Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes (hubs), ensuring that the student model prioritizes the most information-dense spatiotemporal regions. Furthermore, we develop a multi-level distillation strategy that aligns both intermediate features and final predictions through contrastive learning and attention mechanisms. Extensive experiments conducted on four real-world datasets demonstrate that DHKD significantly reduces inference latency while maintaining state-of-the-art performance, validating its viability for deployment on resource-constrained Internet of Vehicles (IoV) edge devices. The implementation code is available at https://anonymous.4open.science/r/DHKD.

The increasing adoption of Internet of Things (IoT) devices and growing demand for real-time applications have driven a shift in the computation paradigm from Cloud computing toward Edge computing, giving rise to theCloud-to-Things Continuum (C2TC). Many real-time IoT applications involve Mobile Nodes (MNs), such as mobile robots or autonomous vehicles, which may join or leave the system dynamically. In addition, future reconfigurable IoT systems will be characterized by multiple applications with heterogeneous requirements dynamically deployed or removed on the same infrastructure. To support such environment, dynamic management mechanisms are needed to ensure that the requirements of different real-time applications are guaranteed despite frequent system reconfigurations (e.g., application deployment and removal). This paper proposes DJ-NECORA, an online algorithm for joint allocation of networking and computing resources in C2TC, capable of guaranteeing the requirements of real-time applications while efficiently managing system reconfigurations and a varying number of MNs. DJ-NECORA is evaluated via simulations in a realistic scenario against two state-of-the-art solutions and an Ideal algorithm. Results show that DJ-NECORA manages system reconfigurations efficiently, provides better Quality of Service (QoS), and performs close to the Ideal algorithm.

Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our findings reveal that only the principal eigenvalue contributes to enhancing this similarity. Moreover, our theoretical analysis applies universally to both clients and servers. Specifically, employing a specialized eigenvalue selection strategy allows for effective optimization of both local and global fairness. Drawing on these insights, we improve dual-perspective fairness through the lens of spectral graph theory without sacrificing utility. Experimental results on two real-world datasets show the superiority of F3GL over existing baselines.

Suicidal ideation is a major psychological problem, and preventing this social risk is recognized as an important research topic. In reality, there can be several reasons why a person experiences suicidal ideation. Each individual can express views, emotions, and several types of symptoms related to suicidal ideation on the most popular social media platforms. In online social networks (OSNs), identification of suicidal ideation is one of the major challenging tasks. Existing studies have shown that the delay in understanding and identifying various risk factors can cause the suicidal event to occur. Due to the scarcity of data and understanding, the genuine intentions of people in their posts are the major challenges to improve the efficacy of suicidal ideation detection. Motivated by the existing psychological research, this article first analyzes an individual’s social behavior from different perspectives, namely, stress-oriented knowledge, tweet behavior, emotion transition sequence, social interaction, and other psychological factors. Next, atweet inspectionframework based on fuzzy deep reinforcement learning (FDRL) model is proposed to detect users with suicidal ideation in OSNs. In addition, a suicidal influential user is proposed by considering a suicidal influence minimization with minimum contextual modification model (SIM-MCM), which reduces the impact of suicidal influence without major changes in the contextual information during the cascading process in OSNs. Experimental results illustrate that the proposed model effectively detects users with suicidal ideation when compared with other deep learning classifier models.

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To address this, we propose the Precision-Aware Hierarchical In-Network Aggregation (PAHInA) framework, the first, to our knowledge, to perform routing optimization for in-network aggregation that explicitly considers precision heterogeneity. The core of PAHInA is an intelligent control-plane scheduler that co-optimizes for gradient priority and path cost, dynamically planning the most cost-effective aggregation strategy for each flow. This fine-grained scheduling guarantees that high-priority gradients are routed through premium, low-latency paths, minimizing global communication overhead. On the data plane, we leverage the eXpress Data Path (XDP) for high-performance packet processing to reduce aggregation-induced overhead. Extensive simulations show that, compared to state-of-the-art baselines, PAHInA significantly mitigates network congestion, reducing end-to-end communication time by up to 33% and boosting overall training throughput by approximately 30%.

Mobility introduces significant challenges for optimal computation offloading, latency minimization, and efficient re source utilization in multi-access edge computing (MEC) systems. A key difficulty lies in leveraging real user trajectories to jointly optimize horizontal (inter-edge) and vertical (edge-to-cloud) task offloading decisions. This paper proposes a two-dimensional offloading scheme for a multi-layer edge–cloud architecture that enables collaborative task execution among resource-constrained edge nodes under mobility conditions. We present MGCO (Mobility-Aware Generative Computation Offloading), a generative AI–driven Transformer-based sequence-to-sequence Deep Q-Network (s2s-DQN) framework that learns from real-time trajectory data to anticipate user movement and optimize task placement dynamically. The Transformer architecture is adopted because its multi-head self-attention effectively captures long range dependencies in mobility and task-demand patterns while avoiding vanishing gradients and sequential bottlenecks inherent to LSTM/GRU models. This design enables parallel contextual reasoning and stable autoregressive action generation, supporting real-time offloading decisions within strict operational latency constraints. Experimental results demonstrate that MGCO consistently outperforms existing methods, achieving up to 41.61% reduction in turnaround time compared to GASTO, and substantial improvements over DMQTO and HMAOA, reaching up to 645.40% and 751.90%, respectively, for longer prediction horizons (48 time slots of 5 seconds each). These results highlight MGCO's robustness, scalability, and effectiveness in managing complex mobility scenarios in dynamic edge–cloud environments.

With continuous growth of IoT applications, service failures are quite inevitable. Due to the complexity and dynamics of IoT services, the root cause analysis (RCA) following an alert can assist in quickly resolving the possible faults. However, the time scales of metrics (e.g., CPU utilization, memory usage) generated by microservices and the dynamic topologies generated by calls between the Application Program Interfaces (APIs) are different. Moreover, the status of devices is an important aspect of RCA in IoT. All these make it extremely challenging to learn failure features of microservice metrics and API calls. Therefore, we propose a novel framework for collaborative identification of root cause analysis (CIRCA) to identify the most potential root cause path with the highest fault scores (weights). In detail, we use both microservice-level and API-level root cause identification (RCI) models to obtain the node fault score in the path. Since we prove the root cause path inference problem is an NP-hard problem, and we then propose a topology-based weighted variable neighborhood search (TWVNS) algorithm and infer the optimal root cause path from two-level scores and call topologies. Our experiments demonstrate CIRCA achieves satisfactory results of RCI and path inference on four public datasets.

Federated learning (FL) has emerged as a transformative paradigm enabling collaborative machine learning without centralizing data, preserving client privacy. This is particularly relevant in the context of edge computing, where the proliferation of Internet of Things devices has led to an explosion of data at the network’s edge. These IoT devices, often battery-powered, are limited by their energy capacities, which pose significant challenges for the adoption of FL in such environments. In this paper, we introduce BatteryFL, a novel framework that coordinates battery-aware clients through FL to maximize their contribution to the global model while ensuring a fair distribution of energy consumption across the clients without compromising accuracy. BatteryFL incorporates an innovative data collection algorithm that prioritizes data diversity to minimize battery usage and a sample relevance-based algorithm to select optimal data for training. We also integrate a client selection strategy into the framework to optimize training loss and fairness (based on the battery energy of the clients) simultaneously. Along with a theoretical analysis, we experimentally demonstrate that BatteryFL significantly improves the energy efficiency of FL, prolonging the data collection and the contributions of the clients.

Federated Learning (FL) is increasingly used in healthcare to enable collaborative model training across decentralized medical institutions while preserving patient privacy. Despite its promise, FL faces significant challenges in medical image processing, such as class imbalance and client data heterogeneity, which can lead to biased models and reduced accuracy in detecting rare diseases. To address these issues, we propose a novel approach, called Syndicated Federated Learning (SyndFL), comprising a multi-layer client selection algorithm that emphasizes fair client representation based on dataset size, learning performance, label distribution, and domain-specific features. SyndFL not only prioritizes clients from minority data clusters but also includes adaptive weighting to ensure that rare conditions receive adequate representation, reducing the risk of bias in model aggregation. Experiments conducted on healthcare image datasets demonstrate that SyndFL achieves a $\mathbf{1 0}-\mathbf{2 0 \%}$ improvement in detecting rare conditions compared to standard FL methods, significantly enhancing both model robustness and fairness in clinical decision-making and diagnostics.

With growing interest in sustainable logistics, electric vehicle (EV)-based deliveries offer a promising alternative for urban distribution. However, EVs face challenges due to their limited battery capacity, requiring careful planning for recharging. This depends on factors such as the charging point (CP) availability, cost, proximity, and vehicles’ state of charge (SoC). We propose CARGO, a framework addressing the EV-based delivery route planning problem (EDRP), which jointly optimizes route planning and charging for deliveries within time windows. After proving the problem’s NP-hardness, we propose a mixed integer linear programming (MILP)-based exact solution and a computationally efficient heuristic method. Using real-world datasets, we evaluate our methods by comparing the heuristic to the MILP solution, and benchmarking it against baseline strategies, Earliest Deadline First (EDF) and Nearest Delivery First (NDF). The results show up to 39% and 22% reductions in the charging cost over EDF and NDF, respectively, while completing comparable deliveries.

Smart farming systems encounter significant challenges, including limited resources, the need for data privacy, and poor connectivity in rural areas. To address these issues, we present eEnergy-Split, an energy-efficient framework that utilizes split learning (SL) to enable collaborative model training without direct data sharing or heavy computation on edge devices. By distributing the model between edge devices and a central server, eEnergy-Split reduces on-device energy usage by up to 86% compared to federated learning (FL) while safeguarding data privacy. Moreover, SL improves classification accuracy by up to 6.2% over FL on ResNet-18 and by more modest amounts on GoogleNet and MobileNetV2. We propose an optimal edge deployment algorithm and a UAV trajectory planning strategy that solves the Traveling Salesman Problem (TSP) exactly to minimize flight cost and extend and maximize communication rounds. Comprehensive evaluations on agricultural pest datasets reveal that eEnergy-Split lowers UAV energy consumption compared to baseline methods and boosts overall accuracy by up to 17%. Notably, the energy efficiency of SL is shown to be model-dependent—yielding substantial savings in lightweight models like MobileNet, while communication and memory overheads may reduce efficiency gains in deeper networks. These results highlight the potential of combining SL with energy-aware design to deliver a scalable, privacy-preserving solution for resource-constrained smart farming environments.

The shortest-time route recommendations offered by modern navigation systems fuel selfish routing in urban vehicular traffic networks and are therefore one of the main reasons for the growth of congestion. In contrast, intelligent transportation systems (ITS) prefer to steer driver-vehicle systems (DVS) toward system-optimal route recommendations, which are primarily designed to mitigate network congestion. However, due to misalignment in motives, drivers may exhibit a lack of trust in the ITS. This paper models the interaction between a DVS and an ITS as a novel, multi-stage routing game where the DVS exhibits dynamics in its trust towards the recommendations of the ITS based on counterfactual and observed game outcomes. Specifically, the DVS and ITS are modeled as a travel-time minimizer and network congestion minimizer, respectively, each having nonidentical prior beliefs about the network state. A novel approximate algorithm to compute the Bayesian Nash equilibrium, called ROSTER (Recommendation Outcome Sampling with Trust Estimation and Re-evaluation), is proposed based on Monte Carlo sampling with trust belief updating to determine the best response route recommendations of the ITS at each stage of the game. Results of simulations between an ITS and a single additional DVS in a traffic network demonstrate that the developed algorithm is able to both mitigate network congestion and reduce driver travel times more effectively than baseline single-stage route recommendation strategies, while the error in the ITS's prediction of DVS's trust converges to zero as the number of interaction stages increases.

The proliferation of loT devices and the growing demand for real-time applications have driven a shift in the computation paradigm, from Cloud computing to Edge computing, creating the Cloud-to-Things Continuum (C2TC). Many real-time loT applications involve Mobile Nodes (MNs), which may dynamically join or leave. In addition, in future reconfigurable loT systems, applications with different requirements will coexist, and will be dynamically introduced or removed. All this asks for dynamic management mechanisms to ensure the requirements of different real-time applications, even when the system configuration changes over time. In this paper, we propose DJ-NECORA, an online algorithm for the joint allocation of networking and computing resources in C2TC that is capable of guaranteeing the requirements of real-time applications and efficiently managing possible changes in the system configuration. We evaluated DJ-NECORA through simulation in a realistic scenario. The results show that DJ-NECORA effectively handles application dynamics and, in some scenarios, outperforms offline resource allocation solutions by supporting 14% more MNs.

Federated Learning (FL) is a distributed learning paradigm that leverages the computational strength of local devices to collaboratively train a model. The clients train the local model on their respective devices and submit the weight updates to the server for aggregation. This paradigm allows the clients to experience diverse data without sharing their local data with other participants or the server. However, FL is susceptible to backdoor attackers that deliberately train the model on altered data, essentially trying to get favor on a specific subtask separated from the main task. In this work, we focus on powerful backdoor attackers who play attacks in a distributed manner to strengthen their impact and to escape a strong detection method. We propose a novel defense algorithm against distributed backdoor attacks, which leverages dynamic model clipping and a reputation-based global model update by filtering adversarial update vectors. While requiring minimal changes to the standard FL framework, our algorithm can be used as a plug-in solution. By simulating various forms of backdoor attacks over three benchmark datasets, we find that with a negligible compromise on the overall performance of the model, our algorithm maintains a lower attack success rate and outperforms the prior solutions.

This paper studies the fundamental problem of energy consumption in the movement of mobile random sensors ensuring k-weak coverage on the domain. In particular, we analyze two notions of k-weak coverage on the unit square, namely (1) $(k, x)$-weak coverage in which every straight-line path across the width of the unit square passes through the sensing range of at least k sensors; and (2) $(k, x, y)$-weak coverage in which every straight-line path across the width and the length of the unit square passes through the sensing range of at least k sensors. The number of reliable and p-reliable sensors (i.e., each sensor is reliable with probability p independently from other sensors) in energy-efficient reallocation is optimized to provide the desired k-weak coverage. For reliable sensors, we discover sharp decrease in the expected minimal energy consumption in the movement to provide the desired k-weak coverage as a function of the number of sensors. For p-reliable sensors, we design and analyze two energy-efficient algorithms to ensure the k-weak coverage. The novelty of the paper is in the theoretical understanding and analysis.

Smart manufacturing, powered by Long Range (LoRa) communication-assisted Industrial Internet of Things (IIoT), offers significant benefits but also incurs security concerns due to device compromise. In addition, various application scenarios and inherent heterogeneity of IIoT devices induce significant challenges for reliable behavior detection of compromised devices. While existing work is mostly on detecting compromised devices and there exists limited work on modeling system behavior, an open question is how to model the per-device behavior in an IIoT deployment and how behavioral changes can be automatically adapted in different scenarios. This paper proposes Misbehav, a novel self-learning device behavior anomaly detection system to detect sophisticated and stealthy attacks. First, Misbehav builds the behavior model per device using events and actions, which enables us to define acceptable and permissible actions. We use an autoencoder based unsupervised approach to train the per-device behavior model and detect malicious actions. This approach guarantees that Misbehav not only detects known attacks, but is equally capable of detecting zero-day attacks. We evaluated Misbehav on a data set collected from standard heterogeneous LoRa devices. Our results show that Misbehav exhibits a significant improvement in robustness, accuracy, and latency. In particular, Misbehav improves the detection accuracy by over 88.25% under different evasion attacks and reduces the detection latency by 11.94% than the state-of-the-art solutions.

In the Industrial Internet of Things (IIoT) landscape, where the cloud-to-things continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve mobile nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this article, we propose joint network and computing resource allocation (J-NECORA), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results.

Logs record various operations and events during system running in text format, which is an essential basis for detecting and identifying potential security threats or system failures, and is widely used in system management to ensure security and reliability. Existing log sequence anomaly detection is limited by log parsing and does not consider all key features of logs, which may cause false or missed detection. In this article, we propose a fast and accurate log parsing method and feed the entire log content into the deep learning network for analysis. To avoid semantic loss during parsing, we replace some variables with tokens containing semantic information and divide logs with appropriate granularity. To ensure the speed and accuracy of parsing, we propose a similarity-based fast merging method to deal with redundant templates. For anomaly detection, we use the complete log content features as input to the model. We use Bidirectional Encoder Representation from Transformers (BERT) to output anomaly detection results directly after considering both the global and local information of log sequences. Experiments show that our log parsing method achieves the best average parsing quality on 16 datasets, and the anomaly detection method achieves optimal results on different datasets.

Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing surveys or tutorials have addressed the importance of ML- or DL-based techniques for LoRaWAN. This article provides a comprehensive survey of current LoRaWAN challenges and recent solutions, particularly using DL and ML algorithms. The primary objective of this survey is to stimulate further research efforts to enhance the performance of LoRa networks and facilitate their practical deployments. We begin by emphasizing the characteristics of LoRaWAN compared to other LPWAN technologies and then examine the technical specifications of LoRaWAN that have been released so far, as well as the current research trends. Furthermore, we discuss an overview of the most utilized DL and ML algorithms for overcoming LoRaWAN challenges. We also present an interoperable reference architecture for LoRaWAN and validate its effectiveness using a wide range of applications. Additionally, we shed light on several evolving challenges of LoRa and LoRaWAN for the future digital network, along with possible solutions. Finally, we conclude our discussion by briefly summarizing our work.

Electric Vehicles (EVs) have become popular in the domain of Intelligent Transportation Systems for their ability to mitigate increasing environmental concerns by reducing carbon footprints and conserving fossil fuels. Due to the scarcity of static charging stations, Vehicle-to-Vehicle (V2V) charge sharing can facilitate the on-demand charging requirement of EVs. However, most of the V2V charge-sharing solutions are either centralized or semi-centralized, causing long waiting times, huge message overhead, and high infrastructural costs. For a large network, assigning a suitable donor EV for an acceptor EV as well as maximizing the matching cardinality in a distributed environment is a challenging problem. In this paper, the problem of V2V matching for charge sharing is mapped to the classical stable matching problem in bipartite graphs. The problem is formulated using integer linear programming that considers flexible decision making for EVs based on multiple charging criteria and constraints. However, as EVs have limited communication ranges, an EV can’t possess knowledge about the entire vehicular network. So we propose two sets of distributed heuristics under the name of Vehicle to Vehicle Distributed Charge Sharing (V2VDisCS), which yield a sub-optimal solution with lower computational and message complexities compared to existing distributed solutions. We analyze the average case matching probabilities and prove the sub-optimality of our approach. Simulation studies show that our heuristics outperform the existing distributed approaches in terms of message overhead and matching percentage. They show a comparable result for matching preference with respect to the standard centralized stable matching algorithm.

Ensuring the safety and reliability of Transportation Cyber-Physical Systems (T-CPS) is critical. However, the increasing interconnectedness of T-CPS exposes them to sophisticated cyberattacks, necessitating robust intrusion detection systems (IDS) to safeguard against evolving threats. This paper aims to enhance the security of T-CPS by addressing two key challenges: effective anomaly detection and handling imbalanced datasets in intrusion detection tasks. In this paper, we propose ReMeNet (Reconstruction Memory Network), a novel intrusion detection model that combines a memory module with a GAN-based architecture to enhance anomaly detection and data reconstruction. To address the challenge of imbalanced datasets, we incorporate a Vector Quantized Wasserstein Generative Adversarial Network (VQ-WGAN) to generate additional samples for underrepresented attack categories, thereby balancing the dataset and improving detection performance. Experimental evaluation on the UNSW-NB15 dataset demonstrates that ReMeNet achieves an accuracy of 91.70%, and an F1-score of 91.63% which outperforms Random Forest by 8.02% and EC-GAN by 3.01%. The results show that ReMeNet effectively handles imbalanced data, improving detection rates across all attack categories in T-CPS.

Reverse skyline query (RSQ) has been widely used in practice since it can pick out the data of interest to the query vector. To save storage resources and facilitate service provision, data owners usually outsource data to the cloud for RSQ services, which poses huge challenges to data security and privacy protection. Existing privacy-preserving RSQ schemes are either based on a two-cloud model or cannot fully protect privacy. To this end, we propose an efficient privacy-preserving reverse skyline query scheme over a single cloud (ePRSQ). Specifically, we first design a privacy-preserving inner product's sign determination scheme (PIPSD), which can determine whether the inner product of two vectors satisfies a specific relation with 0 without leaking the vectors’ information. Next, we propose a privacy-preserving reverse dominance checking scheme (PRDC) based on symmetric homomorphic encryption. Finally, we achieve ePRSQ based on PIPSD and PRDC. Security analysis shows that PIPSD and PRDC are both secure in the real/ideal world model, and ePRSQ can protect the security of the dataset, the privacy of query requests and query results. Extensive experiments show that ePRSQ is efficient. Specifically, for a 3-dimensional dataset of size 1000, the computational and communication overheads of ePRSQ for a query are 79.47 s and 0.0021 MB, respectively. The efficiency is improved by$3.78\times$(300.58 s) and$928.57\times$(1.95 MB) respectively compared with PPARS, and by$61.31\times$(4872.55 s) and$407309\times$(855.35 MB) respectively compared with OPPRS.

Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements such as location, time, velocity, inter-dependencies, and distance. An integrated attention mechanism refines these matrices, ensuring precision in capturing vehicular dynamics. The culmination of these components results in routing decisions that are both responsive and anticipatory. Through detailed empirical experiments using a testbed, simulations with OMNeT++, and theoretical assessments grounded in real-world datasets, we demonstrate the distinct advantages of our methodology. Furthermore, when benchmarked against existing solutions, our technique performs better in model interpretability, delay minimization, rapid convergence, reducing complexity, and minimizing memory footprint.

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to anomaly detection tasks in distributed environments may lead to suboptimal performance results. We propose a federated graph anomaly detection framework via contrastive self-supervised learning (CSSL) [federated CSSL anomaly detection framework (FedCAD)] to address these challenges. FedCAD updates anomaly node information between clients via federated learning (FL) interactions. First, FedCAD uses pseudo-label discovery to determine the anomaly node of the client preliminarily. Second, FedCAD employs a local anomaly neighbor embedding aggregation strategy. This strategy enables the current client to aggregate the neighbor embeddings of anomaly nodes from other clients, thereby amplifying the distinction between anomaly nodes and their neighbor nodes. Doing so effectively sharpens the contrast between positive and negative instance pairs within contrastive learning, thus enhancing the efficacy and precision of anomaly detection through such a learning paradigm. Finally, the efficiency of FedCAD is demonstrated by experimental results on four real graph datasets.

Geo-Distributed Machine Learning (GDML) aims to train large-scale machine learning models across geographically dispersed datacenters. However, the performance of GDML systems is constrained by the limited Wide Area Network (WAN) bandwidth and the presence of the straggler problem. Existing GDML designs often show contradictory effects in addressing these challenges, while in-network computing attempts are typically restricted to single datacenter environments rather than the more complex GDML scenarios. To overcome these limitations, this paper proposes L3DML to facilitate GDML using the P4-based Software-defined Network (SDN). Our approach incorporates three key innovations. Firstly, we introduce a novel network addressing scheme that enables location-specific in-network gradient aggregation for GDML, eliminating the need for parameter servers. Secondly, we utilize the P4 data plane to integrate lossless gradient transmission within switches. Thirdly, we address the straggler problem by employing a unique Deep Reinforcement Learning (DRL) model set and a corresponding rate synchronization routing approach. L3DML is implemented on a prototype system consisting of several Intel Tofino switches and the Spirent network emulator. Experimental results indicate that L3DML outperforms existing solutions in terms of goodput, model accuracy, and training speed gain for large-scale GDML.

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G, so that a given submodular function$f(S)$on S is maximized and the induced subgraph$G[S]$by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in S, the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel$\frac {1-1/e}{2h+2}$-approximation algorithm for the problem, improving the best approximation ratio$\frac {1-1/e}{2h+3}$for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter that depends on the problem and its typical value is 2. In addition, when$h=2$, the algorithm approximation ratio is at least$\frac {1-1/e}{5}$and may be as large as 1 in some special cases when$K\le 23$, and is no less than$\frac {1-1/e}{6}$when$K\ge 24$, compared with the current best approximation ratio$\frac {1-1/e}{7}\left ({{=\frac {1-1/e}{2h+3}}}\right)$for the problem. Finally, experimental results in the application of deploying a UAV network demonstrate that, the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and the throughput of the deployed UAV network by the proposed algorithm is up to 9.7% larger than those by the algorithms. Furthermore, the empirical approximation ratio of the proposed algorithm is between 0.7 and 0.99, which is close to the theoretical maximum value one.

The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building on this, we proposeDynaMOSP, a parallel heuristic forDynamicMultiObjectiveShortestPath searches in large, fully dynamic networks. We provide a theoretical analysis of the conditions to achieve Pareto optimality. Furthermore, we devise a dedicated shared memory CPU implementation along with a version for heterogeneous computing environments. Empirical analysis on eight real-world graphs demonstrates that our method scales effectively. The shared memory CPU implementation achieves an average speedup of 12.74× and a maximum of 57.22×, while on an Nvidia GPU, it attains an average speedup of 69.19×, reaching up to 105.39× when compared to state-of-the-art techniques.

Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL is deployed into the wild, some intelligent clients can deliberately deviate from the standard training process to make the global model inclined toward their local model, thereby prioritizing their local data distribution. We refer to this novel category of misbehaving clients as selfish. In this paper, we propose a Robust aggregation strategy for the FL server to mitigate the effect of Selfishness (in short RFL-Self). RFL-Self incorporates an innovative method to recover (or estimate) the true updates of selfish clients from the received ones, leveraging robust statistics (median of norms) of the updates at every round. By including the recovered updates in aggregation, our strategy offers strong robustness against selfishness. Our experimental results, obtained on MNIST and CIFAR-10 datasets, demonstrate that just 2% of clients behaving selfishly can decrease the accuracy by up to 36%, and RFL-Self can mitigate that effect without degrading the global model performance.

While Federated Learning (FL) manages well data diversity in distrusted learning, it faces additional complexities in scenarios with "frugal labeling," where the client nodes host a mix of partially or fully unlabelled datasets. This paper introduces FedDAFL, a novel approach tailored for label-scarce federated settings, that enables learners with unlabelled datasets to leverage fully labelled teachers, thus incorporating domain adaptation techniques within a multi-teacher multi-learner framework. Through the integration of self-learning and semi-supervised methods, FedDAFL aims to enhance model accuracy using each client’s unlabelled data. Comparative analysis with established techniques like Federated Adversarial Domain Adaptation (FADA) and Federated Knowledge Alignment (FedKA), demonstrates FedDAFL’s superior accuracy across independent and identically or non-identically distributed (non-IID) datasets. Even if the clients possess single class data, FedDAFL adeptly tackles frugal labeling challenges in federated transfer learning, exhibiting competitive performance against existing methods. Results validated on Pneumonia chest X-ray datasets underscore FedDAFL’s adaptability in handling limited labels and non-IID setups, demonstrating its applications to various settings.

Structural hole spanners (SHSs) are nodes that connect different communities to facilitate efficient information dissemination in complex networks. Existing efforts to identify SHS nodes have predominantly focused on undirected networks, rendering them inadequate to capture directional data flow. This paper presents a novel lightweight approach to motif span scores, called mSpan that leverages network substructures called feed forward loop (FFL) motifs, to detect SHS in directed, weighted as well as unweighted social networks. The proposed approach measures the spanning score of a node in terms of its participation in FFL motifs that bridge network communities. Our theoretical analysis establishes a strong association between the variants of the scores for a given node and the likelihood of its removal disrupting connectivity. We also utilize mSpan to detect spanner motifs that bridge the structural holes in social networks. We validate the efficacy of mSpan in detecting SHS in practical scenarios through comparative evaluations of three real-world social networks against existing spanner detection metrics.

The diversity in disease profiles and therapeutic approaches between hospitals and health professionals underscores the need for patient-centric personalized strategies in healthcare. Alongside this, similarities in disease progression across patients can be utilized to improve prediction models in survival analysis. The need for patient privacy and the utility of prediction models can be simultaneously addressed in the framework of Federated Learning (FL). This paper outlines an approach in the domain of federated survival analysis, specifically the Cox Proportional Hazards (CoxPH) model, with a specific focus on mitigating data heterogeneity and elevating model performance. We present an FL approach that employs feature-based clustering to enhance model accuracy across synthetic datasets and real-world applications, including the Surveillance, Epidemiology, and End Results (SEER) database. Furthermore, we consider an event-based reporting strategy that provides a dynamic approach to model adaptation by responding to local data changes. Our experiments show the efficacy of our approach and discuss future directions for a practical application of FL in healthcare.

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low, and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther tool demonstrates that location-based keys as proposed in our protocol are secure against location inference and spoofing attacks among others.

Autonomous Vehicles (AV s) require substantial computational resources to perform operations that safely navigate vehicles in urban road networks. Resource-intensive operations are offloaded to roadside units (RSUs), acting as edge servers, to improve the responsiveness and reduce the energy consumed in execution. In this context, a cooperative execution involving the vehicular on-board units (OBUs) and the RSUs can act as a game changer. However, partial offloading is non-trivial and demands addressing the following research challenges. Firstly, the RSU's resources are limited, necessitating regulated resource assignments. Secondly, capturing distinctive vehicle parameters using a unified ranking scheme is imperative. Thirdly, an efficient partition strategy must consider the energy expended and adhere to the real-time operations' deadline needs. This paper proposes a partial offloading scheme, MOVE, catering to the above-mentioned challenges. A deferred acceptance algorithm (DAA) with preferences is proposed to address the first two challenges, whereas a novel energy-aware partitioning strategy resolves the final challenge. The performance of the proposed scheme is evaluated against baseline algorithms, and we observed a 54.04 % and 52.17 % reduction in offloading latency and energy.

Domain Name System (DNS) tunneling is a well-known cyber-attack that allows data exfiltration - the attackers exploit this tunnel to extract sensitive information from the system. Advanced Persistent Threat (APT) attackers encapsulate malicious traffic in a DNS connection to elude security mechanisms such as Intrusion Detection System (IDS). Although different techniques have been implemented to detect these targeted attacks, their rise induces a threat to Cyber-Physical Systems (CPS). The DNS over HTTPS (DoH) tunnel detection is a challenge because the encrypted data prevents an analysis of DNS traffic content. In this paper, we present a novel detection system that identifies malicious DoH tunnels in real time. We study the normal traffic pattern and based on that, we define a profile. The objective of this system is to detect malicious activity on the system as early as possible through a lightweight packet by packet analysis based on a real-time IDS classifier. This system is evaluated on three available data sets and the results obtained are compared with a machine learning technique. We demonstrate that the identification of anomalous activity, in particular DoH tunnels, is possible by analyzing different traffic features.

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget$K$, the problem is to find a subset$S$with$K$nodes from a graph$G$so that a given submodular function$f (S)$on$S$is maximized while the induced subgraph$G[S]$by the nodes in$S$is connected, where the submodular function$f$can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in$S$, the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel$\frac{1-1/e}{2h+2}$-approximation algorithm for the problem, improving the best approximation ratio$\frac{1-1/e}{2h+3}$for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where$e$is the base of the natural logarithm,$h$is a parameter depends on the problem and its typical value is 2. In addition. when$h= 2$, the algorithm approximation ratio is at least$\frac{1-1/e}{5}$and may be as large as 1 in some special cases when$K$≤21, and is no less than$\frac{1-1/e}{6}$when$K$≥ 22, compared with the current best approximation ratio$\frac{1-1/e}{7}(= \frac{1-1/e}{2h+3})$for the problem. We finally evaluate the algorithm performance in the application of deploying a UAV network. Experimental results demonstrate the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and its empirical approximation ratio is between 0.7 and 0.99, which is close to the theoretical maximum value one.

Autonomous vehicles (AVs) execute compute-intensive control operations like adjusting speed and steering, causing significant energy dissipation and latency due to resource-limited onboard units (OBUs). Offloading these tasks to Roadside Units (RSUs) is a solution, but it faces challenges. First, the stringent latency requirements are impacted by the vehicle’s stochastic velocity. Second, allocating limited RSU resources to numerous vehicles within its coverage area is difficult. This paper proposes the MIME framework to address these issues. We use Discrete Fourier transform (DFT) that computes the average velocity over an aperiodic velocity signal extracted from a real-world dataset. For resource allocation, we model it as matching with externalities, using reactive preferences based on vehicle speed and location. We present an efficient, scalable, stable solution, showing a 24.61% and 11.2% reduction in offloading latency and energy for the inD dataset, and a 5.6% and 5.52% reduction for the SUMO dataset.

Road traffic congestion affects not only the commute delay but also a city's overall social, economic, and environmental growth. Existing approaches for road congestion mitigation primarily adopt a reactive approach by detecting congestion after it occurs and recommending alternate routes to the vehicles, which fails to prevent congestion cascading. In contrast, we propose a pervasive platform called ProCon that proactively infers the driving micro-behaviors that can contribute to congestion formation and assist the drivers in avoiding such maneuvers in real-time during the navigation. Thorough evaluations over multiple real-life and simulated datasets indicate that ProCon can reduce congestion for more than 60% of the scenarios on average while significantly reducing the travel time of the vehicles.

A Stackelberg routing platform (SRP) reduces congestion in one-shot traffic networks by proposing optimal route recommendations to the selfish travelers. Traditionally, Stackel-berg routing is cast as a partial control problem where a fraction of the traveler flow complies with route recommendations, while the remaining responds as selfish travelers. In this paper, we formulate a novel Stackelberg routing framework where the agents exhibit probabilistic compliance by accepting SRP's route recommendations with a trust probability. Specifically, we propose a greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) for SRP to compute unique path recommendations to each traveler flow with a unique demand. Simulation experiments are designed with random travel demands with diverse trust values on real road networks, such as Sioux Falls, Chicago Sketch, and Sydney networks for both single-commodity and multi-commodity flows. The performance of TASR is compared with state-of-the-art Stackelberg routing methods in terms of traffic congestion and trust dynamics over repeated interaction between the SRP and the travelers. Results show that while it may require several interactions for travelers to reach perfect trust, TASR improves network congestion in the single-commodity and multi-commodity settings when compared to the most well-known Stackelberg routing strategies.

The vision behind community-scale smart living applications is to use sensor- actuator devices, the so-called Internet of Things (IoT), to generate sensing data that provide situational awareness of the physical world to improve the quality of human life at the city scale. Examples applications include smart transportation, customer and distribution layers of the smart grid metering, smart water networks, etc. The effects of threats such cyber-attacks, device/network faults and malfunctions, unsafe events, typically manifest themselves as anomalies that need to be promptly detected. However, there are unique challenges in anomaly detection for smart living : (1) behavioral randomness of humans creates dynamic spatiotemporal variations in data patterns making it difficult to learn the profile of benign behavior leading to unusable false alarm frequencies; (2) High non-linearity, non-IID data, random evolving patterns, cause traditional anomaly detection/learning methods to lose detection sensitivity; (3) smart living sensing data often have privacy and individual device profiling concerns; (4) unlabeled threats present while learning benign profile.

It is our great pleasure to welcome you to the 8th IEEE International Workshop on Big Data and IoT Security in Smart Computing (BITS 2024) co-located with the 10th IEEE International Conference on Smart Computing (SMARTCOMP 2024). This year, the BITS 2024 is held in person in Osaka, Japan.

Urban Air Mobility (UAM) encompasses both piloted and autonomous aerial vehicles, spanning from small unmanned aerial vehicles (UAVs) like drones to passenger-carrying personal air vehicles (PAVs), to revolutionize smart transportation in congested urban areas. This emerging paradigm is anticipated to offer disruptive solutions to the mobility challenges in congested cities. In this context, a pivotal concern centers on the sustainability of transitioning to this mode of transportation, especially with the focus on incorporating clean technology into developing innovative solutions from the ground up. Recent studies highlight that a significant portion of the total energy consumption in UAM can be attributed to the flight operations of the aircraft. To address this challenge, this paper introduces a framework POSCA aimed at meeting the energy requirements of UAM flights. It delves into a complex and dynamic route-planning problem. It introduces a novel concept called the Phototropic Index, calculated by considering the traversal distance and solar coverage along the route. To solve the path planning problem, we propose two solutions, S-POSCA and D-POSCA, catering to static and dynamic setups. Simulation results confirm an average increase of 8.81% in static conditions and 10.64% in the dynamic condition for the cumulative Global Horizontal Irradiance (GHI) compared to the baseline approaches.

Unmanned Aerial Vehicles (UAVs) can capture pictures of road conditions in all directions and from different angles by carrying high-definition cameras, which helps gather relevant road data more effectively. However, due to their limited energy capacity, drones face challenges in performing related tasks for an extended period. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multi-agent deep deterministic policy gradient-based (MADDPG) algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on both fronts. Firstly, we define an optimization problem aimed at reducing UAV energy consumption. Secondly, we transform the defined optimization problem into a reinforcement learning problem and design MAUP to solve it. Finally, we optimize energy consumption and memory usage by reducing the number of neurons in the hidden layer of MAUP and conducting fine-grained pruning on connections. The final simulation results demonstrate that our method effectively reduces the energy consumption of UAVs compared to other methods.

The Synchronized Single-hop Multiple Gateway (SHMG) framework has been recently proposed to support mobility in 6TiSCH, the network architecture defined by the IETF for the Industrial Internet of Things (IIoT). SHMG includes a scheduling policy to allocate communication resources to Mobile Nodes (MNs) in order to satisfy the stringent requirements of industrial applications. Current scheduling algorithms, however, manage mobility by simply over-allocating communication resources, without taking into account the position of MNs. In this paper, we propose a Location-Aware Scheduling Algorithm with Rescheduling (LASA-R) that leverages the position of MNs, reported via periodic Position Notification (PN) messages, to optimize the allocation of communication resources. LASA-R also includes a conflict resolution mechanism to modify the schedule, as conflicts are detected. Finally, a mathematical methodology is developed to determine the optimal PN period. LASA-R is assessed through simulations. The results obtained show that it can guarantee a high reliability and a bounded latency, even with a very large number of MNs.

UAVs (Unmanned Aerial Vehicles) are promising tools for efficient data collections of sensors in IoT networks. Existing studies exploited both spatial and temporal data correlations to reduce the amount of collected redundant data, in which sensors are first partitioned into different clusters, a master sensor in each cluster then collects raw data from other sensors and compresses the received data. An energy-constrained UAV finally collects the maximum amount of compressed data from different master sensors. We however notice that the compressed data from only a portion of clusters are collected by the UAV in the existing studies, while the data from other clusters are not collected at all. In this paper, we study a problem of finding a data collection trajectory for an energy-constrained UAV, so that the accumulative utility of collected data is maximized, where the accumulative utility measures the quality of spatiotemporally correlated data collected from different clusters. We propose a novel 16+-approximation algorithm for the problem, where is a given constant with >0. Experimental results with real datasets show that the accumulative utility by the proposed algorithm is at least 23% larger than those by the existing studies, and the number of clusters collected by the proposed algorithm is from 45% to 105% larger than those by the existing studies.

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As a consequence, this leads to approaches that are (1) ineffective due to poor long-term user engagement and (2) too abstract to be used in real-world settings. In this article, we propose an auction theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine learning-based PSPs algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial time algorithm called Greedy Ranking AllocatioN (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this article, we first propose a scalable data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee and compare with the state-of-the-art ML methods to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.

Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly based attack detection. In this paper, we propose a noise resilient learning framework for anomaly based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the effect of false alarms caused by the noise. Specifically, we propose a feature extraction framework that uses some Pythagorean Means of the active power from a cluster of PMUs, reducing multi-dimensional nature of the PMU data streams via quick Big Data summarization. We also propose a robust and noise resilient methodology for learning thresholds based on generalized robust estimation theory of our invariant feature. We experimentally validate our approach and demonstrate improved reliability performance using two completely different datasets collected from real distribution level PMU infrastructures.

The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. This paper focuses on how individuals’ GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, namedMobilyticsdesigned to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) and a deep learning architecture that automatically annotates the GPS log. Additionally, we propose a novel “transfer learning” approach to explore movement dynamics in a geographically distant area by leveraging knowledge obtained from a comparable region, such as an academic campus. In terms of major contributions and novelty, this is the first work to present end-to-end daily mobility trip purpose extraction and mobility knowledge transfer for trip annotation and POI-tagging where the labeled data are insufficient. Experimental results on real-life datasets of five different regions demonstrate the efficacy of our proposed Mobilytics framework which outperforms the baselines for trip-purpose extraction and POI annotations by a significant margin ($\approx$18% to$\approx$30%). Moreover, the analysis on huge volume of simulated traces (10,000 users) illustrates the scalability and robustness of the framework.

Geocast is a one-to-many communication paradigm that enables the transmission of data packets to a designated area rather than an IP address. The most common geocast solutions rely on the application-layer Geolocation-to-IP database. But these IP-based approaches cannot cope with the challenges of flexibility and mobility in a granularity-customizable geocast scenario. While some non-IP network-layer (L3) attempts have resulted in low addressing accuracy and poor routing scalability. Besides, the clean-slate design is incompatible with the existing network. To address these issues, this paper proposes an innovative network-layer geographic addressing scheme that leverages P4-based Software Defined Networks (SDN) to enable flexible geocast with high accuracy. Based on the aggregation relationship of the geographic area, a network-layer routing strategy is designed to enhance routing scalability. Compatibility is improved by deploying the network-layer designs only at the network edge where granularity-customizable geocast is implemented, without requiring changes to the current IP infrastructure. Then, the network-layer functions are integrated with an application-layer mapping service to support intercommunication between different network edges. Furthermore, a prototype system is built to implement and evaluate the proposed L3Geocast, which outperforms the existing approaches in terms of communication latency and mapping overhead.

Wireless Sensor and Actuator Networks (WSANs) are an effective technology for improving the efficiency and productivity in many industrial domains, and are also the building blocks for the Industrial Internet of Things (IIoT). To support this trend, the IEEE has defined the 802.5.4Time-Slotted Channel Hopping (TSCH)protocol. Unfortunately, TSCH does not provide any mechanism to manage node mobility, while many current industrial applications involve Mobile Nodes (MNs), e.g., mobile robots or wearable devices carried by workers. In this article, we present a framework to efficiently manage mobility in TSCH networks, by proposing an enhanced version of the Synchronized Single-hop Multiple Gateway (SHMG) architecture. We first define a flexible scheduling algorithm, calledShared Downstream-Dedicated Upstream (SD-DU), that can be configured to adapt to different types of traffic in industrial applications. Then, we develop a mathematical framework to formalize the problem of Border Routers (BRs) placement to guarantee the complete coverage of the deployment area, in the presence of obstacles and unreliable communication. A methodology for network sizing is also proposed to calculate the maximum number of MNs that can be supported by the network without violating the application requirements. Finally, we evaluate the performance of the proposed solutions, both analytically and through simulations. Our results show that the proposed enhancements allow a very effective management of node mobility, by providing mobility transparency without a significant impact on performance.

Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. However, obtaining the optimal offloading strategy in a multi-task offloading decision-making process is an NP-hard problem. Existing Deep learning techniques with slow learning rates and weak adaptability are not suitable for dynamic multi-user scenarios. In this article, we propose a novel deep meta-reinforcement learning-based approach to the multi-task offloading problem using a combination of first-order meta-learning and deep Q-learning methods. We establish the meta-generalization bounds for the proposed algorithm and demonstrate that it can reduce the time and energy consumption of IoT applications by up to 15%. Through rigorous simulations, we show that our method achieves near-optimal offloading solutions while also being able to adapt to dynamic edge-cloud environments.

Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from the distributed EHRs without sharing the client-side raw data. However, the intrinsic characteristics of the distributed EHRs are typically non-independent and identically distributed (Non-IID), significantly bringing challenges related to data imbalance and leading to a notable decrease in the effectiveness of making healthcare decisions derived from the global model. To address these challenges, we introduce a novel personalized federated learning framework named PEARL, which is designed for disease prediction on Non-IID EHRs. Specifically, PEARL incorporates disease diagnostic code attention and admission record attention to extract patient embeddings from all EHRs. Then, PEARL integrates self-supervised learning into a federated learning framework to train a global model for hierarchical disease prediction. To improve the performance of the client model, we further introduce a fine-tuning scheme to personalize the global model using local EHRs. During the global model updating process, a differential privacy (DP) scheme is implemented, providing a high-level privacy guarantee. Extensive experiments conducted on the real-world MIMIC-III dataset validate the effectiveness of PEARL, demonstrating competitive results when compared with baselines.

Task cooperation emerges as a efficacious strategy for the execution of intricate tasks within the context of challenging wireless edge networks characterized by limited resources and intermittent infrastructure connections. Presently, TCP/IP-based solutions encounter issues related to suboptimal utilization of network resources and a substantial dependency on infrastructure connections. In light of this, Information-Centric Networking (ICN) has surfaced as a promising architectural paradigm aimed at mitigating these challenges. In ICN-based task cooperation, the data reuse characteristic of ICN enhances the efficiency of network resource utilization. However, this also introduces plausible security vulnerabilities to the reused data, encompassing eavesdropping attacks and unauthorized access attacks. In this paper, we propose an ICN-based secure task cooperation scheme to mitigate the above threats without compromising the efficiency of task execution. We present the task cooperation model that quantifies the cost of securing data reuse in task execution. We also introduce a specific naming convention to support the acquisition of collaborative task requests and keys related to the task cooperation. Besides, we introduce a novel design for an enhanced name-based access control scheme that ensure both data confidentiality and access control in collaborative tasks accessed by the same sub-policy, streamlining the encryption process for content keys. Security analysis and experimental results demonstrate that our scheme effectively safeguards the security of data reuse. Furthermore, compared to existing schemes, our scheme incurs lower cost associated with computation and security.

Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visitwaypointsand accomplishactivitiesas part of theirmission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novelMission Scheduling Problem ()that co-schedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the data capture and computing utilities from the activities while meeting the activity deadlines, and the energy and computing constraints. We first prove that is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design five time-efficient heuristic algorithms that provide sub-optimal but fast solutions that are empirically competitive with the optimal solution. Evaluation of these five schedulers using real drone traces demonstrate utility–runtime trade-offs under diverse workloads.

In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying$K$heterogeneous UAVs in the air to form a temporarily connected UAV network such that the network throughput – the number of users served by the UAVs, is maximized, subject to the constraint that the number of people served by each UAV is no greater than its service capacity. We then propose a novel$O(\sqrt{\frac{s}{K}})$-approximation algorithm for the problem, where$s$is a given positive integer with$1 \le s\le K$, e.g.,$s=3$. We also devise an improved heuristic, based on the approximation algorithm. We finally evaluate the performance of the proposed algorithms. Experimental results show that the numbers of users served by UAVs in the solutions delivered by the proposed algorithms are increased by 25% than state-of-the-arts.

In this paper, we study the deployment of$K$heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of$K$heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one type of UAVs can take only visual images while the other type of UAVs can take both visual and thermal infrared images. In this paper, we investigate a problem of scheduling$K$heterogeneous UAVs to monitor PoIs so that the sum of monitoring rewards received by all UAVs is maximized, subject to energy capacity on each UAV. We propose the very first$\frac {1}{3}$-approximation algorithm for this scheduling problem. We also evaluate the performance of the proposed algorithm, using real parameters of commercial UAVs. Experimental results show that the performance of the proposed algorithm is promising, which is improved by 25%, compared with existing algorithms.

The use of drones for collecting information and detecting bugs in orchards covered by nets is a challenging problem. The nets help in reducing pest damage, but they also constrain the drone’s flight path, making it longer and more complex. To address this issue, we model the orchard as an aisle-graph, a regular data structure that represents consecutive aisles where trees are arranged in straight lines. The drone flies close to the trees and takes pictures at specific positions for monitoring the presence of bugs, but its energy is limited, so it can only visit a subset of positions. To tackle this challenge, we introduce the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the orienteering problem, where likely infested locations are prioritized by assigning them a larger profit. Additionally, the drone’s movements have a cost in terms of energy, and the objective is to plan a drone’s route in the most profitable locations under a given drone’s battery. We show that SOAP can be optimally solved in polynomial time, but for larger orchards/instances, we propose faster approximation and heuristic algorithms. Finally, we evaluate the algorithms on synthetic and real datasets to demonstrate their effectiveness and efficiency.

Federated Learning is a training framework that enables multiple participants to collaboratively train a shared model while preserving data privacy. The heterogeneity of devices and networking resources of the participants delay the training and aggregation. The paper introduces a novel approach to federated learning by incorporating resource-aware clustering. This method addresses the challenges posed by the diverse devices and networking resources among participants. Unlike static clustering approaches, this paper proposes a dynamic method to determine the optimal number of clusters using Dunn Indices. It enables adaptability to the varying heterogeneity levels among participants, ensuring a responsive and customized approach to clustering. Next, the paper goes beyond empirical observations by providing a mathematical derivation of the communication rounds for convergence within each cluster. Further, the participant assignment mechanism adds a layer of sophistication and ensures that devices and networking resources are allocated optimally. Afterwards, we incorporate a master-slave technique, particularly through knowledge distillation, which improves the performance of lightweight models within clusters. Finally, experiments are conducted to validate the approach and to compare it with state-of-the-art. The results demonstrated an accuracy improvement of over 3% compared to its closest competitor and a reduction in communication rounds of around 10%.

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties:1) a Gated Recurrent Unit (GRU) based traffic prediction model is established to predict the unexpected and dynamically changing traffic behaviors in advance with the accuracy over 98%; 2) a closed-loop system is formed, in which the prediction model can learn and evolve continuously to respond to more complex scenarios; 3) different states of VNF are introduced and dynamically switched to deal with the current demands with reduced cost by avoiding frequent VNF initialization and destroy.The experimental results indicate that the proposed mechanism outperforms the state-of-the-art methods, which include achieving over 98% prediction accuracy, improving the service acceptance rate by more than 18%, and reducing the overall cost by more than 20%.

In the last few years, Federated Learning (FL) has received extensive attention from the research community because of its capability for privacy-preserving, collaborative learning from heterogeneous data sources. Most FL studies focus on either average performance improvement or the robustness to attacks, while some attempt to solve both jointly. However, the performance disparities across clients in the presence of attackers have largely been unexplored. In this work, we propose a novel Fair Federated Learning scheme with Attacker Detection capability (abbreviated as FFL+AD) to minimize performance discrepancies across benign participants. FFL+AD enables the server to identify attackers and learn their malign intent (e.g., targeted label) by investigating suspected models via top performers. This two-step detection method helps reduce false positives. Later, we introduce fairness by regularizing the benign clients’ local objectives with a variable boosting parameter that gives more emphasis on low performers in optimization. Under standard assumptions, FFL+AD exhibits a convergence rate similar to FedAvg. Experimental results show that our scheme builds a more fair and more robust model, under label-flipping and backdoor attackers, compared to prior schemes. FFL+AD achieves competitive accuracy even when 40% of the clients are attackers.

This paper proposes a novel selective federated learning (FL) algorithm, called fittest aggregation and slotted training (FedFaSt). It relies on a “free-for-all” client training process to score clients' efficiency while applying the “natural selection” principle to elect the fittest clients to be used in FL training and aggregation processes. While relying on a combined data quality and training performance metric for scoring clients, FedFaSt implements a slotted training model enabling teams of fittest clients to participate in the training and aggregation processes for a fixed number of successive rounds, called slots. Performance validation using X-ray datasets reveals that FedFaSt outperforms selective federated learning algorithms like FedAVG, FedRand, and FedPow in terms of accuracy, convergence to the global optimum, time complexity, and robustness against attacks.

Urban Air Mobility (UAM) involving piloted or autonomous aerial vehicles, is envisioned as emerging disruptive technology for next-generation transportation addressing mobility challenges in congested cities. This paradigm may include aircrafts ranging from small unmanned aerial vehicles (UAVs) or drones, to aircrafts with passenger carrying capacity, such as personal air vehicles (PAVs). This paper highlights the UAM vision and brings out the underlying fundamental research challenges and opportunities from computing, networking, and service perspectives for sustainable design and implementation of this promising technology providing an innovative infrastructure for urban mobility. Important research questions include, but are not limited to, real-time autonomous scheduling, dynamic route planning, aerial-to-ground and inter-vehicle communications, airspace traffic management, on-demand air mobility, resource management, quality of service and quality of experience, sensing (edge) analytics and machine learning for trustworthy decision making, optimization of operational services, and socio-economic impacts of UAM infrastructure on sustainability.

Smart spaces system equipped with sensors to collect data that can be used to generate insights about its environmental conditions. Those collected data is then transmitted to the applications to enhance the comfort, quality of life, and security of the space. Long Range (LoRa) technology provides long distance coverage and consumes low energy which makes it suitable for smart space application. There are six virtual channels to transmit data in LoRa, however network faces the interference problem when nodes transmitted data at the same time. The interference problem makes LoRa less suitable for time-critical applications. To mitigate the interference problem, a spreading factor should be allocated in an optimal way. This paper assigns the spreading factor to the LN using Rate-Monotonic scheduler to ensures data transmission within deadline with minimum energy consumption. To quantify delay in receiving the information, we use the ‘Age of Information’ metric. The proposed approach is validated using Network Simulator-3 and results show that it effectively reduces delay and energy and prolongs the network utility.

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the first attempt at identifying SCC for incremental graphs across distributed compute nodes. Our experimental analysis on real and synthesized graphs shows up to 2.8x performance improvement over the state-of-the-art by reducing the overall memory utilized and improving the communication bandwidth.

Smart water metering (SWM) infrastructure collects real-time water usage data that is useful for automated billing, leak detection, and forecasting of peak periods. Cyber/physical attacks can lead to data falsification on water usage data. This paper proposes a learning approach that converts smart water meter data into a Pythagorean mean-based invariant that is highly stable under normal conditions but deviates under attacks. We show how adversaries can launch deductive or camouflage attacks in the SWM infrastructure to gain benefits and impact the water distribution utility. Then, we apply a two-tier approach of stateless and stateful detection, reducing false alarms without significantly sacrificing the attack detection rate. We validate our approach using real-world water usage data of 92 households in Alicante, Spain for varying attack scales and strengths and prove that our method limits the impact of undetected attacks and expected time between consecutive false alarms. Our results show that even for low-strength, low-scale deductive attacks, the model limits the impact of an undetected attack to only C0.2199375 and for high-strength, low-scale camouflage attack, the impact of an undetected attack was limited to C1.434375

The performance of vehicular edge computing (VEC) depends on the effective optimization of task offloading. However, uneven distribution of vehicular traffic, rapidly changing network conditions, and stochastic nature of vehicular networks motivate us to innovate approaches to efficient resource management while maintaining system's stability. To address these challenges, we propose a novel queue length-based stochastic task migration strategy that leverages model predictive control (MPC) and Lyapunov optimization techniques. Our approach employs the queue length at the edge node as the criterion for offloading decisions. The MPC controller dynamically allocates the processing power and bandwidth resources to vehicles based on their current requirements, facilitating prompt offloading decisions. The Lyapunov optimization ensures long-term system stability. Our method also incorporates dynamic request selection from multi-dimensional queuing load optimization and ensures fair and efficient load distribution, thereby enhancing edge server utilization. We evaluate the performance of our proposed approach via simulation experiments and demonstrate its superiority by reducing the queue length at the edge node and adhering to delay constraints of vehicular networks.

The Synchronized Single-hop Multiple Gateway (SHMG) is a framework recently proposed to support mobility into 6TiSCH, the standard network architecture defined for Industrial Internet of Things (IIoT) deployments. SHMG supports industrial applications with stringent requirements by adopting the Shared-Downstream Dedicated-Upstream (SD-DU) scheduling policy, which allocates to Mobile Nodes (MNs) a set of dedicated transmission opportunities for uplink data. Such allocation is performed on all the Border Routers (BRs) of the network without considering the location of MNs. Transmission opportunities are reserved also in BRs far from the current location of the MN, resulting in a waste of resources that limits the maximum number of nodes supported by the network. To overcome this problem, we propose a Location-Aware Scheduling Algorithm (LASA) that takes into account the position of MNs to build and maintain an efficient communication schedule. Specifically, LASA tries to prevent conflicts arising due to node mobility, in a preventive manner, so as to minimize packet dropping. We evaluate LASA via simulation experiments. Our results show that LASA allows to increase the number of MNs by more than four times, with respect to SD-DU, yet guaranteeing a Packet Delivery Ratio higher than 98%.

While traditional real-time systems analysis requires single pessimistic estimates to represent system parameters, the mixed-criticality (MC) design proposes to use multiple estimates of system parameters with different levels of pessimism, resulting in low critical workloads sacrificed at run-time in order to provide guarantees to high critical workloads. Shortcomings of the MC design were improved recently by the precise MC scheduling technique in which the processor speed is increased at run-time to provide guarantees to both low and high critical workloads. Aiming to extend the precise MC scheduling to multiprocessor computing platforms, this paper proposes three novel scheduling algorithms that are based on virtual-deadline and fluid-scheduling approaches. We prove the correctness of our proposed algorithms through schedulability analysis and also present their theoretical effectiveness via speedup bounds and approximation factor calculations. Finally, we evaluate their performance experimentally via randomly generated task sets and demonstrate that the fluid-scheduling algorithms outperform the virtual-deadline algorithm.

Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model calledCoMCLOUDto select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines.

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across multiple levels and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route-planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points ( endpoint location privacy ) and the route itself ( route privacy ). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤ 20% in length from optimal shortest paths, with completion times within ∼ 25 seconds, which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼ 1.0) and good route privacy (≥ 0.8).

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared. However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks. Driven by these motivations, in this article we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify. Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time.

Software-defined networking (SDN) has gained significant attention as the future deployment platform for the Internet and enterprise networks. The major advantages of SDN include effective traffic management, dynamic configuration of policy and flow rules, and better scalability with heterogeneous traffic requirements. However, centralized network control and the use of OpenFlow protocols introduce various security challenges for the underlying network. The attacks on the SDN controller is critical as it hosts all network control functions. Motivated by a systematic analysis of different attack scenarios in SDN using the STRIDE attack model, this article presents an effective security enforcement framework for proactive prevention of potential attacks on SDN controllers. First, based on a signaling game approach, we design a trust-based controller attack detection (TCAD) model that calculates the trust value of each incoming packet to take necessary action. Next, we propose a risk-based attack prevention (RAP) model that detects and filters malicious traffic flows in the network. Finally, we evaluate our proposed security enforcement framework on different scenarios with varying traffic requirements and by injecting attacks based on the STRIDE model. Experimental results show 95% accuracy in the potential attack detection and prevention.

Precision farming has a positive potential in the agricultural industry regarding water conservation, increased productivity, better development of rural areas, and increased income. Blockchain technology is a better alternative for storing and sharing farm data as it is reliable, transparent, immutable, and decentralized. Remote monitoring of an agricultural field requires security systems to ensure that any sensitive information is exchanged only among authenticated entities in the network. To this end, we design an efficient blockchain-enabled authenticated key agreement scheme for mobile vehicles-assisted precision agricultural Internet of Things (IoT) networks called$AgroMobiBlock$. The limited existing work on authentication in agricultural networks shows passive usage of blockchains with very high costs.$AgroMobiBlock$proposes a novel idea using the elliptic curve operations on an active hybrid blockchain over mobile farming vehicles with low computation and communication costs. Formal and informal security analysis along with the formal security verification using the Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool have shown the robustness of$AgroMobiBlock$against man-in-the-middle, impersonation, replay, physical capture, and ephemeral secret leakage attacks among other potential attacks. The blockchain-based simulation on large-scale nodes shows the computational time for an increase in the network and block sizes. Moreover, the real-time testbed experiments have been performed to show the practical usefulness of the proposed scheme.

The industrial Internet of Things technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of artificial intelligence models running on edge devices, such as intelligent cyber physical systems typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a diagnosis, echo state networks (ESNs) are a valid solution to bring the power of neural networks into low complexity models meeting the resource constraints. On the other hand, the use of such a technique has some limitations when applied in unsupervised contexts. In this article, we propose a novel model that combines ESNs and autoencoders (ESN-AE) for the detection of anomalies in industrial systems. Unlike the ESN-AE models presented in the literature, our approach decouples the encoding and decoding steps and allows the optimization of both the processes while performing the dimensionality reduction. Experiments demonstrate that our solution outperforms other machine learning approaches and techniques we found in the literature resulting also in the best tradeoff in terms of memory footprint and inference time.

In a disaster response management (DRM) scenario, communication and coordination are limited, and absence of related infrastructure hinders situational awareness. Unmanned aerial vehicles (UAVs) or drones provide new capabilities for DRM to address these barriers. However, there is a dearth of works that address multiple heterogeneous drones collaboratively working together to form a flying ad-hoc network (FANET) with air-to-air and air-to-ground links that are impacted by: (i) environmental obstacles, (ii) wind, and (iii) limited battery capacities. In this paper, we present a novel environmentally-aware and energy-efficient multi-drone coordination and networking scheme that features a Reinforcement Learning (RL) based location prediction algorithm coupled with a packet forwarding algorithm for drone-to-ground network establishment. We specifically present two novel drone location-based solutions (i.e., heuristic greedy, and learning-based) in our packet forwarding approach to support application requirements. These requirements involve improving connectivity (i.e., optimize packet delivery ratio and end-to-end delay) despite environmental obstacles, and improving efficiency (i.e., by lower energy use and time consumption) despite energy constraints. We evaluate our scheme with state-of-the-art networking algorithms in a trace-based DRM FANET simulation testbed featuring rural and metropolitan areas. Results show that our strategy overcomes obstacles and can achieve 81-to-90% of network connectivity performance observed under no obstacle conditions. In the presence of obstacles, our scheme improves the network connectivity performance by 14-to-38% while also providing 23-to-54% of energy savings in rural areas; the same in metropolitan areas achieved an average of 25% gain when compared with baseline obstacle awareness approaches with 15-to-76% of energy savings.

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications’ power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is$\mathcal {NP}$-Hard, we propose anAnt Colony Optimization(ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%–30% and electricity cost by approximately 25% compared to the baseline.

In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone’s energy is limited, only a subset of locations in the orchard can be visited with a fully charged battery. Those places that are most likely to be infested should be selected to promptly detect the parasite. We study the budgeted constrained position selection problem in the orchard from an algorithmic point of view. We present the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the well-known orienteering problem where the finite resource is the drone’s battery. We first show that SOAP can be optimally solved for aisle-graphs in polynomial time. However, the optimal solution is not efficient for large orchards. Then, we propose two efficient heuristics that work even for large (orchard) instances. After a thorough analysis of the proposed solutions, we evaluate their performance by simulation experiments on both synthetic and real data sets.

Data sharing by cloud computing enjoys benefits in management, access control, and scalability. However, it suffers from certain drawbacks, such as high latency of downloading data, non-unified data access control management, and no user data privacy. Edge computing provides the feasibility to overcome the drawbacks mentioned above. Therefore, providing a security framework for edge computing becomes a prime focus for researchers. This work introduces a new key-aggregate cryptosystem for edge-cloud-based data sharing integrating cloud storage services. The proposed protocol secures data and provides anonymous authentication across multiple cloud platforms, key management flexibility for user data privacy, and revocability. Performance assessment in feasibility and usability paves satisfactory results. Therefore, this work directs a new horizon to detailed new edge-computing-based data sharing services based on the proposed protocol for low latency, secure unified access control, and user data privacy in the modern edge enabled reality.

In the era of a data-driven society with the ubiquity of Internet of Things (IoT) devices storing large amounts of data localized at different places, distributed learning has gained a lot of traction, however, assuming independent and identically distributed data (iid) across the devices. While relaxing this assumption that anyway does not hold in reality due to the heterogeneous nature of devices, federated learning (FL) has emerged as a privacy-preserving solution to train a collaborative model over non-iid data distributed across a massive number of devices. However, the appearance of malicious devices (attackers), who intend to corrupt the FL model, is inevitable due to unrestricted participation. In this work, we aim to identify such attackers and mitigate their impact on the model, essentially under a setting of bidirectional label flipping attacks with collusion. We propose two graph theoretic algorithms, based on Minimum Spanning Tree and k-Densest graph, by leveraging correlations between local models. Our FL model can nullify the influence of attackers even when they are up to 70% of all the clients whereas prior works could not afford more than 50% of clients as attackers. The effectiveness of our algorithms is ascertained through experiments on two benchmark datasets, namely MNIST and Fashion-MNIST, with overwhelming attackers. We establish the superiority of our algorithms over the existing ones using accuracy, attack success rate, and early detection round.

Skill-based volunteering is an expanding branch of crowdsourcing where one may acquire sustainable services, solutions, and ideas from the crowd by connecting with them online. The optimal mapping between volunteers and tasks with collaboration becomes challenging for complex tasks demanding greater skills and cognitive ability. Unlike traditional crowdsourcing, volunteers like to work on their own schedule and locations. To address this problem, we propose a novel two-phase frame-work consisting of Initial Volunteer-Task Mapping (i-VTM) and Adaptive Common Slot Finding (a-CSF) algorithms. The i-VTM algorithm assigns volunteers to the tasks based on their skills and spatial proximity, whereas the a-CSF algorithm recommends appropriate common working time slots for successful volunteer collaboration. Both the algorithms aim to maximise the overall utility of the crowdsourcing platform. Experimenting with the UpWork dataset demonstrates the efficacy of our framework over existing state-of-the-art methods.

We present the first GPU-based parallel algorithm to efficiently update vertex coloring on large dynamic networks. For single GPU, we introduce the concept of loosely maintained vertex color update that reduces computation and memory requirements. For multiple GPUs, in distributed environments, we propose priority-based ordering of vertices to reduce the communication time. We prove the correctness of our algorithms and experimentally demonstrate that for graphs of over 16 million vertices and over 134 million edges on a single GPU, our dynamic algorithm is as much as 20x faster than state-of-the-art algorithm on static graphs. For larger graphs with over 130 million vertices and over 260 million edges, our distributed implementation with 8 GPUs produces updated color assignments within 160 milliseconds. In all cases, the proposed parallel algorithms produce comparable or fewer colors than state-of-the-art algorithms.

Smart transportation networks have become instrumental in smart city applications with the potential to enhance road safety, improve the traffic management system and driving experience. A Traffic Message Channel (TMC) is an IoT device that records the data collected from the vehicles and forwards it to the Road Side Units (RSUs). This data is further processed and shared with the vehicles to inquire the fastest route and incidents that can cause significant delays. The failure of the TMC sensors can have adverse effects on the transportation network. In this paper, we propose a Gaussian distribution based trust scoring model to identify anomalous TMC devices. Then we propose a semi-supervised active learning approach that reduces the manual labeling cost to determine the threshold to classify the honest and malicious devices. Extensive simulation results using real-world vehicular data from Nashville are provided to verify the accuracy of the proposed method.

Task cooperation is an effective way to execute a complex task in challenging wireless edge networks. Existing TCP/IP-based solutions encounter the problem of low network resource utilization and the heavy dependency of infrastructure connections. Information-centric networking(ICN) is a promising architecture to address these issues. In existing ICN-based task cooperation schemes, the data reuse feature of ICN improves the utilization of network resources, which also brings potential security threats to the reused data. To guarantee the security of data reuse in task cooperation without affecting the data reuse feature, we propose an ICN-based secure task cooperation scheme. In our scheme, the specific naming convention is designed to support task cooperation and the acquisition of keys. In addition, our scheme implements fine-grained access control for data reuse in task cooperation combined with attribute-based encryption. Experimental results show that our scheme enhances the security of task cooperation with low cost compared with existing schemes.

While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc. provides livelihood to many drivers, the desire to raise income forces the drivers to work very hard without rest. However, continuous journeys not only affect their health, but also lead to abnormal driving behavior such as rash driving, swerving, side-slipping, sudden brakes, or weaving, leading to accidents in the worst cases. Motivated by the severity of rising accidents and health issues among drivers, this paper proposes a recommendation system, called RsSafe, for the safety of drivers. Aiming to improve the driving quality and the driver's experience, RsSafe suggests that the driver accepts or rejects the next trip based on the predicted driving behavior. In particular, we propose a fusion architecture that learns to predict the driver's behavior for the next trip using information from multiple streams. This architecture consists of Multi-task Learning with Attention (MTLA) that captures individual drivers' personality traits to deal with the adaptability of system. We use publicly available naturalistic driving behavior analysis dataset, namely the UAHDriveSet, results show that the MTLA predicts with F-measure score of 96%; and outperforms the baseline as well as state-of-the-art models.

Dementia, a cognitive disease that affects more than 50 million people, causes some degree of disability in remembering simple things and following basic instructions with unusual delays. Researchers proposed different pre-clinical methods with mediocre performance leaving the door open for further improvement. One of the most successful pre-clinical tests, SLUMS (Saint Louis University Mental Status), incorpo-rates verbal responses in the form of standardized questionnaires. It involves expert judgment to label patients such as dementia, MCI (Mild Cognitive Impairment), or healthy based on an overall score. However, a nonverbal stress response is also taken into account in the Alz-Sense algorithm, which has a few underlying false assumptions, i) uniformity of answering duration, ii) equity of questions stress level, and iii) unfair stress penalty while discarding healthy patient detection. Moreover, the stress data of the corresponding question is manually synchronized using the examiner's hand-shaken data of the wearable device. As a goal to improve the original Alz-Sense algorithm, Alz-Sense+ is proposed to handle these three assumptions by incorporating the windowing process, statistical and visual approach. Be-sides, it also automated the synchronization between questions and corresponding sensor data by estimating time slots while proposing an optimal ordering of questions that mitigates the unintended consequences. Alz-Sense+ achieved 81.39%, 80.76%, and 82.35 % accuracy, sensitivity, and specificity, respectively, which is 7.39%, 0.01 %, and 15.75% improvement over the original Alz-Sense algorithm. In a nutshell, the new Alz-Sense+ algorithm outperformed the existing algorithm by addressing a few underlying assumptions while eliminating a few limitations of the original algorithm.

Prolonged rotating outages and exorbitant energy bills, recently experienced in California and Texas, have exposed the limitations and need for modernizing electric power systems. The occurrence of such events is a consequence of peak loads, often due to extreme outside temperatures that simultaneously trigger Heating Ventilation Air Conditioning (HVAC) systems. Leveraging pervasive computing technologies, such as smart meters and smart thermostats, this paper introduces a comprehensive approach to perform residential HVAC power conservation and prevent these catastrophic events. Differently from previous solutions, our approach models realistic user behavior and HVAC dynamics of individual homes. Specifically, we formulate a novel reverse auction-based problem, called POwer Conservation Optimization (POCO). The goal is to perform power conservation by motivating users to temporarily adjust their HVAC thermostat settings in exchange for financial rewards. We prove that POCO ensures truthfulness and individual rationality of the auction mechanism, although it is an NP-hard problem. Therefore, we propose an efficient heuristic, called Greedy Ranking AllocatioN (GRAN), which we prove ensures the same formal properties, while incurring only a polynomial complexity. To predict power savings resulting from an HVAC thermostat adjustments, we propose a novel machine learning-based technique called Power Saving Prediction (PSP). In addition, we conduct an online survey to study the willingness to adopt the proposed system and to model realistic user behavior. Survey results show willingness of adoption above 79% and a highly heterogeneous and non-linear user behavior. We perform extensive experiments using high-fidelity simulator EnergyPlus. Results show that PSP outperforms a state-of-the-art solution obtaining 85% predictions within a 5% error margin. Furthermore, GRAN achieves near-optimal performance, outperforming a recent state-of-the-art approach obtaining results between 58% and 68% closer to the optimum.

Electric vehicles (EVs) have emerged in the intelligent transportation system (ITS) to meet the increasing environmental concerns. To facilitate on-demand requirement of EV charging, vehicle-to-vehicle (V2V) charge transfer can be employed. However, most of the existing approaches to V2V charge sharing are centralized or semi-centralized, incurring huge message overhead, long waiting time, and infrastructural cost. In this paper, we propose novel distributed heuristic algorithms for V2V charge sharing based on the multi-criteria decision-making policy. The problem is mapped to an alias classical problem (i.e., optimum matching in weighted bipartite graphs), where the goal is to maximize the matching cardinality while minimizing the matching cost. An integer linear programming (ILP)-based problem formulation can not achieve optimum matching because the global network topology is not available with the EVs due to their limited communication range. Our proposed heuristics can yield an almost stable matching with lesser computational and message overhead compared to other existing distributed approaches. An average case matching probability is also calculated. Simulation experiments are conducted to measure the performance of our heuristics in terms of message overhead, matching percentage, and matching preference. The proposed solution outperforms the existing distributed approaches and shows comparable result with respect to standard centralized stable matching algorithm.

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme.

In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required by the anomaly detection framework are stored to enable arbitrary arithmetic calculations over FHE. Furthermore, we adopt a private information retrieval (PIR) approach with FHE to enable approximate search with LUTs, which reduces the execution time of the attack detection service while protecting private information. Besides, we show that by adjusting the significant digits of inputs and outputs in our LUT, we can control the detection accuracy and execution time of the attack detection, even while using FHE. Our experiments confirmed that our proposed method is able to detect the injection of false power consumption in the range of 11–17 secs of execution time, depending on detection accuracy.

We consider a delivery food service operated by Unmanned Aerial Vehicles (UAVs). Due to the absence of a dataset on UAVs deliveries in the literature, and since it is not possible to perform real tests, we create a dataset using an open Air Traffic Simulator (ATS). Precisely, we converted a set of food deliveries operated by wheeled vehicles, proposed in the literature [1], into a set of simulated UAVs deliveries. For each delivery, we ran a UAV flight from the source to the destination. The results showed that, as expected, the UAV’s course is shorter than the vehicle trajectory on the ground because the UAV follows an Euclidean path. Following that path, UAVs can be 5 to 8 times faster than wheeled vehicle, in absence of wind. Highly important, the ATS simulator allows to take care of the wind impact in a realistic way. Tailwind increases UAVs speed which becomes up to 10 times faster than the wheeled vehicles, whereas the headwind and crosswind slowdown the UAVs as the traffic slowdown the wheeled vehicles. Our work proves that air traffic simulators pave the way for realistic simulations of UAVs systems.

Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a game-theoretic approach to estimate the time duration of using a spreading factor that ensures on-time data delivery with maximum network utilization. We incorporate the Age of Information (AoI) metric to capture the freshness of information as demanded by the applications. Our proposed approach is validated through simulation experiments, and its applicability is demonstrated for a crop protection system that ensures real-time monitoring and intrusion control of animals in an agricultural field. The simulation and prototype results demonstrate the impact of the number of nodes, AoI metric, and game-theoretic parameters on the performance of the IoT network.

The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Inter-net of Things (IIoT). Unfortunately, 6TiSCH does not provide mechanisms to manage node mobility, while many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers). In this paper, we consider the Synchronized Single-hop Multiple Gateway framework to manage mobility in 6TiSCH networks. For this framework, we address the problem of positioning Border Routers in a deployment area, which is similar to the "Art Gallery" problem, proposing an efficient deployment policy for Border Routers based on geometrical rules. Moreover, we define a flexible Scheduling Function that can be easily adapted to meet the requirements of various IIoT applications. We analyze the considered Scheduling Function in different scenarios with varying traffic patterns, and define an algorithm for sizing the system in such a way to guarantee the application requirements. Finally, we investigate the impact of mobility on the performance of the system. Our results show that the proposed solutions allow to manage node mobility very effectively, and without significant impact on the performance.

This paper presents a network-based template for analyzing large-scale dynamic data. Specifically, we propose a novel shared-memory parallel algorithm for updating tree-based structures or properties, such as connected components (CC) and minimum spanning trees (MST), on dynamic networks. The underlying idea is to update the information in a rooted tree data structure that stores the edges of the network that are most relevant to the analysis. Extensive experiments on real-world and synthetic networks demonstrate that, with the exception of the inherently sequential component for creating the rooted tree, our proposed updatiing algorithm is scalable and, in most cases, also requires significantly less memory, energy, and time than recomputing-from-scratch algorithm. To the best of our knowledge, this is the first parallel algorithm for updating MST on weighted dynamic networks. The rooted-tree based framework that we propose in this paper can be extended for updating other weighted and unweighted tree-based properties such as single source shortest path and betweenness and closeness centrality.

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed.

N OWADAYS, billions of devices are connected to the In-ternet, enabling Internet of Things (IoT) systems widely deployed, such as smart city, smart healthcare and intelligent plant, to capture a great quantity of sensing data. Consequently, the data transmission, processing and analysis in IoT applications bring a great pressure to the central server. Fortunately, distributed intelligence becomes one of the potential solutions. Distributed intelligence can greatly relieve server pressures via plenty of terminal devices, and these devices collaboratively perceive and handle the mass data to improve the reliability, s-calability and security of industrial IoT systems. As future IoT system will embrace more wireless sensors and devices, the high-performance computing, high-bandwidth and low-latency communication are excessively required, many new research opportunities and challenges for distributed intelligence over Internet of things have arisen. To promote the development of distributed intelligence technology, this special section (SS) focuses on various technologies and platforms regarding industrial IoT systems. This special section received nearly 50 submitted manuscripts, out of which 10 of them have been accepted after a rigorous peer review. Each manuscript is reviewed by multiple rounds of review with at least three or four reviewers, the problems to be solved and the innovation of each manuscript are mainly concerned. Then the accepted papers are summarized as follows in details. Considering the joint optimization of the offloading decision and resource allocation under limited resource constraints in collaborative edge computing networks with multiple IIoT devices and MEC servers, an improved differential evolution algorithm [7] is proposed to minimize the weighted sum of cost of energy consumption and time delay, which can effectively reduce the system delay and energy consumption. In order to improve the performance of task scheduling in cloud computing, Attiya et al. [1] propose a novel hybrid swarm intelligence method MRFOSSA, which uses a modified Manta-Ray Foraging Optimizer (MRFO) and the Salp Swarm Algorithm (SSA). MRFOSSA is superior to other methods in terms of makespan time and cloud throughput. The research goal of the paper [5] is to design an intelligent computing offloading strategy for industrial applications in order to optimize costs and mitigate energy losses. Then the paper proposes to combine a fog controller and AI-based learning techniques so that the fog controller can intelligently assign tasks to the most appropriate fog devices and find the appropriate path to the target. Considering the resource utilization efficiency under dynamic overload requests and network states in IIoT, Chen et al. [2] propose DRL-based intelligent SFC orchestration scheme and jointly optimize the VNF deployment and SFC embedment by the improved DDQN algorithm, which can improve the performance of resource utilization rate, execution cost and delay compared with other representative schemes. To solve the problem of resource allocation and energy cost in Internet of Vehicles, Kong et al. [8] design a joint computing and caching framework and formulate the problem as a reinforcement learning problem to minimize the energy cost. On this basis, the optimization algorithm based on DDPG is proposed, which can effectively decrease energy costs. To reduce the query numbers of the object model when constructing adversarial examples, Zhang et al. [10] propose generating adversarial examples with shadow model (GASM), i.e., transfering the query operations to the designed shadow model, which can achieve high attack success rates. Chen et al. [3] revise a Decentralized-Wireless-Federated-Learning algorithm (DWFL) which utilizes the superposition property of the analog scheme. It can solve the problem of single failure, limited bandwidth resource and privacy protection in wireless federated learning algorithm, which can be applied widely in wireless IoT networks. To reduce the resource consumption in CNN-based applications, Jia et al. [6] propose the CNN-based Resource Optimization APProach which utilizes model compression and computation sharing to optimize inner-model and inter-model respectively, and the comparison results show the superior performance in scalability and the decrease of resource cost. In mobile crowdsensing activities, Gao et al. [4] propose a differential Location Privacy-preserving Mechanism based on Trajectory obfuscation (LPMT) to protect the location privacy of mobile users, which includes three operations: stay points extraction, stay points obfuscation and stay points sampling. In order to mimic the task-free bottom-up visual attention process by predicting salient regions on natural images, Umer et al. [9] propose a Pseudo Knowledge Distillation (PKD) model based on knowledge distillation and pseudo labelling technique, which is computationally efficient and suitable for real-time on-device saliency prediction.

5G technology is foreseen to have a heterogeneous architecture with the various computational capability, and radio-enabled Service Providers (SPs) and Service Requesters (SRs), working altogether in a cellular model. However, the coexistence of heterogeneous network model spawns several research challenges such as diverse SRs with uneven service deadlines, interference management, and revenue maximization of non-uniform computational capacities enabled SPs. Thus, we propose a coexistence of heterogeneous SPs and SRs enabled cellular 5G network and formulate the SPs' revenue maximization via resource allocation, considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a distributed many-to-many stable matching based solution. Moreover, we offer an adaptive stable matching based distributed algorithm to solve the formulated problem in a dynamic network model. Through extensive theoretical and simulation analysis, we have shown the effect of different parameters on the resource allocation objectives and achieves 94\% of optimum network performance.

Localizing ground devices (GDs) is an important requirement for a wide variety of applications, such as infrastructure monitoring, precision agriculture, search and rescue operations, to name a few. To this end, unmanned aerial vehicles (UAVs) or drones offer a promising technology due to their flexibility. However, the distance measurements performed using a drone, an integral part of a localization procedure, incur several errors that affect the localization accuracy. In this paper, we provide analytical expressions for the impact of different kinds of measurement errors on the ground distance between the UAV and GDs. We review three range-based and three range-free localization algorithms, identify their source of errors, and analytically derive the error bounds resulting from aggregating multiple inaccurate measurements. We then extend the range-free algorithms for improved accuracy. We validate our theoretical analysis and compare the observed localization error of the algorithms after collecting data from a testbed using ten GDs and one drone, equipped with ultra wide band (UWB) antennas and operating in an open field. Results show that our analysis closely matches with experimental localization errors. Moreover, compared to their original counterparts, the extended range-free algorithms significantly improve the accuracy.

Multi-domain networking slice orchestration is an essential technology for the programmable and cloud-native 5G network. However, existing research solutions are either based on the impractical assumption that operators will reveal all the private network information or time-consuming secure multi-party computation which is only applicable to limited computation scenarios. To provide agile and privacy-preserving end-to-end network slice orchestration services, this paper proposes NetChain, a multi-domain network slice orchestration architecture based on blockchain and trusted execution environment. Correspondingly, we design a novel consensus algorithm CoNet to ensure the strong security, scalability, and information consistency of NetChain. In addition, a bilateral evaluation mechanism based on game theory is proposed to guarantee fairness and Quality of Experience by suppressing the malicious behaviors during multi-domain network slice orchestration. Finally, the prototype of NetChain is implemented and evaluated on the Microsoft Azure Cloud with confidential computing. Experiment results show that NetChain has good performance and security under the premise of privacy-preserving.

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the ‘freshness’ of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay$B$, e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than$B$. Specifically, we study two variants of the problem: one is that a UAV needs to fly to the location of each IoT device to collect its data; the other is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio$4\frac {4}{7}$for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average.

The Single Source Shortest Path (SSSP) problem is a classic graph theory problem that arises frequently in various practical scenarios; hence, many parallel algorithms have been developed to solve it. However, these algorithms operate on static graphs, whereas many real-world problems are best modeled as dynamic networks, where the structure of the network changes with time. This gap between the dynamic graph modeling and the assumed static graph model in the conventional SSSP algorithms motivates this work. We present a novel parallel algorithmic framework for updating the SSSP in large-scale dynamic networks and implement it on the shared-memory and GPU platforms. The basic idea is to identify the portion of the network affected by the changes and update the information in a rooted tree data structure that stores the edges of the network that are most relevant to the analysis. Extensive experimental evaluations on real-world and synthetic networks demonstrate that our proposed parallel updating algorithm is scalable and, in most cases, requires significantly less execution time than the state-of-the-art recomputing-from-scratch algorithms.

Network service provisioning becomes flexible and programmable with the help of Network Function Virfitualization (NFV), since NFV abstracts various service functions into software components called Virtual Network Function (VNF) and VNFs can be flexibly and quickly composed to form new services. It is commonly known that sharing the same VNF among different services can improve the resource utilization. However, we should be aware that such sharing also leads to serious resource preemption. In addition, VNF sharing aggravates the generation of the performance bottleneck, which then causes the rate mismatch problem between the upstream and downstream VNFs belonging to the same service chain. In this article, we propose a dynamic and flexible algorithm to jointly address the VNF sharing resource allocation and the rate coordination between the upstream and downstream VNFs. Specifically, 1) the VNFs are shared among different service chains with a fairness factor considered for the purpose of reducing the resource preemption probability and improving the resource utilization; 2) the backpressure indicator of each VNF is defined to judge its pressure condition, based on which we can dynamically adjust the processing rates between it and its downstream or upstream VNFs by maximizing the idle resource utilization. The experimental results indicate that the proposed algorithm outperforms the other methods in terms of the average delay, the flow completion time, the throughput and the backlog, etc. Meanwhile, the proposed algorithm achieves more stable performance than the other methods.

In this article, we study the orienteering aisle-graph single-access problem (OASP), a variant of the orienteering problem for a robot moving in a so-called single-access aisle graph, i.e., a graph consisting of a set of rows that can be accessed from one side only. Aisle graphs model, among others, vineyards or warehouses. Each aisle-graph vertex is associated with a reward that a robot obtains when it visits the vertex itself. As the energy of the robot is limited, only a subset of vertices can be visited with a fully charged battery. The objective is to maximize the total reward collected by the robot with a battery charge. We first propose an optimal algorithm that solves the OASP in O (m 2n 2) time for aisle graphs with a single access consisting of m rows, each with n vertices. With the goal of designing faster solutions, we propose four greedy suboptimal algorithms that run in at most O(mn\(m + n)) time. For two of them, we guarantee an approximation ratio of 1 2(1-1 e), where e is the base of the natural logarithm, on the total reward by exploiting the well-known submodularity property. Experimentally, we show that these algorithms collect more than 80% of the optimal reward.

Mobile edge computing (MEC) has been envisioned as a promising paradigm that could effectively enhance the computational capacity of wireless user devices (WUDs) and quality of experience of mobile applications. One of the most crucial issues of MEC is computation offloading, which decides how to offload WUDs’ tasks to edge severs for further intensive computation. Conventional mathematical programming-based offloading approaches could face troubles in dynamic MEC environments due to the time-varying channel conditions (caused primarily by WUD mobility). To address the problem, reinforcement learning (RL) based offloading approaches have been proposed, which develop offloading policies by mapping MEC states to offloading actions. However, these approaches could fail to converge in large-scale MEC due to the exponentially-growing state and action spaces. In this article, we propose a novel online computation offloading approach that could effectively reduce task latency and energy consumption in dynamic MEC with large-scale WUDs. First, a RL-based computation offloading and energy transmission algorithm is proposed to accelerate the learning process. Then, a joint optimization method is adopted to develop the allocating algorithm, which obtains near-optimal solutions for energy and computation resources allocation. Simulation results show that the proposed approach can converge efficiently and achieve significant performance improvements over baseline approaches.

An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a deep learning based compression-decompression model for reducing the size of the energy time series at the Edge devices. Next, it formulates an optimization problem for finding the suitable compressed energy time series to reduce the energy consumption and delay of the system. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed time series to the operator in the given time. Our simulation and prototype results demonstrate the impact of the parameters of the compression model, network, and the number of smart meters and appliances on delay, energy consumption, and accuracy of the system.

A challenging problem in Advanced Metering Infrastructure (AMI) of smart grids is the identification of smart meters under the control of a stealthy adversary, that inject very low margins of stealthy data falsification. The problem is challenging due to wide legitimate variation in both individual and aggregate trends in real world power consumption data, making such stealthy attacks unrecognizable by existing approaches. In this paper, via proposed modified diversity index scoring metric, we propose a novel information-theory inspired data driven device anomaly classification framework to identify compromised meters launching low margins of stealthy data falsification attacks. Specifically, we draw a parallelism between the effects of data falsification attacks and ecological balance disruptions and identify required mathematical modifications in existing Renyi Entropy and Hill's Diversity Entropy measures. These modifications such as expected self-similarity with weighted abundance shifts across various temporal scales, and diversity order are appropriately embedded in our resulting framework. The resulting diversity index score is used to classify smart meters launching additive, deductive, and alternating switching attack types with high sensitivity (as low as 100W) compared to the existing works that perform poorly at margins of false data below 400W. Our proposed theory is validated with two different real smart meter datasets from USA and Ireland. Experimental results demonstrate successful detection sensitivity from very low to high margins of false data, thus reducing undetectable strategy space of attacks in AMI for an adversary having complete knowledge of our method.

Unmanned aerial vehicles or drones provide new capabilities for disaster response management (DRM). In a DRM scenario, multiple heterogeneous drones collaboratively work together forming a flying ad-hoc network (FANET) instantiated by a ground control station. However, FANET air-to-air and air-to-ground links that serve critical application expectations can be impacted by: (i) environmental obstacles, and (ii) limited battery capacities. In this paper, we present a novel obstacle-aware and energy-efficient multi-drone coordination and networking scheme that features a Reinforcement Learning (RL) based location prediction algorithm coupled with a packet forwarding algorithm for drone-to-ground network establishment. We specifically present two novel drone location-based solutions (i.e., heuristic greedy, and learning-based) in our packet forwarding approach to support heterogeneous drone operation as per application requirements. These requirements involve improving connectivity (i.e., optimize packet delivery ratio and end-to-end delay) despite environmental obstacles, and improving efficiency (i.e., by lower energy use and time consumption) despite energy constraints. We evaluate our scheme by comparing it with state-of-the-art networking algorithms in a trace-based DRM FANET simulation testbed. Results show that our strategy overcomes obstacles and can achieve between 81-90% of network connectivity performance observed under no obstacle conditions. With obstacles, our scheme improves network connectivity performance by 14-38 % while also providing 23-54% of energy savings.

Allocating tasks to the best-fit candidates is a classical problem in crowdsourcing (CS). Most of the existing approaches assume that the task and candidate knowledge is known in advance and ignore the effect of enrolled candidates' willingness on the CS system's selection decision. For instance, an unwilling candidate assigned to a task may quit without completing it, thus depreciating the utility of the CS platform. In practice, a task or candidate may arrive or leave the CS system dynamically. Moreover, a complex task may be broken into smaller sub-tasks, each requiring a variety of computations and expertise. To overcome these challenges, based on a greedy algorithm, we propose a novel approach for skill-oriented dynamic task allocation with willingness factor for complex assignments (SWill-TAC). This approach iteratively attempts to delegate candidates (workers) to tasks depending on the skills required for executing the tasks and the candidates' skill set. SWill-TAC also considers the willingness of eligible candidates and keeps track of the budget constraints of tasks. Finally, the feasibility and efficiency of our approach are demonstrated using the UpWork dataset. Experimental results show that SWill-TAC outperforms Online Greedy, TM-Uniform, Random selection-based, and Minimum payment-based task allocations in terms of the completed tasks count, the utility gained, and success ratio.

It is time-critical to administer pre-clinical cognitive health screening of patients to detect dementia and differentiate from mild cognitive impairment (MCI), and ensure early intervention, treatment and caregiving plans. Current medical standard and practice for pre-clinical screening follows standardized cognitive questionnaire test and patients’ verbal response based scoring. However, such practice misses sensing valuable signals of non-verbal stress response relevant to deciding dementia vs. MCI. To this end, we propose a novel approach, Alz-Sense, that integrates non-invasive passive sensing (during the standard cognitive questionnaire test) with an intelligent algorithm based assessment of dementia vs. MCI. The contributions of our Alz-Sense approach are: (i) innovative non-invasive passive sensing of non-verbal stress response from a custom made Smart Chair Cover; (ii) identifying and quantifying a novel indicator of stress response from chair cover multi-sensors data; and (iii) intelligent integration of stress indicator into a revised scoring mechanism. We deployed the Alz-Sense system for patients study in a hospital clinic, and validated its performance through 50 patients dataset. We compare the ROC (Receiver Operating Characteristic) performance of Alz-Sense approach with widely used standardized SLUMS questionnaire based scoring. We also compare performance in the contextual region of ROC curve relevant to pre-clinical cognitive health screening to show advantages of our approach. Further analysis selects optimal model parameters and compares SLUMS performance using medical clinic recommended threshold.

Selective removal of certain subgraphs called motifs based on the spread function value is one of the most powerful approaches to curb the overall influence spread in any complex network. In this paper, we first prove that any general spread function preserves both monotonicity and submodularity properties even under motif removal operations. Next, we propose a scoring mechanism as a novel spread function that quantifies the relative importance of a given motif within the overall influence spread dynamics on the complex network. We design a novel algorithm that eliminates motifs with high spread scores to curb influence spread. We evaluate the performance of our proposed spread control algorithm using simulation experiments in the context of 3-node motifs called feed forward loops (FFLs) in both real and synthetic network topologies. We demonstrate that high-scoring motifs intercept a high number of short paths from the pre-assigned source and sinks, because of which their elimination results in a significant effect on curbing the influence spread. Furthermore, we empirically evaluate the run-time and cost versus performance trade-off of the proposed algorithm.

Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that co-schedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the utility from the activities while meeting activity deadlines as well as energy and computing constraints. We first prove that MSP is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design two efficient heuristic algorithms, jsc and vrc, that provide fast sub-optimal solutions. Evaluation of these three schedulers using real drone traces demonstrate utility-runtime trade-offs under diverse workloads.

The use of drones can be a valuable solution for the problem of delivering goods for many reasons. In fact, they can be efficiently employed in time-critical situations when there is a traffic jam on the roads, to serve customers in hard-to-reach places, or simply to expand the business. However, due to limited battery capacities and the fact that drones can serve a single customer at a time, a drone-based delivery system (DBDS) aims to minimize the drones’ energy usage for completing a route from the depot to the customer and go back to the depot for new deliveries. In general, the shortest delivery route could not be the optimal choice since external factors like the wind (which varies with time) can affect energy consumption. Previous work has mainly considered simplified DBDSs assuming architectures with a single drone and with static costs on paths. Moreover, in these non-centralized architectures, the drones themselves compute the routes on the fly employing their onboard processing resources, making this choice costly. In this paper we develop a centralized system for computing energy-efficient time-varying routes for drones in a multi-depot multi-drone delivery system. Specifically, we propose a novel centralized parallel algorithm called Parallel Shortest Route Update (PSRU) that, over time, updates the drones’ delivery routes avoiding the whole recomputation from scratch. A comprehensive evaluation proves that PSRU is up to 4. 5x faster than the state-of-the-art algorithms.

In this paper, we develop techniques that create smart space in an efficient manner, wherein the efficiency is defined in terms of all-together: energy, security, delay, and cost. We design an energy-efficient smart space system using the Long-Range (LoRa) network. The system consists of various sensors that generate sensory data represented as Multi-dimensional Time Series (MTS). The sensors are connected with an Edge device and LoRa node for processing and transferring the MTS, respectively. The system first proposes a deep learning-based compression-decompression model for reducing the size of MTS at the Edge devices. Next, it uses game theory for finding a minimum-cost security mechanism to facilitate the secure transmission of MTS. Then, we formulate an optimization problem to obtain a suitable compression ratio and security mechanism to reduce the system's energy consumption, delay, and security cost. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed and secured MTS to the application server in the given time period with desired accuracy. We evaluate the proposed system over the simulation platform and demonstrate the impact of the parameters of the compression model, network, security mechanism, and the number of sensors on energy consumption, delay, cost, and system accuracy.

Data collection in Wireless Sensor Networks (WSNs) has been a hot research topic owing to the accelerated development in the Internet of Things (IoT). With high agility, mobility and flexibility, the Unmanned Aerial Vehicle (UAV) is widely considered as a promising technology for data collection in WSNs. Under the one-to-many data collection scheme, where a UAV is able to collect data from multiple sensors simultaneously within its reception range, the identification of hovering locations of the UAV impacts the efficiency of data collection significantly. Most existing studies either neglect this critical issue or discretize the UAV serving area into small regions with a given size, which results in the inevitable utility loss of data collection. In this paper, we jointly consider the hovering location positioning of the UAV and the utility maximization of data collection. Specifically, we first formulate a novel data collection utility maximization problem (UMP) and show that it is an NP-hard problem. We then devise an efficient algorithm for precisely positioning (potential) UAV hovering locations, which improves the data collection utility significantly. We also propose an approximation algorithm for UMP with approximation ratio (1 - 1/e), where e is the base of the natural logarithm. We finally evaluate the performance of the proposed algorithms through simulation experiments, and demonstrate that the proposed algorithms significantly outperform four heuristics.

Vehicular Ad-hoc NETworks (VANETs) enable a countless set of next-generation applications thanks to the technological progress of the last decades. These applications rely on the assumption that a simple network of vehicles can be extended with more complex and powerful network infrastructure, in which several Road Side Units (RSUs) are employed to achieve application-specific goals. However, this assumption is not always satisfied as in many real-world scenarios it is unfeasible to have a conspicuous deployment of RSUs, due to both economic and environmental constraints. With the aim to overcome this limitation, in this paper we investigate how the only Vehicle-to-Vehicle (V2V) communications can be effectively exploited to share data among the vehicles about an event of interest, such as vehicular traffic. In this sense, we propose a novel communication schema based on the Population Protocol model that allows vehicles to be efficiently updated about a given event. Experimental analysis aims to evaluate the performance of the proposed schema, while also highlighting the benefits it might bring in VANETs applications.

Vehicular Social Networks (VSNs) is an emerging communication paradigm, derived by merging the concepts of Online Social Networks (OSNs) and Vehicular Ad-hoc Networks (VANETs). Due to the lack of robust authentication mechanisms, social-based vehicular applications are vulnerable to numerous attacks including the generation of sybil entities in the networks. We address this important issue in vehicular crowdsourcing campaigns where sybils are usually employed to increase their influence and worsen the functioning of the system. In particular, we propose a novel User Recruitment Policy (URP) that, after extracting the participants within the event radius of a crowdsourcing campaign, detects and filters out the sybil vehicles by using a novel sybil detection approach, called SybilDriver. This technique combines the advantages of VANETs and OSNs by means of an innovative concept of proximity graph obtained from the physical vehicular network, in conjunction with a community detection and Random Forest techniques adopted in the OSN domain. Detailed experimental evaluations demonstrate the effectiveness of our approach and also show that it outperforms existing state-of-the-art methods typically used in the OSNs.1

The realization of edge-based cyber-physical systems (CPS) poses important challenges in terms of performance, robustness, security, etc. This paper examines a novel approach to providing a user-centric adaptive route planning service over a network of Road Side Units (RSUs) in smart cities. The key idea is to adaptively select routing task parameters such as privacy-cloaked area sizes and number of retained intersections to balance processing time, privacy protection level, and route accuracy for privacy-augmented distributed route search while also handling per-query user preferences. This is formulated as an optimization problem with a set of parameters giving the best result for a set of queries given system constraints. Processing Throughput, Privacy Protection, and Travel Time Accuracy were developed as the objective functions to be balanced. A Multi-Objective Genetic Algorithm based technique (NSGA-II) is applied to recover a feasible solution. The performance of this approach was then evaluated using traffic data from Osaka, Japan. Results show good performance of the approach in balancing the aforementioned objectives based on user preferences.

The Industry 4.0 paradigm has changed the way industrial systems with hundreds of sensor-actuator enabled devices, including industrial internet of things (IIoT), cooperate and communicate with the physical and human worlds. Given the intricacy, the diagnostics of such systems is extremely important. While anomaly detection is a valid approach to avoid unplanned maintenance or even complete breakdown, its effective realization in IIoT requires the design and implementation of frameworks for efficient monitoring, data collection, and analysis. Most of the existing anomaly detection techniques provide only a diagnosis of the fault without taking into account the uncertainty. Moreover, the lack of ground truth data (which is a typical problem in the industrial context), make their implementation even more challenging. This paper proposes an anomaly detection technique built on top of an industrial framework for the data collection and monitoring. Specifically, we address the lack of labeled data by designing a semi-supervised anomaly detection algorithm that exploits Bayesian Gaussian Mixtures to assess the working condition of the plant while measuring the uncertainty during the diagnosis process and we implement the proposed framework on a real-life IIoT testbed, namely a scale replica assembly plant. Experimental results demonstrate that our anomaly detection algorithm is able to detect the plant working conditions with 99.8% of accuracy, and the semi-supervised approach performs better than a supervised one.

Mobile Crowdsensing System (MCS) applications deploy rating feedback mechanisms to help quantify the trustworthiness of published events which over time improve decision accuracy and establish user reputation. In this paper, we first show that factors such as sparseness, inherent error probabilities of rating feedback labelers, and prior knowledge of the event trust scoring models, can be used by strategic adversaries to hijack the feedback labeling mechanism itself with bad mouthing attacks. Then, we propose a randomized rating sub-sampling technique inspired from moving target defense and cyber deception to mitigate the degradation in the resulting event trust scores of truthful events. We offer a game theoretic strategy under various knowledge levels of an adversary and the MCS in regards to picking an optimal sub-sample size for bad mouthing attacks and event trust calculations respectively, by using a vehicular crowdsensing as a proof-of-concept.

Location estimation in an indoor Internet-of-Things (IoT) environment is a challenging task due to multipath signals and obstacles that cause shadowing and fading effects, and change the received signal power considerably. Most of the existing path-loss-based localization methods assume only a lognormal shadowing model and ignore small scale fading effects. This article considers a generic combined lognormal shadowing and Rayleigh fading model for efficient localization of smart devices in an indoor IoT environment. In particular, the maximum likelihood estimate of the location and path-loss exponent (PLE), and Cramer-Rao lower bound (CRLB) are derived. The localization parameters are estimated using a novel adaptive mini-batch gradient ascent method that maximizes the log-likelihood function with an appropriate batch size based on the convergence factor. Hence, the proposed method addresses the challenge of an arbitrary selection of a fixed batch size for a gradient ascent method by utilizing this convergence factor. Performance evaluation by a simulation study and real experiments from an indoor IoT testbed provide a more accurate joint estimation of model parameters and smart device localization.

In this article, we present the design of an Internet-of-Things (IoT)-based dynamic irrigation scheduling system (AgriSens) for efficient water management of irrigated crop fields. The AgriSens provides real time, automatic, dynamic as well as remote manual irrigation treatment for different growth phases of a crop's life cycle using IoT. A low-cost water-level sensor is designed to measure the level of water present in a field. We propose an algorithm for automatic dynamic-cum-manual irrigation based on farmer requirements. The AgriSens has a farmer-friendly user interface, which provides field information to the farmers in a multimodal manner - visual display, cell phone, and Web portal. It achieves significant results with respect to different performance metrics, such as data validation, packet delivery ratio, energy consumption, and failure rate in various climatic conditions and with dynamic irrigation treatments. Experimental results show that the AgriSens helps improve the crop productivity by at most 10.21% over the traditional manual irrigation method, expands the network's lifetime 2.5 times more than the existing system yet achieving a reliability of 94% even after 500 h of operation.

False power consumption data injected from compromised smart meters in Advanced Metering Infrastructure (AMI) of smart grids is a threat that negatively affects both customers and utilities. In particular, organized and stealthy adversaries can launch various types of data falsification attacks from multiple meters using smart or persistent strategies. In this paper, we propose a real time, two tier attack detection scheme to detect orchestrated data falsification under a sophisticated threat model in decentralized micro-grids. The first detection tier monitors whether the Harmonic to Arithmetic Mean Ratio of aggregated daily power consumption data is outside a normal range known as safe margin. To confirm whether discrepancies in the first detection tier is indeed an attack, the second detection tier monitors the sum of the residuals (difference) between the proposed ratio metric and the safe margin over a frame of multiple days. If the sum of residuals is beyond a standard limit range, the presence of a data falsification attack is confirmed. Both the `safe margins' and the `standard limits' are designed through a `system identification phase', where the signature of proposed metrics under normal conditions are studied using real AMI micro-grid data sets from two different countries over multiple years. Subsequently, we show how the proposed metrics trigger unique signatures under various attacks which aids in attack reconstruction and also limit the impact of persistent attacks. Unlike metrics such as CUSUM or EWMA, the stability of the proposed metrics under normal conditions allows successful real time detection of various stealthy attacks with ultra-low false alarms.

As a new computing paradigm, edge computing emerges in various fields. Many tasks previously relied on cloud computing are distributed to various edge devices that cooperate to complete the tasks. However, circumstantial factors in the edge network (e.g., functionality, transmission efficiency, and resource limitation) become more complex than those in cloud computing. Consequently, there is instability that cannot be ignored in the cooperation between the edge devices. In this article, we propose a novel framework to optimize edge cooperative network (ECN), called ECN-Opt, to improve the performance of edge computing tasks. Specifically, we first define the evaluation metrics for cooperation. Next, the cooperation of an ECN is optimized to improve the performance of specific tasks. Extensive experiments using real datasets from wearable sensors on the players in soccer teams demonstrate that our ECN-Opt framework performs well, and it also validate the effectiveness of the proposed optimization algorithm.

Spurious power consumption data reported from compromised meters controlled by organized adversaries in the Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid’s operations. While existing research on data falsification in smart grids mostly defends against isolated electricity theft, we introduce a taxonomy of various data falsification attack types, when smart meters are compromised by organized or strategic rivals. To counter these attacks, we first propose a coarse-grained and a fine-grained anomaly-based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to indicate: (i) occurrence, (ii) type of attack, and (iii) attack strategy used, collectively known as attack context . Leveraging the attack context information, we propose three attack response metrics to the inferred attack context: (a) an unbiased mean indicating a robust location parameter; (b) a median absolute deviation indicating a robust scale parameter; and (c) an attack probability time ratio metric indicating the active time horizon of attacks. Subsequently, we propose a trust scoring model based on Kullback-Leibler (KL) divergence, that embeds the appropriate unbiased mean, the median absolute deviation, and the attack probability ratio metric at runtime to produce trust scores for each smart meter. These trust scores help classify compromised smart meters from the non-compromised ones. The embedding of the attack context, into the trust scoring model, facilitates accurate and rapid classification of compromised meters, even under large fractions of compromised meters, generalize across various attack strategies and margins of false data. Using real datasets collected from two different AMIs, experimental results show that our proposed framework has a high true positive detection rate, while the average false alarm and missed detection rates are much lesser than 10% for most attack combinations for two different real AMI micro-grid datasets. Finally, we also establish fundamental theoretical limits of the proposed method, which will help assess the applicability of our method to other domains.

In this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a time. To this end, we propose a novel framework based on time-dependent cost graphs to properly model the MFP and tackle the delivery dynamics. When the drone moves in the delivery area, the global wind may change thereby affecting the drone's energy consumption, which in turn can increase or decrease. This issue is addressed by designing three algorithms, namely: (i) compute the route of minimum energy once, at the beginning of the mission, (ii) dynamically reconsider the most convenient trip towards the destination, and (iii) dynamically select only the best local choice. We evaluate the performance of our algorithms on both synthetic and real-world data. The changes in the drone's energy consumption are reflected by changes in the cost of the edges of the graphs. The algorithms receive the new costs every time the drone flies over a new vertex, and they have no full knowledge in advance of the weights. We compare them in terms of the percentage of missions that are completed with success (the drone delivers the goods and comes back to the depot), with delivered (the drone delivers the goods but cannot come back to the depot), and with failure (the drone neither delivers the goods nor comes back to the depot).

In this article, we first formulate the joint resource allocation, interference minimization, user-level, and cell-level fairness for maximum resource reuse in 5G heterogeneous small cell networks as an NP-hard problem. We then propose three algorithms - centralized, distributed, and randomized distributed algorithms - to efficiently solve the formulated resource allocation problem while minimizing interference, maximizing fairness, and resource reuse. Through extensive real data analysis and network simulations, we show that our proposed solutions outperform state-of-the-art schemes, namely interfering model (INT) and distributed random access (DRA), for both low and high-density 5G networks.

The inter-domain routing with BGP is highly vulnerable to malicious attacks, due to the lack of a secure means of verifying authenticity and legitimacy of inter-domain routes. Resource Public Key Infrastructure (RPKI) is a new security infrastructure to prevent the most devastating prefix hijacks in BGP by maintaining a Route Origin Authorization (ROA) repository. However, RPKI is a centralized hierarchical architecture that may empower the centralized authorities to unilaterally revoke or compromise any IP prefixes under their control. To eliminate the risks of RPKI, we present ROAchain, a novel BGP security infrastructure based on blockchain. Different from RPKI, ROAchain is a decentralized architecture, in which each AS maintains a globally consistent and tamper-proof ROA repository, authenticating the legitimacy of route origin and preventing BGP prefix hijacks. To ensure the strong consistency, scalability, and security of ROAchain, a novel consensus algorithm is proposed, in which the credence value, collective signing, sharding, and a penalty mechanism are introduced. Moreover, a compatibility design is proposed without changing the current BGP protocol. Finally, ROAchain is implemented in Golang and validated on the Google Cloud.

The Internet of Vehicles (IoV) offers an emerging paradigm that deals with interconnected vehicles interacting with the infrastructure, roadside units (RSUs), sensors, and mobile devices with a goal to sense, compute, store, and transmit vital information or data over a common channel while vehicles are moving. Secure and reliable communication and efficient on-device performance are thus crucial challenges in this paradigm, particularly in presence of limited computation resources. This paper presents a novel secure and energy-efficient message communication system, called MComIoV, using a one-way hash function and elliptic curve cryptography (ECC). We evaluate MComIoV through security proof and analysis against various attacks to verify its robustness. The proposed system is also implemented and tested on Raspberry Pi 3B+. Experimental results demonstrate the efficiency in computation time, storage cost, communication overhead, and energy consumption.

In this article we study a generalized team orienteering problem (GTOP), which is to find service paths for multiple homogeneous vehicles in a network such that the profit sum of serving the nodes in the paths is maximized, subject to the cost budget of each vehicle. This problem has many potential applications in IoTs and smart cities, such as dispatching energy-constrained mobile chargers to charge as many energy-critical sensors as possible to prolong the network lifetime. In this article, we first formulate the GTOP problem, where each node can be served by different vehicles, and the profit of serving the node is a submodular function of the number of vehicles serving it. We then propose a novel (1 - (1/e)1/2+e)-approximation algorithm for the problem, where ε is a given constant with 0 <; ε ≤ 1 and e is the base of the natural logarithm. In particular, the approximation ratio is about 0.33 when ε = 0.5. In addition, we devise an improved approximation algorithm for a special case of the problem where the profit is the same by serving a node once and multiple times. We finally evaluate the proposed algorithms with simulation experiments, and the results of which are very promising. Especially, the profit sums delivered by the proposed algorithms are up to 14% higher than those by existing algorithms, and about 93.6% of the optimal solutions.

This article deals with reliable and unreliable mobile sensors having identical sensing radius r , communication radius R , provided that r ≤ R and initially randomly deployed on the plane by dropping them from an aircraft according to general random process. The sensors have to move from their initial random positions to the final destinations to provide greedy path k 1 -coverage simultaneously with k 2 -connectivity. In particular, we are interested in assigning the sensing radius r and communication radius R to minimize the time required and the energy consumption of transportation cost for sensors to provide the desired k 1 -coverage with k 2 -connectivity. We prove that for both of these optimization problems, the optimal solution is to assign the sensing radius equal to r = k 1 || E [S]||/2 and the communication radius R = k 2 || E [S]||/2, where || E [S]|| is the characteristic of general random process according to which the sensors are deployed. When r < k 1 || E [S]||/2 or R < k 2 || E [S]||/ 2, and sensors are reliable, we discover and explain the sharp increase in the time required and the energy consumption in transportation cost to ensure the desired k 1 -coverage with k 2 -connectivity.

In this paper, we study the Orienteering Aislegraphs Single-column Problem (OASP), which is a variant of the route planning problem for an entity/robot moving along a specific aisle-graph consisting of a set of rows connected via just one column at one endpoint of the rows. Such constrained aislegraph may model, for instance, a vineyard or warehouse, where each vertex is assigned with a reward that a robot gains when visiting it for accomplishing a task. As the robot is energy limited, it must visit a subset of vertices before going back to the depot for recharging, while maximizing the total reward gained. It is known that the OASP for constrained aisle-graphs composed by m rows of length n is polynomially solvable in O(m2n2) time, which can be prohibitive for graphs of large dimensions. With the goal of designing more time efficient solutions, we propose four algorithms that iteratively build the solution in a greedy manner. These solutions take at most O(mn (m + n)) time, thus improving the optimal solution by a factor of n. Experimentally, we show that these algorithms collect more than 80% of the optimum reward. For two of them, we also guarantee an approximation ratio of 1/2(1 - 1/e)on the reward function by exploiting the submodularity property, where e is the base of the natural logarithm.

Reliable automation of smart grids depends on decisions based on situational awareness extracted via real time system monitoring and accurate state estimation. The Phasor Measurement Units (PMU) at distribution and transmission layers of the smart grid provide high velocity real time information on voltage and current magnitudes and angles in a three phase electrical grid. Naturally, the authenticity of the PMU data is of utmost operational importance. Data falsification attacks on PMU data can cause the Energy Management Systems (EMS) to take wrong decisions, potentially having drastic consequences on the power grid's operation. The need for an automated data falsification attack detection and isolation is key for EMS protection from PMU data falsification. In this paper, we propose an automated distributed stream mining approach to time series anomaly based attack detection that identifies attacks while distinguishing from legitimate changes in PMU data trends. Specifically, we provide a real time learning invariant that reduces the multi-dimensional nature of the PMU data streams for quick big data summarization using a Pythagorean means of the active power from a cluster of PMUs. Thereafter, we propose a methodology that learns thresholds of the invariant automatically, to prove the predictive power of distinguishing between small attacks versus legitimate changes. Extensive simulation results using real PMU data are provided to verify the accuracy of the proposed method.

Location-based services assume significant importance in the Internet of Things (IoT) based systems. In the scenarios where the satellite signals are not available or weak, the Global Positioning System (GPS) accuracy degrades sharply. Therefore, opportunistic signals can be utilized for smart device localization. In this paper, we propose a smart device localization method using residual neural networks. The proposed network is generic and performs smart device localization using opportunistic signals such as Wireless Fidelity (Wi-Fi), geomagnetic, temperature, pressure, humidity, and light signals in the IoT network. Additionally, the proposed method addresses the two significant challenges in IoT based smart device localization, which are noise and device heterogeneity. The experiments are performed on three real datasets of different opportunistic signals. Results show that the proposed method is robust to noise, and a significant improvement in the localization accuracy is obtained as compared to the state-of-the-art localization methods.

In this paper we study a team orienteering problem, which is to find service paths for multiple vehicles in a network such that the profit sum of serving the nodes in the paths is maximized, subject to the cost budget of each vehicle. This problem has many potential applications in IoT and smart cities, such as dispatching energy-constrained mobile chargers to charge as many energy-critical sensors as possible to prolong the network lifetime. In this paper, we first formulate the team orienteering problem, where different vehicles are different types, each node can be served by multiple vehicles, and the profit of serving the node is a submodular function of the number of vehicles serving it. We then propose a novel (1 - (1/e)1/2+ε)approximation algorithm for the problem, where c is a given constant with 0 ≤ ε ≤ 1 and ε is the base of the natural logarithm. In particular, the approximation ratio is no less than 0.32 when ε = 0.5. In addition, for a special team orienteering problem with the same type of vehicles and the profits of serving a node once and multiple times being the same, we devise an improved approximation algorithm. Finally, we evaluate the proposed algorithms with simulation experiments, and the results of which are very promising. Precisely, the profit sums delivered by the proposed algorithms are approximately 12.5% to 17.5% higher than those by existing algorithms.

Driver Stress and Behavior prediction is a significant feature of the Advanced Driver Assistance System. This system can improve driving safety by alerting the driver to the danger of unsafe or risky driving conditions. In this paper, we analyzed historical trip data to calculate the driving stress and its impact on different driving behavior. We used Long Short-Term Memory Fully Convolutional Network to predict the corresponding stress level of the driver. We further established a relationship between stress and driving behavior and developed an intelligent recommendation system for cab companies to recommend the driver for a subsequent trip. To meet the demand for Artificial Intelligence in the Intelligent Transportation System, we leverage Federated Learning in Vehicular Edge Computing in the proposed system architecture. It enables Road Side Units to do all computing of data on it. The model has been tested on the UAH-DriveSet dataset. We observed that the proposed model predicts the stress with an accuracy of 95% and assists in enhancing the driving quality and experience.

The source-based routing using quality of service (QoS) metrics results in alternate path discovery considering link-availability time, link costs, and path delays to overcome the barrier of information blocking on a selected path by choosing alternative routes. However, multipath selection, as well as the cost of selecting a path, make the route selection a challenging task. This paper proposes a vehicle orientation based QoS routing in vehicular ad-hoc networks (VANETs), called OBQR that exploits vehicle orientational information instead of magnitude information. The cosine similarity concept with a preliminary scalarization model converts the multi-constraint objectives into a single constraint objective to find a set of possible paths to the destination. We evaluate the performance of our approach and compare it with existing state-of-the-art schemes based on QoS routing and clustering. Experimental results demonstrate that the proposed scheme significantly improves path selection and load balancing with better QoS routing performance.

Diagnosis of plant health conditions is gaining significant attention in smart agriculture. Timely recognition of early symptoms of a disease can help avoid the spread of epidemics on the plantations. In this regard, most of the existing solutions use some AI techniques on smart edge devices (IoTs or intelligent Cyber Physical Systems), typically equipped with a hardware like sensors and actuators. However, the resource constraints on such devices like energy (power), memory and computation capability, make the execution of complex operations and AI algorithms (neural network models) for disease detection quite challenging. To this end, compression and quantization techniques offer viable solutions to reduce the memory footprint of neural networks while maximizing performance on the constrained devices. In this paper, we realized a real intelligent CPS on top of which we implemented an AI application, called Deep Leaf running on a microcontroller of the STM32 family, to detect coffee plant diseases with the help of a Quantized Convolutional Neural Network (Q-CNN) model. We present a quantitative analysis of Deep Leaf by comparing five different deep learning models: a 32-bit floating point model, a compressed model, and three different types of quantized models exhibiting differences in terms of accuracy, memory utilization, average inference time, and energy consumption. Experimental results show that the proposed Deep Leaf detector is able to correctly classify the plant health condition with an accuracy of 96%, thus demonstrating the feasibility of our approach on a Smart Edge platform.

Software-Defined Networking (SDN) is an innovational network architecture which gives network administrators the ability to directly control the whole network by programming on a centralized controller. Due to network complexity, networks are unlikely to be bug-free. The ability to verify data plane properties will make network management easier for network administrators in SDN. In this paper, we present a novel atomic predicates based data plane properties verification method for SDN using Spark which is a big data processing framework. First, we verify packet reachability which is a fundamental data plane property. Then, we verify other data plane properties such as loop-freedom and nonexistence of black holes. In addition, the proposed method can detect a security threat existing in SDN called firewall bypass threat with packet reachability verification. By adopting atomic predicates, we achieve less computational and storage overhead. We implement the methods and study the performance. The results of experiments show that we can efficiently and accurately detect loops, black holes and firewall bypass threats.

Abstract Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known asgrammatical inference. We also introduce a measure ofsimilaritythat accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity.

Vehicular cyber-physical systems (VCPS), among several other applications, may help address an ever-increasing challenge of traffic congestion in large cities. Nevertheless, VCPS can be hindered by information falsification problem, resulting due to the wrong perception of a traffic event or deliberate faking by the participating vehicles. Such information fabrication causes the re-routing of vehicles and artificial congestion, leading to economic, safety, environmental, and health hazards. Thus, it is imperative to infer truthful traffic information in real-time to restore the operational reliability of the VCPS. In this work, we propose a novel reputation scoring and decision support framework, called Spoofed and False Report Eradicator (SAFE) , which offers a cost-effective and efficient solution to handle information falsification problem in the VCPS domain. The framework includes humans in the sensing loop by exploiting the paradigm of participatory sensing , a concept of a mobile security agent (MSA) to nullify the effects of deliberate false contribution, and a variant of the distance bounding mechanism to thwart location-spoofing attacks. A regression-based model integrates these effects to generate the expected truthfulness of a participant’s contribution. To determine if any contribution is true or false, a generalized linear model is used to transform the expected truthfulness into a Quality of Contribution (QoC) score. The QoC of different reports is aggregated to compute user reputation. Such reputation enables classification of different participation behaviors. Finally, an Expected Utility Theory (EUT) -based decision model is proposed that utilizes the reputation score to determine if event-specific information should be published or dropped. To evaluate the SAFE framework through experimental study, we used both simulated and real data to compare its reputation-based user segregation performance with state-of-the-art frameworks. Experimental results exhibit that SAFE captures the fine differences in participants’ behavior through the quality and quantity of participation, and the accuracy of their informed location. It also significantly improves operational reliability through publishing the information of only legitimate events.

The IETF is currently defining the 6TiSCH architecture for the Industrial Internet of Things to ensure reliable and timely communication. 6TiSCH relies on the IEEE TSCH MAC protocol and defines different scheduling approaches for managing TSCH cells, including a distributed ( neighbor-to-neighbor ) scheduling scheme, where cells are allocated by nodes in a cooperative way. Each node leverages a Scheduling Function (SF) to compute the required number of cells, and the 6top (6P) protocol to negotiate them with neighbors. Currently, the Minimal Scheduling Function (MSF) is under consideration for standardization. However, multiple SFs are expected to be used in real deployments, in order to accommodate the requirements of different use cases. In this article, we carry out a comprehensive analysis of 6TiSCH distributed scheduling to assess its performance under realistic conditions. Firstly, we derive an analytical model to assess the 6P protocol, and we show that 6P transactions take a long time to complete and may also fail. Then, we evaluate the performance of MSF and other distributed SFs through simulations and real experiments. The results show that their performance is affected by the failure of 6P transactions and the instability of the routing protocol, which may lead to congestion from which the network is unable to recover. Finally, we propose a new SF (E-OTF) and show, through simulations and real experiments, that it can effectively improve the overall performance, by allowing nodes to quickly recover from congestion.

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both `quality' (accuracy of contribution) and `quantity' (degree of participation) of their contributions. Specifically, QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT), we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.

This paper analyses the performance of target detection and localization methods in heterogeneous sensor networks using compartmental model, which is an attenuation model expressing the variation of received signal strength (RSS) with propagation distance. First, we compute the threshold for the proposed target detection scheme, based on the decision fusion of different sensors and without requiring a priori probability. We also derive the bound on the threshold and subsequently the lower and upper bounds on the detection and false-alarm probabilities. Next, the location of the detected target is estimated using iterative mini-batch Singular Value Decomposition (SVD) methods in the presence of sensor location uncertainty. We highlight that the method for localization has low computational complexity which is suitable for Internet of Things (IoT) networks. The effectiveness of the compartmental model is demonstrated using both simulation study and real experiments. The model parameters are estimated using WiFi signal strength received on the mobile phones from the access points in an indoor environment.

Ubiquitous Computing (UbiComp) is a computational paradigm that enhances the use of computing devices by making them available to the user anywhere and anytime. From the energy perspective, it is often very important to compute the entire UbiComp task within a specific deadline with minimum energy. The literature on determining the energy consumption of the system for computing the task does not consider periodic tasks and different sampling rate of the sensors, which eliminates the deadline constraints in the analysis. Since the period of the tasks is not fixed, the estimated delay without considering the fixed period is lower than the actual value. In this paper, we assume that an Edge, Fog, and Cloud layers based UbiComp system computes the periodic task within the specific deadline. We derive the expressions of total delay and energy consumption of the UbiComp system. Using the derived expressions, we estimate fractions of the task that are computed at each layer to reduce the energy consumption such that the task is computed within a specific deadline. Our numerical and prototype results demonstrate the impact of the data size, network topologies, deadline, and characteristics of the sensors on the energy consumption, delay, and accuracy of the system.

According to the Smart City Council, an adequate telecommunications infrastructure is vital for the success of businesses, industries as well as residents of Smart cities. However, currently available standard and cellular technologies, such as 3/4G, GSM (Global System for Mobile Communications) and LTE (Long-Term Evolution), are rapidly reaching their limit mainly due to increased traffic demand. Such limitations are only going to worsen in the next years, due to the advent of Internet of Things technologies that are expected to interconnect billions of devices to the Internet. In this paper, we propose a novel network architecture that supports several delay-tolerant (non-real-time) Smart city applications and services (e.g., gathering air pollution information), and therefore, a promising approach to address the burdening of increased traffic demand to Smart city's legacy standard and cellular communication infrastructure. The proposed architecture is based on an innovative diverse band-aware Dynamic Spectrum Access (d-DSA) paradigm, that allows a certain wireless device to opportunistically access idle channels in multiple licensed/unlicensed spectrum bands. d-DSA radio devices are mounted on Smart city's urban vehicles (e.g., taxis) that act as mobile routers to gather, carry, and forward various types of data traffic. This results in a time-varying and unpredictable delay-tolerant network (DTN) where each node can access whitespace channels and transmit in multiple spectrum bands. Given lack of research in efficient routing schemes for such d-DSA DTN networks, we propose a distributed and lightweight d-DSA aware Geographical Routing (dDSA-GR) protocol, that utilizes a weighted linear metric for selecting a suitable spectrum band, and classic georouting principle for choosing next hop node in the path route between any node pair in d-DSA DTNs. Results on realistic traces based on the map of Lexington, KY, USA, show that our dDSA-GR routing protocol outperforms baseline approaches in terms of network delay, message delivery ratio, and energy efficiency, under all considered scenarios.

Electronic communication is crucial for monitoring the rescue-relief operations and providing assistance to the affected people during and after disasters. Given the ubiquity of smartphones, we envision that smartphones with lost connection (due to damage) to the communications infrastructure are nevertheless integrated seamlessly into the network as far as possible. To achieve this, we propose to build ad hoc subnetworks of disconnected smartphones using the WiFi tethering technology and ultimately connect them to either the emergency communication equipment deployed in the disaster area or to other smartphones that have still the network connectivity. The proposed architecture for such integration and a defined software-based control through the emergency control center (ECC) enables battery aware collection of critical data through smartphone sensors. The developed solution supports mobility of all smartphones, including those that have lost direct cellular connectivity as well as those that have not and are willing to act as gateways. We demonstrate how the proposed scheme can be tied to the standardized wireless emergency alert service and how it can effectively handle mobility tolerant device discovery and data transfer.

In this paper we study the min-max cycle cover problem with neighborhoods, which is to find a given number of K cycles to collaboratively visit n Points of Interest (POIs) in a 2D space such that the length of the longest cycle among the K cycles is minimized. The problem arises from many applications, including employing mobile sinks to collect sensor data in wireless sensor networks (WSNs), dispatching charging vehicles to recharge sensors in rechargeable sensor networks, scheduling Unmanned Aerial Vehicles (UAVs) to monitor disaster areas, etc. For example, consider the application of employing multiple mobile sinks to collect sensor data in WSNs. If some mobile sink has a long data collection tour while the other mobile sinks have short tours, this incurs a long data collection latency of the sensors in the long tour. Existing studies assumed that one vehicle needs to move to the location of a POI to serve it. We however assume that the vehicle is able to serve the POI as long as the vehicle is within the neighborhood area of the POI. One such an example is that a mobile sink in a WSN can receive data from a sensor if it is within the transmission range of the sensor (e.g., within 50 meters). It can be seen that the ignorance of neighborhoods will incur a longer traveling length. On the other hand, most existing studies only took into account the vehicle traveling time but ignore the POI service time. Consequently, although the length of some vehicle tour is short, the total amount of time consumed by a vehicle in the tour is prohibitively long, due to many POIs in the tour. In this paper we first study the min-max cycle cover problem with neighborhoods, by incorporating both neighborhoods and POI service time into consideration. We then propose novel approximation algorithms for the problem, by exploring the combinatorial properties of the problem. We finally evaluate the proposed algorithms via experimental simulations. Experimental results show that the proposed algorithms are promising. Especially, the maximum tour times by the proposed algorithms are only about from 80% to 90% of that by existing algorithms.

Web-based collaboration provides a platform which allows users from different domains to share and access information. In such an environment, mitigating threats from insider attacks is challenging, particularly if state-of-the-art token-based access control is used to authorize (permit or deny) requests. This entails the need for an additional layer of authorization based on soft-security factors such as the reputation of the requesters, risks involved in requests, and so on to make the final decision. In this paper, we propose a novel risk quantification framework, called $R2Q$, which exploits a weighted regression approach to compute the expected threat related to a collaboration request. Our model combines the shared object's sensitivity, access mode of the request, requester's security level and reputation, and maps the expected threat to a risk score using the prospect theory (PT) inspired value functions to actualize decision making under uncertainty of economic outcomes (loss or gain). Simulation-based performance evaluation validates the efficacy of our framework and demonstrates that it can classify requesters based on their past behaviours, and also enables the collaboration platform to achieve higher rates of successful authorization.

This paper presents a novel network architecture, termed neXt-generation CHAllenged NeTwork (X-CHANT), for improving connectivity in rural environments. The underlying idea is to deploy diverse Dynamic Spectrum Access (d-DSA) radio devices on the public transportation vehicles, such as buses. This results in a d-DSA enabled delay-tolerant network in which the devices can operate in various (un)licensed bands (e.g., TV, LTE, ISM, CBRS), if available. Given the lack of research in efficient routing for such time-varying d-DSA enabled networks, we propose a novel diverse DSA aware routing (dDSAaR) protocol that jointly exploits various (un)licensed bands besides the time-varying yet sufficiently predictable mobility of public transportation vehicles. We compare X-CHANT, utilizing dDSAaR, to the conventional non-DSA/DSA architectures, utilizing a standard (single band) routing protocol (e.g., Epidemic). We use real bus mobility traces collected at the University of Massachusetts, Amherst campus. Results show that X-CHANT achieves better message delivery, negligible message overhead, and better energy expenditure, at the expense of a slight increase in delay. Never-theless, the delay improves with higher predictable mobility.

The IETF is currently defining the 6TiSCH architecture for the Industrial Internet of Things to provide reliable and timely communication. 6TiSCH includes a distributed management mode, in which network resources are computed autonomously by nodes and allocated in a cooperative way. Specifically, nodes use the 6top protocol to negotiate network resources with their neighbors. In this paper, we investigate the performance of 6top protocol through a set of experimental measurements on a testbed. We show that the time required to complete a 6top transaction in a real environment is not negligible, as often assumed in many studies. In addition, a significant percentage of 6top transactions fails, with a negative impact the performance of the application. We investigate the reasons for such failures and propose some guidelines to reduce their number.

The next-generation wireless network is expected to be highly dense with a large number of Device-to-Device (D2D) communication enabled IoT devices in fog computing based cellular networks. The dense deployment of the heterogeneous network architecture is expected to fulfill the smart devices' growing data demand, lower power consumption, and lower latency constraint. The 5G technology is expected to have such multi-tier architecture with various computational capability and radio enabled IoT devices. However, the coexistence of such heterogeneous network model spawns research challenges such as interference management, non-uniform computational capacity with non-uniform devices connectivity and service deadline. Thus, in this paper, we propose a coexistence of D2D-IoT (D-IoT) and fog computing model in cellular networks and formulate the resource allocation problem in such a multi-tier architecture considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a distributed many-to-many stable matching based solution. Through extensive theoretical and simulation analysis, we have shown the effect of different parameters on the resource allocation objectives and achieve more than 94% of optimum network performance.

In the aftermath of large-scale disasters, such as earthquakes or hurricanes, existing communication infrastructures are often critically impaired, preventing timely information exchange between the survivors, responders, and the coordination center. Smart devices, movable base stations, easily deployable WiFi routers, and unimpaired communication towers can be used to set up temporary networks, called disaster response networks (DRNs). However, such networks are challenged by rapid energy depletion of smart devices as well as component failures. To address these issues, in this paper we propose a novel energy-efficient yet robust DRN topology, termed Bio-DRN, that mimics the inherent robustness of a biological network of living organisms, called gene regulatory network (GRN). Specifically, the Bio-DRN is a subgraph of the DRN topology generated by one-to-one mapping between the structurally similar genes and DRN components, i.e., survivors, points of interest like shelter points, and the coordination center. We first formulate the construction of Bio-DRN topology as an integer linear programming optimization problem, and show that it is NP-hard. Then, we present a sub-optimal heuristic that constructs the Bio-DRN topology as a common subgraph of both GRN and DRN topologies. Our experimental study on a real disaster prone region in Bhaktapur, Nepal, shows that Bio-DRN preserves the topological properties of GRN, such as low graph density and motif abundance, and achieves both energy efficiency and network robustness, while ensuring timely message delivery.

Internet-of-Things (IoT) is a networking architecture where promising, intelligent services are designed via leveraging information from multiple heterogeneous sources of data within the network. However, the availability of such information in a timely manner requires processing and communication of raw data collected from these sources. Therefore, the economic feasibility of IoT-enabled networks relies on the efficient allocation of both computational and communication resources within the network. Since fog computing and 5G cellular networks approach this problem independently, there is a need for joint resource-provisioning of both communication and computational resources in the networks. As the solution to this problem, we propose a novel three-dimensional matching based resource provisioning algorithm that minimizes average service latency in the presence of various resource constraints, task deadlines and non-identical preferences at IoT devices, fog access points (FAPs) and small-cell access points (SAPs) in 5G networks. We prove the stability and termination of the proposed algorithm and also demonstrate that our proposed algorithm outperforms other state-of-the-art algorithms through both, simulation and real-world experiments on the laboratory test-bed.

Recent years have seen a proliferation of intelligent (automated) decision support systems for various smart city applications such as energy management, transportation, healthcare, environment monitoring, and so on. A key enabler in the smart city paradigm is the Internet-of-Things (IoT) network of smart sensing and actuation devices assisting in real-time detection and monitoring of physical phenomena. The underlying IoT network must be energy-efficient for application sustainability and also quality of information (QoI)-aware for near-perfect device actuation. To this end, this paper proposes bioSmartSense, a novel bio-inspired distributed event sensing and data collection framework, based on the gene regulatory networks (GRNs) in living organisms. The idea is to make the sensing and reporting tasks energy-efficient through self-modulation of IoT device energy levels, analogous to the activation or repression of genes by the regulating proteins, called Transcription Factors (TFs). To support energy-efficient and QoI-aware information dissemination, we first customize a heuristic designed for the Maximum Weighted Independent Set problem encompassing both `quality' and `quantity' of sensed data, where the former depends on the device energy levels while the latter on the number of events sensed. We utilize the heuristic to propose a sub-optimal device selection mechanism constrained on the IoT network's overall residual energy. Simulation experiments demonstrate that the bioSmartSense framework achieves better energy-efficiency while maximizing event reporting compared to a state-of-the-art data collection approach for smart city applications.

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory test-bed and comparing with the state-of-the-art algorithm.

Data quality, or sometimes referred to as data credibility, is a critical issue in mobile crowd sensing (MCS) and more generally Internet of Things (IoT). While candidate solutions, such as incentive mechanisms and data mining have been well explored in the literature, the power of crowds has been largely overlooked or under-exploited. In this paper, we propose a cross validation approach which seeks a validating crowd to ratify the contributing crowd in terms of the sensor data contributed by the latter, and uses the validation result to reshape data into a more credible posterior belief of the ground truth. This approach consists of a framework and a mechanism, where the framework outlines a four-step procedure and the mechanism implements it with specific technical components, including a weighted random oversampling (WRoS) technique and a privacy-aware trust-oriented probabilistic push (PATOP2) algorithm. Unlike most prior work, our proposed approach augments rather than redesigning existing MCS systems, and requires minimal effort from the crowd, making it conducive to practical adoption. We evaluate our proposed mechanism using a real-world MCS IoT dataset and demonstrate remarkable (up to 475%) improvement of data quality. In particular, it offers a unified solution to reconciling two disparate needs: reinforcing obscure (weakly recognizable) ground truths and discovering hidden (unrecognized) ground truths.

Current state-of-the-art systems in the literature using wearables are not capable of distinguishing a large number of fine-grained and/or complex human activities, which may appear similar but with vital differences in context, such as lying on floor versus lying on bed versus lying on sofa. This paper fills the gap by proposing a novel system, called HuMAn, that recognizes and classifies complex at-home activities of humans with wearable sensing. Specifically, HuMAn makes such classifications feasible by leveraging selective multi-modal sensor suites from wearable devices, and enhances the richness of sensed information for activity classification by carefully leveraging placement of the wearable devices across multiple positions on the human body. The HuMAn system consists of the following components: (a) a practical feature set extraction method from selected multi-modal sensor suites; and (b) a novel two-level structured classification algorithm that improves accuracy by leveraging sensors in multiple body positions; and (c) improved refinement in classification of complex activities with minimal external infrastructure support (e.g., only a few Bluetooth beacons used for location context). The proposed system is evaluated with 10 users in real home environments. Experimental results demonstrate that the HuMAn system can detect 21 complex at-home activities with high degree of accuracy. For same-user evaluation strategy, the average activity classification accuracy is as high as 95 percent over all of the 21 activities. For the case of 10-fold cross-validation evaluation strategy, the average classification accuracy is 92 percent, and for the case of leave-one-out cross-validation strategy, the average classification accuracy is 75 percent.

Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform the state of the art work by improving the efficiency in recruiting participants by 30 percent.

The industrial Internet of Things (IIoT) is expected to revolutionize the current industry. The capillary introduction of sensors and actuators for real-time monitoring and remote control and their seamless integration into existing information systems will represent a technological breakthrough that will help to reshape the industrial processes. To this aim, the definition of wireless communication standards will play a crucial role in reducing deployment costs and minimizing the time for installation. In this context, the new IPv6 over the TSCH mode of IEEE 802.15.4e communication stack, 6TiSCH, represents the current leading standardization effort that aims at achieving both reliable and timed wireless communication and integration within IPv6 communication networks for industrial systems. In this paper, the network formation dynamics of 6TiSCH networks are assessed, considering the current guidelines for the so-called minimal configuration, a static initial configuration pre-configured to guarantee control communication during network bootstrap. It is shown that the minimal configuration might lead to long network formation and suboptimal performance of the routing algorithm which may result into a disconnected network. In order to overcome this issue, a dynamic resource management algorithm to be executed during network bootstrap is proposed. Simulation and experimental results show that the proposed solution allows to minimize the network formation time and also helps in optimizing routing operations leading to the discovery of better routes.

Time-dependent pricing (TDP) sets different prices in different time slots in order to motivate users to shift their delay-tolerant flows from congested time slots to less congested ones, thus helping Internet service providers (ISPs) utilize their network capacity more efficiently. In existing TDP approaches, however, once a flow is delayed to a less congested time slot by a user, the user has to wait until that time slot to consume the flow, even if there is idle capacity in earlier time slot(s) to accommodate the flow. In addition, in case that the traffic usage shifted to some time slots is so aggressive that new congestion is caused, it is hard for the ISP to accommodate more bursty traffic. To address these issues, in this paper we propose an approach to pre-schedule the delayed flows before their deadlines. Our results from extensive simulations show that the proposed approach could benefit both users and ISPs. For example, an ISP can smooth its bandwidth usage, which in turn makes it possible to accommodate more bursty traffic.

Thanks to the collective action of participating smartphone users, mobile crowdsensing allows data collection at a scale and pace that was once impossible. The biggest challenge to overcome in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior, thus compromising the accuracy of the data collection process. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To address this crucial issue, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST) that leverages mobile trusted participants (MTPs) to securely assess the reliability of sensing reports. FIRST models and solves the challenging problem of determining before deployment the minimum number of MTPs to be used to achieve desired classification accuracy. After a rigorous mathematical study of its performance, we extensively evaluate FIRST through an implementation in iOS and Android of a room occupancy monitoring system and through simulations with real-world mobility traces. Experimental results demonstrate that FIRST reduces significantly the impact of three security attacks (i.e., corruption, on/off, and collusion) by achieving a classification accuracy of almost 80% in the considered scenarios. Finally, we discuss our ongoing research efforts to test the performance of FIRST as part of the National Map Corps project.

The MAC standard amendment IEEE 802.15.4e is designed to meet the requirements of industrial and critical applications. In particular, the Time Slotted Channel Hopping (TSCH) mode divides time into periodic, equally sized, slotframes composed of transmission timeslots. Then, it combines time slotted access with multichannel and channel hopping capabilities, providing large network capacity, high reliability, and predictable latency while ensuring energy efficiency. Since every network node considers the same timeslots at each slotframe and selects physical channels according to a periodic function, TSCH produces a steady channel utilization pattern. This can be exploited by a selective jammer to entirely thwart communications of a victim node in a way that is stealthy, effective, and extremely energy efficient. This article shows how a selective jamming attack can be successfully performed even though TSCH uses the IEEE 802.15.4e security services. Furthermore, we propose DISH, a countermeasure which randomly permutes the timeslot and channel utilization patterns at every slotframe in a consistent and completely distributed way without requiring any additional message exchange. We have implemented DISH for the Contiki OS and tested its effectiveness on TelosB sensor nodes. Quantitative analysis for different network configurations shows that DISH effectively contrasts selective jamming with negligible performance penalty.

In this paper, we present a novel community sensing paradigm CSWA –Community Sensing Without Sensor/Location Data Aggregation. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea of the target area, without aggregating sensor and location data collected by community members. CSWA operates on top of a secured peer-to-peer network over the community members and proposes a novel Decentralized Spatial-Temporal Compressive Sensing framework based on Parallelized Stochastic Gradient Descent. Through learning the low-rank structure via distributed optimization, CSWA approximates the value of the sensor data in each subarea (both covered and uncovered) for each sensing cycle using the sensor data locally stored in each member’s mobile device. Simulation experiments based on real-world datasets demonstrate that CSWA exhibits low approximation error (i.e., less than 0.2 centi-degree in city-wide temperature sensing task and 10 units of PM2.5 index in urban air pollution sensing) and performs comparably to (sometimes better than) state-of-the-art algorithms based on the data aggregation and centralized computation.

Computing single-source shortest paths (SSSP) is one of the fundamental problems in graph theory. There are many applications of SSSP including finding routes in GPS systems and finding high centrality vertices for effective vaccination. In this paper, we focus on calculating SSSP on big dynamic graphs, which change with time. We propose a novel distributed computing approach, SSSPIncJoint, to update SSSP on big dynamic graphs using GraphX. Our approach considerably speeds up the recomputation of the SSSP tree by reducing the number of map-reduce operations required for implementing SSSP in the gather-apply- scatter programming model used by GraphX.

Compromised smart meters sending false power consumption data in Advanced Metering Infrastructure (AMI) may have drastic consequences on the smart grid»s operation. Most existing defense models only deal with electricity theft from individual customers (isolated attacks) using supervised classification techniques that do not offer scalable or real time solutions. Furthermore, the cyber and interconnected nature of AMIs can also be exploited by organized adversaries who have the ability to orchestrate simultaneous data falsification attacks after compromising several meters, and also have more complex goals than just electricity theft. In this paper, we first propose a real time semi-supervised anomaly based consensus correction technique that detects the presence and type of smart meter data falsification, and then performs a consensus correction accordingly. Subsequently, we propose a semi-supervised consensus based trust scoring model, that is able to identify the smart meters injecting false data. The main contribution of the proposed approach is to provide a practical framework for compromised smart meter identification that (i) is not supervised (ii) enables quick identification (iii) scales classification error rates better for larger sized AMIs; (iv) counters threats from both isolated and orchestrated attacks; and (v) simultaneously works for a variety of data falsification types. Extensive experimental validation using two real datasets from USA and Ireland, demonstrates the ability of our proposed method to identify compromised meters in near real time across different datasets.

Computing the single-source shortest path (SSSP) is one of the fundamental graph algorithms, and is used in many applications. Here, we focus on computing SSSP on large dynamic graphs, i.e. graphs whose structure evolves with time. We posit that instead of recomputing the SSSP for each set of changes on the dynamic graphs, it is more efficient to update the results based only on the region of change. To this end, we present a novel two-step shared-memory algorithm for updating SSSP on weighted large-scale graphs. The key idea of our algorithm is to identify changes, such as vertex/edge addition and deletion, that affect the shortest path computations and update only the parts of the graphs affected by the change. We provide the proof of correctness of our proposed algorithm. Our experiments on real and synthetic networks demonstrate that our algorithm is as much as 4X faster compared to computing SSSP with Galois, a state-of-the-art parallel graph analysis software for shared memory architectures. We also demonstrate how increasing the asynchrony can lead to even faster updates. To the best of our knowledge, this is one of the first practical parallel algorithms for updating networks on shared-memory systems, that is also scalable to large networks.

Access control is an important mechanism in cloud computing. The Key Policy Attribute Based Encryption (KP-ABE) is an important method to implement the access control in cloud service. However, conventional user revocation scheme in KP-ABE costs huge computational overhead. In this paper, we focus on the important user revocation issue in KP-ABE. We introduce several control parameters, including version value, check value and user list. We combine KP-ABE with salt encryption for the implementation. We provide a novel user revocation scheme for KP-ABE to improve the user revocation issue, which can reduce the heavy computational overhead when user being revoked. The performance evaluation shows that the proposed user revocation scheme gives good performance with KP-ABE.

Unmanned Aerial Vehicles (UAVs) have enormous potentials for several important applications, such as search and rescue and structural health monitoring. An important requirement for these applications is the ability to accurately localize objects, such as sensors or ``smart-things'', equipped with wireless communication capability. However, most previous works in this area neglect the unavoidable errors that are involved in the localization process, thus resulting in poor performance in practice. In this paper, for the first time, we express the measurement error on the ground as a function of the rolling, altitude, and instrumental precision provided by the hardware on the drone. We takeaway two lessons from this analysis: to limit the ground error (i) all the waypoints used to measure the same node must be at a sufficiently large ground distance from the node itself, and (ii) they must not be collinear among themselves nor with the node. We validate the error expressions derived analytically through real experiments using the 3DR Solo Drone.

Software-Defined networking (SDN), as a new paradigm, fixes the shortage that traditional network does not support the dynamic, scalable computing and storage needs of more computing environments. SDN, however, also faces security problems such as vulnerable to DDoS attacks. DDoS attacks are well-known and powerful attacks. DDoS detection and DDoS traffic separation for SDN environments are still an open research issue. DDoS attacks in SDN environments will not only bring damage to target server, but also takes exact impact on SDN system. In this paper, we identify a new type DDoS attack, specifically aiming SDN environment, which is harder to be detected. We propose a novel real-time DDoS detection scheme for SDN environment, by using Principal Component Analysis (PCA) scheme to analyze the network status on traffic packets data. We separate the network into different parts, to reduce the total calculation burden. We compare our scheme with sample entropy, showed our scheme achieves better detecting ability for DDoS attacks.

The scheduling of mixed-criticality (MC) systems with graceful degradation is considered, where LO-criticality tasks are guaranteed some service in HI mode in the form of minimum cumulative completion rates. First, we present an easy to implement admission-control procedure to determine which LO-criticality jobs to complete in HI mode. Then, we propose a demand-bound-function-based MC schedulability test that runs in pseudo-polynomial time for such systems under EDF-VD scheduling, wherein two virtual deadline setting heuristics are considered. Furthermore, we discuss a mechanism for the system to switch back from HI to LO mode and quantify the maximum time duration such recovery process would take. Finally, we show the effectiveness of our proposed method by experimental evaluation in comparison to state-of-the-art MC schedulers.

Bike-Sharing Systems (BSSs) are being introduced to more and more cities recently, and therefore they have generated huge amounts of data. Mobike is a station-less BSS which is suffering from the chaotic parking problem. To solve this problem, it is necessary to predict where the bikes are going. Traditional works dealing with destination prediction mainly focus on station-based BSSs, and they merely leverages context-aware information technically. Thus it is naturally promising to investigate how to improve the destination prediction of station-less bikes by context information. To that end, in this paper, we develop a multi-view machine (MVM) method, by incorporating the context information from Point of Interest (POI) data and human mobility data into destination prediction. Specifically, we first describe three different views, namely start position, start time and destination by features extracted from POI data and human mobility data. Then, we capture the relationship between these three views' interactions and the trip's possibility by a multi-view machine. Finally, since multi-view machine contains too many parameters to be optimized, we leverage tensor factorization (TF) to reduce the computation costs. The experimental results show that the model can effectively capture the potential relationship of three views with trip's possibility and the approach is thus much more effective than traditional prediction methods for destination.

Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset.

The large number of internet-connected devices will continue to drive growth in data traffic in an exponential way, forcing network operators to increase the capacity of wireless networks. To do so in the cost-effective way a paradigm shift is occurring in 5G cellular networks from high power macro base station to small cell heterogeneous networks known as microcells, picocells, and femtocells. This paradigm shift of 5G cellular networks gives many opportunities ranging from increase capacity to reuse the scarce spectrum resources to coexistence to interference minimization etc. The coexistence of heterogeneous small cells makes the resource allocation, interference management, and maximum fairness among the users more complicated. In this paper, we formulate the resource allocation for spectrum reuse maximization, interference minimization and user level fairness in heterogeneous small cells 5G cellular networks as a NP-hard problem. We design centralized and probability based heuristic for the above resource allocation problem in-order to minimize interference and to achieve maximum spectrum reuse and fairness among the users in feasible computational complexity. We show through extensive network simulations that our proposal outperforms existing centralized interfering model (INT) and distributed random access (DRA) in both low and high-density networks.

The IETF 6TiSCH Working Group (WG) is currently standardizing a novel architecture to integrate IEEE 802.15.4e TSCH Wireless Networks into the Internet of Things. Within the proposed 6TiSCH architecture, a Scheduling Function (SF) is used to manage the allocation of communication resources (i.e., TSCH timeslots). Although many SFs have been proposed in literature, the 6 TiSCH WG is considering the On-The-Fly (OTF) Bandwidth Reservation Algorithm as the reference SF. In this paper, we carry out an extensive simulation analysis of OTF to evaluate if, in its current definition, it can ensure the low latency and high reliability requirements of critical applications targeted by TSCH. Specifically, we investigate how its performance is affected by the RPL protocol and the 6top protocol used for routing and distributed resource negotiation, respectively. Our results show that the OTF performance is significantly affected by (i) frequent changes in the preferred parent selected by RPL, and (ii) failures in resource negotiation. These events typically lead to congestion, from which the network can hardly recover. To overcome such limitations, we propose a modified version of the Bandwidth Reservation Algorithm, named Enhanced-OTF. We show that proposed modifications can effectively improve the overall network performance by allowing network nodes to recover from congestion in a short time.

In this paper, we propose a tractable semi-analytical approach for the network performance analysis of uplink (UL) cellular networks, which is based on a deterministic network analysis using a Gaussian approximation (DNA-GA). The key contribution of this paper is to investigate the UL signal-to-interference ratio (SIR) performance using the DNA-GA analysis. In particular, the SIR is modeled as a ratio of two random variables (RVs), representing the signal power and the aggregate interference power, respectively. The signal power is further characterized by a product of two RVs, i.e., a lognormal RV and an RV with an arbitrary distribution. The former RV comes from a common assumption of lognormal shadow fading, and the latter one takes the rest of random factors into account, such as random user positions, arbitrary types of multi-path fading, and so on. The aggregate interference power is approximated by an RV with a power lognormal distribution. The proposed DNA-GA analysis has several desirable features: 1) it naturally considers lognormal shadow fading; 2) it can treat arbitrary shape and/or size of cell coverage areas; 3) it can handle non-uniform user distributions; 4) it can cope with any type of multi-path fading; and 5) it can be applied to multi-antenna base stations. These features make the DNA-GA analysis very useful for the network performance analysis of the 5th generation systems with general cell deployment and user distribution.

Kinetic energy harvesting (KEH) may help combat battery issues in wearable devices. While the primary objective of KEH is to generate energy from human activities, the harvested energy itself contains information about human activities that most wearable devices try to detect using motion sensors. In principle, it is therefore possible to use KEH both as a power generator and a sensor for human activity recognition (HAR), saving sensor-related power consumption. Our aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE). We evaluate the performance of HARKE using two independent datasets: (i) a public accelerometer dataset converted into KEH data through theoretical modeling; and (ii) a real KEH dataset collected from volunteers performing activities of daily living while wearing a data-logger that we built of a piezoelectric energy harvester. Our results show that HARKE achieves an accuracy of 80 to 95 percent, depending on the dataset and the placement of the device on the human body. We conduct detailed power consumption measurements to understand and quantify the power saving opportunity of HARKE. The results demonstrate that HARKE can save 79 percent of the overall system power consumption of conventional accelerometer-based HAR.

Opportunistic computing is a paradigm for completely self-organised pervasive networks. Instead of relying only on fixed infrastructures as the cloud, users’ devices act as service providers for each other. They use pairwise contacts to collect information about services provided and amount of time to provide them by the encountered nodes. At each node, upon generation of a service request, this information is used to choose the most efficient service, or composition of services, that satisfy that request, based on local knowledge. Opportunistic computing can be exploited in several scenarios, including mobile social networks, IoT, and Internet 4.0. In this paper, we propose an opportunistic computing algorithm based on an analytical model, which ranks the available (composition of) services, based on their expected completion time. Through the model, a service requester picks the one that is expected to be the best. Experiments show that the algorithm is accurate in ranking services, thus providing an effective service-selection policy. Such a policy achieves significantly lower service provisioning times compared to other reference policies. Its performance is tested in a wide range of scenarios varying the nodes mobility, the size of input/output parameters, the level of resource congestion, and the computational complexity of service executions.

Smart and connected communities (SCCs) are emerging as a novel paradigm that allows the community residents to be connected with surrounding environments through smart technologies. However, there remain important challenges to fully exploit the potential of SCCs in improving societal well-being and prosperity. In particular, there is a need for designing green communication systems that are also capable of providing high quality of service (QoS) to distribute and collect information to and from SCCs. However, simultaneously satisfying both of these criteria is difficult due to varying demands posed by heterogeneous sensing modalities, lack of dedicated infrastructure in rural/sub-urban areas, and certain sustainability constraints. While low-power short-range technologies often fail to achieve high QoS, using 3G or 4G technologies (LTE, LTE-A, GSM) for SCCs will eventually face spectrum scarcity and cross technology interference. In recent times, Dynamic spectrum access (DSA) has been proposed as a solution to overcome policy constraints and improve spectrum scarcity by spectrum sharing. In this article, we show that harnessing DSA in the context of SCCs can also achieve notable benefits in terms of energy efficiency and sustainability. Specifically, we propose a novel architecture for designing sustainable SCCs using a small-scale DSA-enabled overlay network that improves end-to-end energy efficiency of the network while guaranteeing QoS. We also propose a dynamic spectrum band selection approach that intelligently matches any message requirement to a suitable band type by exploiting distinct electro-magnetic characteristics of various bands. Since data generated in SCCs are typically valuable only when delivered within a certain hard (or soft ) deadline, we formulate a linear optimization problem for determining the most energy-efficient path that ensures a delivery time within the hard deadline. After proving that such a problem is NP-Hard, we propose an exact pseudo-polynomial time dynamic programming algorithm to solve it followed by a polynomial time greedy heuristic. Additionally, we formulate a non-linear optimization problem to find the optimal path when the message delivery time is defined as a soft deadline and extend our greedy heuristic to handle soft deadlines. Compared to the homogeneous band access approaches that opportunistically access free channels within a given spectrum band, our extensive simulation study shows that the proposed dynamic multi-band selection approach significantly improves the achievable energy efficiency while meeting various hard and soft deadlines.

Next-generation wireless networks are going to have highly dense, small cell structure with a large number of femtocells. The dense deployment of the femtocell network architecture is expected to meet the growing data demand by leveraging millimeter-wave structure of 5G wireless networks. However, arbitrary deployment of large number of femtocells underlying a macrocell will pose a challenge for collision and confusion-free Physical Cell ID (PCID) assignments as the total number of available PCIDs is limited to 504. In this paper we propose a distributed, randomized k-clustering algorithm for collision and confusion-free PCID assignment problem, which is known to be NP-complete. To reduce the total control message flow, we create overlapping clusters in ultra-dense femtocellular networks, where each cluster head runs the distributed randomized PCID allocation algorithm and locally monitors the conflicts to avoid the collision and confusion constraints. We prove the correctness of our proposed algorithm and analyze its time and message complexity. Through simulation experiments, we also show the effect of different parameters on the PCID allocation objectives.

Running is one of the most important workouts to keep our body fit. This paper presents RunnerPal - a runner monitoring and advisory system by harmonizing the rhythms of breathing, heart beating and striding based on smart devices. RunnerPal is a convenient, biofeedback-based, automated music recommendation system, which utilizes Bluetooth headset, Apple Watch and smartphone to obtain body sensed data. To improve the accuracy of the detection, we propose a novel approach to calibrate the result by integrating ambient sensed data with a physiological model called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the striding and breathing frequencies. RunnerPal uses the sensed data and runner's contextual information to provide dynamic music suggestions to help the user achieve a target heart rate. We perform an empirical study to show the effect of music on heart rate and devise a Proportional Integral Differentiation Controller (PID - Controller) that recommends appropriate music to the user. RunnerPal has been validated by extensive experiments, and experimental results demonstrate that it can help runners achieve a target heart rate and maintain a stable running rhythm for indoor/outdoor running 91.6 percent of the time. In addition, RunnerPal can provide some advice to improve exercise effectiveness for runners.

Compromised smart meters reporting false power consumption data in Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid's operations. Most existing works only deal with electricity theft from customers. However, several other types of data falsification attacks are possible, when meters are compromised by organized rivals. In this paper, we first propose a taxonomy of possible data falsification strategies such as additive, deductive, camouflage and conflict, in AMI micro-grids. Then, we devise a statistical anomaly detection technique to identify the incidence of proposed attack types, by studying their impact on the observed data. Subsequently, a trust model based on Kullback-Leibler divergence is proposed to identify compromised smart meters for additive and deductive attacks. The resultant detection rates and false alarms are minimized through a robust aggregate measure that is calculated based on the detected attack type and successfully discriminating legitimate changes from malicious ones. For conflict and camouflage attacks, a generalized linear model and Weibull function based kernel trick is used over the trust score to facilitate more accurate classification. Using real data sets collected from AMI, we investigate several trade-offs that occur between attacker's revenue and costs, as well as the margin of false data and fraction of compromised nodes. Experimental results show that our model has a high true positive detection rate, while the average false alarm rate is just 8%, for most practical attack strategies, without depending on the expensive hardware based monitoring.

One of the most intriguing aspects of network analysis is how links or interactions occur over time between a pair of nodes and whether we can have a model to accurately predict the occurrence of links ahead of time, and with what accuracy. In contrast to the existing approaches, this paper proposes a novel Markov prediction model over the time-varying graph of an underlying social network. The model considers the effect of multiple time scales in leveraging temporal analysis for link prediction. The analysis considers fine-grained and coarsegrained time scales, along with associated local (links) and semiglobal (clusters) structural evolution, respectively. The model takes into account correlated evolution and rate of evolution in selecting start and end nodes, and the corresponding interaction probability. Finally, we use temporal data of two heavily dynamic real world social networks (e.g., Twitter and Facebook), and a relatively lesser dynamic network (e.g., DBLP) to demonstrate the prediction accuracy that our Markov model outperforms two recent dynamic approaches in the range of 7.5% to 19.81%.

The real-world large scale networks motivate the need for parallel and distributed evaluation of network analysis and computational tasks for computational efficiency and application effectiveness. One of the essential tasks for parallel and distributed evaluation, is to have partitions over the underlying network graph. Over these partitions the computational or network analysis tasks are in turn processed in a distributed or parallel manner. It is interesting to use intrinsic communities of social networks as partitions, to be used as basic components in parallel and distributed computation. We propose two novel graph compaction algorithms that generate the desired compact graph of communities as a preprocessing stage to the parallel and distributed evaluation of computational tasks. To comply with heterogeneity in community structure and size, we use a flexible limit on them. We evaluate the structure and quality of our algorithms and hence its resulting communities over two distinct application networks. We show that the generated community structure, reasonably complies with the modular structure of the network. We evaluate the quality of the partitions, relative to the partitions generated using existing state-of-the-art approach, and compare the approaches to show better quality of our partitions in terms of number of graph cuts.

Gene Regulatory Networks (GRNs) are biological networks that have been widely studied for their ability to regulate protein synthesis in cells by robust signal propagation. The innate biological robustness of GRN is attributed to the occurrence of statistically significant subgraphs, called motifs. While Wireless Sensor Network (WSN) topologies designed using GRN graphs, called bio-WSNs, have been proven to exhibit significant improvement in packet delivery and network latency over random graph-based WSNs, it is still not clear what role motifs play in the observed performance improvement of bio-WSNs. This work explores why a dominant 3-node motif, called Feed Forward Loop (FFL), typifies the robustness of GRN motifs. We also employ graph centrality metrics to corroborate biological studies that have shown motifs to provide pathways for signal propagation in GRNs. Finally, we perform graph-theoretic and simulation experiments on GRN subgraphs and their corresponding bio-WSNs to demonstrate that nodes with high FFL motif participation offer multiple short and robust communication pathways, despite the failure of random and targeted nodes and links.

Large scale disasters require prompt rescue and relief operations to restrict further casualties. To carry out such operations, it is essential to have a communication infrastructure between survivors and responders, which is often impaired due to the disaster. Off-the-shelf wireless devices such as smartphones, PDAs and Laptops offer an effective solution towards the establishment of makeshift communication infrastructure. However, in the absence of bonafide power sources, it becomes imperative to judiciously utilize energy (battery power) of such devices such that the network is functional until primary infrastructure is restored. This paper proposes a novel approach, called Cluster based Topological Routing (CTR) that prolongs the longevity of the network by exploiting the natural gathering of survivors in shelter points. In particular, the clustering algorithm identifies such survivor groups combined with a data forwarding approach, to minimize the number of data transmissions yet guaranteeing the required packet delivery and network latency. Our extensive simulation study shows that CTR yields twice the network lifetime than existing routing approaches in disaster response networks, while ensuring comparable packet delivery and network latency.

Wireless Sensor Networks (WSNs) will represent a crucial enabling technology to interconnect sensors and actuators in future smart cities. Since many applications will demand for reliable and low-latency communication, current standardization efforts are focusing in the definition of novel wireless standard architectures, e.g., the 6TiSCH architecture, to improve reliability and introduce support for quality of service. In this paper, we evaluate the performance of the 6TiSCH architecture during the initial network formation. Specifically, the performance of the 6top protocol, defined in 6TiSCH for distributed negotiation of resources, is evaluated. Simulations highlighted how the initial negotiation of resources is influenced by the routing protocol and the link-quality estimation mechanism adopted. The performance evaluation allowed to draw a set of guidelines for network configuration to guarantee the reliability of the initial allocation of resources.

With advances in Internet technology and prominence of mobile and smart devices in our lives, opportunistic and pervasive networks are now ubiquitous in solving many existing service limitations. The challenge lies in the underlying time-varying graph of the network due to mobility and intermittent connectivity. This introduces technical limitations in successful realization of services and applications e.g., efficient routing, maximal coverage with minimal latency, data offloading, and effective dissemination over mobile networks. Efficient solution to these inter-related problems lies in the novel prediction strategies for most accurate future contacts (i.e., links or interactions). In contrast to the existing strategies that consider either network structure or regular pattern and periodic nature of contacts, we propose novel use of seasonal autoregressive integrated moving average model and recurrent neural network model that are capable of capturing multi-periodic, dependent contact patterns. We predict the number of contacts relative to a node and over all nodes in any future interval over a given user and a pair of users. Finally, we validate our models with three distinct empirical data set, and compare with doubly recurrent and homogeneous Poisson process model to demonstrate the superiority of our prediction models.

Time division multiple access (TDMA) is often used in wireless sensor networks (WSNs), especially for critical applications, as it provides high energy efficiency, guaranteed bandwidth, bounded and predictable latency, and absence of collisions. However, TDMA is vulnerable to selective jamming attacks. In TDMA transmission, slots are typically pre-allocated to sensor nodes, and each slot is used by the same node for a number of consecutive superframes. Hence, an adversary could thwart a victim node's communication by simply jamming its slot(s). Such attack turns out to be effective, energy efficient, and extremely difficult to detect. In this paper, we present JAMMY, a distributed and dynamic solution to selective jamming in TDMA-based WSNs. Unlike traditional approaches, JAMMY changes the slot utilization pattern at every superframe, thus making it unpredictable to the adversary. JAMMY is decentralized, as sensor nodes determine the next slot utilization pattern in a distributed and autonomous way. Results from performance analysis of the proposed solution show that JAMMY introduces negligible overhead yet allows multiple nodes to join the network, in a limited number of superframes.

Recently, urban traffic management has encountered a paradoxical situation which is the empty carrying phenomenon for taxi drivers and the difficulty of taking a taxi for passengers. In this paper, through analyzing the quantitative relationship between passengers' getting on and off taxis, we propose a time-location-relationship (TLR) combined taxi service recommendation model to improve taxi drivers' profits, uncover the knowledge of human mobility patterns, and enhance passengers' travel experience. Moreover, the TLR model uses Gaussian process regression and statistical approaches to acquire passenger volume, mean trip distance, and average trip time in functional regions during every period on weekdays and weekends, and allows drivers to pick up more passengers within a short time frame. Finally, we compare our proposed model with the autoregressive integrated moving average model, the back-propagation neural network model, the support vector machine model, and the gradient boost decision tree model by using the real taxi GPS data in Beijing. The experimental results show that our optimizing taxi service recommendation can predict more accurately than others by considering the 3-D properties.

The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the inference process. At the highest tier, a self-optimization process dynamically reconfigures the sensory infrastructure, by sampling a subset of sensors in order to minimize energy consumption and maximize inference accuracy. A Bayesian approach allows to deal with the imprecision of sensory measurements, due to environmental noise and possible hardware malfunctions. The effectiveness of our approach is demonstrated with the application scenario of the user activity recognition in an Ambient Intelligence system managing a smart home environment. Experimental results show that the proposed solution outperforms static approaches for context-aware multi-sensor fusion, achieving substantial energy savings whilst maintaining a high degree of inference accuracy.

Wireless energy transfer has emerged as a promising technology for wireless sensor networks to power sensors with controllable yet perpetual energy. In this paper, we study sensor energy replenishment by employing a mobile charger (charging vehicle) to charge sensors wirelessly in a rechargeable sensor network, so that the sum of charging rewards collected from all charged sensors by the mobile charger per tour is maximized, subject to the energy capacity of the mobile charger, where the amount of reward received from a charged sensor is proportional to the amount of energy charged to the sensor. The energy of the mobile charger will be spent on both its mechanical movement and sensor charging. We first show that this problem is NP-hard. We then propose approximation algorithms with constant approximation ratios under two different settings: one is that a sensor will be charged to its full energy capacity if it is charged; another is that a sensor can be charged multiple times per tour but the total amount of energy charged is no more than its energy demand prior to the tour. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are very promising, and the solutions obtained are fractional of the optimum. To the best of our knowledge, the proposed algorithms are the very first approximation algorithms with guaranteed approximation ratios for the mobile charger scheduling in a rechargeable sensor network under the energy capacity constraint on the mobile charger.

Smartphones have become the most pervasive devices in people’s lives and are clearly transforming the way we live and perceive technology. Today’s smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as an accelerometer, a gyroscope, a microphone, and a camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this article, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing and analyze in depth the current state of the art on the topic. We also outline novel research challenges, along with possible directions of future work.

Robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their network topology and on how devices can react to disruptions, including node and link failures. This article presents a novel solution to obtain robust WSNs by exploiting principles of biological robustness at nanoscale. Specifically, we consider Gene Regulatory Networks (GRNs) as a model for the interaction between genes in living organisms. GRNs have evolved over millions of years to provide robustness against adverse factors in cells and their environment. Based on this observation, we apply a method to build robust WSNs, called bio-inspired WSNs, by establishing a correspondence between the topology of GRNs and that of already-deployed WSNs. Through simulation in realistic conditions, we demonstrate that bio-inspired WSNs are more reliable than existing solutions for the design of robust WSNs. We also show that communications in bio-inspired WSNs have lower latency as well as lower energy consumption than the state of the art.

The challenge in predicting future links over large scale networks (social networks) is not only maintaining accuracy, but also coping with the time-varying network graph. In contrast to the existing approaches, in this work we propose building a Markov prediction model. It not only incorporates temporal snapshots reflecting the dynamic network graph, but also considers effect of multiple timescales, along with corresponding local and global structural evolution (links and clusters respectively), correlated evolution and rate of evolution. The resulting edge selection in our approach exhibits the power law degree distribution, as exhibited in real world networks. Finally, we use two heavily dynamic real world network temporal data set (e.g. Twitter and Enron) and one relatively less dynamic network data set (e.g. DBLP), and existing state-of-the-art static and recent dynamic measures, to evaluate the prediction accuracy of our proposed Markov model and show that it out performs existing approaches.

Real world large scale networks exhibit intrinsic community structure, with dense intra-community connectivity and sparse inter-community connectivity. Leveraging their community structure for parallelization of computational tasks and applications, is a significant step towards computational efficiency and application effectiveness. We propose a weighted depth-first-search graph partitioning algorithm for community formation that preserves the needed community dependency without any cycles. To comply with heterogeneity in community structure and size of the real world networks, we use a flexible limiting value for them. Further, our algorithm is a diversion from the existing modularity based algorithms. We evaluate our algorithm as the quality of the generated partitions, measured in terms of number of graph cuts.

Participatory sensing (PS) is an emerging socio-technological paradigm in which citizens voluntarily participate and contribute to a distributed information system using applications installed in their hand-held devices. It can be found in a number of real-life applications, viz. traffic monitoring, air/sound pollution, garbage monitoring, social networking, commodity pricing, and so on. In these systems, information sensed by the user helps the peers in decision making. Present work considers vehicular participatory sensing systems, where registered user senses (perceives) the traffic incident and submits its report(s) to a PS application server. PS application server in turn, broadcasts those reports as alerts to its subscribers. To promote the participation, the PS systems used to have incentive schemes for the participants. However, a common problem in participatory sensing is the generation of false reports either due to wrong perception of an event or to maliciously increase the degree of participation to gain undue incentives. Such false reports make the usage of the PS system unreliable and vulnerable to the illusion attack. This work proposes a novel approach to make PS applications more reliable by identifying and filtering out the falsely reported event through automated confidence assignment based on a probabilistic model. Waze traffic alerts have been used as the dataset to validate the proposed filtering mechanism. Finally, simulation-based experiments and performance evaluation have been done to demonstrate that the proposed approach is relatively accurate.

Breathing plays an important role in the process of running. A stable and harmonic breathing rhythm can postpone runners' fatigue and help to improve their running performances. This paper presents a method that can detect runner's breathing frequency continuously. We utilize Bluetooth headset and smart phone to obtain sensed data, such as striding frequency and breathing frequency. Due to the interference of ambient noise, the detection will be inaccurate. In order to cope with this problem, we calibrate the detection result by leveraging a physiological model, called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the stride and breathing frequencies. Our method has been validated by extensive experiments and the experimental results indicate that it can accurately detect the breathing frequency for runners.

A large majority of population spend substantial amount of time each day in sedentary positions, which make the chairs potentially rich source of information and insight about people daily activities and behavior patterns. These information, which often gets under-utilized, can reveal valuable knowledge about the users's wellness level and quality of life. Considering this we have designed Care-Chair, a simple and cost effective smart sensing system with just four pressure sensors on the backrest of a chair, equipped with intelligent data analytics. Our proposed Care-Chair system has been able to classify among large number of 19 fine-grained and complex user sedentary activities. To best of our knowledge this is the first work to detect user functional activities and user emotion based activities (in addition to static and movement based sedentary activities and postures) with just 4 pressure sensors on chair and sensor data analytics. The performance is validated with 5 users combined dataset (thus considering user specific variations) with 86% overall accuracy. Our system is also validated to achieve precise measurement in user breathing rate during relatively static sedentary postures.

The reliability of location-based services (LBS) is strongly dependent on the accuracy of the location of the users. However, existing LBS systems are not able to efficiently validate the position of users in large-scale outdoor environments, leading to possible location spoofing attacks by malicious users. To this end, we present an efficient and scalable Location Validation System (LVS) that secures LBS systems from location spoofing attacks. In particular, the user location is verified with the help of mobile WiFi hotspots (MHSs), who are users activating the WiFi hotspot capability of their smartphones and accept connections from nearby users, thereby validating their position inside the sensing area. The system also comprises a novel verification technique called Chains of Sight, which tackles collusion-based attacks effectively. LVS also includes a reputation-based algorithm that rules out sensing reports of location-spoofing users.

In emergency scenarios such as earthquakes, fires, avalanches, or building collapses, it is necessary to discover people trapped under debris or anyway hidden from eyesight. In this paper, we propose RescuePal, an energy-efficient smartphone-based system that does not require any interaction by the victim and does not use energy-expensive GPS. RescuePal leverages a wake-up system based on sounds that activates the WiFi interface of the victim's smartphone only when the rescuer is close, to save energy. After presenting the system, we mathematically formulate an optimization problem so as to find the sound frequency and power level that minimizes WiFi false activations and yet guarantees high discovery efficiency. RescuePal has been implemented on off-the-shelf Android-based devices, and its performance has been evaluated on a realistic use-case scenario of victims inside a building. Finally, the energy consumption of RescuePal has been calculated using the Power Monitor hardware tool. Results demonstrate that RescuePal is highly effective and saves more than 60% of energy with respect to an approach based only on WiFi.

Online collaboration service has become a popular offering of present day Software-as-a-Service (SaaS) clouds. It facilitates sharing of information among multiple participating domains and accessing them from remote locations. Owing to loosely-coupled nature of such collaborations, access request from a remote user is made in the form of a set of permissions. The cloud vendor maps the requested permissions into appropriate local roles in order to allow resource access. However, coexistence of such multiple simultaneous role activation requests may introduce conflicts which violate the principle of security. In this paper, we propose a distributed secure collaboration framework which enables collaborating domains to detect and remove these conflicts. Two features of our framework are: (i) it requires only local information, and (ii) it detects and removes conflicts on-the-fly. Formal proofs have been provided to establish the correctness of our approach. Experimental results and qualitative comparison with related work demonstrate the efficacy of our approach in terms of response time, thus addressing the scalability requirement of cloud services.

Crowdsourcing can be modeled as a principal-agent problem in which the principal (crowdsourcer) desires to solicit a maximal contribution from a group of agents (participants) while agents are only motivated to act according to their own respective advantages. To reconcile this tension, we propose an all-pay auction approach to incentivize agents to act in the principal’s interest, i.e., maximizing profit, while allowing agents to reap strictly positive utility. Our rationale for advocating all-pay auctions is based on two merits that we identify, namely all-pay auctions (i) compress the common, two-stage “bid-contribute” crowdsourcing process into a single “bid-cum-contribute” stage, and (ii) eliminate the risk of task nonfulfillment. In our proposed approach, we enhance all-pay auctions with two additional features: an adaptive prize and a general crowdsourcing environment. The prize or reward adapts itself as per a function of the unknown winning agent’s contribution, and the environment or setting generally accommodates incomplete and asymmetric information, risk-averse (and risk-neutral) agents, and a stochastic (and deterministic) population. We analytically derive this all-pay auction-based mechanism and extensively evaluate it in comparison to classic and optimized mechanisms. The results demonstrate that our proposed approach remarkably outperforms its counterparts in terms of the principal’s profit, agent’s utility, and social welfare.

Many analytical models have been proposed for evaluating the performance of event-driven 802.15.4 Wireless Sensor Networks (WSNs), in Non-Beacon Enabled (NBE) mode. However, existing models do not provide accurate analysis of large-scale WSNs, due to tractability issues and/or simplifying assumptions. In this paper, we propose a new approach called Event Chains Computation (ECC) to model the unslotted CSMA/CA algorithm used for channel access in NBE mode. ECC relies on the idea that outcomes of the CSMA/CA algorithm can be represented as chains of events that subsequently occur in the network. Although ECC can generate all the possible outcomes, it only considers chains with a probability to occur greater than a pre-defined threshold to reduce complexity. Furthermore, ECC parallelizes the computation by managing different chains through different threads. Our results show that, by an appropriate threshold selection, the time to derive performance metrics can be drastically reduced, with negligible impact on accuracy. We also show that the computation time decreases almost linearly with the number of employed threads. We validate our model through simulations and testbed experiments, and use it to investigate the impact of different parameters on the WSN performance, in terms of delivery ratio, latency, and energy consumption.

Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers' types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.

Socially-aware networking is an emerging paradigm for intermittently connected networks consisting of mobile users with social relationships and characteristics. In this setting, humans are the main carriers of mobile devices. Hence, their connections, social features, and behaviors can be exploited to improve the performance of data forwarding protocols. In this paper, we first explore the impact of three social features, namely physical proximity, user interests, and social relationship on users' daily routines. Then, we propose a multi-dimensional routing protocol called Proximity-Interest-Social (PIS) protocol in which the three different social dimensions are integrated into a unified distance function in order to select optimal intermediate data carriers. PIS protocol utilizes a time slot management mechanism to discover users' movement similarities in different time periods during a day. We compare the performance of PIS to Epidemic, PROPHET, and SimBet routing protocols using SIGCOMM09 and INFOCOM06 data sets. The experiment results show that PIS outperforms other benchmark routing protocols with the highest data delivery ratio with a low communication overhead.

In pervasive computing environments, understanding the context of an entity is essential for adapting the application behavior to changing situations. In our view, context is a high-level representation of a user or entity's state and can capture location, activities, social relationships, capabilities, etc. Inherently, however, these high-level context metrics are difficult to capture using uni-modal sensors only and must therefore be inferred using multi-modal sensors. A key challenge in supporting context-aware pervasive computing is how to determine multiple high-level context metrics simultaneously and energy-efficiently using low-level sensor data streams collected from the environment and the entities present therein. A key challenge is addressing the fact that the algorithms that determine different high-level context metrics may compete for access to low-level sensors. In this paper, we first highlight the complexities of determining multiple context metrics as compared to a single context and then develop a novel framework and practical implementation for this problem. The proposed framework captures the tradeoff between the accuracy of estimating multiple context metrics and the overhead incurred in acquiring the necessary sensor data streams. In particular, we develop two variants of a heuristic algorithm for multi-context search that compute the optimal set of sensors contributing to the multi-context determination as well as the associated parameters of the sensing tasks (e.g., the frequency of data acquisition). Our goal is to satisfy the application requirements for a specified accuracy at a minimum cost. We compare the performance of our heuristics with a brute-force based approach for multi-context determination. Experimental results with SunSPOT, Shimmer and Smartphone sensors in smart home environments demonstrate the potential impact of the proposed framework.

In past literature, it has been demonstrated that the use of mobile sinks (MSs) increases dramatically the lifetime of wireless sensor networks (WSNs). In applications where the MSs are humans, animals, or transportation systems, the mobility of the MSs is often uncontrollable and could also be random and unpredictable. This implies the necessity of algorithms tailored to handle uncertainty on the MS mobility. In this article, we define the lifetime optimization of a WSN in the presence of uncontrollable sink mobility and Quality of Service (QoS) constraints. After defining an ideal scheme (calledOracle) which provably maximizes network lifetime, we present a novelSwarm-Intelligence-based Sensor Selection Algorithm(SISSA), which optimizes network lifetime and meets predefined QoS constraints. Then we mathematically analyze SISSA and derive analytical bounds on energy consumption, number of messages exchanged, and convergence time. The algorithm is experimentally evaluated on practical experimental setups, and its performances are compared to that by the optimalOraclescheme, as well as with the IEEE 802.15.4 MAC and TDMA schemes. Results conclude that SISSA provides on the average the 56% of the lifetime provided byOracleand outperforms IEEE 802.15.4 and TDMA in terms of yielded network lifetime.

Participatory sensing is a powerful paradigm that takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users’ willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users’ active and reliable participation. In this article, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.

In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex inhome activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect coarse-grained ADLs (Activities of Daily Living) but not large number of fine-grained and complex IADLs (Instrumental Activities of Daily Living). These are also not able to distinguish similar activities but with different context (such as sit on floor vs. sit on bed vs. sit on sofa). Our solution helps accurate detection of in-home ADLs/ IADLs and contextual activities, which are all critically important for remote elderly care in tracking their physical and cognitive capabilities. A-Wristocracy makes it feasible to classify large number of fine-grained and complex activities, through Deep Learning based data analytics and exploiting multi-modal sensing on wrist-worn device. It exploits minimal functionality from very light additional infrastructure (through only few Bluetooth beacons), for coarse level location context. A-Wristocracy preserves direct user privacy by excluding camera/ video imaging on wearable or infrastructure. The classification procedure consists of practical feature set extraction from multi-modal wearable sensor suites, followed by Deep Learning based supervised fine-level classification algorithm. We have collected exhaustive home-based ADLs and IADLs data from multiple users. Our designed classifier is validated to be able to recognize very fine-grained complex 22 daily activities (much larger number than 6-12 activities detected by state-of-the-art works using wearable and no camera/ video) with high average test accuracies of 90% or more for two users in two different home environments.

The emerging cloud market introduces a multitude of cloud service providers, making it difficult for consumers to select providers who are likely to be a low risk from a security perspective. Recently, significant emphasis has arisen on the need to specify Service Level Agreements that address security concerns of consumers (referred to as SecSLAs) -- these are intended to clarify security support in addition to Quality of Service characteristics associated with services. It has been found that such SecSLAs are not consistent among providers, even though they offer services with similar functionality. However, measuring security service levels and the associated risk plays an important role when choosing a cloud provider. Data breaches have been identified as a high priority threat influencing the adoption of cloud computing. This paper proposes a general analysis framework which can compute risk associated with data breaches based on pre-agreed SecSLAs for different cloud providers. The framework exploits a tree based structure to identify possible attack scenarios that can lead to data breaches in the cloud and a means of assessing the use of potential mitigation strategies to reduce such breaches.

Gene Regulatory Networks (GRNs) represent the interactions of genes in living organisms, which have evolved over millions of years to provide a near-optimal structure for rapid adaptation to the environment. On the other hand, robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their topology and how quickly the network can recover from node and link failures. This article proposes a novel approach to design robust WSNs by exploiting GRNs. Specifically, we build bio-inspired WSNs based on the topology of GRNs. Our approach embeds the physical communication graph of the WSN into the GRN graph under the optimization criterion of minimizing the interference between different nodes. Furthermore, we propose an algorithm to identify data collection points (i.e., sinks) and improve robustness by maximizing the expansion of the network. Through an analytical evaluation, we show that our bio-inspired graph embedding approach leads to robust WSNs which preserve the structural properties of GRNs.

Participatory sensing enables to collect a vast amount of data from the crowd by allowing a wide variety of sources to contribute data. However, the openness of participatory sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth the important issues of false data detection and correction in participatory sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. In this work, we propose DECO, a general framework to detect false values for participatory sensing in the presence of missing data. By applying a tailored spatio-temporal compressive sensing technique, DECO is able to accurately detect the false data and estimate both false and missing values for data correction. We validate our design through an experimental case study.

Evaluating influential nodes is one of the fundamental problems in large scale networks having wide range of applications. The centrality metric, in particular betweenness centrality plays a significant role in ranking influential nodes. Existing exact algorithms for evaluating betweenness centrality metric consider the entire network and hence incur high computational cost. In this paper, we reduce computational cost by leveraging network structural properties. We propose a community detection algorithm that uses right-skewed nature of degree distribution with incremental accumulation and semi-local optimal node selection giving computational cost O(|V|2- m|V|k2), where k, |V| and m represent average degree, number of vertices and modularity respectively. Additionally, we use predefined upper bound (O(√|V|)) equation on the number and size of communities to propose an algorithm for evaluating exact betweenness centrality indices that exploit the dense intra-modular and sparse intermodular connections of large scale networks, leading to the computational cost of O(|V|2+ 1/2|V|3/2log|V|). We validate our algorithms using real world networks. The computational cost incurred due to community detection and betweenness centrality evaluation holds irrespective of graph density and out performs existing exact algorithms. To the best of our knowledge this is the first work to leverage structural properties in community detection and exact betweenness centrality evaluation over large scale networks.

In past literature, it has been demonstrated that the use of mobile sinks (MSs) increases dramatically the lifetime of wireless sensor networks (WSNs). In applications where the MSs are humans, animals, or transportation systems, the mobility of the MS is often random and unpredictable, implying the necessity of novel and specific algorithms able to deal with large uncertainty on the MS mobility. In this paper, we define the yet unsolved problem of optimizing the lifetime of a WSN in the presence of uncontrollable and random sink mobility with QoS constraints. Then, we present a novel Swarm-Intelligence-based Sensor Selection Algorithm (SISSA), which optimizes network lifetime and meets pre-defined QoS constraints. Next, we mathematically analyze SISSA and derive analytical bounds on energy consumption, number of messages exchanged, and convergence time. The efficiency of SISSA and the accuracy of the model are experimentally evaluated with a testbed composed by 40 sensors, and the network lifetime provided by SISSA is compared to that by an ideal scheme. Experimental and analytical results conclude that SISSA is highly scalable and energy-efficient, and provides on the average the 56% of the lifetime provided by the ideal scheme in all the considered network parameter sets.

In absence of fixed infrastructure in Opportunistic Networks (OppNet), connectivity between OppNet nodes (usually characterized by human-portable devices), is one of the most challenging issues. The traditional assumption considers every proximity triggered human contact to be an effective OppNet connection. However, the high dynamicity of human mobility impairs the interchangeable notion of human contact and effective oppnet connection, thus necessitating the consideration of other critical contact properties like contact volume, defined as the maximum amount of data transferable during a contact. Recently a few works were proposed to predict the contact volume, using the instantaneous movement direction and velocity of the users. However none of those considered previous mobility history of the users which has a significant role on the future estimations. In this paper, we propose a novel scheme called HiPCV, which uses a distributed learning approach to capture preferential movements of the individuals, with spatial contexts and directional information and paves the way for mobility history assisted contact volume prediction. Experimenting on real world human mobility traces, HiPCV first learns and structures human walk patterns, along her frequently chosen trails. By creating a Mobility Markov Chain (MMC) out of this pattern and infusing it into HiPCV algorithm, we then devise a decision model for data transmissions during opportunistic contacts. Experimental results show the robustness of HiPCV in terms mobility prediction, reliable opportunistic data transfers and bandwidth saving, at places where people show regularity in their movements.

With rapid technological advancements, cloud marketplace witnessed frequent emergence of new service providers with similar offerings. However, service level agreements (SLAs), which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services to store and process their clients' data. If loss of data occurs due to an outage, the customer's business gets affected. Therefore, the major challenge for a customer is to select an appropriate service provider to ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP, which combines trustworthiness and competence to estimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider's SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms.

In most wireless sensor network (WSN) applications, data are typically gathered by sensor nodes and reported to a data collection point called sink. To support such a data collection pattern, a tree structure rooted at the sink is defined. Depending on various factors, including the WSN topology and the availability of resources, the energy consumption of nodes in different paths of the data collection tree may vary largely, thus affecting the overall network lifetime. This paper addresses the problem of lifetime maximization of WSNs based on data collection trees. Specifically, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai), that aims at extending the lifetime of WSNs through load balancing. Given an initial data collection tree, RaSMaLai randomly switches some sensor nodes from their original paths to other paths with lower load. We prove that, under appropriate settings of the operating parameters, RaSMaLai converges with a low time complexity. We further design a distributed version of our algorithm. Through an extensive performance evaluation study that includes simulation of large-scale scenarios and real experiments on a WSN testbed, we show that the proposed RaSMaLai algorithm and its distributed version achieve a longer network lifetime than the state-of-the-art solutions.

Emerging applications in areas such as advanced manufacturing and healthcare systems demand predictable delivery of prioritized message streams over multihop wireless systems. The dominance protocol is an important approach in which transmitting nodes use priority identifiers to dynamically regulate access to the shared medium. This protocol, however, has an unresolved problem called the Multihop Competing Problem (MCP). Despite recognizing that the problem could occur, the current literature offers no characterization of the problem or when it can occur. Because MCP could occur, it was not possible to analytically determine the end-to-end latency bounds of the message streams. When MCP occurred, the end-to-end latency of the message streams was affected adversely. We characterize MCP and show that it is an exposed terminal problem that occurs when the nodes use certain priority identifiers in adjacent two-hop neighborhoods of a transmitting node. Using a random disk graph model, we show that MCP occurs often in such systems. We utilize our characterization of MCP to derive priority identifiers that guarantee that MCP will not occur. The MCP free labels propose enable the predictable delivery of multihop message streams, improve end-to-end latency of the streams, and improve throughput in well-engineered and ad hoc networks.

Utility theory has served as a bedrock for modeling risk in economics. Where risk is involved in decision-making, for solving Markov decision processes (MDPs) via utility theory, the exponential utility (EU) function has been used in the literature as an objective function for capturing risk-averse behavior. The EU function framework uses a so-called risk-averseness coefficient (RAC) that seeks to quantify the risk appetite of the decision-maker. Unfortunately, as we show in this paper, the EU framework suffers from computational deficiencies that prevent it from being useful in practice for solution methods based on reinforcement learning (RL). In particular, the value function becomes very large and typically the computer overflows. We provide a simple example to demonstrate this. Further, we show empirically how a variance-adjusted (VA) approach, which approximates the EU function objective for reasonable values of the RAC, can be used in the RL algorithm. The VA framework in a sense has two objectives: maximize expected returns and minimize variance. We conduct empirical studies on a VA-based RL algorithm on the semi-MDP (SMDP), which is a more general version of the MDP. We conclude with a mathematical proof of the boundedness of the iterates in our algorithm.

In this paper, we study properties of distributed consensus in layered sensor networks of the multi-layer multi-group (MLMG) structure. We show that properly designed MLMG networks maintain decentralized communication, whereas show the advantage of centralized structures. In particular, they require less number of transmissions required to reach consensus. This feature is critical for efficient distributed computing in large-scale sensor network applications. For typical classes of MLMG networks, we mathematically characterize the reduced number of transmissions compared to equivalent egalitarian decentralized structures of the same consensus dynamics. This explicit characterization based on simple graphical characteristics of MLMG structures permits an efficient design of large-scale network structures to meet desired performance requirements. In addition, we characterize the asymptotic and transient properties of consensus in MLMG networks of limited channel rates, using the probabilistic quantization schemes.

In this paper, we propose a novel architecture called Energy Aware Disaster Recovery Network using WiFi Tethering (E-DARWIN). The underlying idea is to make use of WiFi Tethering technology ubiquitously available on wireless devices, like smartphones and tablets, to set up an ad hoc network for data collection in disaster scenarios. To this end, we design novel mechanisms, which aid in autonomous creation of the ad hoc network, distribution of data capturing task among the devices, and collection of data with minimum delay. Specifically, we design and implement a distributed coalition formation game for distributing the data capturing task among wireless devices based on their capabilities, available energy, and network participation for higher network lifetime. Finally, we evaluate the performance of the proposed architecture using a prototype application implemented on Android platform and large-scale simulations.

In a mobile grid computing framework where mobile devices are used as computing resources, minimizing the task offloading time remains an important issue. A task is an independent unit of execution consisting of a input data volume for execution and optionally a target-specific executable. We consider a mobile grid infrastructure where mobile devices are connected via Wi-Fi network and the grid infrastructure has a set of tasks (i.e. a set of data volumes) to be transferred to a subset of the mobile devices. In a Wi-Fi network, mobile devices usually associate themselves to the access points (APs) having the strongest radio signal. In this paper, we address the problem of AP activation (by frequency assignment) and association of AP with devices in the context of minimizing the overall data-transfer completion time. We present a constraint based formulation and also a heuristic as solutions. Simulations results are presented which contrast our proposed methods with some of the earlier works.

In this paper, we describe a practical model of analyzing the performance of 802.11n Wireless Local Area Networks (WLANs) carrying real-time voice over IP (VoIP) traffic. In particular, we determine the response-time and packet loss of client nodes using traffic generators and network monitors. We demonstrate that there exists a correlation between the number of VoIP calls on a WLAN and the response time and packet loss between the nodes. We also determine maximum number of calls that can be handled on a WLAN before call quality is degraded. In addition, we calculate bandwidth utilization of the WLAN based on two-way and three-way calls. The proposed approach is practical given that this is done with live hardware and software rather than simulations and theory. The results are measured and recorded based on an extensive set of real-world experiments by SolarWinds Network Performance Monitor in a testbed.

Participatory sensing is an approach to data collection where individuals use their smartphones to capture different scenarios, and upload to a server to create knowledge. A fundamental issue in such data collection process is the coverage of the collected data that reflects how well the targeted area is monitored. To ensure coverage requirements, the traditional approaches require the server to know the location of the participating mobile devices that takes significant amount of energy. In this paper, we propose a novel framework called PLUS for data collection in participatory sensing ensuring partial coverage, where a mobile device does not send location updates to the server. We also propose an energy efficient localization scheme called sLoc, designed for a mobile device to participate in the data collection process with PLUS. Finally, simulating a continuous monitoring application, we show that PLUS can save significant amount of energy, as compared to traditional approaches.

Participatory sensing is an approach to data collection for monitoring different scenarios with the help of smartphone sensors. As more and more sensors are being added to smartphones, monitoring a wide range of scenarios has become possible with participatory sensing. An important issue in such participatory sensing application is the coverage of the collected data that reflects how well the data samples represent the monitored area. In the traditional approach, the data collection process is assisted by a server that knows the location of the participating devices and selects the necessary ones to cover the monitored area efficiently. However, for battery powered devices like smartphones, sending frequent location updates to the server is quite energy expensive. In this paper, we propose a framework, called STREET, for data collection from urban streets that can address the coverage problem where a participating mobile device is not required to send location updates to the server. In particular, our framework can collect data samples to ensure the requirements of a specified partial coverage, full coverage and k-coverage. STREET is assisted by a simple localization scheme for mobile devices that minimizes the usage of location sensor (e.g., GPS) while participating in the data collection process. Experiments from simulation studies show that our approach can significantly reduce energy consumption of the participating mobile devices.

Periodic data collection is an important application in wireless sensor networks (WSNs). Since sensors are power constrained, building energy efficient data collection topology offers significant challenge. Compression of correlated data is one of the widely used techniques in WSNs where sensory data are compressed along their routes toward the sink. Consequently, a data compression tree is formed in which a sensor (say, child) selects its parent based on the degree of correlation among their sensed data, and the data of the child node is compressed at the parent node. In periodic data collection, the data collected by individual sensors can be considered as a time series. The amount of correlation between time series of two sensory data streams may not be constant over time. Most existing works in this direction do not consider the temporal effect of correlation among data streams generated by periodic sensing. Moreover, the compression can introduce some imperfection that may affect the reliability of the collected data. In this paper, we address the problem of energy efficient data gathering in WSNs while considering variability of correlation among data streams of neighboring sensors. We propose a bucket approximation based framework named EFFECT (energy efficient framework for compression tree) that produces a compression tree based on the compression ratio of data streams from the neighboring sensors in a given sensor network. We perform experiments on real data sets and show that our framework can produce trees with significantly higher lifetime while reducing the average energy consumption of the sensors by at least 20%.

Participatory sensing (PS) has recently attracted tremendous attention given its potential for a wide variety of sensing applications. Due to the fact that PS systems rely completely on the data provided by the users, incentivizing users' active participation while guaranteeing data reliability is paramount to effectively employ PS systems in practical scenarios. In this paper, we first define a set of attacks which compromise data reliability of existing PS applications. Next, we propose a scalable and secure trust-based framework, called FIDES, which relies on the concept of mobile security agents (MSAs) and Josang's trust model to rule out incorrect reports and reward reliable users. By simulating the FIDES framework on mobility traces of taxi cabs in San Francisco, we demonstrate that FIDES secures the PS system from the proposed attacks, guarantees high data reliability, and saves significant amount of revenue with respect to existing reward mechanisms.

Opportunistic networks have so far been seen mostly as delay tolerant in nature. As a result, real-time and soft real-time applications, which demand consistent and instant connectivity of the network, are usually considered intangible in opportunistic communication environments. In this paper we seek to reveal the inherent connected virtual backbone in an opportunistic network through the consociation of the neighbors in the network. This backbone can pave the way for designing an architecture for real-time social applications. The backbone may change in terms of time, location and crowd density. Experimenting on real world as well as synthetic human mobility traces and pause times, we first structure the pattern of human halt durations at popular places. Infusing this pattern, we then prove the existence of the intrinsic backbone in those networking environments, where people show regularity in their movements. Applying graph-theoretic concepts like Minimum Connected Dominating Set and Unit Node Weighted Steiner Tree we further optimize and ensure the robustness of the backbone. Simulation results show the effectiveness of our approach in exposing a newer dimension in the form of real time interaction prospects in opportunistic networks.

Providing reliable and efficient communication under fading channels is one of the major technical challenges in wireless sensor networks (WSNs), especially in industrial WSNs (IWSNs) with dynamic and harsh environments. In this work, we present the Reliable Reactive Routing Enhancement (R3E) to increase the resilience to link dynamics for WSNs/IWSNs. R3E is designed to enhance existing reactive routing protocols to provide reliable and energy-efficient packet delivery against the unreliable wireless links by utilizing the local path diversity. Specifically, we introduce a biased backoff scheme during the route-discovery phase to find a robust guide path, which can provide more cooperative forwarding opportunities. Along this guide path, data packets are greedily progressed toward the destination through nodes' cooperation without utilizing the location information. Through extensive simulations, we demonstrate that compared to other protocols, R3E remarkably improves the packet delivery ratio, while maintaining high energy efficiency and low delivery latency.

Recent studies have demonstrated that mobile elements (MEs) are an efficient solution to help decrease dramatically energy consumption in wireless sensor networks (WSNs). However, in most of cases, sensors use duty cycle schemes to save energy, and unless the ME mobility pattern is deterministic, each sensor node has to discover the presence of the ME in the nearby area before starting to exchange data with it. Therefore, in such wireless sensor networks with mobile elements (in short, WSN-MEs), the definition and analysis of a protocol for efficient ME discovery becomes of fundamental importance. In this paper, we propose an extensive performance analysis of an easy-to-implement, hierarchical discovery protocol for WSN-MEs, called Dual Beacon Discovery (2BD) protocol, taking into account stochastic, multi-path, variable speed ME mobility patterns. We also derive the optimal parameter values that minimize the energy consumption of sensor nodes, while guaranteeing the minimum node throughput required by the applications under consideration. Finally, we compare the 2BD protocol with a classical solution based on Periodic Listening (PL). Our results show that 2BD can exploit its hierarchical mechanism and thus significantly increase lifetime, especially when the ME discovery phase is relatively long.

QoS routing is an important research issue in wireless sensor networks (WSNs), especially for mission-critical monitoring and surveillance systems which requires timely and reliable data delivery. Existing work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant energy cost. In this work, we exploit the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in WSNs. Existing GOR protocols are not efficient for QoS provisioning in WSNs, in terms of the energy efficiency and computation delay at each hop. To improve the efficiency of QoS routing in WSNs, we define the problem of efficient GOR for multiconstrained QoS provisioning in WSNs, which can be formulated as a multiobjective multiconstraint optimization problem. Based on the analysis and observations of different routing metrics in GOR, we then propose an Efficient QoS-aware GOR (EQGOR) protocol for QoS provisioning in WSNs. EQGOR selects and prioritizes the forwarding candidate set in an efficient manner, which is suitable for WSNs in respect of energy efficiency, latency, and time complexity. We comprehensively evaluate EQGOR by comparing it with the multipath routing approach and other baseline protocols through ns-2 simulation and evaluate its time complexity through measurement on the MicaZ node. Evaluation results demonstrate the effectiveness of the GOR approach for QoS provisioning in WSNs. EQGOR significantly improves both the end-to-end energy efficiency and latency, and it is characterized by the low time complexity.

In most wireless sensor network (WSN) applications, data are typically gathered by the sensor nodes and reported to a data collection point, called the sink. In order to support such data collection, a tree structure rooted at the sink is usually defined. Based on different aspects, including the actual WSN topology and the available energy budget, the energy consumption of nodes belonging to different paths in the data collection tree may vary significantly. This affects the overall network lifetime, defined in terms of when the first node in the network runs out of energy. In this paper, we address the problem of lifetime maximization of WSNs in the context of data collection trees. In particular, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai) that aims at maximizing the lifetime of WSNs through load balancing with a low time complexity. We further design a distributed version of our algorithm, called D-RaSMaLai. Simulation results show that both the proposed algorithms outperform several existing approaches in terms of network lifetime. Moreover, RaSMaLai offers lower time complexity while the distributed version, D-RaSMaLai, is very efficient in terms of energy expenditure.

In this paper we introduce a mathematical model to analyze the performance of Wi-Fi networks carrying voice calls, TCP controlled file downloads, and video streaming traffic. We derive the voice call capacity, TCP throughput, video streaming throughput, and bandwidth utilization using the proposed five-dimensional Markov model. We show that there exists a correlation among the queue size of client nodes and the capacity and throughput of the network. We also demonstrate the effect of variable packet arrival rates on bandwidth utilization. Moreover, the proposed model is generic because some of the existing approaches become special cases of our model for a specific queue size. The analytical results match well with the experimental results generated by Qualnet simulator.

Exploiting opportunistic contacts between mobile devices to enable deployment of real applications through reliable and efficient data transfers poses a significant research challenge. Indeed, accurate prediction of contact volume, defined as the maximum amount of data transferable during a contact, can improve performance of deployments. However, existing schemes for estimating contact volume that make use of preconceived patterns or contact time distributions may not be applicable in uncertain environments. In this paper, we propose a novel scheme called PCV that predicts contact volume in soft real-time to enable efficient and reliable data transfers in opportunistic networks. An Android Application that learns data rate profiles has been developed to facilitate PCV. In addition, an analytical model has been developed to depict variable data rates between mobile devices. Extensive simulations are carried out on both synthetic and real world mobility traces to validate the usefulness of PCV. Experimental results show the effectiveness of our approach in terms of reliable data transfers.

Wireless sensor networks are usually densely deployed, and it is quite common for sensors to gather and transmit redundant information, which results in unnecessary energy consumption. Sleep scheduling is quite helpful for reducing overall energy consumption of the network, and thus prolongs the network lifetime. In this paper, we propose an Information Entropy Approach for Sleep Scheduling (IEASS). Information entropy is exploited in the algorithm to characterize the correlation of data which is used for determining the eligibility of node sleeping. The main objective of IEASS is to achieve adaptive coverage while keeping network connectivity. From the simulation results, IEASS performs well on coverage ratio and coverage degree with much less active sensor nodes. Moreover, IEASS achieves high flexibility by adjusting the algorithm parameters.

In this paper we introduce a mathematical model to analyze the performance of Wi-Fi networks carrying voice calls and TCP controlled file downloads. We derive the voice call capacity, the TCP throughput, and the bandwidth utilization using the proposed five-dimensional Markov model. We show that there exists a correlation among the queue size of client nodes and the capacity and throughput of the network. We also demonstrate how bandwidth utilization is affected by variable packet arrival rates. The analytical results match well with the simulation results generated by Qualnet simulator. Moreover, we conduct an experimental study of the Enhanced Distributed Channel Access (EDCA) mechanism of the IEEE 802.11e standard with a real-environment testbed consisting of 1 access point and 11 laptops to further validate the analytical and simulation results.

In this paper we introduce a simple model to analyze the performance of Wi-Fi networks carrying real-time video traffic. In particular, we estimate the throughput, capacity and bandwidth utilization using two-dimensional Markov model. We demonstrate that there exists a correlation between the queue size of client nodes and the capacity of the network. We also compute bandwidth utilization for variable packet arrival rates. The two-dimensional approach is shown to be simpler and more efficient than some of the existing models because it reduces the total number of Markov states in the system. Moreover, the proposed model is generic in the sense that some of the existing approaches become special cases of our model based on the queue size. Experimental results generated by Qualnet simulator validate our analytical results.

A novel channel detection timing scheme for periodic sensing is proposed to minimize the sensing cost of the secondary users while protecting the transmission of the primary users. The framework consists of a Cognitive Radio Network where secondary users operate in a periodic fashion while the primary users operate in continuous-time. The scheme has been presented for both non-erroneous and erroneous channel sensing conditions. Numerical results show the effectiveness of our scheme for erroneous sensing conditions when compared to non-erroneous sensing conditions.

Recently, content-based publish/subscribe (pub/sub) services have become a significant research field in opportunistic mobile networks (OppNets). Pub/sub is an asynchronous messaging paradigm, in which content transmissions are guided by the interest. Since selfish behavior is common in reality, nodes often behave selfishly with an aim to maximize their own utilities without considering performance of other nodes. Therefore, how to encourage nodes to collect, store and share network content efficiently is one of the key challenges under this paradigm. In this paper, we propose an incentive-based pub/sub scheme, called ConSub, for OppNets. In ConSub, Tit-For-Tat (TFT) mechanism is employed to deal with selfish behavior. ConSub also implements a content exchange protocol between two interacting node, thus encouraging them to play as businessmen and carry contents to satisfy each other's interest. Specifically, the exchange order is determined by the content utility, which is calculated by contact probability and cooperation level between the current node and its neighbors subscribing to the interest. Extensive realistic trace-driven simulation results show that ConSub is superior to existing schemes in terms of delivered packets and transmission hops with reasonable transmission cost.

Wireless Sensor Networks (WSNs) are embracing an increasing number of real-time applications subject to strict delay constraints. Utilizing the methodology of potential field in physics, in this article we effectively address the challenges of real-time routing in WSNs. In particular, based on a virtual composite potential field, we propose the Potential-based Real-Time Routing (PRTR) protocol that supports real-time routing using multipath transmission. PRTR minimizes delay for real-time traffic and alleviates possible congestions simultaneously. Since the delay bounds of real-time flows are extremely important, the end-to-end delay bound for a single flow is derived based on the Network Calculus theory. The simulation results show that PRTR minimizes the end-to-end delay for real-time routing, and also guarantees a tight bound on the delay.

Optimal routing in hierarchical wireless sensor networks is an NP-complete problem of immense practical importance for which exact solutions exist for very small problem sizes. This paper presents a new memetic algorithm, based on differential evolution, that solves routing problems of more than a thousand relay nodes; in comparison, the best-known approach in the literature handles up to 312 nodes. Simulation results show that the proposed method consistently produces better-quality solutions than three competing approaches.

This study suggests to use popularity based caching for IP-based TV (IPTV) services. Our main focus is on caching the most popular video files nearby the clients, in order to reduce the IPTV service delay, increase the quality of service provided to the clients, and reduce the traffic over the Internet backbone. The proposed framework was applied on real data traces from live P2P networks. The results demonstrate a significant improvement over the Least Recently Used (LRU) cache management scheme.

Biological networks (specifically, genetic regulatory networks) exhibit an optimized sparse topology and are known to be robust to various external perturbations. We have earlier utilized such networks, particularly, the gene regulatory network of E. coli, for constructing smart communication structures in bio-inspired Wireless Sensor Networks (WSNs) having high packet transmission efficiency. In this paper, we present machine learning approaches to relate the graph topology based characteristics of such bio-inspired WSNs to their network-level robustness in terms of average packet transmission efficiency. In particular, we generate a support vector regression model using the graph metric features as input data. The model predicts the percentage of packets received by the highest degree sink node and a theoretical estimate for the overall network robustness.

Recent advances in technology and algorithms for smart environments have made it possible to collect and store large amounts of data about many aspects of human life and the surrounding environment with limited effort and cost. However, such data become useful to lay users with no background in data analysis only if they are presented in a fashion that supports intuitive interaction to spot the patterns and trends, thus transforming the data into valuable information. In this paper, we introduce FuseViz, a framework for Web-based fusion and visualization of data in smart environments. FuseViz addresses the challenges posed by large, live, heterogeneous, and dynamic data streams from autonomous data sources, and lay users, with two basic features: fusion and visualization. CouchDB, a schemaless database with a ReSTful API and MapReduce support, is used to fuse data streams from multiple sources, while Web-based visualization is implemented on top of D3, a JavaScript library for manipulation of data-driven documents. We demonstrate the capabilities of FuseViz with E2Home, a case study application for energy-efficient smart home environments. We show how the precise information provided by E2Home can help the user easily improve the home energy efficiency by more than 10%.

Convergecast is a typical form of data collection in wireless sensor networks (WSNs), wherein nodes sample data from the environment and send them to a common destination. In order to prolong the network lifetime, a duty-cycle mechanism is usually coupled with a routing tree structure, in which nodes are organized in clusters. Each cluster aggregates data and sends them towards the root of the tree. However, clusters can interfere each other if their active time is not properly chosen. Furthermore, scheduling can lead to a long data delivery delay when a duty-cycle mechanism is used. In this article, we introduce a receiver-oriented scheduling algorithm for cluster-tree WSNs which provides a bounded latency for convergecast data collection. In contrast with most of the existing works in the literature, where two nodes are assumed to interfere if they are at most 2 hops away, we address the more general and realistic case where interfering nodes can be up to t hops away from each other, where te2. We first show that the minimum-latency convergecast problem is NP-hard for cluster-based WSNs with arbitrary topologies. We then focus on tree-based WSNs and derive bounds on the latency for convergecast data collection. We also propose a heuristic to obtain a t-interference-free scheduling in O(nt) time, where n is the number of clusters in the WSN. We finally validate our findings by simulation on both synthetic topologies and routing trees obtained from WSN deployments.

This paper investigates the interactions between two-way TCP connections over 3GPP LTE networks. In the LTE network, the two-way TCP flows share buffers on a common bottleneck, i.e., the radio access links. The behaviors of TCPs significantly influence the others in the opposite direction. Specifically, the radio links of LTE are asymmetric, which may induce drastic interactions of TCPs and rapid draining of downlink buffer. The periodic idleness of downlink is a huge waste of the precious radio bandwidth and results in considerable performance degradation. In the viewpoint of Coupled Queues, we thoroughly understand the interacting TCPs and explain the reason for performance degradation. Based on a straightforward modeling procedure, we formalize the evolution of two-way TCPs and model the bottleneck queue size in every slot. The model indicates the queues are close coupled, which is verified with simulations on NS2. If the uplink (queue) is fully utilized, the downlink (queue) will always be underutilized even idle, and vice versa. Furthermore, an effective solution called Preemptive ACK Queueing (PAQ) is designed to decouple the queues, which improves the performance of two-way TCPs over LTE networks.

As the organizing committee, it is our pleasure to present the proceedings of the 3rd IEEE International Workshop on Data Security and PrivAcy in wireless Networks (D-SPAN), held on June 25, 2012, in San Francisco, California, USA. The goal of this one-day workshop, organized in conjunction with the 13th IEEE WoWMoM 2012, is to exchange cutting-edge ideas for securing the next-generation wireless networks, systems and applications. The scope of D-SPAN includes a wide variety of topics, including security and privacy of data collection, transmission, storage, publishing, and sharing in wireless networks broadly defined such as cellular and mobile ad hoc networks (MANET), vehicular ad hoc networks (VANET), cognitive and sensor networks to applying data analytics techniques to address security and privacy challenges in these networks. D-SPAN provides a forum for academic and industry researchers to present research ideas that build bridges across three communities: wireless networks and databases, and security.

In a pervasive system, users have very dynamic and rich interactions with the environment and its elements, including other users. To efficiently support users in such environments, a high-level representation of the system (namely, context) is usually exploited. However, since pervasive environments are inherently people-centric, context might consist of sensitive information. As a consequence, privacy concerns arise, especially in terms of how to control information disclosure to third parties (e.g., other users). In this paper we propose context-aware approaches to privacy preservation in wireless and mobile pervasive environments. Specifically, we design two schemes: (i) to reduce the interactions between the user and the system, and (ii) to exploit the interactions between different users. Both of our solutions are adaptive, thus suitable for dynamic scenarios. In addition, our schemes require limited computational and storage resources, so that they can be implemented on resource-constrained personal and sensing devices. We apply our solutions to a smart healthcare scenario, and show that our schemes not only effectively protect the user privacy, but also significantly reduce the interactions with the system, thus improving the user experience.

Wireless Sensor Networks (WSNs) are emerging as an effective solution for a wide range of real-life applications. In scenarios where a fine-grain sensing is not required, sensor nodes can be sparsely deployed in strategic locations and special Mobile Elements (MEs) can be used for data collection. Since communication between a sensor node and a ME can occur only when they are in the transmission range of each other, one of the main challenges in the design of a WSN with MEs is the energy-efficient and timely discovery of MEs. In this paper, we consider a hierarchical ME discovery protocol, namely Dual beacon Discovery (2BD) protocol, based on two different beacon messages emitted by the ME (i.e., Long-Range Beacons and Short-Range Beacons). We develop a detailed analytical model of 2BD assuming a sparse network scenario, and derive the optimal parameter values that minimize the energy consumption at sensor nodes, while guaranteeing the minimum throughput required by the application. Finally, we compare the energy efficiency and performance of 2BD with those of a traditional discovery protocol based on a single beacon. Our results show that 2BD can provide significant energy savings, especially when the discovery phase is relatively long.

doi: 10.1093/comjnl/bxr022 Comp J 2011;54(4): 549–569 This paper was intended to be published as part of the Advances in Network and System Security special issue butwas published separately in issue 54(4) of The Computer Journal in error.We would like to apologise to the authors for this oversight.

To extend the lifetime of a wireless sensor network, sensor nodes usually duty cycle between dormant and active states. Duty cycling schemes are often evaluated in terms of connection delay, connection duration, and duty cycle. In this article, we show with experiments on Sun SPOT sensors that duty cycling time (energy) efficiency, that is, the ratio of time (energy) employed in ancillary operations when switching from and into deep sleep mode, is an important performance metric too. We propose a novel randomized duty cycling scheme based on Markov chains with the goal of (i) reducing the connection delay, while maintaining a given time (energy) efficiency, or (ii) keeping a constant connection delay, while increasing the time (energy) efficiency. Analytical and experimental results demonstrate that the Markov chain-based scheme can improve the performance in terms of connection delay without affecting the time efficiency, or vice versa, as opposed to the trade-off observed in traditional schemes. We extend the proposed duty cycling scheme to a partially randomized scheme, where wireless nodes can switch into active state beyond their schedules when their neighbors are active to anticipate message forwarding. The analytical and experimental results confirm the relationship between connection delay and time efficiency also for this scheme.

We model the user's quality of experience (QoE) in a wireless voice over IP (VoIP) service as a function of the amount of effort the user has to put to continue her conversation. We assume that users would quit or terminate an ongoing call if they have to put more efforts than they could tolerate. Not knowing the tolerance threshold of each individual user, the service provider faces a decision dilemma of whether to fix the network problem immediately whenever he detects a user effort in the VoIP system, or ignore it with the hope that the user may still continue the call anyway. In this paper, we formulate the provider's dilemma as a non-cooperative game between the provider and the VoIP user experiencing a deteriorating QoE. We demonstrate that providers implementing the equilibrium solutions can expect to not only increase their revenues, but also reduce the number of cases when users quit out of frustration thus minimizing potential churning. We also discuss conditions under which a sophisticated user may or may not benefit from faking unwarranted efforts with a goal of receiving a better service from the provider. Finally, we conduct a subjective experiment of VoIP over WiFi, which verifies the key model assumption that perceptual quality is negatively correlated to the amount of effort the user has to put to continue the call.

Abstract We propose novel localization and routing protocols in an actor‐centric wireless sensor network consisting of an actor node and a large number of energy‐constrained sensors operating under L different periodic sleep–awake schedules. Specifically, we propose a semidistributed localization algorithm in which a small subset of sensors extracts their positions in polar coordinates based on the messages received from the actor, and subsequently localizes (also in polar coordinates) the remaining sensors. By modeling the deployed sensors as a two‐dimensional Poisson point process and applying well‐known results from the coupon collector's problem and Chernoff bounds, we analytically derive and also validate, by simulation, the sensor density required to localize all sensors in the network with high probability. The actor‐centric network can be modeled by a cluster adjacency graph G with the help of the already localized polar coordinates that logically partition the network into concentric coronas (around the actor), each subdivided in a varying number of clusters (of almost the same area). To avoid intercluster collisions in G, sensors in different clusters transmit on different channels. A lower bound on the number of channels required to schedule the transmissions without collisions is obtained by solving a distance‐2 vertex coloring problem on G. Optimal and quasioptimal fully distributed algorithms are provided to determine the channel assigned to each cluster in constant time. Finally, we apply these results to develop a geographic routing protocol: the messages generated from the sensors in a given cluster are routed toward the actor through the unique shortest path of G that starts from the node associated with the cluster and goes up to the corona where the actor resides. In each cluster, to avoid redundant retransmissions toward the actor, we select L leaders, one for each periodic sleep–awake schedule. © Wiley Periodicals, Inc. NETWORKS, Vol. 2012.

ABSTRACT Task allocation for wireless sensor networks with multiple concurrent applications (such as target tracking and event detection) requires sharing applications' tasks (such as sensing and computation) and available network resources. In this paper, we model the distributed task allocation problem for multiple concurrent applications by using a reverse combinatorial auction, in which the bidders (sensor nodes) are supposed to bid cost values (according to their available resources) for accomplishing the subset of the applications' tasks. Trust management schemes consist of a powerful tool for the detection of unexpected node behaviors (such as faulty or malicious). It is critical for participants (i.e., bidders and auctioneer) to estimate each other's trustworthiness before initiating the task allocation procedure. To address this issue, we introduce a real‐time trust management module for our auction system that is able to validate the reliable bid value and determine faulty nodes and malicious entities. The main objective of our task allocation scheme is to maximize the network lifetime by sharing tasks and network resources within applications, while enhancing the overall application quality of service (e.g., deadline). We also propose a heuristic two‐phase winner determination protocol to deal with the combinatorial reverse auction problem. Simulation results show that the proposed scheme offers the promising performance and efficiency. Copyright © 2012 John Wiley & Sons, Ltd.

Sensing coverage is an essential functionality of wireless sensor networks (WSNs). However, it is also well known that coverage alone in WSNs is not sufficient, and hence network connectivity should also be considered for the correct operation of WSNs. In this paper, we address the problem of k-coverage in WSNs such that in each scheduling round, every location in a monitored field (or simply field) is covered by at least k active sensors while all active sensors are being connected. Precisely, we study sensors duty-cycling strategies for generating k-coverage configurations in WSNs. First, we model the k-coverage problem in WSNs. Second, we derive a sufficient condition of the sensor spatial density for complete k-coverage of a field. We also provide a relationship between the communication and sensing ranges of sensors to maintain both k-coverage of a field and connectivity among all active sensors. Third, we propose four configuration protocols to solve the problem of k-coverage in WSNs. We prove that our protocols select a minimum number of sensors to achieve full k-coverage of a field while guaranteeing connectivity between them. Then, we relax some widely used assumptions for coverage configuration in WSNs, to promote the use of our proposed protocols in real-world sensing applications. Our simulation results show that our protocols outperform an existing distributed k-coverage configuration protocol.

Due to the unattended nature of wireless sensor networks, an adversary can physically capture and compromise sensor nodes and then mount a variety of attacks with the compromised nodes. To minimize the damage incurred by the compromised nodes, the system should detect and revoke them as soon as possible. To meet this need, researchers have recently proposed a variety of node compromise detection schemes in wireless ad hoc and sensor networks. For example, reputation-based trust management schemes identify malicious nodes but do not revoke them due to the risk of false positives. Similarly, software-attestation schemes detect the subverted software modules of compromised nodes. However, they require each sensor node to be attested periodically, thus incurring substantial overhead. To mitigate the limitations of the existing schemes, we propose a zone-based node compromise detection and revocation scheme in wireless sensor networks. The main idea behind our scheme is to use sequential hypothesis testing to detect suspect regions in which compromised nodes are likely placed. In these suspect regions, the network operator performs software attestation against sensor nodes, leading to the detection and revocation of the compromised nodes. Through quantitative analysis and simulation experiments, we show that the proposed scheme detects the compromised nodes with a small number of samples while reducing false positive and negative rates, even if a substantial fraction of the nodes in the zone are compromised. Additionally, we model the detection problem using a game theoretic analysis, derive the optimal strategies for the attacker and the defender, and show that the attacker's gain from node compromise is greatly limited by the defender when both the attacker and the defender follow their optimal strategies.

For wireless multimedia sensor networks (WMSNs) deployed in noisy and unattended environments, it is necessary to establish a comprehensive framework that protects the accuracy of the gathered multimedia information. In this paper, we jointly consider data aggregation, information trust, and fault tolerance to enhance the correctness and trustworthiness of collected information. Based on the multilayer aggregation architecture of WMSNs, we design a trust-based framework for data aggregation with fault tolerance with a goal to reduce the impact of erroneous data and provide measurable trustworthiness for aggregated results. By extracting statistical characteristics from different sources and extending Josang's trust model, we propose how to compute self-data trust opinion, peer node trust opinion, and peer data trust opinion. According to the trust transfer and trust combination rules designed in our framework, we derive the trust opinion of the sink node on the final aggregated result. In particular, this framework can evaluate both discrete data and continuous media streams in WMSNs through a uniform mechanism. Results obtained from both simulation study and experiments on a real WMSN testbed demonstrate the validity and efficiency of our framework, which can significantly improve the quality of multimedia information as well as more precisely evaluate the trustworthiness of collected information.

Pervasive computing applications often involve sensor-rich networking environments that capture various types of user contexts such as locations, activities, vital signs, and so on. Such context information is useful in a variety of applications, for example, monitoring health information to promote independent living in "aging-in-place” scenarios, or providing safety and security of people and infrastructures. In reality, both sensed and interpreted contexts are often ambiguous, thus leading to potentially dangerous decisions if not properly handled. Therefore, a significant challenge in the design and development of realistic and deployable context-aware services for pervasive computing applications lies in the ability to deal with ambiguous contexts. In this paper, we propose a resource-optimized, quality-assured context mediation framework for sensor networks. The underlying approach is based on efficient context-aware data fusion, information-theoretic reasoning, and selection of sensor parameters, leading to an optimal state estimation. In particular, we apply dynamic Bayesian networks to derive context and deal with context ambiguity or error in a probabilistic manner. Experimental results using SunSPOT sensors demonstrate the promise of this approach.

In wireless sensor networks, different applications feature different requirements in terms of such performance metrics as sensing coverage and data reporting latency. In most applications, it is usually sufficient to provide a Desired Sensing Coverage (DSC) lower than full coverage at any instance with the guarantee that the whole area will eventually be covered within a specified delay bound. Due to the fact that these applications are also expected to run for longer periods of time and at the same time battery recharging and replacement are costly, energy consumption in wireless sensor networks should be minimized while achieving the application goals. In this article, we propose a novel framework for application-specific data gathering which exploits a trade-off between coverage and latency, thereby minimizing energy consumption and extending the network lifetime. The proposed energy-efficient, constant-time, randomized scheme, called Coverage-Adaptive raNdom SEnsor sElection (CANSEE), selects a subset of k sensors to report at each round so as to fulfill the application-specific requirement of desired sensing coverage and bounded latency, instead of always guaranteeing full coverage and minimum latency. We present a probabilistic model to estimate: (i) the connectivity of those selected k sensors and the number of additional sensors needed to guarantee connectivity; (ii) a lower bound on k in each round; and (iii) the probability of almost surely having k data reporters using the Chernoff bound. The immediate event detection capability achieved by the proposed CANSEE scheme is also analyzed to compare the performance of our framework with other data gathering schemes that allow 100% coverage. Simulation results demonstrate that our framework leads to a significant conservation of energy (and thus extended network lifetime) with a small trade-off between coverage and data reporting latency, yet providing the required data reporting capability.

Motivated by the increasing need for developing automated decision-support tools for cyber-physical networks subject to uncertainties, we have been pursuing development of a new control-theoretic framework for network security and vulnerability. In this paper, we build on the proposed framework to put forth concrete definitions for security and (dually) discoverability, for a class of models that can represent dynamics of numerous cyber-physical networks of interest: namely, dynamical network spread models. These security and discoverability definitions capture whether or not, and to what extent, a stakeholder can infer the temporal dynamics of the spread from localized and noisy measurements. We then equivalence these security and security-level definitions to the control-theoretic notions of observability and optimal estimation, and so obtain explicit algebraic and spectral conditions for security and analyses of the security level. Further drawing on graph-theory constructs, a series of graphical conditions for security, as well as characterizations of security levels, are derived. A case study on zoonotic disease spread is also included, to illustrate concrete application of the analyses in management of cyber-physical infrastructure networks.

Abstract Traditional mobile multicast schemes have higher multicast tree reconfiguration cost or multicast packet delivery cost. Two costs are very critical because the former affects the service disruption time during handoff while the latter affects the packet delivery delay. Although the range‐based mobile multicast (RBMoM) scheme and its similar schemes offer the trade‐off between two costs to some extent, most of them do not determine the size of service region, which is critical to the network performance. Hence, we propose a dynamic region‐based mobile multicast (DRBMoM) to dynamically determine the optimal service region for reducing the multicast tree reconfiguration and multicast packet delivery costs. DRBMoM provides two versions: (i) the per‐user version, named DRBMoM‐U, and (ii) the aggregate‐users version, named DRBMoM‐A. Two versions have different applicability, which are the complementary technologies for pursuing efficient mobile multicast. Though having different data information and operations, two versions have the same method for finding the optimal service region. To that aim, DRBMoM models the users' mobility with arbitrary movement directional probabilities in 2‐D mesh network using Markov Chain, and predicts the behaviors of foreign agents' (FAs') joining in a multicast group. DRBMoM derives a cost function to formulate the average multicast tree reconfiguration cost and the average multicast packet delivery cost, which is a function of service region. DRBMoM finds the optimal service region that can minimize the cost function. The simulation tests some key parameters of DRBMoM. In addition, the simulation and numerical analyses show the cost in DRBMoM is about 22∼50% of that in RBMoM. At last, the applicability and computational complexity of DRBMoM and its similar scheme are analyzed. Copyright © 2010 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) are usually assigned tasks for a single application. Recently, the concept of shared sensor networks, which support multiple concurrent applications, has emerged, reducing the deployment and administrative costs, and increasing the usability and efficiency of the network. Supporting task allocation for multiple concurrent applications in sensor networks (such as target tracking, event detection, etc.) requires sharing applications' tasks (such as sensing, computation, etc.) and available network resources. In this paper, we model the distributed task allocation problem for multiple concurrent applications using a reverse combinatorial auction, in which the bidders (sensor nodes) bid the cost value (in terms available resources) for accomplishing the subset of the applications' tasks. The main objective is to maximize the network lifetime by sharing tasks and network resources among applications, while enhancing the overall application QoS (e.g., deadline). We also propose a heuristic two-phase winner determination protocol to solve the combinatorial reverse auction problem. Simulation results show that the proposed scheme offers efficiency and network scalability.

Wireless ad hoc networks can experience significant performance degradation under fading channels. In this work, we present a robust forwarding extension (RFE) for reactive routing protocols in wireless ad hoc networks. RFE is designed to enhance existing reactive routing protocols to provide reliable and energy-efficient packet delivery against the unreliable wireless links. Specifically, we introduce a biased backoff scheme during the route discovery phase to find a robust virtual path, which can provide more cooperative forwarding opportunities. Along this virtual path, data packets are greedily progressed toward the destination through nodes cooperation. We extend the widely used AODV routing protocol with RFE to study its performance. Through extensive simulations, we demonstrate that AODV-RFE effectively improves the reliability, end-to-end energy efficiency and latency.

NA

To extend the life time of a wireless sensor network, sensor nodes usually switch between dormant and active states according to a duty cycling scheme. In randomized schemes, sensors use only partial or no information about their neighbors, and rely on randomness to generate working schedules. Duty cycling schemes are often evaluated in terms of the connection delay, i.e., the time until two neighboring nodes are simultaneously active, and the connection duration, i.e., the time until at least one of them switches to the dormant state. In this paper, we argue that duty cycling time (energy) efficiency, i.e., the ratio of time (energy) employed in ancillary operations when switching from and into deep sleep mode, is an important performance metric too. We present experimental results using Sun SPOT sensors that support our claim and highlight the performance trade-off between connection delay and time (energy) efficiency for a traditional scheme based on independent and identically distributed (i.i.d.) random variables. We propose a novel randomized duty cycling scheme based on Markov chains with the goal of (i) reducing the connection delay, while maintaining a given time (energy) efficiency, or (ii) keeping a constant connection delay, while increasing the time (energy) efficiency. The proposed scheme is analyzed mathematically by deriving the time efficiency, connection delay and duration in terms of the time slot length, duty cycle, and cost of set up and tear down operations. Analytical results demonstrate that the Markov chain-based scheme can improve the performance in terms of connection delay without affecting the time efficiency, or vice versa, as opposed to the trade-off observed in traditional schemes. Experimental results using Sun SPOT sensor nodes with the minimum number of operations during transitions from and into deep sleep mode confirm the mathematical analysis of the proposed Markov chain-based randomized scheme.

In this paper, we study the dynamic node activation schedule for the utility based coverage problem in solar-powered wireless sensor networks. We assume that the utility achieved by a WSN for coverage service is a sub modular function over the set of sensors that will provide the service. We first present an integer programming formulation with sub modular objective functions. We then present an efficient simple greedy hill-climbing algorithm such that the achieved average utility of the computed schedule is at least $1/2$ times that achieved by the optimal schedule. To the best of our knowledge, this is the first polynomial time algorithm that can ensure a good constant approximation of the achieved utility for multi-target coverage problem. We conduct extensive evaluations to study the performances of our proposed aggregation scheduling algorithm on real testbed. Our evaluation results corroborate our theoretical analysis.

We propose a Markov model for performance evaluation and capacity estimation of IEEE 802.11e-EDCA (Enhanced Distributed Channel Access) WLAN. EDCA defines four traffic classes (voice, video, best effort and background) based on their quality of service requirements. With the help of the proposed Markov model, we derive analytically the service rate of access point and bandwidth utilization, and then validate our model by comparing analytical results with simulation experiments. We also show that there exists a trade-off between user satisfaction and bandwidth utilization.

On behalf of the Technical Program Committee, we welcome all of you to the IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC 2011) in the beautiful campus of Bahcesehir University, Istanbul, Turkey! We are indeed delighted that this year's IEEE IWCMC accomplishes its goal under the conference theme “Making Wireless Communities,” and continues its tradition of providing the premier forum for presentation of research results and experience reporting on the cutting edge research in the general areas of wireless communications and mobile computing. This year, we received more than 1000 submissions from 51 countries worldwide. Each paper received at least three peer technical reviews, comprised of 49 Symposia Chairs/Co-Chairs and a total of more than 450 TPC members from academia, government laboratories, and industries. After carefully examining all the received review reports, the IEEE IWCMC 2011 TPC finally selected about 35% high-quality papers for presentation at the conference and publication in the IEEE IWCMC 2011 proceedings. The conference program starts on Monday July 4thwith a full day Tutorials that is free of charge to all our attendees. Then, each day starts with a keynote speaker chosen from renowned world-class leaders in the area-Dr. Rick Stevens, Dr. Mario Gerla, and Dr. Sajal Das, highlighting the latest research trends in the wireless communications, mobile computing, and networks. This year, the technical sessions reflect the continued and growing interests in a wide range of spectrum, including wireless communications and networks, cross-layer design and optimization, mobile computing, wireless sensor networks, network security, and use of wireless technologies in social emergency applications. We also added a special Workshop this year to address practical aspects of Wireless Communications and Mobile Computing, such as Multihop Wireless Network Testbeds and Experiments, Network and Communications for Advanced Society, and Federated Wireless Sensor Systems (FedSenS). There are five special sessions composed of invited papers from renowned experts from around the world. Outstanding papers will be selected for four Special Issues in well known international journals. Our objective in the future is to reduce the acceptance rate further to reach 30% and less. In addition, we would like to reduce the number of Symposia and Workshops as well to meet the conference theme.

In pervasive computing environments, understanding the context of an entity is essential for adapting the application behavior to changing situations. In our view, context is a high-level representation of a user or entity's state and can capture location, activities, social relationships, capabilities, etc. Inherently, however, these high-level context metrics are difficult to capture using uni-modal sensors only, and must therefore be inferred with the help of multi-modal sensors. However a key challenge in supporting context-aware pervasive computing environments, is how to determine in an energy-efficient manner multiple (potentially competing) high-level context metrics simultaneously using low-level sensor data streams about the environment and the entities present therein. In this paper, we first highlight the intricacies of determining multiple context metrics as compared to a single context, and then develop a novel framework and practical implementation for this problem. The proposed framework captures the tradeoff between the accuracy of estimating multiple context metrics and the overhead incurred in acquiring the necessary sensor data stream. In particular, we develop a multi-context search heuristic algorithm that computes the optimal set of sensors contributing to the multi-context determination as well as the associated parameters of the sensing tasks. Our goal is to satisfy the application requirements for a specified accuracy at a minimum cost. We compare the performance of our heuristic based framework with a brute-forced approach for multi-context determination. Experimental results with SunSPOT sensors demonstrate the potential impact of the proposed framework.

No abstract available.

It has been shown that sink mobility provides an energy-efficient approach to data delivery in wireless sensor networks (WSNs). Most of the approaches targeted to WSNs with mobile sinks (MSs) addressed the problem of data delivery where only a few messages are reported during a long time frame. However, transmitting streaming data is becoming relevant in WSNs, as more and more multimedia sensor nodes - equipped with image, audio, and video capabilities - are being used to characterize the sensing environment. In this scenario, a sequence of messages propagates into the network, hence the problem of finding an effective routing path for delivering data to MSs becomes even more challenging, since the communication overhead for reaching the MS might also be significant. In this paper, we present an energy-efficient streaming data delivery (SDD) protocol for cluster-based WSNs with MSs. Different from existing works, we focus on the mobility support for the delivery of streaming data in hierarchical WSNs. By introducing a cross-cluster handover mechanism and a path redirection scheme, SDD maintains the end-to-end connectivity between the source and the MS, while avoiding the constant transmission of the MS location as it moves across multiple clusters. We evaluate the performance of the proposed SDD protocol, and compare it with a hierarchical cluster-based data dissemination protocol. Simulation results demonstrate its effectiveness, in terms of both end-to-end delivery delay and energy-efficiency.

As the organizing committee, it is our pleasure to present the proceedings of the 2ndIEEE International Workshop on Data Security and PrivAcy in wireless Networks (D-SPAN), held on June 20, 2011, in Lucca, Italy. The goal of this one-day workshop, organized in conjunction with the 12thIEEE WoWMoM 2011, is to exchange cutting-edge ideas for securing the next-generation wireless networks, systems and applications. The scope of D-SPAN includes a wide variety of topics, including security and privacy of data collection, transmission, storage, publishing, and sharing in wireless networks broadly defined - such as cellular and mobile ad hoc networks (MANET), vehicular ad hoc networks (VANET), cognitive and sensor networks - to applying data analytics techniques to address security and privacy challenges in these networks. D-SPAN provides a forum for academic and industry researchers to present research ideas that build bridges across three communities: wireless networks and databases, and security.

The availability and diffusion of wireless sensor nodes, personal communication devices (e.g., smartphones), as well as application-specific devices (e.g., surveillance cameras) has changed the typical sensing application scenarios where data are collected from the environment for the purpose of monitoring a phenomenon and detecting events. The combination of highly heterogeneous devices, in terms of sensing, processing, and communication capabilities, has become a key feature to collaborative, distributed, and multimodal sensing applications. However, the heterogeneity of devices also raises a number of challenges for the application developers. In this paper, we present a general software framework for heterogeneous and multimedia wireless sensor networks. The framework abstracts from the individual sensing devices and platforms, and enables collaborative and distributed sensing applications. We present a reference application scenario represented by Assisted Living Environments (ALEs).We show the potential of our proposed framework by a preliminary testbed implementation consisting in a multimodal application for fall detection of elderly people.

Cognitive networks address the spectrum shortage problem exploiting unused chunks of the frequency spectrum, i.e. channels. With the aim to opportunistically use these channels, cognitive devices (CDs) have been defined. We propose two game theoretic frameworks with the peculiarity of capture the interactions between selfish CDs exhibiting both non-cooperative and cooperative behaviors. The first framework is formulated as a multi-player Non-cooperative Repeated game (NoRa), which is treated as a potential game. The second framework is a Hedomc Coalirional Formation game (HeCtor) characterized by cooperation among CDs that belong to the same coalition (i.e., a sub-group of devices). Extensive experiments demonstrate that cooperation among CDs increases network throughput, however, when licensed users change their transmission parameters quickly, the performance gain is comparable to that of the non-cooperative framework because of the faster adaptability that characterizes the non-cooperative framework as compared to the cooperative one.

The objective of self-healing key distribution is to enable group users to recover session keys by themselves, without requesting additional transmissions from the group manager (GM), even when they miss some broadcast messages. One major benefit of the self-healing key distribution mechanism is the reduction of energy consumption due to the elimination of such additional transmission. Also in some applications, e.g., uni-directional broadcast channel from the GM, the self-healing key distribution mechanism seems to be the ideal solution. Desired features of self-healing key distribution schemes include energy awareness, short broadcast message, efficient users addition, revocation and so on. A primary challenge is managing the trade-off between providing an acceptable level of security and conserving scarce resources in particular energy which is critical for wireless network operations. Over a decade, a great number of self-healing key distribution schemes have been proposed for establishing a group key amongst a dynamic group of users over an unreliable, or lossy, network. In this paper a comprehensive survey is conducted on the state-of-the-art in the field of self-healing key distribution. First, we clarify the security requirements of self-healing key distribution scheme for their special application environment. Then, we present a classification of self-healing key distribution schemes according to different cryptographic primitives, and give an insight to their features and goals. Furthermore, we consider several problems, namely authentication on broadcast messages, sponsorization and mutual-healing, related to the robustness of self-healing key distribution schemes. At last, we delineate their similarities and differences and outline several future research directions.

A major concern in wireless sensor networks (WSNs) is energy conservation, since battery-powered sensor nodes are expected to operate autonomously for a long time, e.g., for months or even years. Another critical aspect of WSNs is reliability, which is highly application-dependent. In most cases it is possible to trade-off energy consumption and reliability in order to prolong the network lifetime, while satisfying the application requirements. In this paper we propose an adaptive and cross-layer framework for reliable and energy-efficient data collection in WSNs based on the IEEE 802.15.4/ZigBee standards. The framework involves an energy-aware adaptation module that captures the application's reliability requirements, and autonomously configures the MAC layer based on the network topology and the traffic conditions in order to minimize the power consumption. Specifically, we propose a low-complexity distributed algorithm, called ADaptive Access Parameters Tuning (ADAPT), that can effectively meet the application-specific reliability under a wide range of operating conditions, for both single-hop and multi-hop networking scenarios. Our solution can be integrated into WSNs based on IEEE 802.15.4/ZigBee without requiring any modification to the standards. Simulation results show that ADAPT is very energy-efficient, with near-optimal performance.

Recent work has shown that sink mobility along a constrained path can improve the energy efficiency in wireless sensor networks. However, due to the path constraint, a mobile sink with constant speed has limited communication time to collect data from the sensor nodes deployed randomly. This poses significant challenges in jointly improving the amount of data collected and reducing the energy consumption. To address this issue, we propose a novel data collection scheme, called the Maximum Amount Shortest Path (MASP), that increases network throughput as well as conserves energy by optimizing the assignment of sensor nodes. MASP is formulated as an integer linear programming problem and then solved with the help of a genetic algorithm. A two-phase communication protocol based on zone partition is designed to implement the MASP scheme. We also develop a practical distributed approximate algorithm to solve the MASP problem. In addition, the impact of different overlapping time partition methods is studied. The proposed algorithms and protocols are validated through simulation experiments using OMNET++.

Due to the unattended nature of wireless sensor networks, an adversary can capture and compromise sensor nodes, make replicas of them, and then mount a variety of attacks with these replicas. These replica node attacks are dangerous because they allow the attacker to leverage the compromise of a few nodes to exert control over much of the network. Several replica node detection schemes have been proposed in the literature to defend against such attacks in static sensor networks. However, these schemes rely on fixed sensor locations and hence do not work in mobile sensor networks, where sensors are expected to move. In this work, we propose a fast and effective mobile replica node detection scheme using the Sequential Probability Ratio Test. To the best of our knowledge, this is the first work to tackle the problem of replica node attacks in mobile sensor networks. We show analytically and through simulation experiments that our scheme detects mobile replicas in an efficient and robust manner at the cost of reasonable overheads.

Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.

Energy-efficient and reliable transmission of sensory information is a key problem in wireless sensor networks. To save more energy, in-network processing such as data fusion is a widely used technique, which, however, may often lead to unbalanced information among nodes in the data fusion tree. Traditional schemes aim at providing reliable transmission to individual data packets from source node to the sink, but seldom offer the desired reliability to a data fusion tree. In this paper, we explore the problem of Minimum Energy Reliable Information Gathering (MERIG) when performing data fusion. By adaptively using redundant transmission on fusion routes without acknowledgments, packets with more information are delivered with higher reliability. For different data fusion topologies, such as star, chain, and tree, we provide optimal solutions to compute the number of transmissions for each node. We also propose practical, distributed approximation algorithms for chain and tree topologies. Analytical proofs and simulation results show that energy-efficient information reliability can be guaranteed in an unreliable wireless environment with the help of our proposed schemes.

The congestion problem in Wireless Sensor Networks (WSNs) is quite different from that in traditional networks. Most current congestion control algorithms try to alleviate the congestion by reducing the rate at which the source nodes inject packets into the network. However, this traffic control scheme always decreases the throughput so as to violate fidelity level required by the applications. In this paper, we present a solution that sufficiently exerts the idle or underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. To achieve this goal, a traffic-aware dynamic routing (TADR) algorithm is proposed to route packets around the congestion areas and scatter the excessive packets along multiple paths consisting of idle and underloaded nodes. Utilizing the concept of potential in classical physics, our TADR algorithm is designed through constructing a hybrid virtual potential field using depth and normalized queue length to force the packets to steer clear of obstacles created by congestion and eventually move toward the sink. The simulation results show that the proposed solution improves the overall throughput by around 370 percent as compared to MintRoute, which is one of benchmark routing protocols. Furthermore, TADR scheme has low overhead suitable for large-scale, dense sensor networks.

Energy is an extremely critical resource for battery-powered wireless sensor networks (WSN), thus making energy-efficient protocol design a key challenging problem. Most of the existing energy-efficient routing protocols always forward packets along the minimum energy path to the sink to merely minimize energy consumption, which causes an unbalanced distribution of residual energy among sensor nodes, and eventually results in a network partition. In this paper, with the help of the concept of potential in physics, we design an Energy-Balanced Routing Protocol (EBRP) by constructing a mixed virtual potential field in terms of depth, energy density, and residual energy. The goal of this basic approach is to force packets to move toward the sink through the dense energy area so as to protect the nodes with relatively low residual energy. To address the routing loop problem emerging in this basic algorithm, enhanced mechanisms are proposed to detect and eliminate loops. The basic algorithm and loop elimination mechanism are first validated through extensive simulation experiments. Finally, the integrated performance of the full potential-based energy-balanced routing algorithm is evaluated through numerous simulations in a random deployed network running event-driven applications, the impact of the parameters on the performance is examined and guidelines for parameter settings are summarized. Our experimental results show that there are significant improvements in energy balance, network lifetime, coverage ratio, and throughput as compared to the commonly used energy-efficient routing algorithm.

Mobile IPv6 (MIPv6) offers a basic solution to support mobility in IPv6 networks. Although Hierarchical MIPv6 (HMIPv6) has been designed to enhance the performance of MIPv6 by hierarchizing the network, it does not always outperform MIPv6. In fact, two solutions have different application scopes. Existing work studies the impact of various parameters on the performance of MIPv6 and HMIPv6, but without analyzing their application scopes. In this paper, we propose a model to analyze the application scopes of MIPv6 and HMIPv6, through which an Optimal Choice of Mobility Management (OCMM) scheme is designed. Different from the existing work that either propose new mobility management schemes or enhance existing mobility management schemes, OCMM chooses the better alternative between MIPv6 and HMIPv6 according to the mobility and service characteristics of users, addressing whether to hierarchize the network. Besides that, OCMM chooses the best mobility anchor point and regional size when HMIPv6 is adopted, addressing how to hierarchize the network. Simulation results demonstrate the impact of key parameters on the application scopes of MIPv6 and HMIPv6 as well as the optimal regional size of HMIPv6. Finally, we show that OCMM outperforms MIPv6 and HMIPv6 in terms of total cost including average registration and packet delivery costs.

Abstract Sensor networks are mainly used to gather strategic information in various monitored areas. Sensors may be deployed in zones where their internal memory, or the sensors themselves, can be corrupted. Since deployed sensors cannot be easily replaced, network persistence and robustness are the two main issues that have to be addressed while efficiently deploying large scale sensor networks. The sensing radius of a sensor is the distance within which a sensor can monitor certain events. The communication radius of a sensor is the distance within which a sensor can transmit and receive data. A sensor is said to cover a particular monitored area if a circular area, with radius equal to that sensor's sensing radius, covers that area. A set of sensors is said to be strongly connected if any two sensors in the set can communicate with each other, either directly or indirectly. The goal of forming a minimum connected cover of a query region in sensor networks is to select a subset of nodes that entirely covers a particular monitored area, which is strongly connected, and which does not contain a subset with the same properties. Selecting a minimal number of connected sensors is an NP hard problem. In our work, we address minimality in terms of inclusion. In this paper, we consider the most general case, wherein every sensor has a different sensing and communication radius. We propose two novel and robust solutions to the minimum connected cover problem that can cope with both transient faults (corruptions of the internal memory of sensors) and sensor crash/join. Also, our proposal includes extended versions which use multi‐hop information. We also prove the self‐stabilization property of our solutions, both analytically and through extended simulations. A self‐stabilizing system is a system that, when started from an arbitrary state, is always guaranteed to recover following the occurrence of (transient) faults and converge to a desired behavior (legitimate state) in a finite number of steps.Viasimulations, we also conclude that our solutions provide better performance, in terms of coverage, than preexisting self‐stabilizing solutions. Moreover, we observe that multi‐hop solutions produce a better approximation to an optimal cover set. Copyright © 2009 John Wiley & Sons, Ltd.

Energy efficient multicast routing is one of the fundamental problems in wireless sensor networks (WSNs). Previous work has shown that when the goal is to find multicast trees with minimum transmission cost, the problem becomes NP-complete. In this work, we present a heuristic distributed minimum transmission multicast routing protocol (MTMRP) for WSNs. By introducing the biased backoff scheme and taking advantage of the broadcast nature of wireless communication, MTMRP chooses the forwarding routes which can connect more multicast receivers. Moreover, MTMRP introduces a path handover scheme, which can prune redundant routes for multicast routing. As a result, the multicast transmission cost is reduced in a distributed manner. We conduct extensive evaluations to study the performance of the proposed MTMRP compared with existing protocols. Simulation results demonstrate that our scheme effectively improves the multicast routing energy efficiency.

A scalable framework for mobile real-time group communication services is developed in this paper. Examples for possible applications of this framework are mobile social networks, mobile conference calls, mobile instant messaging services, and mobile multi-player on-line games. A key requirement for enabling a real-time group communication service is the tight constraint imposed on the call delivery delay. Since establishing such communication service for a group of independent mobile users under a tight delay constraint is NP-hard, a two-tier architecture is proposed, that can meet the delay constraint imposed by the real-time service requirement for many independent mobile clients in a scalable manner. The time and memory complexity associated with the group services provided by the proposed framework are O(N) for each service, where N is the number of nodes being served, while a distributed scheme requires O(N2) for both time and memory complexity.

Mobile handset manufacturers are introducing new features that allow a user to configure the same handset for seamless operation with multiple wireless network providers. As the competitiveness in the wireless network service market intensifies, such products will deliver greater freedom for the mobile users to switch providers dynamically for a better price or quality of experience. For example, when faced with an unexpected wireless link quality problem, the user could choose to physically switch the provider, or she could be more strategic and use her freedom of switching provider as a ‘psychological weapon’ to force the current provider upgrading the link quality without delay. In this paper, we explore the latter option where users threaten to quit the current provider unless he (the provider) takes immediate actions to improve the link quality. By threatening the provider, the user will have to accept the risk of having to disconnect from the current provider and reconnect to another in the middle of a communication session, should the provider defies the threat. The user therefore will have to carefully assess the merit of issuing such threats. To analyze the dynamics of this scenario, we formulate the problem as a brinkmanship game theory model. As a function of user's and provider's payoff or utility values, we derive conditions under which the user could expect to gain from adopting the brinkmanship strategy. The effect of uncertainties in payoff values are analyzed using Monte Carlo simulation, which confirms that brinkmanship can be an effective strategy under a wide range of scenarios. Since user threats must be credible to the provider for the brinkmanship model to work, we discuss possible avenues in achieving threat credibility in the context of mobile communications.

In this paper, we exploit the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in wireless sensor networks (WSNs). Recent work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant energy cost. We also find that existing GOR protocol may not be suitable for QoS provisioning in WSNs, due to the large computation delay at each hop. To improve the efficiency of QoS routing in WSNs, we study the problem of efficient GOR for multiconstrained QoS provisioning in WSNs, which can be formulated as a multiobjective multiconstraint optimization problem. We look in depth at the properties of the multiple objectives. Based on the analysis and observations, we then propose a heuristic efficient GOR (EGOR) algorithm for QoS provisioning in WSNs. We evaluate EGOR by comparing it with the multipath routing approach through ns-2 simulation and evaluate its time complexity through measurement on the MicaZ node. Evaluation results demonstrate that EGOR can significantly improve both the end-to-end energy efficiency and latency for multiconstrainted QoS provisioning in WSNs, and that EGOR is characterized by its low time complexity.

The Wireless Sensor Network (WSN) is embracing an increasing number of real-time applications subject to strict delay constraints. Utilizing the methodology of potential field in physics, we present an effective way to address the challenges in real-time transmission. We propose the Potential based Real-Time Routing (PRTR) protocol, which provides real-time transmission using multi-path routing algorithm based on a composite potential field. PRTR features a delay-minimized real-time routing as well as alleviating congestion simultaneously.

Wireless voice over IP (VoIP) is subject to unpredictable link conditions which directly contribute to user irritation. Standard technological means available to the network provider to remedy wireless link problems require additional radio resources to be allocated. Given that radio resource is limited, it is not clear whether such allocation strategies to reduce user irritation are economically rewarding for the wireless providers. We model the resource allocation dilemma to reduce user irritation as a noncooperative game between the provider and the VoIP user suffering from a link quality problem. We demonstrate that providers implementing Nash equilibrium can expect to optimize their revenues and avoid potential churning.

In a wireless sensor network (WSN), connectivity enables the sensors to communicate with each other, while sensing coverage reflects the quality of surveillance. Although the majority of studies on coverage and connectivity in WSNs consider 2D space, 3D settings represent more accurately the network design for real-world applications. As an example, underwater sensor networks require design in 3D rather than 2D space. In this paper, we focus on the connectivity and k-coverage issues in 3D WSNs, where each point is covered by at least k sensors (the maximum value of k is called the coverage degree). Precisely, we propose the Reuleaux tetrahedron model to characterize k-coverage of a 3D field and investigate the corresponding minimum sensor spatial density. We prove that a 3D field is guaranteed to be k-covered if any Reuleaux tetrahedron region of the field contains at least k sensors. We also compute the connectivity of 3D k-covered WSNs. Based on the concepts of conditional connectivity and forbidden faulty sensor set, which cannot include all the neighbors of a sensor, we prove that 3D k-covered WSNs can sustain a large number of sensor failures. Precisely, we prove that 3D k-covered WSNs have connectivity higher than their coverage degree k. Then, we relax some widely used assumptions in coverage and connectivity in WSNs, such as sensor homogeneity and unit sensing and communication model, so as to promote the practicality of our results in real-world scenarios. Also, we propose a placement strategy of sensors to achieve full k-coverage of a 3D field. This strategy can be used in the design of energy-efficient scheduling protocols for 3D k-covered WSNs to extend the network lifetime.

Existing code update protocols for reprogramming nodes in a sensor network are either unsuitable or inefficient when used in a mobile environment. The prohibitive factor of uncertainty about a node's location due to their continuous movement coupled with the obvious constraint of a node's limited resources, pose daunting challenges to the design of an effective code dissemination protocol for mobile sensor networks. In this paper, we propose ReMo, an energy-efficient, multihop reprogramming protocol for mobile sensor networks. Without making any assumptions on the location of nodes, ReMo uses the LQI and RSSI measurements of received packets to estimate link qualities and relative distances with neighbors in order to select the best node for code exchange. The protocol is based on a probabilistic broadcast paradigm with the mobile nodes smoothly modifying their advertisement transmission rates based on the dynamic changes in network density, thereby saving valuable energy. Contrary to previous protocols, ReMo downloads pages regardless of their order, thus, exploiting the mobility of the nodes and facilitating a fast transfer of the code. Our simulation results show significant improvement in reprogramming time and number of message transmissions over other existing protocols under different settings of network mobility. Our implementation results of ReMo on a testbed of SunSPOTs also showcase its better performance than existing reprogramming protocols in terms of transfer time and number of message transmissions.

A low sampling rate leads to reduced congestion and hence energy consumption in the resource-constrained wireless sensor networks. In this article, we propose asynchronous sampling that shifts the sampling time instances of sensor nodes from each other. For lossy data gathering scenarios, the proposed approach provides more information about the physical phenomena in terms of increased entropy at a low sampling rate. For lossless data gathering scenarios, on the other hand, the sampling rate is lowered without sacrificing critical knowledge required for signal reconstruction. As lower sampling rates lead to smaller energy consumption for processing and transmitting the collected sensory data, the proposed asynchronous sampling strategies are capable of achieving a better trade-off between the lifetime of the network and the quality of collected information. In addition to mathematical analysis, simulation results based on real data also verify the benefits of our asynchronous sampling.

We address issues related to establishing a defender's reputation in anomaly detection against two types of attackers: 1) smart insiders, who learn from historic attacks and adapt their strategies to avoid detection/punishment, and 2) naïve attackers, who blindly launch their attacks without knowledge of the history. In this paper, we propose two novel algorithms for reputation establishment--one for systems solely consisting of smart insiders and the other for systems in which both smart insiders and naïve attackers are present. The theoretical analysis and performance evaluation show that our reputation-establishment algorithms can significantly improve the performance of anomaly detection against insider attacks in terms of the tradeoff between detection and false positives.

Data reporting strategy in heterogeneous wireless sensor networks can be differentiated based on task-specific requirements. In this paper, we propose a novel two-phase data reporting (TDR) scheme that supports class-based QoS to sensor nodes in different priority classes. In the first phase, time slots are divided into separate data reporting round defined for each class, while in the second phase, the sensor nodes in the same class are scheduled to particular time slots depending on the given number of slots calculated in the first phase. In TDR, sensor nodes compete with other nodes in the same class only, while nodes in different classes have differentiated channel access opportunity. TDR is performed in a single-hop cluster-based topology, and a cluster head acts as a node assignment manager (NAM). Sensor nodes wake up for their scheduled time slots; otherwise, they go into sleep mode to save energy. TDR supports both schedule- based and contention-based channel access mechanisms and is scalable due to its distributed nature.

Due to the unattended nature of wireless sensor networks, an adversary can capture and compromise sensor nodes, generate their replicas, and thus mount a variety of attacks with these replicas. Such attacks are dangerous because they allow the attacker to leverage the compromise of a few nodes to exert control over much of the network. Several replica node detection schemes have been proposed in the literature to defend against such attacks in static sensor networks. However, these schemes rely on fixed sensor locations and hence do not work in mobile sensor networks, where sensors are expected to move. In this work, we propose a fast and effective mobile replica node detection scheme using the Sequential Probability Ratio Test. To the best of our knowledge, this is the first work to tackle the problem of replica node attacks in mobile sensor networks. We show analytically and through simulation experiments that our scheme provides effective and robust replica detection capability with reasonable overheads.

Most of geographic forwarding protocols assume that all sensors are always on. Such an assumption is unrealistic for applications where sensors are switched on or off. This paper focuses on k-covered wireless sensor networks (WSNs), where each point in a field is covered by at least k sensors. First, we characterize k-coverage. Then, we propose an energy-efficient scheduling protocol for k-covered WSNs. Third, we propose the first design of a geographic forwarding protocol for duty-cycled k-covered WSNs with data aggregation. Finally, we evaluate the performance of our joint k-coverage and geographic forwarding protocol and compare it to the Coverage Configuration Protocol (CCP) with BVGF on top of it. Simulation results show that our joint protocol outperforms the resulting protocol, CCP+BVGF.

In standard IEEE 802.11 based systems, when the wireless client migrates away from the radio range of the currently associated access point (AP), network applications temporarily loose connectivity till the client is able to re-associate itself with a new AP. The delay that occurs during the break-off interval can vary from a few hundreds of microseconds to a few seconds. However, delay sensitive applications such as Voice over IP (VoIP) or streaming multimedia applications usually are unable to tolerate such long connectivity delays that fall beyond the range of 50 - 200 ms. This results in dropped calls or frozen video frames. In this paper we describe the design, implementation, and evaluation of a software based framework that facilitates seamless and transparent handoff between different APs in standard IEEE 802.11 based wireless local area networks (WLANs). Although different solutions are available in the literature that seek to address the handoff latency, most of them propose changes that are outside the purview of the current 802.11 standards. We have specifically kept such compatibility restrictions in mind and have devised a software based client side solution that is capable of reducing handoff delays to an average value of 20 ms. It is available as a driver update to the client and requires no additional support from the network. As part of our solution, we have successfully implemented and tested our proposed solution framework on Atheros AR5212 chipsets using the open source MadWifi driver.

Ubiquitous (or smart) healthcare applications envision sensor rich computing and networking environments that can capture various types of contexts of patients (or inhabitants of the environment), such as their location, activities and vital signs. Such context information is useful in providing hea

Due to the unattended nature of wireless sensor networks, an adversary can physically capture and compromise sensor nodes and then mount a variety of attacks with these compromised nodes. To minimize the damage incurred by compromised nodes, the system should detect and revoke them as soon as possible. To meet this need, we propose a zone-based node compromise detection and revocation scheme in sensor networks. The main idea of the proposed scheme is to use the sequential hypothesis testing to detect suspect regions in which compromised nodes are likely placed. In these suspect regions, the network operator performs software attestation against sensor nodes, leading to the detection and revocation of the compromised nodes. Through analysis and simulation, we show that the proposed scheme provides effective and robust node compromise detection and revocation capability with little overhead.

Sensing coverage reflects the quality of surveillance of a field by a wireless sensor network (WSN). This paper investigates the problem of minimum connected k-coverage in WSNs, where each point in a field is covered (or sensed) by at least k active sensors while minimizing the necessary total number of active sensors and ensuring connectivity between them. In this paper, we propose two k-coverage protocols using different scheduling approaches. In the first protocol, called self-scheduling driven k-coverage (SSCk), each sensor turns itself on based on the local information it has about its sensing neighbors in order to k-cover its sensing range. The second protocol, called triggered-scheduling driven k-coverage (TSCk), allows a sensor to trigger a necessary number of its sensing neighbors to become active in order to achieve k-coverage of its sensing range. Then, we relax some commonly used assumptions for coverage configuration protocols in WSNs to promote the use of SSCkand TSCkin realworld sensing applications. Simulation results show that TSCkoutperforms SSCkwith regard to the number of sensors required for connected k-coverage as well as the network lifetime. We find that SSCkoutperforms an existing connected k-coverage protocol for WSNs.

Recent work shows that sink mobility along a constrained path can improve the energy efficiency in wireless sensor networks. However, due to the path constraint, a mobile sink with constant speed has limited communication time to collect data from the sensor nodes deployed randomly. This poses significant challenges in simultaneously improving the amount of data collected and reduction in energy consumption. To address this issue, we propose a novel data collection scheme, called the maximum amount shortest path (MASP), that increases network throughput as well as conserves energy to optimize the assignment of sensor nodes. MASP is formulated as an integer linear programming problem and then solved with the help of a genetic algorithm. A two-phase communication protocol is designed to implement the MASP scheme. Simulations experiments using OMNET++ show that MASP outperforms the shortest path tree (SPT) and static sink methods in terms of system throughput and energy efficiency.