Indoor localization is critical for Internet of Things (IoT) applications, yet challenges such as non-Gaussian noise, environmental interference, and measurement outliers hinder the robustness of traditional methods. Existing approaches, including Kalman filtering and its variants, often rely on Gaussian assumptions or static thresholds, limiting adaptability in dynamic environments. This paper proposes a hierarchical robust framework integrating Variational Bayesian (VB) parameter learning, Huber M-estimation, and Conformal Outlier Detection (COD) to address these limitations. First, VB inference jointly estimates state and noise parameters, adapting to time-varying uncertainties. Second, Huber-based robust filtering suppresses mild outliers while preserving Gaussian efficiency. Third, COD provides statistical guarantees for outlier detection via dynamically calibrated thresholds, ensuring a user-controlled false alarm rate. Theoretically, we prove the Semi-positive Definiteness of Huber-based Kalman filtering covariance and the coverage of sliding window conformal prediction. Experiments on geomagnetic fingerprint datasets demonstrate significant improvements: fingerprint matching accuracy increases from 81.25% to 93.75%, and positioning errors decrease from 0.62–4.37 m to 0.03–1.53 m. Comparative studies further validate the framework’s robustness, showing consistent performance gains under non-Gaussian noise and outlier conditions, achieving 95% outlier detection precision with controlled false alarms.

Network Function Virtualization (NFV) is a foundational technology for Mobile Edge Computing (MEC). It delivers network services by chaining Virtual Network Functions (VNFs) into sequential Service Function Chains (SFCs). One of the most critical challenges in MEC is how to provide continuous and stable services to high-mobility user, such as intelligent vehicles and drones. However, current mobility-aware SFC migration methods remain constrained by either post-hoc reaction or myopic prediction horizons, failing to reconcile the divergent timescales of network services and user mobility, thus resulting in suboptimal resource allocation and service delivery. In this paper, we first formulate the predictive mobility-aware SFC migration problem as an NP-hard Integer Linear Programming (ILP) problem. Aiming to mitigate service disruption for mobile users in MEC networks, we propose PreSFC, a predictive SFC migration framework that integrates multi-slot mobility forecasting with fine-grained network state tracking. We first design Gformer, a deep learning-based long sequence time-series forecasting model, which operates on short time slots (less than 200 ms) to sensitively capture network dynamics while predicting over multiple slots (e.g., 50 slots) to effectively track user mobility. This dual-scale design explicitly addresses the temporal disparity between mobility patterns and service requirements. Based on the predictions, we further propose an Optimal Sub-period Partitioning Migration (OSPM) algorithm to determine migration timing and locations. Extensive simulations show that our approach reduces the maximum and average downtime by approximately 55% and 40%, respectively, compared to benchmark methods.

The growing demand for intelligent mobile applications has made the deployment and operation of Deep Neural Networks (DNNs) on mobile Edge Devices (EDs) increasingly essential. However, the highly dynamic nature of edge environments and the limited computational resources of EDs result in significant energy consumption and compromised inference quality. To address these issues, we propose the Demand-Aware Evolutionary Reinforcement Learning (DA-ERL) framework, a novel approach for optimizing Partitioned Collaborative Inference (PCI) across multiple EDs and Mobile Edge Computing (MEC) servers. At the core of DA-ERL is a Demand-Aware Spatio-Temporal Graph Convolutional Network (DA-STGCN). This new architecture creates a predictive state representation by uniquely integrating two channels: a Spatial Graph Channel using Graph Convolutional Networks to model the network topology, and a Temporal Prediction Channel using Temporal Convolutional Networks to capture the evolution of system dynamics. Moreover, we design and formulate a task dynamic demand index to model the dynamic task characteristics, which guides the agent's learning policy. Furthermore, we train DA-ERL within a Cross-Entropy Method (CEM) based evolutionary framework that leverages elite-guided exploration to enhance sample efficiency in complex search spaces. Extensive simulations demonstrate that the proposed DA-ERL framework significantly outperforms conventional methods, achieving a 23.4% reduction in system cost while maintaining a near-perfect task completion rate in high-density scenarios.

The adoption of federated learning (FL) in industrial IoT (IIoT) facilitates the deployment of field-level industrial intelligence by multi-node collaborative and distributed learning. Numerous studies on FL primarily concentrate on enhancing model accuracy under non-independent and identically distributed data. Nevertheless, this focus is inadequate for time-critical industrial systems, as these systems necessitate real-time processing during the FL model training phase and any strategy that prioritizes incremental accuracy gains at the expense of latency risks violating real-time deadlines. However, the system heterogeneity of computing and communication capabilities will impose a formidable bottleneck to the overall time consumed for FL model training. In this paper, we present a FL-enabled Time-Sensitive IIoT (FETI) framework that integrates FL with Time-Sensitive Networking (TSN) to support the deterministic forwarding of FL flows in industry. Aiming to speed up FL convergence within a targeted accuracy gap, we formulate a heterogeneity-aware FL-TSN joint optimization problem, which is theoretically transformed into a stochastic mixed- integer programming problem solved at each FL round. To address this problem, we propose a hierarchical reinforcement learning-based scheduling scheme, called HierTFL, with two interacting layers of policies. With the assistance of a proposed spatial-temporal state encoder, the high-level policy dynamically selects client participants based on data quality and resource availability in each FL round, while the low-level policy optimizes TSN flow scheduling of each selected client using non-cumulative Bellman updates. Experimental results under industrial monitoring datasets have shown the effectivity of HierTFL in achieving a balanced trade-off between model precision and convergence time compared to existing benchmarks.

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are expected to provide greater flexibility and manageability for next-generation IoT networks. In this context, network services should be modeled as Virtual Network Function Forwarding Graphs (VNF-FGs). A key challenge is efficient allocation of resources for sequentially arriving network service requests, a process known as VNF-FG placement. Most existing algorithms either manually or partially extract features from the physical network and VNF-FG or adopt a greedy approach, allocating resources as long as a feasible solution exists, which may over-allocate resources to VNF-FG requests, ultimately harming infrastructure providers’ long-term revenue. In this paper, we propose a VNF-FG placement and admission control algorithm based on hierarchical reinforcement learning, called EAC. It consists two levels of agents: a coarse-level agent that generates placement strategies and rejects requests with no feasible placement strategies, and a refine-level agent that implements admission control and rejects requests that are detrimental to long-term revenue. To fully capture the topological features of both the physical network and the VNF-FG, we employ a customized Graph Attention Network (GAT) that incorporates link feature awareness and enables deeper exploration. To fully explore historical temporal information for admission control, we construct state triples and feed them into a Recurrent Neural Network (RNN). Using Proximal Policy Optimization (PPO) as the foundational training algorithm, the corresponding agents are trained hierarchically. Extensive experimental results demonstrate that the proposed EAC algorithm outperforms existing state-of-the-art solutions in terms of acceptance rate, revenue-to-cost ratio, and long-term average revenue.

Low Earth Orbit (LEO) satellite networks hold great promise in the field of wireless communication due to their global coverage. However, the long communication distances and massive data computations present significant challenges for current satellite networks. To overcome these barriers, we propose SkyLink, a universal Integrated Ground-Air-Space Collaborative Edge Computing system that leverages horizontal collaboration among aerial platforms (AirXs) as well as vertical collaboration among Ground-Air-Space. We propose a bi-level optimization framework based on a Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) with Hybrid Action Space and constructe a latent representation space for each agent to allow the agent to learn the latent policy. This enabling each AirX to act as an agent and autonomously optimize its hybrid action decisions to improve system efficiency in real-time based on the dynamic network environment, a capability not achievable by conventional DRL methods. This includes continuous optimization variables such as AirX deployment (location changes) and resource allocation, as well as discrete optimization variables for collaborative task offloading decisions. Extensive experiments against state-of-the-art algorithms (e.g., MADDPG, QMIX) demonstrate that the proposed system improves energy efficiency by 27.2% and task completion rate by 6.8% compared to traditional Integrated Ground-Air-Space (IG) Network.

Ensuring the quality of service (QoS) in the Space-Air-Ground Integrated Network (SAGIN) requires implementing appropriate service function chain (SFC) embedding strategies to provide reliable general services and AI services. However, the complicated topology, limited infrastructure resources, and the heterogeneity in hardware and software in SAGIN hinder existing reliable embedding solutions from delivering high QoS. To address the challenges, this paper takes into account the heterogeneous reliable-aware SFC embedding (HRSE) problem in SAGIN. Specifically, we formulate the HRSE problem as a nonlinear integer programming problem that is NP-hard. Subsequently, an algorithm called HRSFCEA, which consists of deep reinforcement learning (DRL) and greedy rules, is proposed to tackle the challenges. Initially, leveraging the autonomous decision-making capability of DRL, SFC embedding is executed to select backups efficiently. The proposed algorithm reduces frequent trial and error by modifying its actions. Next, the paper gives the algorithm analysis including the complexity and unreliable environment. Finally, simulation results based on real-world datasets demonstrate that the DRL scheme converges to high rewards with minimal differences across varying numbers of hidden layer neurons. The proposed algorithm can improve the acceptance rate by up to 82% while incurring no more than 5% additional cost compared to the state-of-art algorithms.

Federated fine-tuning (FFT) of large language models (LLMs) has recently emerged as a promising solution to enable domain-specific adaptation while preserving data privacy. Despite its benefits, FFT on resource-constrained clients relies on the high computational and memory demands of full-model fine-tuning, which limits the potential advancement. This paper presents FedSODA, a resource-efficient FFT framework that enables clients to adapt LLMs without accessing or storing the full model. Specifically, we first propose a similarity group pruning (SGP) module, which prunes redundant layers from the full LLM while retaining the most critical layers to preserve the model performance. Moreover, we introduce an orchestrated distillation alignment (ODA) module to reduce gradient divergence between the sub-LLM and the full LLM during FFT. Through the use of the QLoRA, clients only need to deploy quantized sub-LLMs and fine-tune lightweight adapters, significantly reducing local resource requirements. We conduct extensive experiments on three open-source LLMs across a variety of downstream tasks. The experimental results demonstrate that FedSODA reduces communication overhead by an average of 70.6%, decreases storage usage by 75.6%, and improves task accuracy by 3.1%, making it highly suitable for practical FFT applications under resource constraints.

The growing demand for intelligent mobile applications has made the deployment and operation of Deep Neural Networks (DNNs) on mobile Edge Devices (EDs) increasingly essential. However, the limited computational resources of EDs often result in significant energy consumption and compromised inference quality. To address these challenges, we propose a Partitioned Collaborative Inference (PCI) system that reduces on-device model inference costs by distributing the inference process across multiple EDs and MEC servers. To dynamically model the relationships between computing nodes, inference tasks, and resources, we employ Graph Neural Networks to construct the current state representation of the system. Furthermore, we develop a Cross-Entropy Method (CEM) based Evolutionary Reinforcement Learning algorithm, which leverages negative temporal difference (TD) error as a population fitness metric to generate elite individuals. The elite produces high-quality samples to improve learning efficiency, thereby obtaining optimal partitioned collaborative inference decisions and resource allocation in highly dynamic and complex search spaces. Extensive simulations demonstrate that the proposed approach significantly outperforms existing methods and benchmark schemes, achieving a 57. 5% increase in the inference task completion rate and a 65.7% reduction in system costs.

Present Advanced Driving Assistance System (ADAS) responds to the dangerous crossing of pedestrians after the occurrence of the incident, occasionally causing severe accidents due to the stringent response window. Inference of pedestrian crossing intention may help vehicles operate in advance and enhance the safety of the vehicle by predicting the crossing probability. Recent studies usually ignore the demand of real-time forecast that required in the realistic driving scenario, and mainly focus on improving the model representation capacity on public datasets by increasing modality and observation time. Consequently, a new framework named EfficientPIE is proposed to predict the pedestrian crossing intention in real time with sole observation of the incident. To achieve reliable predictions, we propose incremental learning based on intention domain to relieve forgetting and promote performance with a progressive perturbation method. Our EfficientPIE outperforms all the SOTA models on two datasets PIE and JAAD, running nearly 7.4x faster than the previously fastest model. Our code is available at https://github.com/heinideyibadiaole/EfficientPIE.

As key enabling technologies for 5G, edge computing pushes computing resources to the edge close to IoT users, enabling low-latency services. Meanwhile, network function virtualization abstracts complex services into service function chains (SFCs) consisting of multiple virtualized network functions (VNFs), significantly simplifying service management. However, the highly dynamic traffic in edge environments may cause the routing configurations of SFC requests and the resource configurations of VNFs to become outdated. The outdated configurations may result in VNF load imbalance or overload, severely disrupting service availability and degrading user quality of service (QoS). Therefore, this paper studies the novel joint optimization of SFC routing update and elastic resource configuration problem, aiming to minimize the resource leasing cost of the service provider, while subject to multi-resource capacity and update delay constraints. Considering all these characteristics, we formalize this problem as an integer linear programming and prove its NP-hardness. To tackle this problem, we propose a rounding-based routing update and resource configuration algorithm to find cost-effective routing paths and configuration types for requests and VNFs. Further, we design a backtracking-based greedy improvement algorithm that upgrades VNF resource configurations and adjusts request routing paths to accommodate more requests, thus boosting network throughput. Extensive simulation results show that compared with state-of-the-art solutions, our scheme can reduce the leasing cost by 17.8%, while improving network throughput and meeting user QoS.

As a key enabler for 6G and symbiotic IoT applications, edge computing brings computing resources closer to end users, ensuring low-latency services, while network function virtualization (NFV) enables flexible service deployment by abstracting complex functionalities into service function chains (SFCs) composed of virtual network functions (VNFs). However, in dynamic edge environments, traffic changes and frequent network failures can lead to outdated routing configurations, resulting in load imbalance, network congestion, and Quality of Service (QoS) degradation. These issues will damage the robustness and real-time response capability required for symbiotic IoT systems. Although existing work separately optimizes VNF assignment or SFC routing for real-time and robust SFC updates, it fails to jointly optimize both, leading to potential VNF overload and throughput degradation. Therefore, this article studies the joint optimization of the VNF assignment and SFC routing problem, considering limited resource capacity, robustness and delay requirements, and bounded migration costs, aiming to maximize the network throughput. To capture all these characteristics, we formalize the problem as integer linear programming (ILP) and prove its NP-hardness. To tackle this problem, we propose a QoS-aware VNF assignment and routing update algorithm that first relaxes the ILP to linear programming (LP), and then randomly rounds the fractional solution obtained by solving LP to obtain a nearly optimal integer solution. Furthermore, we propose a backtracking-based greedy improvement algorithm, which greedily adjusts VNF assignments and request paths to accommodate more requests and satisfy all constraints. Extensive simulation results show that compared to state-of-the-art solutions, the proposed method can improve throughput by 22.98% while meeting user QoS.

The utilization of federated learning (FL) has witnessed notable advancements in the domain of edge computing (EC). However, limited edge resources and heterogeneous devices restrict the accelerated training of the FL model. To address this issue, we introduce the biological evolutionary mechanism and momentum gradient descent (MGD) update approach into FL, called the EMAFL scheme, aiming to achieve accelerated model training and maximize resource utilization, simultaneously. Specifically, we first update the local model with particle swarm optimization (PSO) for each device and perform MGD on the updated local model. Next, by a toy example, we illustrate the necessity of adopting the different number of local iterations for heterogeneous devices in a resource-limited environment. Analytical convergence of the EMAFL scheme, premised on a delineated resource budget is subsequently explored. This yields a mathematical delineation correlating the quantity of local iterations for heterogeneous devices with the optimal model parameters. Predicated on the prior theoretical examinations, an adaptive control algorithm is devised to ascertain the local iteration count pertinent to each device following every communication round. Finally, through a lot of experiments compared with the benchmarks, the advantages of EMAFL in model accuracy, resource consumption, and Non-IID issues are verified.

In the field of medical imaging driven by the Internet of Things (IoT), with the rapid growth of the number of medical devices and the widespread application of edge computing (EC) technology, efficient collaborative computing on resource-constrained end medical devices has become the key to improving diagnostic efficiency, thereby bringing a more patient-friendly diagnosis and treatment experience. Computed tomography perfusion (CTP) images play an irreplaceable role in the assessment of brain tissue ischemia in patients with acute ischemic stroke (AIS), with high diagnostic accuracy in identifying ischemic lesions and distinguishing infarction from penumbra, but it has the disadvantages of high radiation dose and high cost. To this end, we propose a CTP image synthesis framework based on edge-end collaboration (EESyn-CTP), which aims to use non-contrast CT (NCCT), CT angiography (CTA), and delayed CTA (CTA+8s) images to synthesize CTP images with arbitrary time to optimize AIS diagnosis. The framework consists of two stages: the pre-training stage on the edge server and the fine-tuning stage on the end device. Specifically, we first deploy a temporal residual generative network, t-UNet, on the edge server for pre-training. This process utilizes multiple CTP images, which share similar perfusion features with CTA, CTA+8s, and NCCT images, to effectively learn the gap in perfusion information between the inputs and outputs. Subsequently, the pre-trained t-UNet model parameters are frozen and broadcast to the edge medical device. A UNet adapter is introduced before the model, and fine-tuning is performed on the adapter weights using real NCCT, CTA, and CTA+8s images as input. This approach facilitates the synthesis of CTP images at arbitrary time points. Finally, experiments on an internal data set showed that the quality of Syn-CTP images synthesized by the EESyn-CTP framework is comparable to that of real CTP images and significantly reduces computation latency and energy overhead.

A training set with sufficient quantity and reliable quality is crucial for achieving satisfactory results in data-driven acoustic impedance inversion. However, effective data augmentation still faces significant challenges when labeled well data are sparse. In this study, it is proposed a novel impedance sequence augmentation strategy based on the SinGAN multiscale generative adversarial networks under few well conditions. SinGAN requires only a single impedance sequence for training and supports two augmentation modes: Random Impedance Generation Mode (RIGM) and Controllable Impedance Generation Mode (CIGM). RIGM controls diversity between synthetic and true impedance by adjusting the Start Generation Scale (SGS), while CIGM synthesizes impedance by fusing a known low-frequency reference model with high-frequency details derived from well data, the SGS determines the proportion of their integration. Three training sets were established through data augmentation using broadcasting, RIGM, and CIGM on the Marmousi2 model, and were subsequently fed into a CNN-GRU fusion network with identical hyperparameters. Experimental results show that the correlation coefficients (R²) between the estimated and true impedance values reach 0.9111, 0.9282, and 0.9423 for the broadcasting, RIGM, and CIGM methods, respectively. Meanwhile, the CIGM-based model achieves the best overall performance, with an MSE of 0.006 and an SSIM of 0.966, and it accurately characterizes impedance variations across stratigraphic layers and clearly delineates the water–strata interface and associated sand bodies. These findings verify that the proposed augmentation strategy effectively expands the training sample space and enhance impedance prediction accuracy, offering a new promising approach for seismic inversion tasks with sparse labeled data.

For reliability, security and scalability, Multicast Request flows (MRs) need to traverse a Service Function Chain (SFC) that consists of series of Virtual Network Functions (VNFs) such as firewalls, encoder-decoder in Network Function Virtualization-enabled Software-Defined Networks (NFV-enabled SDNs). There are typically multiple identical VNF-instances in the network, it brings significant challenges when dynamically choosing or placing the requisite VNF-instances to construct a Service Function Tree (SFT) consisting of SFCs for fulfilling the MRs's routing. This paper investigates the Delay and Jitter Aware Dynamic SFT Embedding and Routing Problem (DJADSERP) considering VNF placement, network resources, delay and jitter constraints as well as network load balance in NFVEnabled SDNs. First, we formulate DJA-DSERP as an integer linear programming model and prove it to be NP-hard. Then, an auxiliary edge-weighted graph and an Optimal Link Selection Function (OLSF) are devised, and SFT Embedding Algorithm (SFT-EA) is proposed to address the problem aiming at minimizing the resource consumption costs while satisfying multiple QoS constraints and network load balance. Furthermore, we theoretically prove the effectiveness of the OLSF and the SFTEA. Simulation results demonstrate that the SFT-EA exhibits superior performance compared to existing algorithms in terms of throughput, traffic acceptance rate, and network load balance.

Compared with synchronous federated learning (FL), asynchronous FL (AFL) has attracted more and more attention in edge computing (EC) fields because of its strong adaptability to heterogeneous application scenarios. However, the non-independent and identically distributed (Non-IID) data across devices and the staleness-aware estimation of unreliable wireless connections and limited edge resources make it much more difficult to achieve better AFL-related applications. To handle this problem, we propose anAdaptiveStaleness-awareMomentumAcceleratedAFL(ASMAFL) algorithm to reduce the resources consumption of heterogeneous wireless communication EC (WCEC) scenarios, as well as decrease the negative impact of Non-IID data for model training. Specifically, we first introduce the staleness-aware parameter and a unified momentum gradient descent (GD) framework to reformulate AFL. Then, we establish global convergence properties of AFL, derive an upper bound on AFL convergence rate, and find that the bound is related to the staleness-aware parameter and Non-IIDness. Next, we formulate the bound into a minimization problem of resource consumption under given model accuracy, and the corresponding staleness-aware parameter of devices will be recomputed after each asynchronous aggregation to eliminate the differences of local models’ contribution to global model aggregation. Finally, extensive experiments are carried out to validate the superiority of ASMAFL in model accuracy, convergence rate, resources consumption, Non-IID issue, etc.

The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy. In this paper, we propose a novel hierarchically Federated Region-learning framework of Autonomous Vehicles (FedRAV) that adaptively divides a large area containing vehicles into sub-regions based on the defined region-wise distance, and achieves personalized vehicular models and regional models. Specifically, the architecture employs a designated hypernetwork to learn personalized mask vectors per vehicle used in the linear combination of models shared by vehicles in the same region. This approach ensures that the updated vehicular model adopts the beneficial models while discarding the unprofitable ones. We validate our FedRAV framework against existing federated learning algorithms on four real-world autonomous driving datasets in various heterogeneous settings. Extensive experiment results demonstrate that FedRAV framework achieves superior performance than the state-of-the-art algorithms, and improves the accuracy by 9.36%. The source code of FedRAV is available at:https://github.com/yjzhai-cs/FedRAV.

The emergence of 5G technology has enabled the development of Metaverse applications that provide users with immersive experiences through augmented reality (AR) devices, and the integration of federated learning (FL) with the Metaverse AR (MAR) systems can enable many edge intelligence services in 5G. However, the presence of nonindependent and identically distributed (Non-IID) data across all AR users' devices, coupled with limited edge communication resources, makes it challenging to achieve human-centric Metaverse-related applications such as target detection or image classification that combine virtual content with real-world. To address these challenges, we propose a novel adaptive resource-efficient Metaverse-based FL (AMFL) algorithm for AR applications that mitigates the negative effect of Non-IID data and reduces resource costs as well as improves the quality of experience (QoE). We first analyze the impact of wireless communication factors such as CPU frequency, bandwidth, and transmission power on FL training performance by a toy example in the MAR systems. Based on this analysis, furthermore, we establish a Non-IID degree, model accuracy, and resource consumption-related QoE maximization problem under given resource budgets, which is a stochastic optimization problem with strongly coupled variables, including bandwidth, CPU frequency, and transmission power. Guided by the theoretical analysis, to solve this issue, AMFL employs a deep reinforcement learning (DRL)-based method to adaptively allocate resources. Numerical results demonstrate that AMFL can significantly improve the QoE by up to 30.28%, and reduce communication round and energy costs by up to 81.08% and 72.20%, respectively, even under the worst Non-IID case, compared to benchmarks.

As a novel promising computational paradigm, wireless powered mobile edge computing (WPMEC) has been proposed to offer real-time energy and computing services for Internet of Things (IoT) devices. However, time-varying limited resources such as communication quality and residual energy pose great challenges in devising suitable real-time task offloading and resource allocation strategies to meet users' requirements for low latency and energy consumption. Existing studies either employ raw data offloading methods with significant communication overhead, or utilize ordinary data compression methods that result in poor compression effects. To cope with the challenges, this paper considers introducing the state-of-the-art lossless data compression technology into WPMEC and study the online joint optimization problem of task offloading decision, charging time allocation, and compression proportion allocation with the goal of optimizing task completion time. To tackle this problem, we propose an Intelligent Compression Offloading and adaptive resource Allocation algorithm called ICOA. We first put forward a well-devised framework based on deep reinforcement learning to generate a offloading decision vector set in real-time. Then we standardize the resource allocation problem as a linear programming problem and solve it using the simplex method. The experimental results on a real dataset show that, compared with the benchmark algorithms, the proposed algorithm can effectively reduce the task accomplishing time and energy consumption, and achieve the best approximate ratio. Moreover, ICOA requires low runtime, and can satisfy the real-time and effectiveness requirements very well.

Generative Adversarial Networks (GANs) training in federated learning (FL) is becoming increasingly popular in solving practical applications. However, the training is hampered by non-independent and identically distributed (non-IID) data and resource constraints. The issue of client-drift caused by non-IID data may lead to the model’s poor performance and failure to converge to the global optimum. To address these issues, we propose an adaptive RCFL-GAN framework that combines federated learning to train GANs and deep reinforcement learning (DRL) to adaptively select better-performing clients for aggregation and optimize client-local training epochs. The evaluation of contribution weights for each local model is achieved through the utilization of the maximum mean square deviation (MMD) score. The minimization issue of the RCFL-GAN model loss is formulated in a specified resource budget. Additionally, a theoretical analysis is conducted to examine the impact of client selection and the number of local training epochs on the training performance of the RCFL-GAN model. According to experimental results, our RCFL-GAN framework can improve learning performance while addressing the problems caused by non-IID data and limited resources in FL.

In the era of Big Data, graph data is often collected from dispersed addresses and stored separately, making our research on this data subject to transmission and privacy constraints. So we often introduce federated learning, a novel distributed machine learning algorithm, in our studies of graph data. However, the application of federated learning in the field of graph data research is constrained by issues such as the surge in communication costs and the decrease in model accuracy caused by data heterogeneity. To address both the communication stress problem and the accuracy impairment caused by heterogeneity in graph federated learning, we propose a communication-efficient personalized federated learning algorithm, named FedGDC-P. In this algorithm, we introduce the dataset condensation technique in the global information collection process to transfer the features of local data with a one-time transmission of the distilled data instead of multiple transmissions of the model parameters, helping the model on the server to build a panorama of data distribution in order to reduce the amount of communication data during the training process. Subsequently, we execute the obtained model locally to personalize the training process to improve the adaptation of the model to personalized local data in order to enhance the overall training effect. The experiments demonstrate that our algorithm can achieve or even outperform SOTA with only 5% communication budget.

The integrated of the fifth generation (5G) and time-sensitive networking (TSN) is a promising approach to meet the requirements of deterministic forwarding with extremely low latency and high flexibility for the Industrial Internet of Things (IIoT). However, due to the large dissimilarity of protocol stacks, the 5G system normally serves as a logical bridge for TSN, performing hold-and-forward operations in Base Stations (BSs) under the current 5G-TSN frameworks. To provide ubiquitous and seamless connectivity for IIoT devices, this paper focuses on the integrated enhancement of 5G and TSN by optimizing the scheduling of time-sensitive flows with end-to-end latency of 5G-TSN transmission taken into consideration. Specifically, we propose a novel architecture named GF-CQF, which combines 5G Grant-free (GF) access with TSN Cyclic Queuing and Forwarding (CQF). Subsequently, the distributed flow scheduling problem based on GF-CQF architecture is established due to the high timeliness demands. To alleviate the impact of the uncertainty inherent in 5G channels on end-to-end deterministic transmission, a feature-aware decentralized real-time scheduling (FDRS) policy based on Multi-agent Reinforcement Learning is proposed. FDRS allows each agent at BS to adaptively allocate TSN injection slots for flows mainly based on dynamic 5G transmission performance and TSN network resource state so that TSN queue overflow can be avoided and flow delay constraints can be guaranteed. Simulations show that FDRS offers superior scheduling capabilities under the limited TSN queue resources.

Network Function Virtualization (NFV) supported Mobile Edge Cloud (MEC) is considered an ideal platform for low-latency Internet of Things (IoT) applications, where IoT application requests are represented as Service Function Chains (SFCs) which consists of a sequence of ordered Virtual Network Functions (VNFs). However, MEC’s limited resources can only support a limited number of IoT applications. In this scenario, how to effectively place SFCs to improve resource utilization and service quality under latency, resource constraints while considering the dynamic changes of network is a critical concern for infrastructure providers. In this paper, we study the SFC placement problem in NFV-enabled MEC and propose a Proximal Policy Optimization (PPO) based online SFC placement algorithm called SFCP-PPO. SFCP-PPO achieves the goal of maximizing long-term average revenue through the integration of two critical components: the Multi-Head Attention Mechanism (MHA), capable of extracting information from diverse network representation spaces, and the Recurrent Neural Network (RNN) that addresses scalability challenges posed by varying sizes of SFCs and reduces the frequency of acquiring physical network states during the SFC placement process. We demonstrate the effectiveness of SFCP-PPO through extensive experiments. Compared to existing benchmark algorithms, SFCP-PPO achieves an improvement of 8% in acceptance ratio and 6.5% in long-term average revenue with low running time.

Advances in containerization technology and edge device performance enable applications to run on a wide range of devices through virtualization, enhancing service quality in decentralized edge networks. However, edge devices often lack the computational power of cloud infrastructure and may experience connection fluctuations, which makes node reliability crucial when providing Virtual Network Functions (VNFs). To provide Service Function Chain (SFC) which combines a series of ordered VNFs, it is necessary to determine the redundancy of VNFs to meet reliability requirement and decide whether to deal with these VNF requests immediately or defer them. Therefore, this paper addresses this problem and formulates it as a reliability-aware SFC scheduling problem in container environment (RASCE) and prove it to be NP-hard. To solve this problem, we propose a reliability-aware scheduling algorithm via priority-based node selection (SSAP) using Deep Reinforcement Learning (DRL), which consists of long-task prioritization redundancy strategy considering dynamic node reliability, priority-based node selection, and SFC scheduling based on DRL. The simulation demonstrates that our approach can enhance the success rate by a minimum of 5.78% in comparison to the state-of-the-art algorithm.

Federated learning may protect user privacy, reduce the transmission of a large amount of raw data, and is more compatible with smart home applications. Current federated learning faces two major problems including non-independent and identically (Non-IID) distributed data and high communication overhead. Personalized federated learning is a good method to deal with Non-IID data, but current personalized federated learning methods overlook the shared features of users' living habits in the same region. Hierarchical federated learning can reduce traffic on the core network, but its potential for personalization for smart home applications has not been considered. Therefore, to address these issues simultaneously, we propose hierarchical personalized federated learning. Specifically, we adopt a three-layer federated learning architecture of cloud-edge-client. On this basis, we use differential learning classification loss (DLCL), hierarchical balance loss (HBL) and balanced edge data selection (BEDS) methods to achieve the personalization of models on both the device side and the edge side. Finally, our experiments demonstrate that compared to state-of-the-art federated learning methods, hierarchical personalized federated learning has improvements in model accuracy and communication overhead.

The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy. In this paper, we propose a novel hierarchically Federated Region-learning framework of Autonomous Vehicles (FedRAV), a two-stage framework, which adaptively divides a large area containing vehicles into sub-regions based on the defined region-wise distance, and achieves personalized vehicular models and regional models. This approach ensures that the personalized vehicular model adopts the beneficial models while discarding the unprofitable ones. We validate our FedRAV framework against existing federated learning algorithms on three real-world autonomous driving datasets in various heterogeneous settings. The experiment results demonstrate that our framework outperforms those known algorithms, and improves the accuracy by at least 3.69%. The source code of FedRAV is available at: https://github.com/yjzhai-cs/FedRAV.

The promising wireless powered mobile edge computing (MEC) can offer sustainable energy and fast network service response for nearby wireless terminals (WTs) to satisfy real-time and flexible requirements. Online task offloading and wireless power transfer (WPT) are critical for the wireless powered MEC system to realize powerful function. However, existing researches usually schedule the task offloading of WTs serially to prevent mutual signal interference, and suffer from high task offloading time. Hence, to lower the task offloading time, we adopt the successive interference cancellation (SIC) technology and realize task concurrent offloading of multiple WTs to the edge server (ES). Specifically, we study the SIC-enabled online task concurrent offloading problem with the aim of optimizing the total task completion time. We prove this optimization problem to be NP-hard, and decompose this problem to reduce the problem solving difficulty. With the support of the SIC and deep reinforcement learning (DRL) technology, we present an efficient and intelligent algorithm named SIOA. Our SIOA algorithm provides online offloading decision generating strategies for WTs through the idea of task concurrent offloading and a well-devised DRL structure. Moreover, our SIOA algorithm assigns the ES’s WPT time via a feasible area analysis approach. Our SIOA algorithm can offer demonstrable feasibility assurance, and requires lower task completion time than existing baseline algorithms with low program running time, which is verified by experiments on a real dataset.

Cloud and edge computing paradigms are increasingly being applied to data processing for Internet of Things (IoT) sensors. Meanwhile, unmanned aerial vehicles (UAVs) can assist these sensor systems in data acquisition, especially in smart applications such as environmental monitoring and smart agriculture, where direct network connectivity for sensors is limited due to remote deployment. In this work, the Age of Information (AoI) is introduced for wireless sensor networks to measure the freshness of data information. We also develop a hierarchical UAV-assisted data processing framework to minimize AoI, where the multi-UAVs hover over the sensor clusters to collect data and conduct computing offloading by flexibly using the computation resources of edge server or cloud. Then, we innovatively propose a joint service association, trajectory scheduling and computing offloading mechanism for UAVs oriented by AoI. Specifically, we design a sensor clustering and sensor-hovering point (HP) association management scheme to improve the efficiency of data collection, and then propose an HP clustering model to establish the HP-UAV association. Further, a multi-objective optimization model is solved by devising a learning-based trajectory scheduling scheme. Simulation results show that the proposed ATSCO can not only converge well and improve the freshness of data information, but also realize superior performance than the mainstream schemes in various situations.

Mobile edge computing (MEC) is a burgeoning paradigm that MEC servers provide the computing capabilities to release the workload of the mobile devices by transferring the computational tasks, which can vastly reduce the latency and energy cost for executing tasks. In consideration of the battery capacity limitation with the mobile devices, the computation task process may be interrupted. To improve the computational service capacity as well as the popularity of the green computing, the energy of mobile devices is considered to be supplied effectively by energy harvesting (EH), capturing the energy from the environment. We propose an effective task allocation strategy that minimizes the weight sum of energy cost and computational latency of mobile devices in an MEC system with EH. Furthermore, we construct a task queue to fetch the upcoming tasks for mobile devices. On the basis of the Lyapunov optimization approach, we propose an online Lyapunov optimization-based dynamic task allocation (LODTA) algorithm that determines the task assignment policy through adjusting mobile devices with the CPU execution frequency and the transmission power caused by offloading. The LODTA algorithm has a superiority that only the current system state is necessary for the task allocation strategy, but without predicting the future state. In our simulation, the proposed model and algorithm can stabilize the battery energy level with a trade-off between energy consumption and execution latency.

Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to variations in drivers’ driving styles and vehicle types, speed predictions for different target vehicles may significantly differ. Existing methods may not realize personalized vehicle speed prediction while protecting drivers’ data privacy. We propose a Federated learning framework with Personalized Aggregation Weights (FedPAW) to overcome these challenges. This method captures client-specific information by measuring the weighted mean squared error between the parameters of local models and global models. The server sends tailored aggregated models to clients instead of a single global model, without incurring additional computational and communication overhead for clients. To evaluate the effectiveness of FedPAW, we collected driving data in urban scenarios using the autonomous driving simulator CARLA, employing an LSTM-based Seq2Seq model with a multi-head attention mechanism to predict the future speed of target vehicles. The results demonstrate that our proposed FedPAW ranks lowest in prediction error within the time horizon of 10 seconds, with a 0.8% reduction in test MAE, compared to eleven representative benchmark baselines.

Wireless powered Internet of Things (WPIoT) has gained great concern due to its benefits of high deployment flexibility and low maintenance overhead. The minimum delay broadcast scheduling problem is very critical for many applications of WPIoT. However, traditional broadcast scheduling algorithms assume that Internet of Things (IoT) devices always possess sufficient energy to support data transmission or reception, which does not hold in WPIoT with the special feature of using the store-charge-and-forward communication mode. Furthermore, existing solutions rely heavily on the interference-avoiding technology to handle the signal interference problem, and overlook the powerful interference processing capability of the successive interference cancellation (SIC) technology. To efficiently resolve this problem, this article proposes a delay-efficient energy-aware broadcast scheduling algorithm called EABS. EABS algorithm incorporates a novel broadcast link scheduling method by fully considering the special feature of WPIoT and efficiently utilizing the advantage of the SIC technology to significantly improve the broadcast delay. Extensive experiments based on a real-world dataset are conducted to evaluate the performance of our algorithm, and the results demonstrate the better performance of our algorithm than the baseline algorithms.

Unmanned aerial vehicle (UAV) edge computing systems provide easy-to-deploy and low-cost services at those areas with inadequate infrastructure by deploying UAVs as moving edge servers for large-scale users. However, user devices are generally distributed unevenly in a large area, which makes it difficult for existing efforts to cope with this realistic scenario for optimal deployment of UAVs. Therefore, this paper considers a multiple UAV (Multi-UAV) Collaborative edge Computing (UCC) system by utilizing collaboration among them to split computation tasks at UAVs to balance the load and improve resource utilization. In order to maximize the energy-efficiency of the UCC system under the satisfaction of the delay constraint, we study the joint problem of UAV deployment, task collaborative offloading, computation and communication resource allocation in UCC system. We propose a bi-level optimization framework to solve the formulated non-convex mixed-integer optimization problem. In the upper level, the UAV deployment is optimized based on an improved differential evolution (DE) algorithm, and in the lower level the offloading decision and resource allocation are optimized based on a Reinforcement Learning (RL) algorithm with Twin Delayed Deep Deterministic policy gradient. Experimental results demonstrate the effectiveness and superiority of multi-UAV collaborative computing, with the proposed framework achieving a 32.4% reduction in energy consumption and an average 30% increase in task completion rate compared to DDPG, ToDeTaS, and other benchmark schemes.

While a variety of innovative travel modes, such as taxi service and ridesourcing service, have been launched to improve the transportation efficiency, people still encounter travel problems in real life. The major cause is the imbalance between transportation supply and demand. To strike a balance, it is well-recognized that an accurate and timely passenger demand prediction model is the foundation to enable high-level human intelligence (i.e., taxi drivers) or machine intelligence (i.e., ride-hailing platforms) to allocate resources in advance. Although quite a lot of deep models have been designed to model the complicated spatial and temporal dependencies in a data-driven way, they focus on the demand prediction of a single mode and ignore the fact that passengers may shift between different modes, especially between taxis and ridesourcing cars. In this paper, we target a co-prediction problem that considers the prediction of taxi and ridesourcing as two coupled and associated tasks, and propose a novel Temporal and Spatial Intertwined Network (TSIN) that consists of two twin components and an intertwined component. Each twin in the TSIN model is able to extract spatial and temporal dependencies from its corresponding travel mode separately (i.e., intra-mode features), and the in-between intertwined component is designed to bridge the twins and allow them to exchange information (i.e., inter-mode features), thus enabling better prediction. We first evaluate our model on four real-world datasets. Results demonstrate the outstanding performance of our model and the necessity to take into account the influence between modes. Based on an additional demand data from bike in NYC, we then discuss the generalizability in coupling more transportation modes. Further results demonstrate that our proposed intertwined neural network is highly flexible and extendable, and can yield better prediction performance.

Edge computing has emerged as a powerful technology for enhancing the performance of on-device video analytics, which is critical to support real-time applications. Nevertheless, there still lack of effective metrics to guide the offloading decision of video analytics tasks between device and edge server. Additionally, these existing optimization mechanisms either presume prior knowledge of the ground-truth of previous inferences or involve high training overheads, thereby rendering them unsuitable for real-time situations. To address these challenges, this paper presents a system model of edge-assisted online video analytics, where a lightweight object tracking module and a complex DNN-based model are deployed at the device and edge server, respectively. We formulate the resolution and deviation-based offloading (RDO) problem by considering heterogeneous computation resources and dynamic network bandwidth, aiming at maximizing inference accuracy and processing rate concurrently. We propose a context-aware offloading (CO) algorithm based on Bayesian optimization, which learns the optimal parameter settings by evaluating reward based on Gaussian process. Notably, the CO is proved to offer near-optimal solution with sublinear regret. Finally, we build a testbed and test algorithm performance on three realistic video datasets. The simulation results illustrate that the proposed CO outperforms other existing solutions in various service scenarios.

The Internet of Vehicles (IoV) enhances data availability by equipping a plethora of sensors, driving the automotive industry towards data-driven Predictive Maintenance (PreM) models. However, traditional centralized PreM solutions, requiring complete access to training data, raise concerns about data privacy. PreM in the automotive domain is more challenging than in many other fields, partly due to the varying distribution nature of data samples and the limited network connectivity time caused by vehicle mobility. To address these challenges, we propose the PreM-FedIoV framework, extending single-agent Double Deep Q-Network (DDQN) to Multi-Agent Double Deep Q-Network (MADDQN). In each round, each vehicle client uploads a data packet to the server based on the current contention window, containing its local model, local test Mean Absolute Error (MAE), and a timestamp. The server initially performs federated aggregation on the received local models. The MADDQN module then dynamically adjusts the contention window of each vehicle for the next round based on the local test MAE and communication statistical state, aiming to optimize communication costs and predictive performance. Additionally, we utilize NS-3 to create IoV simulations and deploy the PreM-FedIoV framework within NS3-gym. We choose Federated Averaging (FedAvg) and FedAdam following the IEEE 802.11p standard as baselines. The experiments demonstrate significant improvements in our framework compared to state-of-the-art algorithms. On the C-MAPSS dataset, we achieve reductions of up to 10.2% in MAE, 26.31% in average communication clock time per round, and 65.6% in the number of participating clients per round. For the Random Battery Usage dataset, with up to 4.55%, 24.44%, and 36.58% improvements in the respective metrics.

Traditional traffic classification methods struggle to identify emerging network traffic due to the need for model retraining, which hampers the real-time response of deployed edge devices. Furthermore, emerging network traffic samples are often scarce, traditional methods often treat a session as a single image, thereby overlooking essential structural features. These factors can result in poor generalization ability of the trained model. To overcome these challenges, we propose ConViTML (Convolutional Vision Transformer-based Meta-Learning), a real-time end-to-end network traffic classification framework that employs meta-learning to avoid model retraining. We propose a novel feature extraction network, Convolutional Visual Transformer (ConViT), merging Convolutional Neural Network (CNN) and Visual Transformer (ViT). ConViT can directly extract low-dimensional discriminative features containing basic and structural features of the session, which is vital for improving detection accuracy and accelerating convergence in a data-scarce environment. Furthermore, we employ a Packet-based Relation Network (PRN) to analyze the matching degree of support samples and query samples. Therefore, accurate classification in novel traffic identification tasks can be achieved with just a few labeled samples, eliminating extensive data collection and labeling operations. Finally, we replace various feature extractors and compare our approach with the classic meta-learning framework Relation Network (RelationNet). Extensive experimental results demonstrate that ConViTML outperforms others with various performance indicators.

The success rate of current adversarial attacks remains low on real-world 3D face recognition tasks because the 3D-printing attacks need to meet the requirement that the generated points should be adjacent to the surface, which limits the adversarial example’ searching space. Additionally, they have not considered unpredictable head movements or the non-homogeneous nature of skin reflectance in the real world. To address the real-world challenges, we propose a novel structured-light attack against structured-light-based 3D face recognition. We incorporate the 3D reconstruction process and skin's reflectance in the optimization process to get the end-to-end attack and present 3D transform invariant loss and sensitivity maps to improve robustness. Our attack enables adversarial points to be placed in any position and is resilient to random head movements while maintaining the perturbation unnoticeable. Experiments show that our new method can attack point-cloud-based and depth-image-based 3D face recognition systems with a high success rate, using fewer perturbations than previous physical 3D adversarial attacks.

The advent of Industry 4.0 has resulted in a significant increase in data availability, leading to the development and deployment of data-driven models for predicting the Remaining Useful Life (RUL) of machines. However, traditional centralized Predictive Maintenance (PreM) solutions that require complete access for training data give raise to concerns regarding data privacy. To address this challenge, Federated Learning (FL) has emerged as a promising and practical approach to enhance task performance while preserving data privacy within network nodes. Nevertheless, the presence of Non-Independent and Identically Distributed (Non-IID) data samples across devices can present challenges in terms of the convergence and speed of FL. Additionally, the heterogeneity of devices can lead to issues such as local model discarding and high communication costs, which are important considerations in FL. To address these challenges, this paper proposes Fedrated Predictive Maintenance (FedPreM), a novel federated reinforcement learning-based PreM scheme. FedPreM selectively involves a subset of devices in each communication round and employs an improved Perturbed Gradient Descent (PGD) optimizer to achieve flexible workload distribution among participating devices. By conducting experiments on a widely used turbofan dataset, our results demonstrate the effectiveness of FedPreM in reducing the number of communication rounds and minimizing prediction errors in distributed Industry 4.0 scenarios.

With rapid development of vehicular sensing and mobile communication technologies, cooperative sensing becomes an emerging paradigm of future intelligent transportation systems (ITSs). This paper investigates a LiDAR based cooperative sensing scenario in Vehicular Edge Computing (VEC). Specifically, we present the system architecture, in which vehicles with on-board LiDAR are able to detect objects via local processing of the sensed point-cloud data, and the outputs can be further shared via vehicle-to-vehicle (V2V) /vehicle-to-infrastructure (V2I) communications and fused in edge nodes. Then, we formulate the Edge Assisted Task Offloading (EATO) problem by considering the heterogeneous computation and communication capacities of vehicles and edge nodes, aiming at minimizing the average delay of the cooperative sensing tasks. Further, we propose a Multi-Armed Bandit (MAB)-based algorithm to make task offloading decisions adaptively. Finally, we implement the system prototype and give a comprehensive performance evaluation, which demonstrates the effectiveness of the proposed algorithm.

Most of federated learning (FL) researches mainly focus on image and voice data at the expense of graph data. However, Graph Federated Learning (GFL) is specialized for FL on graph, and received little attention. Subgraph FL is a branch of GFL. In the Subgraph FL situation, a graph is not stored centrally but is distributed among clients as multiple subgraphs. Each client owns a subgraph of the original graph and faces a unique challenge, i.e., missing information cross clients. Ignoring the missing information cross subgraphs will result in deterioration of the performance of the local model. In this paper, we consider data heterogeneity and bring up a more practical problem, i.e., the subgraphs on the clients are from multiple Non-IID graphs rather than the same global graph. Then, to address the issues, we propose a subgraph FL framework FedSG which can learn a personalized model for each client, benefiting from its ability to effectively separate and combine topology information and feature information among the subgraphs. Finally, our experimental results show that FedSG achieves higher accuracy performance and faster convergence, compared with the existing approaches.

Data immutability, transparency and decentralization of blockchain make it widely used in various fields, such as Internet of things, finance, energy and healthcare. With the advent of the Big Data era, various companies and organizations urgently need data from other parties for data analysis and mining to provide better services. Therefore, data sharing and data exchange have become an enormous industry. Traditional centralized data platforms face many problems, such as privacy leakage, high transaction costs and lack of interoperability. Introducing blockchain into this field can address these problems, while providing decentralized data storage and exchange, access control, identity authentication and copyright protection. Although many impressive blockchain-based schemes for data sharing or data exchange scenarios have been presented in recent years, there is still a lack of review and summary of work in this area. In this paper, we conduct a detailed survey of blockchain-based data sharing and data exchange platforms, discussing the latest technical architectures and research results in this field. In particular, we first survey the current blockchain-based data sharing solutions and provide a detailed analysis of system architecture, access control, interoperability, and security. We then review blockchain-based data exchange systems and data marketplaces, discussing trading process, monetization, copyright protection and other related topics.

Deep integration of wireless power transmission and mobile edge computing (MEC) promotes wireless powered MEC to become a new research hotspot in the field of Internet of Things. In this paper, we focus on the joint optimization problem of online offloading decision and charging resource allocation for minimizing task accomplishing time in dynamic time-varying wireless channel scenarios. The optimal solution involves addressing a mixed integer programming problem in real time, which is proved to be NP-hard, and imposes nontrivial challenges to design with conventional optimization methods. To efficiently address this problem, we leverage the deep reinforcement learning (DRL) technology to propose an energy-aware online offloading algorithm called EAOO. EAOO algorithm learns empirically the online offloading decision policies via a well-designed DRL framework, and adopts the feasible solution region analysis method to implement the charging resource allocation. We further propose a novel feasible decision vector generation method, and incorporate the crossover and mutation technology to expand the offloading vector search space with the provable feasibility guarantee. Extensive experimental results show that, our EAOO algorithm outperforms existing baseline algorithms, and achieves near-optimal performance with low CPU execution latency, which well satisfies the practical requirements of real-time and efficiency.

Mobile edge computing (MEC) is envisioned as a promising solution to real-time services in Internet of Vehicles (IoV) by enabling edge caching, computing and communication. However, it is still challenging to implement multimedia streaming in MEC-based IoV due to dynamic vehicular environments and heterogeneous network resources. In this paper, we present an MEC-based architecture for adaptive-bitrate-based (ABR) multimedia streaming in IoV, where each multimedia file is segmented into multiple chunks encoded with different bitrate levels. Then, we formulate a joint resource optimization (JRO) problem by synthesizing heterogeneous edge cache and communication resource constraints, which aims at achieving both smooth play and high-quality service by optimizing chunk placement and transmission. For chunk placement, a multi-armed bandit (MAB) algorithm is proposed for online scheduling with low overhead but slow convergence. Further, a deep-Q-learning algorithm is proposed to improve cache reward and speed up convergence by using replay memory for repeatedly training. For chunk transmission, we design an adaptive-quality-based chunk selection (AQCS) algorithm, which determines bandwidth allocation and quality level based on a benefit function incorporating quality level, available playback time, and freezing delay. Lastly, we build the simulation model and give comprehensive performance evaluation, which demonstrates the superiority of proposed algorithms.

Mobile edge computing (MEC) can use wireless access network (RAN) to provide users with nearby information technology (IT) services and cloud computing functions, which creates a high-performance and low latency service environment. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission. However, the time-varying and dynamic of the content requests may lead to the base station to cache the useless contents. In this paper, we study a distributed caching optimization problem in edge networks (ENs) with the spatio-temporal requirements. In the considered ENs, the cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the total cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original optimization problem is transformed into a mean field game (MFG). We propose a distributed caching iterative control algorithm that decouples the information interaction between the general SBS and others through the mean field distribution. In addition, we obtain the optimal edge caching control strategy, while the existence and uniqueness of the mean field equilibrium (MFE) can also be guaranteed. Simulation results demonstrate that our proposed caching control algorithm can average reduce 27.12% storage cost and achieve better performance than other existing schemes.

The variation of wireless signal in dynamic indoor parking environments may seriously compromise the performance of fingerprint-based localization methods. In this regard, this paper investigates the problem of robust WiFi fingerprint-based vehicle tracking in dynamic indoor parking environments, aiming at designing an online learning framework to continuously train the localization model and counteract the effect of signal variation. Specifically, a Hidden Markov Model (HMM) based Online Evaluation (HOE) method is firstly proposed to assess the accuracy of localization results by measuring the inconsistency of locations inferred by WiFi fingerprinting and Dead Reckoning (DR). Further, an Online Transfer Learning (OTL) algorithm is designed to improve the robustness of the fingerprinting localization, which consists of a weight allocation scheme to combine two classification models (i.e., the batch model and the online model) and an instance-based transferring scheme to resample the offline fingerprints and retrain the batch model. Finally, we implement the system prototype and give comprehensive performance evaluation, which demonstrates that the proposed solutions can outperform the state-of-the-art localization algorithms around 28%$\sim$58% on vehicle tracking accuracy in dynamic indoor parking environments.

Deduplication (redundancy elimination) and cooperative video delivery are two effective ways to save the bandwidth and energy consumption and ensure video collection in damaged networks. However, deduplication in mobile crowd sensing (MSC) is primarily performed on texts and images. Furthermore, most of deduplication technologies require global information and are separated from video routing. To solve such problems, this paper propose a cooperative upload method for sensing videos, which performs the local video deduplication without excessive comparison and feature exchange. Also, we combine the content-aware deduplication with the dynamic relay selection to avoid the propagation of redundant items caused by the content-free video routing. Besides, we integrate a novel mutual-assisted mechanism into our method to motivate relay cooperation and load balance. We formulate the deduplication-supported cooperative video upload as a multi-stage decision problem. To solve the uncertainty of destinations in the decision problem, we develop a stepwise Mutual-Assisted Video Upload Algorithm (MAVU) to schedule video chunks and remove duplicates. Extensive experiments are conducted to compare MAVU with the existing algorithms. The numerical results validate that our MAVU has advantages over the other algorithms in collected video size and upload latency

Smart contract technologies can be used to implement almost arbitrary business logic. They can revolutionize many businesses such as payments, insurance, and crowdfunding. The resulting birth of decentralized finance (DeFi) has gained significant momentum. Smart contracts and DeFi are now attractive targets for attacks. An important research question is how to protect deployed smart contracts and DeFi. Smart contracts cannot be modified once deployed, namely vulnerabilities cannot be fixed by patching. In this case, vulnerabilities in deployed contracts and DeFi might cause devastating consequences. In this paper, we put forward SolSaviour, a framework for protecting deployed smart contracts and DeFi. The core of SolSaviour is to build a smart contract protection mechanism based on democratic voting using a distributed trusted execution environment (TEE) cluster. Once a vulnerability in deployed contracts or DeFi is found, SolSaviour can destroy the defective contract and redeploy a patched contract via the distributed TEE cluster. Moreover, SolSaviour can migrate funds and state variables from the destroyed contract to the patched one. Compared with previous work, our approach can protect smart contracts and DeFi in a distributed manner, avoiding reliance on privileged users or trusted third parties. Our experiment results show that SolSaviour can protect smart contracts and complex DeFi protocols with feasible overhead.

With the development of edge computing and sharing economy, more services and contents are decentralized to the edge of the network. At present, most existing studies combine the content caching with service offloading mechanisms. However, few studies focus on what strategies the content providers (CPs) can implement to maximize their utilities. In this paper, we first characterize the content supply and demand model, the CPs' cost and utilities in edge content market by considering both the time sensitivity of edge content and the heterogeneity of CAs. Furthermore, according to the edge content market environment characteristics, we divide the edge content market into a monopoly environment, where the content is only provided by a certain CP, and an open environment, where content services are provided by multiple CPs. In the monopoly environment, we establish a two-stage Stackelberg game to design the incentive mechanism. In the open environment, also we formulate the competitive behavior among CPs as a stochastic game. Since the CPs are not aware of each other's strategies and environmental uncertainty, the reinforcement learning-based algorithm (RLIMO) is used to derive the pricing strategy of CP. Finally, numerical results show that the proposed incentive mechanisms are reliable and effective.

Computation offloading is an effective method to relieve user equipment (UE) from the limited battery capacity and computation resource in mobile edge computing (MEC) networks. However, it is challenging to obtain offloading strategy timely and accurately under diverse computation task requirements and changeable network channel states in multi-user and resource-constrained network environment. In this paper, we consider the network dynamics and UE's resource constraints and aim to minimize the energy consumption of all UEs by jointly optimizing the offloading decision, the central processing unit (CPU) frequency and the power split ratio in a dynamic MEC network. To be specific, we introduce simultaneous wireless information and power transmission (SWIPT) technology in MEC networks to prolong UE's operation time. More importantly, we propose an online computation offloading algorithm based on deep deterministic policy-gradient (DDPG), named Enhanced DDPG (EDDPG), to solve the energy consumption minimization problem. In particular, EDDPG can make real-time decisions without complete network information and adapt to time-varying environments and different requirements. Furthermore, we introduce the priority experience replay technology in EDDPG to accelerate the convergence by using experience tuples. Simulation results show that our proposed algorithm can effectively reduce the energy consumption of UEs and enable them complete more computing tasks within the time limit. Compared with other baseline methods, it can accelerate the convergence and improve the system performance effectively.

Traditional relational databases are nowadays over-whelmed by the increasing data volume and concurrent access. NoSQL databases can manage large-scale data, but most of them do not support complete transactions and standard SQL languages. NewSQL is proposed for both high scalability and transactional properties with SQL languages support. One type of NewSQL builds distributed systems from scratch, which is too radical for some critical applications. The other type of NewSQL, i.e., data sharding among relational databases, is a better option for these scenarios. This paper presents Apache ShardingSphere, the first top-level open-source platform for data sharding in Apache, which enables developers to use sharded databases like one database. Specifically Apache, ShardingSphere integrates six databases and designs and implements a complete SQL engine to route requests correctly and intelligently. Additionally it encapsulates three types of distributed transactions and provides two adaptors for different scenarios. Moreover it proposes a novel AutoTable strategy and a query language i.e DistSQL allowing database maintainers to easily configure the sharded databases. Further-more it provides many other pluggable features to better shard data. Extensive experiments are conducted using two famous benchmarking tools proving that Apache ShardingSphere is more efficient than eight state-of-the-art systems in our settings. All experimental source codes are publicly released. More than 170 companies are currently using Apache ShardingSphere.

Network function virtualization (NFV) is able to reduce the delay and improve the flexibility of network services in mobile edge computing (MEC) networks via deploying the service function chain (SFC) that consists of a sequence of ordered virtual network functions. However, it is still challenging to deploy SFC with delay guarantees and resource efficiency while taking into account the real-time network variations and dispersed edge server nodes in NFV/MEC-enabled networks. To address the issue, this paper proposes a self-attention mechanism-based double deep Q-network algorithm (SA-DDQN) for SFC deployment to jointly minimize the resource consumption on servers and bandwidth consumption on links within delay limits in dynamic NFV/MEC-enabled networks. In particular, we introduce the self-attention mechanism in the deep neural network structure, which enables the agent to pay its attention on more valuable physical nodes when making decisions, thus improving the efficiency of SFC deployment. Additionally, we utilize the Markov decision process (MDP) model to solve the problem of real-time network state variations. Finally, extensive simulation results show that our proposed SA-DDQN SFC deployment algorithm can reduce resource consumption by 25% and delay by 18.4% compared with the state-of-the-art algorithm.

Caching adaptive bitrate video at edge nodes (ENs) can provide multi-version video-on-demand (VoD) services to end users (EUs) with better experience. However, due to the limited cache capacity of ENs, it is important to decide which video content and corresponding bitrate version to be cached in the EN. In this paper, we first propose a user request hit profit (RHP) model, and then based on the RHP model we envision a mobile edge computing (MEC)-assisted collaborative caching scheme (MACC). Specifically, we model the communication links between ENs and EUs as a bipartite graph to employ the collaborative caching among ENs; and we consider the transcoding relationship between different versions to effectively utilize the processing capacity of ENs. Due to the NP-completeness of the cache placement problem, we prove it is a monotone submodular function maximization problem, and propose the proactive cache placement based on maximum RHP increment (PCP-MRI) algorithm and the reactive cache replacement based on maximum RHP increment (RCR-MRI) algorithm. Extensive simulation results show that, compared with existing methods, the proposed MACC has significant performance improvements in cache hit ratio, initial waiting delay and backhaul traffic load.

Traditional traffic event monitoring and detection solutions mainly rely on roadside surveillance cameras. However, existing solutions cannot be applied for traffic event augmentation due to both restricted monitoring angles and limited camera coverage. Therefore, this paper investigates a novel architecture for traffic event augmentation via vehicular edge computing. In particular, multiple vehicles can collaborate with roadside infrastructures for detecting, re-identification and augmenting certain traffic event via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. To enable such an application, we formulate the problem of multi-view augmentation task offloading (MATO) by considering the heterogeneous capabilities of vehicles and edge servers, which aims at minimizing average request delay. On this basis, we design the offloading scheduling framework and propose an adaptive real-time offloading algorithm (ARTO), which makes online offloading decision of object detection and re-identification, by balancing real-time workload among heterogeneous devices. Finally, we implement the hardware-in-the-loop testbed for performance evaluation. The comprehensive results demonstrate the superiority of the proposed algorithm in various realistic traffic scenarios.

Mobile edge computing (MEC) is a novel computing paradigm that sinks the computing capacity of cloud servers into edge nodes to reduce network latency. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission in MEC. However, the dynamic and time-varying of the content requests may increase the network cost. In this paper, we study a distributed edge caching optimization problem in MEC scenario with the spatio-temporal requirements. The considered cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original problem can be transformed into a mean field game (MFG). We propose a caching iterative control algorithm that decouples the information interactions between the general SBS and others with the mean field distribution. In addition, we obtain the optimal caching strategy which achieves the existence and uniqueness of the mean field equilibrium (MFE). Simulation results demonstrate that our proposed algorithm can reduce more storage space and total cost compared to the Kim's approach.

In the last few years, a large amount of private and sensitive data generated by Internet of Things (IoT) devices need to be transmitted, stored, and processed in security, which become a serve challenge because these resource-constrained IoT devices cannot satisfy the requirements of conventional encryption ciphers. In this article, we propose the extended Type-1 generalized Feistel networks (Type-1 EGFNs) to improve the diffusion speed of Type-1 generalized Feistel networks (Type-1 GFNs). We instantiate Type-1 EGFNs into a lightweight block cipher that is named ALLPC, and analyze the performance of the cipher in terms of software implementation, hardware implementation, and security. The Type-1 EGFNs overcome the shortcoming of slow diffusion of Type-1 GFNs, and the ALLPC cipher is quite suitable for the resource-constrained IoT environment. Compared with other existing lightweight block ciphers, the ALLPC cipher can achieve compact hardware and low-cost software (e.g., low memory) while having low execution time, high throughput, and low energy consumption on the 32-bit ARM-Cortex M3 STM32F103 platform. In our security analysis, the ALLPC cipher can attain sufficient security degree against differential cryptanalysis, zero-correlation attack, and linear cryptanalysis, and owns a meaningful avalanche effect. Experimental results shows that our ALLPC cipher outperforms other existing lightweight ciphers in throughput, execution time and energy consumption.

Federated learning (FL) is an effective paradigm for training a machine-learning model based on data distributed at a large quantity of users in Internet of Things (IoT) without sharing their raw data. However, federated optimization of the global model in heterogeneous IoT—while considering the heterogeneity among users and limited network constraints—remains to be an open challenge. In this article, we propose a novel adaptive federated optimization algorithm, Adp-FedProx, to achieve the optimal learning performance within the limited computation and communication resources at the edge. In particular, we analyze how the training loss is affected by each user’s global update frequency and the time and energy used for learning by obtaining the novel convergence bound of federated training loss in heterogeneous IoT. With our proposed algorithm, all users can dynamically adjust their number of local iterations in each global interval and will not drop out during the training process for resource exhaustion, so as to impair the negative effect of heterogeneity among users and guarantee the convergence of the training model. In addition, we can get the optimal learning performance by minimizing the gap between the final loss function and the optimal one within limited resources. Finally, extensive numerical results demonstrate the algorithmic advantages in adapting system heterogeneity and admirable performance of the proposed methodologies in speeding up FL 5%–10% and reduce the energy consumption in training about 10% compared with FedProx.

Edge computing has been widely used in recent years for bringing services closer to end users, resulting in faster response for applications. However, the sensitive information that leaves the data owner is at risk of being disclosed because the service provider is generally honest-but-curious. Federated learning (FL) is a popular method for preserving privacy by transferring the model from the edge node to local devices and training on the local data set. Nonetheless, the training parameter that communicates between local mobile devices and the edge node may contain the original data and be guessed by adversaries. In order to address the privacy threats, we propose the PL-FedIPEC scheme in this article, which is a privacy-preserving and low-latency FL method that transmits parameters encrypted with the improved Paillier, a homomorphic encryption algorithm, to protect the privacy of end devices without transmitting data to the edge node. Our method introduces the improved Paillier encryption, which brings a new hyperparameter and previously computes multiple random intermediate values in the key generation phase so that the time for the encryption phase has a significant reduction. With this new algorithm, the time for model training is decreased, and the sensitive information is in ciphertext format and cannot be analyzed. To evaluate the efficiency of our proposed scheme, we conduct extensive experiments and the results validate and demonstrate that our scheme with the improved Paillier algorithm can achieve the same accuracy as the original Paillier algorithm and the baseline FedAVG algorithm. At the same time, our method can save a massive amount of time when training the learning model with various settings.

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.

Compared with the classical structure with only one controller in software-defined networking (SDN), multi-controller topology structure in SDN provides a new type of cross-domain forwarding network architecture with multiple centralized controllers and distributed forwarding devices. However, when the network includes multiple domains, lack of trust among the controllers remains a challenge how to verify the correctness of cross-domain forwarding behaviors in different domains. In this paper, we propose a novel secure multi-controller rule enforcement verification (BlockREV) mechanism in SDN to guarantee the correctness of cross-domain forwarding. We first adopt blockchain technology to provide the immutability and privacy protection for forwarding behaviors. Furthermore, we present an address-based aggregate signature scheme with appropriate cryptographic primitives, which is provably secure in the random oracle model. Moreover, we design a verification algorithm based on hash values of forwarding paths to check the consistency of forwarding order. Finally, experimental results demonstrate that the proposed BlockREV mechanism is effective and suitable for multi-controller scenarios in SDN.

Software defined networking (SDN) with separated control plane and data plane brings new opportunities for traffic measurement in data center networks. However, in the SDN-enabled switches, available TCAM (Ternary Content Addressable Memory) resources for traffic measurement are limited. Thus, it is necessary to utilize traffic matrix (TM) estimation to derive a hybrid network monitoring scheme through combining the partial direct measurement offered by SDN with some inference techniques. Although large size flows play an important role in improving TM estimation accuracy, directly monitoring each flow and finding out large size flows consume massive channel bandwidth resource between control plane and data plane. Therefore, in this paper, we identify large size flows from multiple historical TMs instead of monitoring each flow. First, we analyze multiple historical TMs and observe that origin-to-destination (OD) pair whose flow size is selected as large size flow at last time slot is most likely to be selected for per-flow monitoring at next time slot, so these OD pairs are identified by gradient boosting machine and are directly regarded as sampled OD pairs in order to reduce resource consumption. Then, we propose a greedy heuristic algorithm to solve SDN-enabled switch selection problem to best utilize the TCAM resources and guarantee that most of sampled OD pairs are measured in the flow table. We also present a source node prefix tree based bit merging aggregation (SPTBMA) scheme to design feasible forwarding rules to be inserted in TCAM of SDN-enabled switches and reserve more TCAM space for sampled OD pairs. Finally, the experimental results based on real traffic dataset demonstrate that our proposed scheme outperforms the existing algorithms in terms of improving TM estimation accuracy and overcoming limitation of TCAM resources.

With the prosperity of cloud computing and video services, the demand for network resources has increased dramatically, leading to the remarkable growth in the amount of network energy consumption, a key factor restricting the development of data centers. Numerous existing works reduce network energy consumption by optimizing data transmission, but they ignore the energy consumption for data transmission preparation, such as activating devices and installing rules. In this paper, we jointly optimize device activation, rule installation and data transmission to minimize network energy consumption. Specifically, we first formulate the minimization problem of the energy consumption of device activation, rule installation, and data transmission. We then prove that it is NP-complete to get the optimal solution of the minimization problem, furthermore, we propose a heuristic algorithm to plan the path with minimum network energy consumption for each flow. The simulation results show that the energy consumption of our algorithm is close to the optimal solution solved by Gurobi, and our algorithm has lower complexity. Compared with the state-of-the-art algorithm, our algorithm always consumes less energy and has shorter flow completion time.

The prediction of taxi-passenger’s destination with the partial GPS trajectory left by moving taxis is an important yet challenging research issue. The high uncertainty of human mobility and limited clue provided by the unfinished trajectory are two major barriers to developing effective predictors. In general, such a prediction task is often converted to the identification one among given candidate destinations. Hence, how to extract the discriminative knowledge from the partial trajectory becomes crucial. It is well-recognized that the sequence of visited locations by the taxi has inherent relationship with the heading destination. Inspired by the idea, we propose a novel approach that jointly combines the GPS embedding and attention-based BiLSTM techniques for the prediction of passenger’s destination. Specifically, we propose two GPS embedding methods to encode the geographic proximity and multi-scale spatiality of GPS points into embedding vectors, so as to reveal the spatial context of visited locations in the urban space. After converting GPS trajectories into embedding sequences, we further establish an attention-based dual BiLSTMs neural network to model the relationship between the heading destination and the bidirectional sequential context of visited locations. Meanwhile, the discriminative capability of visited locations in determining the destination can be captured by the attention mechanism. In addition, the OT (origin and time) information is aggregated into the neural network as auxiliary features. Stepping closer to smarter passenger services, rather than telling destinations in terms of drop-off clusters, our proposed model outputs the destinations in terms of historical passengers’ destination clusters. Finally, we evaluate the system performance based on two real large-scale datasets. Results show the superior performance of our proposed model.

The security of HTTPS fundamentally relies on SSL/TLS certificates issued by Certificate Authorities (CAs), which, however, are vulnerable to be compromised to issue unauthorized certificates (i.e., certificates issued without domains’ permission). Current countermeasures such as Certificate Transparency (CT) can only detect unauthorized certificates rather than preventing them. In this article, we presentPistis, a framework for issuing authorized and trusted certificates with the distributed ledger and Trusted Execution Environment (TEE) technology. InPistis, TEE nodes validate whether the domain in a requested certificate passes the domain ownership validation (i.e., under corresponding applicants’ control) and submit attested results to a smart contract in the distributed ledger. The smart contract issues a certificate to the applicant when an attested result shows a pass. Therefore,Pistiscan ensure its issued certificates are authorized due to the domain ownership validation mechanism in the TEE. Furthermore, as the issued certificates are stored in a Merkle Patricia Tree (MPT) inPistis, they are trusted and can be verified by a normal user easily. The security ofPistisis formally proved in the Universally Composable (UC) framework. Compared with state-of-the-art,Pistisavoids potential damages by preventing unauthorized certificates from issuing.

With the aggravation of data explosion and backhaul loads on 5 G edge network, it is difficult for traditional centralized cloud to meet the low latency requirements for content access. The federated learning (FL)-basedproactive contentcaching (FPC) can alleviate the matter by placing content in local cache to achieve fast and repetitive data access while protecting the users’ privacy. However, due to the non-independent and identically distributed (Non-IID) data across the clients and limited edge resources, it is unrealistic for FL to aggregate all participated devices in parallel for model update and adopt the fixed iteration frequency in local training process. To address this issue, we propose a distributed resources-efficient FPC policy to improve the content caching efficiency and reduce the resources consumption. Through theoretical analysis, we first formulate the FPC problem into a stacked autoencoders (SAE) model loss minimization problem while satisfying resources constraint. We then propose an adaptive FPC (AFPC) algorithm combined deep reinforcement learning (DRL) consisting of two mechanisms of client selection and local iterations number decision. Next, we show that when training data are Non-IID, aggregating the model parameters of all participated devices may be not an optimal strategy to improve the FL-based content caching efficiency, and it is more meaningful to adopt adaptive local iteration frequency when resources are limited. Finally, experimental results in three real datasets demonstrate that AFPC can effectively improve cache efficiency up to 38.4$\%$and 6.84$\%$, and save resources up to 47.4$\%$and 35.6$\%$, respectively, compared with traditional multi-armed bandit (MAB)-based and FL-based algorithms.

Despite increasingly emerging applications, a primary concern for blockchain to be fully practical is the inefficiency of data query. Direct queries on the blockchain take much time by searching every block, while indirect queries on a blockchain database greatly degrade the authenticity of query results. To conquer the authenticity problem, we propose a Verifiable Query Layer (VQL) that can be deployed in the cloud to provide both efficient and verifiable data query services for blockchain systems. The middleware layer extracts data from the underlying blockchain system and efficiently reorganizes them in databases. To prevent falsified data from being stored in the middleware, a cryptographic fingerprint is calculated based on each constructed database. The database fingerprint will be first verified by miners and then written into the blockchain. Moreover, public users can verify the entire databases or several databases that interest them in the middleware layer. We implement VQL together with the verification schemes and conduct extensive experiments based on a practical blockchain system. The evaluation results demonstrate that VQL can efficiently support various data query services and guarantee the authenticity of query results for blockchain systems.

Mobile edge computing (MEC) can significantly improve the performance of mobile applications by leveraging nearby servers as the edge cloud to provide task offloading execution service for a smart mobile device (SMD) through wireless access points (APs). However, the edge cloud and AP will not provide free services. Their radio frequency resources and computing resource are limited but the service requests from various mobile devices could be massive. The goal of this article is to provide a pricing mechanism to efficiently allocate limited resources in the MEC system according to the budget of SMDs. To this end, we first present a market model of MEC resources that can give a real insight into the incentives for resource sharing at network edges. In the model, computation, and radio resources can be traded between resource suppliers (AP and edge cloud) and buyers (SMDs). Furthermore, we employ the microeconomic theory to get an optimal budget allocation strategy for the SMD to maximize its utility within a limited budget. Moreover, we propose an Equilibrium Price Finding (EPF) algorithm to find the equilibrium price of the MEC system, maximizing the whole system utility and leading to optimal resource allocation. Finally, simulation results show that, compared with state-of-the-art resource allocation methods, our optimal budget allocation algorithm can find budget allocation strategy more effectively and our equilibrium price finding algorithm can achieve market equilibrium to optimally allocate computation and radio resources in the MEC system.

Mobile edge computing (MEC) has become a promising technique to accommodate demands of resource-constrained mobile devices by offloading the task onto edge clouds nearby. However, most existing works only focus on whether to offload or where to offload the task but ignore the motivations of edge clouds to offer service. To stimulate service provisioning by edge clouds, it is essential to design an incentive mechanism that charges mobile devices and rewards edge clouds. In this paper, we first propose an incentive mechanism in a non-competitive environment. We utilize market-based profit maximization pricing model to establish the relationship between the resources provided by edge clouds and the price charged to mobile devices. By solving the optimization problem, we provide a reasonable pricing strategy to not only ensure the profit of resource providers but guarantee the quality of experience (QoE) of mobile devices. Furthermore, we design an online profit maximization multi-round auction (PMMRA) mechanism for the resource trading between edge clouds as sellers and mobile devices as buyers in a competitive environment. The mechanism can effectively determine the price paid by buyers to use the resources provided by sellers and make the corresponding match between edge clouds and mobile devices. Finally, numerical results show that proposed mechanism outperforms other existing algorithms in maximizing the profit of edge clouds.

A smart contract cannot be modified once deployed. Bugs in deployed smart contracts may cause devastating consequences. For example, the infamous reentrancy bug in the DAO contract allows attackers to arbitrarily withdraw ethers, which caused millions of dollars loss. Currently, the main countermeasure against contract bugs is to thoroughly detect and verify contracts before deployment, which, however, cannot defend against unknown bugs. These detection methods also suffer from possible false negative results.

Efficient optimization is a key enabler for emerging intelligent applications in Internet of Vehicles (IoV). However, existing studies in IoV only focus on solving a single domain-specific optimization problem at a time, which undermines their efficiency on tackling various cross-domain optimization tasks in IoV. In this paper, we make the first effort on investigating a novel optimization framework in IoV for cross-domain tasks via vehicular edge computing. Specifically, two typical cross-domain tasks in IoV are presented, namely, the data dissemination (DD) task and the computing offloading (CO) task. Then, a cross-domain problem called DD-CO is formulated to facilitate the sharing of task features and knowledge during the solution searching. On this basis, we propose an evolutionary multitasking approach named EMA, which consists of an integer based unified representation scheme for encoding both the DD and CO tasks in a single solution, a corresponding decoding operator for task-specific solution evaluation, and a new population evolution mechanism for better adaptation to the cross-domain problem optimization. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrate the advancement of the new optimization framework via vehicular edge computing and the effectiveness of the proposed EMA method.

In order to solve the problem of flow scheduling in cluster computing framework, the scheduling strategy based on coflow has become a research hot spot. A coflow is a collection of data flows between two different stages of the same parallel computing task. Coflow scheduling in the case of unknown prior information depends on the data flow information of the sent part to infer the data size of coflow and allocate the scheduling sequence for coflow, which is easy to cause congestion. In this paper, we design an effective coflow scheduling mechanism namely, Classification According to Ports Number (CAPN). In the mechanism, firstly, coflows are quickly classified according to the Few Ports Number Scheduling First (FPSF) algorithm, and then coflows with different priorities are scheduled and adjusted, which greatly reduce the average coflow completion time (CCT). Simulation results show that compared with the classical Aalo and MCS mechanisms, our CAPN mechanism can reduce the completion time of coflow by by 31.32% and 25.72%, respectively.

As millions of intelligent devices are gradually deployed at the edge of networks, a large amount of private and sensitive data generated at those devices need to be transmitted, stored and processed safely among these devices, which become a serve challenge because these resource constrained IoT devices cannot satisfy the requirements of conventional encryption ciphers. In this paper, we propose the extended Type-1 generalized Feistel networks (Type-1 EGFNs) and instantiate it into a lightweight block cipher called ALLPC. We analyze performance of the cipher in security, software implementation and hardware implementation. The Type-1 EGFNs overcomes the shortcomings of slow diffusion of Type-1 GFNs. Compared with other existing lightweight block ciphers, experimental results shows that ALLPC cipher can achieve compact hardware and low-performance software (e.g. low memory) while having low execution time, high throughput and low energy consumption on 32-bit ARM-Cortex M3 STM32F103 platform. In our security analysis, ALLPC cipher can attain adequate security level against many different attacks.

With the rapid development of multimedia services in wireless mobile networks, the video data traffic has increased exponentially. Traditional video traffic service based on cloud computing caused a large amount of traffic load and longer access delay, which severely reduces the quality of service (QoS) of users. Mobile edge caching as one of the use cases of mobile edge computing (MEC), can directly serve user requests so as to greatly reduce the traffic load and shorten access delay. At present, distributed caching is widely used in the caching deployment of base stations (BSs). However, the caching capacity of a single BS is generally particularly limited, which will degrade the performance of wireless mobile network. In this paper, a collaborative caching strategy in the heterogeneous MEC networks is designed, and the edge caching of macro base station (MBS) and small base stations (SBSs) is utilized to bring storage resources closer to users. In addition, an optimization problem of content caching is formulated to minimize the total delay cost of all users requesting content in the MEC networks. In order to solve this problem, cache management strategy (GCS) is proposed, which consists of greedy cache placement strategy and greedy cache update strategy. Finally, numerical simulations demonstrate that the GCS scheme effectively improves the cache hit rate, and significantly reduces the average delay and backhaul traffic load.

With increasing expansion and urbanization of cities, the gap is constantly widening between the current provision of urban public toilets and the fast-growing toileting demand. Building new ones becomes a promising way to alleviate such issue. Nevertheless, where to build them in a city is challenging. Different from other location selection (e.g., commercial sites) problems, the selection of public toilet locations is hard to quantify and evaluate. On one hand, the toileting demand that determines whether the new public toilet is needed cannot be measured accurately. On the other hand, the modeling of the toileting demand is also complicated, being influenced by multiple factors, e.g., human mobility, human activity, and geographical characteristics. In this article, we propose a novel data-driven framework named ToiletBuilder to address it, which consists of three components, i.e., region identification, region representation, and region classification. Specifically, region identification obtains many reachable regions with the reasonable size. Region representation extracts city-specific features from multiple urban data to characterize location selection influencing factors for each region. A deep embedding model is further applied to learn a high-order and concise semantic representation. By labeling some regions with the true positive label (i.e., having public toilets served in these regions) in advance, region classification trains a positive-unlabeled (PU) learning model from these samples to identify unlabeled positive ones. Finally, we conduct extensive experiments based on four real-world data sets including road network, river network, taxi trajectory, and POI data, in the city of Chongqing, China. Results demonstrate the effectiveness of our proposed approach.

The 5G heterogeneous network architecture integrates different radio access technologies (RATs), which will support the large-scale communication connection of massive Internet-of-Things (IoT) devices. However, as the rapid growth of IoT connections, personalized requirements of services requested and heterogeneity deepening of the network system, how to design an intelligent network selection scheme for user devices (UDs) is becoming a crucial challenge in the 5G heterogeneous network system. Most of the existing network selection methods only optimize the selection strategies from the user side or network side, which results in heavy network congestion, poor user experience, and system performance degradation. Accordingly, we propose a multiagent$Q$-learning network selection (MAQNS) algorithm based on Nash$Q$-learning, which can learn a joint optimal selection strategy to improve system throughput and reduce user blocking on the premise of ensuring the requirements of IoT services. In particular, we apply the discrete-time Markov chains to model the network selection, and the analytic hierarchy process (AHP) and gray relation analysis (GRA) are jointly utilized to obtain user preferences for each network. Finally, performance evaluation demonstrates that comparing to the existing schemes, MAQNS proposed cannot only improve system throughput and reduce user blocking but also promote user experience on average energy efficiency and delay.

Mobile-edge computing (MEC) is considered as a promising technology in 5G, as it can solve the contradiction between the explosive growth of computation-intensive tasks and the limited computation power and battery life of local devices. However, in the 5G environment, most of the existing studies on task offloading in MEC have either failed to study the compatible multiple access technologies or have not considered the hierarchical relationship between small cell base station (SBS) and macro base station (MBS). Therefore, to explore the MEC offloading problem under the unique 5G architecture is of great significance at present. In light of this, we study the task offloading strategy in the nonorthogonal multiple access (NOMA)-enabled small cell MEC network. Specifically, we first describe a noval small cell MEC architecture in which MBS and SBS are both deployed with edge servers and there is a hierarchical relationship between the two. Based on this architecture, we have established the communication model and computation model, respectively. Then, we formulate the energy and delay weighted sum minimization problem, which aims at minimizing the total cost of task offloading under different requirements and takes into account the constraints of computation capabilities. To solve the problem, we develop a hybrid genetic hill climbing (HGHC) algorithm that can quickly find the optimal solution. Moreover, we perform a lot of simulation experiments to evaluate the performance of our algorithm under different parameters. The experimental results show that our algorithm can converge within about 20 iterations, which is superior to traditional heuristic algorithms.

Mobile cloud computing (MCC) as an emerging computing paradigm enables mobile devices to offload their computation tasks to nearby resource-rich cloudlets so as to augment computation capability and reduce energy consumption of mobile devices. However, due to the mobility of mobile devices and the admission of cloudlets, the connection between mobile devices and cloudlets may be unstable, which will affect offloading decision, even cause offloading failure. To address such an issue, in this paper, we propose a robust computation offloading strategy with failure recovery (RoFFR) in an intermittently connected cloudlet system aiming to reduce energy consumption and shorten application completion time. We first provide an optimal cloudlet selection policy when multiple cloudlets are available near mobile devices. Furthermore, we formulate the RoFFR problem as two optimization problems, i.e., local execution cost minimization problem and offloading execution cost minimization problem while satisfying the task-dependency requirement and application completion deadline constraint. By solving both optimization problems, we present a distributed RoFFR algorithm for CPU clock frequency configuration in local execution and transmission power allocation and data rate control in cloudlet execution. Experimental results in a real testbed show that our distributed RoFFR algorithm outperforms several baseline policies and existing offloading schemes in terms of application completion cost and offloading data rate.

This paper makes the first effort on proposing a fog computing empowered architecture together with a dedicated scheduling algorithm for data dissemination in software defined heterogeneous vehicular ad-hoc networks (VANETs). Specifically, the architecture supports both the logically centralized control via the cloud node in the core network and the distributed data dissemination via the fog nodes at the network edge. A problem calledfog assisted cooperative service(FACS) is formulated, which takes network coding and vehicular caching into consideration, and aims at minimizing the overall service delay via the cooperation of vehicle-to-cloud (V2C), vehicle-to-fog (V2F) and vehicle-to-vehicle (V2V) communications. Further, we derive an equivalence problem of FACS and prove that FACS is NP-hard. On this basis, we propose a Clique Searching based Scheduling (CSS) algorithm at the SDN controller, which considers the heterogeneous communication interfaces and vehicle mobility in scheduling, and enables the collaborative data encoding and transmission among the cloud, fog nodes and vehicles. The complexity analysis demonstrates the feasibility of the proposed algorithm. Finally, we build the simulation model and give a comprehensive performance evaluation based on real vehicular trajectories extracted from different time and space. The simulation results conclusively demonstrate the superiority of the proposed solution.

Software middlebox-based services can be flexibly managed by software defined networking (SDN) and network function virtualization (NFV). Meanwhile, traffic routing can be simplified and the number of routing rules in the SDN-enabled switches can be reduced through the consolidated middlebox model. However, different network functions in middleboxes may alter the volume of processed traffic, so high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in the data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data planes. In this article, we first formulate this problem as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and prove its NP-hardness. Then, we design a two-phase algorithm to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, compared with the existing algorithms through extensive simulations, it demonstrates that our method has near-optimal controller load balancing and link load balancing performance and can improve response time by 9.7% compared with static scheme.

Due to that Service Function Chain (SFC) permits the forwarding of flows along a predetermined sequence chain of Virtual Network Functions (VNFs), it has become a common service in Network Function Virtualization (NFV)-enabled Software Defined Networks (SDNs). Generally, since there are multiple same VNF-instances in NFV-Enabled SDNs, this brings a great challenge for selecting or placing the required VNF-instances to satisfy the routing of SFC Request flows (SRs). In this paper, we study the routing problem for SRs by jointly considering dynamical VNF placement and multiple Resources and Quality of Service (QoS) constraints in NFV-Enabled SDNs. Specifically, we first define two optimization problems: one is the Dynamical VNF Placement and Routing Problem for SRs (DVPRP) and the other is the Delay, packet Loss and Jitter Aware Dynamical VNF Placement and Routing Problem for SRs (DLJA-DVPRP). We then formulate the two problems as Integer Linear Programming (ILP) problems. Next, we creatively devise an auxiliary edge-weight graph and propose two efficient algorithms to solve the problems with the aim of minimizing the resource consumption costs as well as ensuring multiple QoS constraints. Especially, we utilize the shortest path algorithm based on Lagrange relaxation method to solve the DLJA-DVPRP with multiple QoS constraints. Compared with existing algorithms, simulation results demonstrate our proposed algorithms have better performance in terms of throughput, traffic acceptance rate and load balance.

Ahstract— With the rapid development of vehicular applications and mobile devices, demands for resources to process time-critical and computation-intensive tasks are increasingly prominent. In this paper, we propose a two-layer Vehicular Fog Computing (VFC) architecture, including the client layer and the fog layer. Vehicles may generate tasks as clients, which are further assigned to the nodes in the fog layer for processing. The fog layer aggregates available resources of vehicles and infrastructures by exploiting their communication, computation and storage capabilities. Each task requires certain amount of resources for processing at the fog nodes. We formulate a distributed task allocation (DTA) problem, which takes deadline, vehicle mobility and fog capacity into consideration, and aims at maximizing the overall resource utilization of system, via the cooperation of vehicles and fog nodes. We linearize DTA into a 0–1 integer linear programming (ILP) problem to obtain the optimal solution. Further, we design a heuristic algorithm to obtain near-optimal performance with low computational overhead, which decomposes DTA into two subprocess and schedules tasks in each fog node independently. Finally, we build the simulation model and conduct a series of experiments based on real-world vehicle trajectories, which demonstrate the effectiveness and scalability of the proposed algorithm.

Edge computing means that computing tasks are executed on edge devices closer to the data source. It can effectively improve system response speed and reduce the risk of user data leakage. However, current data access control schemes usually focus on cloud computing and rarely on edge computing. Although attribute-based encryption (ABE) scheme can realize flexible and reliable access control, computing cost is too high with the increase of access policy complexity. Therefore, combining computation outsourcing technology with dynamic policy updating technology, we propose a data access control scheme based on ciphertext-policy ABE (CP-ABE) for edge computing. We outsource part of storage service and part of decryption computing to edge nodes, effectively reducing the computing pressure of users. When data owner requires a new access policy, policy update key is generated timely and transmitted to cloud service provider, which is used to update the access policy, reducing the risk of bandwidth consumption and leakage of the ciphertext back and forth transmission. Finally, security analysis and experiment results verify the safety and effectiveness of our scheme.

Smart grid is a highly intelligent power system integrating advanced communication technology, sensor measurement and automatic control technology, which is gradually replacing the traditional power grid. However, the smart grid faces challenges of balancing privacy, efficiency and functionality when processing massive amount of data. In this paper, a smart grid model based on edge computing paradigm is established, and then an efficient privacy-preserving multidimensional data aggregation scheme is proposed. This scheme adopts an improved identity-based signature algorithm and Paillier homomorphic cryptosystem to protect the privacy of users. In addition, super-increasing sequence is used in the proposed scheme to enable smart meters to report multiple types of data in a single reporting message, so that the Service Center (SC) can perform one-way analysis of variance on the data to provide users with more personalized services. Also the security analysis indicates that the proposed scheme works in protecting user's electricity consumption privacy. Finally, performance analyses indicate that this scheme can effectively reduce the computational overhead.

IoT-blockchain applications have advantages of managing massive IoT devices, achieving advanced data security, and data credibility. However, there are still some challenges when deploying IoT applications on blockchain systems due to limited storage, power, and computing capability of IoT devices. Applying current consensus protocols to IoT applications may be vulnerable to Sybil node attacks or suffer from high-computational cost and low scalability. In this paper, we propose G-PBFT (Geographic-PBFT), a new location-based and scalable consensus protocol designed for IoT-blockchain applications. The principle of G-PBFT is based on the fact that most IoT-blockchain applications rely on fixed IoT devices for data collection and processing. Fixed IoT devices have more computational power than other mobile IoT devices, e.g., mobile phones and sensors, and are less likely to become malicious nodes. G-PBFT exploits geographic information of fixed IoT devices to reach consensus, thus avoiding Sybil attacks. In G-PBFT, we select those fixed, loyal, and capable nodes as endorsers, reducing the overhead for validating and recording transactions. As a result, G-PBFT achieves high consensus efficiency and low traffic intensity. Moreover, G-PBFT uses a new era switch mechanism to handle the dynamics of the IoT network. To evaluate our protocol, we conduct extensive experiments to compare the performance of G-PBFT against existing consensus protocol with over 200 participating nodes in a blockchain system. Experimental results demonstrate that G-PBFT significantly reduces consensus time, network overhead, and is scalable for IoT applications.

Software defined networking (SDN) and network function virtualization (NFV) can flexibly manage software middlebox based services, and the consolidated middlebox model is able to simplify traffic routing and reduce the number of routing rules in the SDN-enabled switches. However, different network functions in middleboxes may change the volume of processed traffics, thus high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data plane. This paper first formulates it as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and proves its NP-hardness. Then, a two-phase RL_RFRD algorithm is designed to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, the extensive simulation results demonstrate that the proposed algorithm has near-optimal controller load balancing and link load balancing performance and reduces response time by about 2x-5x compared with other algorithms.

In the heterogeneous wireless networks, it remains a significant challenge to achieve an efficient network selection strategy to satisfy the demands of a massive number of edge users and novel 5G services. In this paper, we formulate the network selection problem for edge users as a discrete-time Markov model, and propose a Nash Q-learning based intelligent network access algorithm for multi-agent system, named MAQNS. We consider the joint optimization of network selection strategies among different types of networks, aiming at maximizing the long-term performance of multi-agent system. Meanwhile, we use Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to characterize the user preferences for networks. Experimental results show that comparing to the existing network selection algorithms, the proposed MAQNS has better performance in terms of system throughput, user blocking probability, average energy efficiency and average delay.

Recent advances in wireless communication, sensing, and computing technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Nevertheless, it is challenging to process data and computation intensive tasks with strict time constraints due to heterogeneous communication, storage, and computation capacities of IoV network nodes, spotty wireless connections in vehicles and infrastructures, unevenly distributed workload, and high vehicles mobility. In this paper, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the fog nodes, and the terminals on processing data and computation intensive IoV tasks. Then, we formulate the real-time task offloading model, aiming at maximizing the task service ratio. Further, considering the dynamic requirements and resource constraints, we propose a real-time task offloading algorithm to adaptively categorize all tasks into four types, and then cooperatively offload them. Finally, we build the simulation model and give a comprehensive performance evaluation, which validates the performance of the proposed method.

Summary Data transmission in current networks is usually associated with strict priority enforcement for the purpose of quality of service (QoS). Under the case that priority flow requests are injected into the network sequentially without the information of future flow request arrivals, it is a challenging to achieve network throughput maximization for on‐line flow requests under the joint constraints of the flow's priority, bandwidth demand, and resource capacity. Software Defined Networking (SDN) can effectively solve the flow scheduling equilibrium problem between the priority of dynamic flow requests and the maximization of network throughput. Therefore, in this paper, we study on‐line flow request admission in SDN, the goal of which is to maximize the network throughput under the constraints of critical network bandwidth resources, flow priority, and bandwidth demands. First, we present the concept of flow routing cost and profit and a model to characterize the cost of using link resources and routing paths. Then, we propose an efficient on‐line priority flow scheduling algorithm (OPFSA) to solve priority flow request scheduling problem and analyze the competitive ratio of OPFSA. Our on‐line algorithm can reach throughput within of the highest possible throughput that can be achieved by an off‐line algorithm, where n is the number of node in the network. Finally, experimental results demonstrate that compared with SHORTEST‐SC, our proposed algorithm can enhance the cumulative bandwidth about 9% and 40% when general network size is 30 and 170 nodes, respectively, and improve the throughput about 25% in Fat‐tree network when pod size is 4.

Summary In wireless sensor networks (WSNs), the volume of data is increasing at an unpredictable rate, which inevitably leads to high spatial‐temporal correlation. To eliminate data redundancy, some researchers have proposed many data aggregation methods. However, a few of aggregation approaches can handle energy consumption and latency simultaneously. Therefore, in this paper, we propose an efficient algorithm, called Delay‐Minimum Energy‐Balanced (DMEB) data aggregation, which benefits from the superiority of the sparse random measurement matrix and minimum delay algorithm. Owing to the sparsity characteristics of the measurement matrix, only the nodes whose corresponding elements in the matrix are non‐zero take part in the measurement. Each measurement can form an aggregation tree with minimum delay. After a sink node receives all the measurements, original readings can be recovered precisely. In addition, we adopt a novel scheduling method to avoid information interference. Experiment results demonstrate that, under recovering the original data accurately, the proposed data aggregation algorithm can not only shorten delay in data collection process but also reduce communication cost and prolong network lifetime during data transmission process.

The network selection in heterogeneous wireless networks is considered as a crucial technology to take advantage of network resource in the coming fifth‐generation (5G) mobile networks. Considering the emergence of 5G novel services and the guarantee of quality of service requirements, in the study, the authors propose a network selection algorithm based on evolutionary game named NS‐EG, by using analytic hierarchy process to jointly analyse user preferences and service requirements. The utility is structured as a joint function of network decision attributes and available capacity. In addition, the dynamic behaviour of users accessing different networks with replicator dynamics are explicitly provided. In order to verify the superiority of the algorithm proposed, the authors evaluate the evolutionary equilibria as well as the iteration of the algorithm by comparing with the simple additive weighting algorithm, multiplicative exponent weighting algorithm and Q‐learning based algorithm. Simulation results confirm that the proposed algorithm outperforms the contrast algorithms and achieve network load balancing.

Although indoor localization has been studied over a decade, it is still challenging to enable many IoT applications, such as activity tracking and monitoring in smart home and customer navigation and trajectory mining in smart shopping mall, which typically require meter-level localization accuracy in a highly dynamic and large-scale indoor environment. Therefore, this article aims at designing and implementing an adaptive and scalable indoor tracking system in a cost-effective way. First, we propose a zero site-survey overhead (ZSSO) algorithm to enhance the system scalability. It integrates the step information and map constraints to infer user's positions based on the particle filter and supports the auto labeling of scanned Wi-Fi signal for constructing the fingerprint database without the extra site-survey overhead. Further, we propose an iterative-weight-update (IWU) strategy for ZSSO to enhance system robustness and make it more adaptive to the dynamic changing of environments. Specifically, a two-step clustering mechanism is proposed to delete outliers in the fingerprint database and alleviate the mismatch between the auto-tagged coordinates and the corresponding signal features. Then, an iterative fingerprint update mechanism is designed to continuously evaluate the Wi-Fi fingerprint localization results during online tracking, which will further refine the fingerprint database. Finally, we implement the indoor tracking system in real-world environments and conduct a comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of the proposed algorithms.

Edge cache service (ECS), as a prospective edge network service paradigm, can significantly reduce the data transmission latency and improve the Quality of Service (QoS) of digital content providers by offloading content data to edge devices in the network. Compared to centralized content service, ECS can provide digital content from nearby edge devices via a high-speed wireless network with fewer hops. However, how to motivate edge devices to share their cache resource and ensure the reliability of content data under the diversity of device behavior remains a challenging issue. In this article, we aim to design an ECS framework for cache resource trading and digital content sharing in the edge network. By using blockchain-based credentials, we first provide the cache resource trading mechanism for the trading between the content provider and edge devices. Then, we give a double auction mechanism for digital content trading between edge devices. The experimental results show the proposed framework can greatly improve the matching efficiency of cache resources and reduce the data transmission overhead in edge networks.

With the recent development of wireless communication, sensing, and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Nevertheless, it is challenging to process time-critical tasks due to unique characteristics of IoV, including heterogeneous computation and communication capacities of network nodes, intermittent wireless connections, unevenly distributed workload, massive data transmission, intensive computation demands, and high mobility of vehicles. In this article, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog, and the mobile fog on processing time-critical tasks in IoV. Then, we give a motivational case study by implementing a prototype of a traffic abnormity detection and warning system, which demonstrates the necessity and urgency of developing adaptive task offloading mechanisms in such a scenario and gives insight into the problem formulation. Furthermore, we formulate the offloading model, aiming at maximizing the completion ratio of time-critical tasks. On this basis, we propose an adaptive task offloading algorithm (ATOA). Specifically, it adaptively categorizes all tasks into four types of pending lists by considering the dynamic requirements and resource constraints, and then tasks in each list will be cooperatively offloaded to different nodes based on their features. Finally, we build the simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of ATOA.

With the technological advances in wireless multimedia domain, these videos made by mobile edge devices dominate network traffics. The video summarization technology enables users to understand the storyline of a video before a client requests the complete video content. Summarizing a video on edge devices and transmitting the summary between them requires a user-oriented and adaptive solution due to the limited capability and the dynamic wireless links of edge devices. Therefore, it is beneficial to improve the user's viewing experience and the bandwidth utilization ratio if we generate and transmit a video summary based on network connections and the user's tolerant latency. Unfortunately, previous summarization approaches are incapable of adjusting the summary size adapted to the varying network bandwidth and the user's attitude towards latency. To timely and flexibly deal with mobile videos, we first formulate the video summarization optimization problem with the elastic number of selected representative segments and the outlier detection within a bounded time budget. Furthermore, we develop an online greedy algorithm called the Elastic Video Summarization Algorithm (EVS) to solve the NP hard problem. We analyze the properties associated with EVS and further design an improved EVS-II to reduce computation complexity. Finally, the experimental results demonstrate that our proposed algorithms outperform other existing researches in fitting network bandwidth and detecting outliers.

With the ever-increasing demand of location-based services (LBS), Wi-Fi based indoor localization has attracted increasing attentions. This paper is dedicated to addressing two critical problems: a) signal fluctuation due to unforeseeable interferences during the offline training phase; b) insufficient real-time signal measurements at certain point due to the target movement during the online localization phase. Specifically, we first give an intensive analysis on the characteristics of received signal strength indicator (RSSI) in indoor environments with respect to both time-domain and frequency-domain. Then, inspired from the advantages of Stacked Denosing Autoencoder (SDA) in terms of recognizing and stabilizing the original features, we propose a dual-band SDA (DBSDA) based model to create more distinguishable fingerprints by extracting the RSSI features at each reference point (RP). In this model, both 2.4GHz and 5GHz RSSIs are exploited to train the SDA neural network and construct the offline fingerprint database. On this basis, we propose a data generation scheme, which is designed based on the observation that environmental interferences are similar in proximate spots. So, the designed scheme can generate signal values at certain point based on its nearby RSSI measurements when there are not enough inputs for the SDA neural network. Finally, we propose a locally weighted liner regression (LWLR) based method to predict the coordinate of the target. For performance evaluation, we implement the system prototype and give comprehensive experiments in real-world environments, which demonstrate the effectiveness and robustness of the proposed solutions.

Mobile edge computing (MEC) has become a promising technique to accommodate demands of resource-constrained mobile devices by offloading the task onto edge clouds nearby. However, most existing works only focus on whether or where a task is offloaded but ignore the motivation of the edge cloud to offer service. To stimulate service provisioning by edge clouds, it is essential to design an incentive mechanism that charges mobile devices and rewards edge clouds. In this paper, we utilize market-based pricing model to establish a relationship between the resources provided by edge clouds and the price paid by the mobile devices in a non-competitive environment. Furthermore, we design a profit maximization multi-round auction (PMMRA) mechanism for the resource trading between edge clouds as sellers and mobile devices as buyers in a competitive environment. The mechanism can effectively determine the price paid by the buyers to use the resources provided by the sellers and make the corresponding match between edge clouds and mobile devices. Finally, numerical results show that proposed mechanism outperforms other existing algorithms in maximizing the profits of resource providers.

With the popularity of mobile multimedia services, the explosive increase in video traffic not only causes huge load and energy consumption of base station (BS), but also affects the QoS of users. The device to device (D2D) multicast technology can effectively reduce the number of redundant video transmissions and improve energy efficiency of the BS. However, the existing researches about D2D technology assume that there is not unconstrained communication between local users, which is not in line with the actual situation. Moreover they do not take full advantage of attributes of users in mobile social networks. In this paper, we first propose a clustering method and select the cluster head users (CHUs) as relay nodes to distribute videos by considering the user's physical conditions and user's social attributes based on Chinese Restaurant Process (CRP). Then we put forward with a video distribution and sharing mechanism based on scalable video coding (SVC). In this mechanism, edge users collaboratively share videos on the basis of user's mobility, where Zipf distribution combines with SVC to effectively reduce the energy consumption of the BS. Finally, the experimental results show that the proposed strategy can not only reduce the energy consumption of the BS, but also improve the throughput and the stability and flexibility of video distribution.

Energy-efficient data collection plays an imperative role in maximizing the lifetime of wireless sensor networks. In data collection, mobile sink can balance the energy consumption between sensor nodes and extend the network lifetime, but the route between sensor node to mobile sink changes frequently when the sink moves, which consumes more energy and brings more transmission delay. Static sinks can achieve low transmission delay and do not change the routing frequently. Based on this consideration, this paper proposes an energy-efficient routing scheme based on the collaborative work of dual sink nodes (mobile sink and static sink). This scheme considers static sinks to collect data in the center of the networks. The mobile sink moves along the network edge to gather the data, i.e., achieve mobile edge computing based data gathering. Mathematical analysis and simulation results demonstrate that the energy consumption of a node depends to a large extent on the position of the node. Compared with the previous scheme, the proposed method can greatly enhance the network lifetime and decrease the corresponding transmission delay.

With recent development of wireless communication, sensing and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Services with low communication latency and high reliability are necessary to enable safety-critical applications in IoV. Nevertheless, it is challenging to satisfy the service requirement due to unique characteristics of IoV, including limited wireless communication bandwidth, high vehicle mobility, massive data transmission, and overwhelming computation overhead. In view of this, we propose a novel vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog and the mobile fog by defining corresponding service modes. On this basis, we further formulate a task offloading model, which quantitatively analyzes the characteristics of the three service modes and enables task offloading based on particular service requirements. Finally, we implement a traffic abnormity detection and warning system based on the proposed architecture as a case study. The hardware-in-the-loop performance evaluation not only demonstrates the effectiveness of the proposed architecture, but also enlightens future research directions on developing adaptive task offloading for dynamic IoV applications.

Mobile Edge Computing (MEC) has recently become an important paradigm of bringing computing and caching resources to the edge networks. In mobile edge networks, data content can be cached in MEC servers to efficiently improve the performance of mobile users' service by sharing data among MEC servers rather than sending data requests to remote content server. However, the resource of edge network is relatively limited, and only a small amount of application data can be cached in edge server. Thus, it is necessary to figure out the wise caching decision to minimize edge computing latency. In this paper, we consider the scenario where multiple mobile users offload duplicate tasks to the edge network and share the data required for computing tasks. We design a joint computation offloading and data caching model to minimize the overall execution latency for all mobile users. Moreover, we propose an efficient online algorithm based on Lyapunov optimization, which jointly schedules computation offloading and allocates data caching for computation/data requests sent by mobile users. The simulation results show that the algorithm can effectively reduce the computation delay of end users while maintaining low energy consumption.

Smart city as a new paradigm for future city development leads to a large amount of computing workload and high network latency especially with artificial intelligence algorithms. Fog computing, as one of the mobile edge computing paradigms, deploys some servers at the edge of mobile networks to solve these problems. However, it still remains a challenging issue how to obtain the energy-effective cooperation policy among fog nodes (FNs) to enhance the users' quality of experience (QoE) under fairness, where the fairness ensures that FNs are willing to take part in cooperations. Therefore, we first build up a cooperative fog computing system to process offloading workload on the entire fog layer by data forwarding. Then, we formulate a joint optimization problem of QoE and energy in integrated fog computing process with fairness. After that, we prove the convexity of the optimization problem and design a fairness cooperation algorithm (FCA) to obtain the optimal fairness cooperation policy of all FNs. Finally, numerical results show that our FCA can quickly converge to its solution compared with three traditional convex optimization approaches, and FCA can effectively reduce the time overhead and the energy consumption compared to baseline algorithm and distributed optimization algorithm.

Mobile edge computing (MEC) as an emerging and prospective computing paradigm, offloads the computation-intensive tasks from resources-constrained smart mobile devices (SMDs) to edge cloud or server so as to enhance computation capability of SMDs. Meanwhile, it is expected that wireless power transfer (WPT) is applied to MEC (WPT-MEC) in order to prolong operation time of the battery. However, how to achieve the energy-effective computation offloading in WPT-MEC system under the hard constraint remains a challenge issue. To address this challenge, this paper considers energy-effective resource allocation policy in a two-user WPT-MEC system. We first formulate the maximization minimum energy efficiency (EE) problem to ensure the fairness of users. Then, due to the “doubly near-far” problem, we propose a user cooperative scheme in which the near user can forward the far user's tasks to the edge cloud utilizing its more harvested energy. Considering the green network, we attach the scheme to maximize the users' EE, defined as a ratio of the user throughput to its harvested or consumptive energy, subject to the constraints of the computational tasks in two schemes. We convert two problems into their equivalent parameterized subtractive form and provide the corresponding optimal solutions via two efficient optimization algorithms. Numerical results show that the optimal WPT-MEC system with cooperation has significant performance enhancement over the systems without cooperation.

Most indoor navigation technologies need to provide structure maps of buildings, but it is difficult to obtain these structure maps in practice. Therefore, we design a geomagnetism-based indoor navigation system without structure map of buildings by applying offloading strategy in narrow band Internet of Things. Why the geomagnetic signal can be used to achieve indoor navigation is due to its stability. We divide the indoor navigation into two parts: 1) the construction of signal fingerprint database and 2) the user navigation. First, the navigator carries out the signal acquisition of navigation route, and transfers the original signals to cloud server so as to construct the fingerprint database. Second, users can choose online navigation or offline navigation according to the actual situation. When choosing offline navigation, the users will employ dynamic time warping algorithm for fingerprint matching. When users would like to obtain better navigation results, they can choose online navigation. In this case, navigation system will offload the users' sampling data to remote cloud, and then the cloud matches the fingerprint of geomagnetic signals by using particle filter algorithm and sends the computation results to the users so as to indicate the users' moving direction. Experiment results demonstrate that our method is effective and the turning error is only 5 cm.

With the coming of the fifth-generation (5G) mobile communications, in mobile edge computing (MEC), the growth of user services and the personalization of QoS requirements have posed great challenges for heterogeneous wireless networks (HWNs) access selection. Based on the multiattribute decision theory and the fuzzy logic theory, we propose a novel network selection scheme for multiservice QoS requirements in MEC. The main procedures of the scheme include dynamic adaptive process, fuzzy process, hierarchical analysis, and integrated attributes assessment. The scheme proposed contributes to efficiently reduce the ping-pong effect and effectively select accurate network in a dynamic environment. Simulation results show that our scheme can select network access according to the type of user services and whether to switch networks. In addition, compared with commonly used simple additive weighting (SAW), random access selection (RAS), and price-based and QoS-based network selection scheme, our scheme has better performance in improving average user satisfaction and reducing access failures.

Data aggregation scheduling is a critical issue in WSNs. This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel asynchronous Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing studies, nevertheless, either focus on non-sleeping scenarios or assume that nodes communicate with one single channel, and thus may have poor performance if directly applied to multi-channel asynchronous duty-cycled scenarios. We first show that the DDACD problem is NP-hard. Then, we propose two new concepts of candidate active conflict graphs (CACGs) and feasible active conflict graphs (FACGs) to depict the relationship of the data aggregation links and present two coloring methods to well separate the links at different time slots or on different channels. Based on these two new concepts and two coloring methods, we propose an efficient data aggregation scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. To reduce unused time slots or channels, we further propose a novel algorithm called NDAS by making full use of the characteristics of multi-channel asynchronous duty-cycled WSNs. We prove that our algorithms can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithms.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control, and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.

Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. Despite some research on urban safety analysis, the accuracy and granularity of safety index inference are both very limited. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this paper, we propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban location-based data. We first extract spatially-related and temporally-related features from various urban location-based data, including urban map, housing rent and density, population, positions of police stations, point of interests (POIs), crime event records, and taxi GPS trajectories. Then, these features are fed into a novel sparse auto-encoder (SAE) framework with feature correlation constraint to obtain the final discriminative feature representation. Finally, we design a new co-training-based learning method, which consists of two separated classifiers, to calculate safety index accurately. We implement U-Safety and conduct extensive experiments by utilizing various real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods.

Network-based malware has posed serious threats to the security of host machines. When malware adopts a private TCP/IP stack for communications, personal and network firewalls may fail to identify the malicious traffic. Current firewall policies do not have a convenient update mechanism, which makes the malicious traffic detection difficult.

Fog Computing as one of Mobile Edge Computing (MEC) paradigms deploys servers to the edge of networks to reduce the transmission latency. However, how to obtain the energy-effective cooperation policy among fog nodes to enhance the users' quality of experience (QoE) under fairness still remains a challenging issue, where the fairness ensures that fog nodes are encouraged to take part in cooperations. Therefore, we first build up a cooperative fog computing system to process offloading workload on the entire Fog layer by data forwarding. Then we propose a joint optimization problem of QoE (average response time) and energy (average energy consumption) in integrated fog computing process with fairness. After that, we prove the convexity of the optimization problem and design a Fairness Cooperation Algorithm to obtain the optimal fairness cooperation policy of all fog nodes. Finally, by comparing with baseline algorithm and Distributed Optimization Algorithm, the numerical results show that our algorithm can effectively reduce response time reduction and energy consumption.

In this paper, we propose a shortest travel route planning scheme that takes into account the spatial characteristics of wireless transmissions for mobile data gathering in wireless sensor networks. We formulate the shortest travel route problem (STRP) as a covering salesman problem (CSP), which is regarded as a mixed integer nonlinear programming and also as a non- convex programming problem. To solve the STRP problem, we propose a heuristic algorithm named decomposition algorithm (DA), which decomposes the STRP problem into two subproblems: access sequence problem and position determining problem. We conduct extensive simulation to verify the effectiveness of the proposed algorithm and show that the DA algorithm can plan the shortest travel route in large scale WSNs other than small scale WSNs by the classical traveling salesman problem (TSP) algorithms.

Mobile cloud computing (MCC), as a prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich center cloud. Compared to a center cloud, an edge cloud can provide services to nearby SMDs with lower latency. However, the edge cloud may be mobile and its resources are limited to multiple nearby users. In this paper, we aim to minimize the total execution cost of multiple devices by offloading the computation from SMDs onto edge clouds in an edge cloud computing (ECC) system. By considering the mobility of SMDs and edge clouds, we first formulate the total cost minimization problem under the constraints of application completion deadline and connection time between SMDs and edge clouds as well as the limited computing resource of both edge clouds and SMDs. Then, by solving the minimization problem, we propose an optimal offloading selection strategy based on a game model, and an edge cloud payoff competition algorithm to optimally allocate edge cloud resource to SMDs to achieve the minimum total execution cost. Experimental results show that our offloading strategy can effectively reduce energy consumption and application completion time compared with the state-of-the-art methods.

Mobile edge computing is an emerging computing paradigm to augment computational capabilities of mobile devices by offloading computation-intensive tasks from resource- constrained smart mobile device onto edge clouds nearby with potential computation capability. However, in general, edge clouds have limited computation resource and energy. Thus it is critical to achieve high energy efficiency while ensuring satisfactory user experience. In this paper, we first formulate the computation offloading problem for mobile edge computing into the system cost minimization problem by taking into account the completion time and energy. We then transform the optimization problem into a convex problem and propose a distributed algorithm consisting of offloading strategy selection, clock frequency configuration, transmission power allocation and channel rate scheduling. Finally, the experimental results show that our algorithm can achieve energy-efficient offloading performance compared to other existing algorithms.

Mobile-edge cloud computing (MEC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and prolong the lifetime of mobile devices (MDs) by offloading computation-intensive tasks to the cloud. This paper considers applying simultaneous wireless information and power transfer (SWIPT) technique to a multi-user computation offloading problem for mobile-edge cloud computing, where energy-limited mobile devices (MDs) harvest energy form the ambient radio-frequency (RF) signal. We investigate partial computation offloading by jointly optimizing MDs' clock frequency, transmit power and offloading ratio with the system design objective of minimizing energy cost of mobile devices. To this end, we first formulate an energy cost minimization problem constrained by task completion time and finite mobile- edge cloud computation capacity. Then, by exploiting alternative optimization (AO) based on difference of convex function (DC) programming and linear programming, we design an iterative algorithm for clock frequency control, transmission power allocation, offloading ratio and power splitting ratio to solve the non-convex optimization problem. Our simulation results reveal that the proposed algorithm can converge within a few iterations and yield minimum system energy cost.

In the 5G wireless networks, non‐orthogonal multiple access (NOMA) is a promising paradigm to improve its high spectrum efficiency. This study considers applying simultaneous wireless information and power transfer (SWIPT) technique to cooperative NOMA wireless networks, where energy‐constrained relay nodes harvest the ambient radio‐frequency signal and use the harvested energy to forward the packets from sources to destinations. To this end, the authors first formulate the energy‐efficient cooperative transmission problem for SWIPT in NOMA with imperfect channel state information. Then, by exploiting alternative optimisation and dual decomposition, they design an iterative algorithm for power allocation and power splitting (PS) to solve the non‐convex optimisation problem. The authors' simulation results reveal that the proposed algorithm can converge within a few iterations and yield optimal system energy efficiency.

The emerging software defined networking enables the separation of control plane and data plane and saves the resource consumption of the network. Breakthrough in this area has opened up a new dimension to the design of software defined method in wireless sensor networks (WSNs). However, the limited routing strategy in software defined WSNs (SDWSNs) imposes a great challenge in achieving the minimum traffic load. In this paper, we propose a flow splitting optimization (FSO) algorithm for solving the problem of traffic load minimization (TLM) in SDWSNs by considering the selection of optimal relay sensor node and the transmission of optimal splitting flow. To this end, we first establish the model of different packet types and describe the TLM problem. We then formulate the TLM problem into an optimization problem which is constrained by the load of sensor nodes and the packet similarity between different sensor nodes. Afterwards, we present a Levenberg-Marquardt algorithm for solving the optimization problem of traffic load. We also provide the convergence analysis of the Levenberg-Marquardt algorithm. Finally, we implement the FSO algorithm in the NS-2 simulator and give extensive simulation results to verify the efficiency of FSO algorithm in SDWSNs.

With the development of sensing technology, smartphones can provide various kinds of data, including inertial sensing data, WiFi data, depth data, and images. These data make it possible to construct accurate indoor floor plans that are the critical foundations of flourishing indoor location-based services for smartphone. However, even with the popular crowdsourcing approach, the wide construction of indoor floor plans has not yet to be realized due to the intensive time consumption. In this paper, we utilize deep learning techniques to build PlanSketcher, a system that enables one user to construct fine-grained and facility-labeled indoor floor plans accurately. First, the proposed system extracts novel integrated features to recognize diverse landmarks. Second, traverse-independent hallway topologies are constructed based on the sensing data, depth data, and images through the proposed hallway construction algorithms. Finally, PlanSketcher constructs the room shape and labels recognized facilities in their corresponding positions to generate a complete indoor floor plan. Because PlanSketcher exploits different kinds of data collected from smartphones with new feature extraction method, it can obtain accurate indoor floor plan topology and facility labels. We implement PlanSketcher and conduct extensive experiments in three large indoor settings. The evaluation results show that the 90th percentile accuracy of positions and orientations of facilities are 1 m–2.5 m and 4°–6°, while 85%–95% facilities are recognized and labeled precisely.

With the proliferation of mobile devices, indoor fingerprinting-based localization has caught considerable interest on account of its high precision. Meanwhile, channel state information (CSI), as a promising positioning characteristic, has been gradually adopted as an enhanced channel metric in indoor positioning schemes. In this paper, we propose a CSI amplitude fingerprinting-based localization algorithm in Narrowband Internet of Things system, in which we optimize a centroid algorithm based on CSI propagation model. In particular, in the fingerprint matching, we utilize the method of multidimensional scaling (MDS) analysis to calculate the Euclidean distance and time-reversal resonating strength between the target point and the reference points and then employ the K-nearest neighbor (KNN) algorithm for location estimation. By conjugate gradient method, moreover, we optimize the localization error of triangular centroid algorithm and combine the positioning result with MDS and KNN's estimated position to get the final estimated position. Experiment results show that compared to some existing localization methods, our proposed algorithm can effectively reduce positioning error.

Accurate digital maps play a crucial role in various location-based services and applications. However, store information is usually missing or outdated in current maps. In this paper, we propose CrowdGIS, an automatic store selfupdating system for digital maps that leverages street views and sensing data crowdsourced from mobile users. We first develop a new weighted artificial neural network to learn the underlying relationship between estimated positions and real positions to localize user's shooting positions. Then, a novel text detection method is designed by considering two valuable features, including the color and texture information of letters. In this way, we can recognize complete store name instead of individual letters as in the previous study. Furthermore, we transfer the shooting position to the location of recognized stores in the map. Finally, CrowdGIS considers three updating categories (replacing, adding, and deleting) to update changed stores in the map based on the kernel density estimate model. We implement CrowdGIS and conduct extensive experiments in a real outdoor region for 1 month. The evaluation results demonstrate that CrowdGIS effectively accommodates store variations and updates stores to maintain an up-to-date map with high accuracy.

In mobile wireless sensor networks (MWSNs), scavenging energy from ambient radio frequency (RF) signals is a promising solution to prolonging the lifetime of energy-constrained relay nodes. In this paper, we apply the Simultaneous Wireless Information and Power Transfer (SWIPT) technique to a MWSN where the energy harvested by relay nodes can compensate their energy consumption on data forwarding. In such a network, how to maximize system energy efficiency (bits/Joule delivered to relays) bytrading off energy harvesting and data forwarding is a critical issue. To this end, we design a resource allocation (ResAll) algorithm by considering different power splitting abilities of relays undertwo scenarios. In the first scenario, the power received by relays is split into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, the received power is only split into a discrete set of power streams with fixed power splitting ratios. For each scenario above, we formulate the ResAll problem in a MWSN with SWIPT as a non-convex energy efficiency maximization problem. By exploiting fractional programming and dual decomposition, we further propose a cross-layer ResAll algorithm consisting of subalgorithms for rate control, power allocation, and power splitting to solve the problem efficiently and optimally. Simulation results reveal that the proposed ResAll algorithm converges within a small number of iterations, and achieves optimal system energy efficiency by balancing energy efficiency, data rate, transmit power, and power splitting ratio.

With the explosive expansion of data centers, a huge amount of identical or similar data are usually requested repeatedly over networks by users, which causes serious waste of network bandwidth, and further increases network energy consumption remarkably. Existing solutions achieve energy saving by increasing network link capacity as well as eliminating the data redundancy in routers. However, such redundancy elimination (RE) causes the increase of router's energy consumption. To solve this problem, we propose a flow preemption routing scheme with RE called RE-FPR. RE-FPR scheme uses software defined networking (SDN) technology to select different routing paths for flows and control RE function on the corresponding router under two modes, i.e., traffic peak and traffic valley. We then formulate the RE-FPR problem as a power consumption minimization problem. Furthermore, we solve the optimization problem by using the maximum entropy principle and propose the RE-FPR algorithm. The simulation results show that RE-FPR algorithm outperforms the traditional flow scheduling algorithms in terms of flow completion time and number of active RE- routers / links.

Multicast can not only shorten task completion time of applications, but also effectively reduce overall bandwidth consumption in data center networks (DCNs). However, load imbalance and sudden link blocking will seriously impact the network performance owing to the fundamental characteristics of huge traffic in DCNs. To address this problem, in this paper, we propose a novel multicast scheduling algorithm in software defined fat-tree DCNs, which can improve network performance by reducing the blocking of multicast traffic. In particular, the multicast scheduling algorithm selects the minimum blocking cost of links as optimal paths. By our scheduling algorithm, multicast flows are evenly distributed over the available links so as to achieve load balance. In addition, the DCNs are controlled globally by the software defined networking (SDN) technology, therefore, the multicast traffic cannot be interfered by the unexpected flow requests. Furthermore, the multicast scheduling algorithm can lead to asymptotically minimum network blocking probability, and has a low time complexity. Simulation results verify the effectiveness of our proposed multicast scheduling algorithm in different network traffic intensities.

Multicast can improve network performance by eliminating sending unnecessary duplicated flows in the data center networks (DCNs), thus it can significantly save network bandwidth and improve the network Quality of Service (QoS). However, the network multicast blocking causes the retransmission of a large number of data packets, and seriously influences the traffic efficiency of data center networks, especially for the multicast traffic in the fat-tree DCNs owing to multi-rooted tree structure. In this paper, we propose a novel multicast scheduling strategy to reduce the network multicast blocking. In order to decrease the operation time of the proposed algorithm, therefore, the remaining bandwidth the selected uplink connecting to available core switch should be close to and greater the three times than the bandwidth of multicast requests. Then the blocking probability of downlink at next time-slot is calculated by using markov chain theory. Furthermore, we select the downlink with minimum blocking probability as the optimal path at next time slot. In addition, theoretical analysis shows that the blocking probability of scheduling algorithm is close to zero and has lower time complexity. Simulation results verify the effectiveness of our proposed multicast scheduling algorithm.

Summary Multicast traffic scheduling in data center networks can not only improve network efficiency but also save network resources. However, the existing multicast scheduling algorithms cannot appropriately schedule flows to achieve traffic load balance so that the network may occur heavy blocking. This prevents the full utilization of high degree of link parallelism and causes unpredictable reduction of network performance. To address the problem, in this paper, we propose a blocking cost‐driven multicast scheduling algorithm by using optimization theory in fat‐tree data center networks. In particular, a model of multicast traffic subnetwork is established on the basis of the blocking probability of available paths at next time slot, which can reflect the blocking characteristics of multicast network and predict network state at next time slot. With the multicast blocking model, we derive the minimum blocking probability of multicast subnetworks, denoted as blocking cost. In addition, the algorithm can select the multicast subnetwork with minimum blocking cost to transfer multicast flows. Time complexity analysis and simulation results demonstrate the effectiveness and efficiency of our proposed multicast scheduling algorithm for different network traffic intensities.

Summary Multi‐domain multi‐controller software‐defined networks have an inevitable potential to improve the scalability and reliability of massive data center networks because the single controller cannot match the rapid expansion of cloud computing. The inconsistency among controllers can significantly degrade the network performance, because of that the data center network in software‐defined networks has physically distributed control plane but operates with logically centralized plane. Existing synchronization among multi‐controllers is based on the periodic synchronization (PS) that is stiffly triggered by time. However, the PS neglects potential inconsistency and further produces much packet loss and poor load balance in the whole network. Longer synchronization period increases synchronization overhead and decreases network performance. To keep the load consistency among controllers, we propose an active synchronization algorithm that is agilely triggered by event, ie, synchronization is triggered when the load of specific server exceeds the average load of its domain. With such an event triggered mechanism, we cannot only eliminate the correlation between synchronization and time but also avoid forwarding loop in the global network. Simulation results show that compared to existing PS‐based schemes, our active synchronization algorithm can achieve better load balance with less synchronization overhead and lower packet loss rate.

In the simultaneous wireless information and power transfer (SWIPT) system, due to the broadcast nature of wireless radio, the energy receiver may act as a potential eavesdropper to eavesdrop the information sent to the information receiver. To address this issue, in this paper, we propose an optimization framework of target secrecy rate and power allocation ratio based on secrecy outage probability and effective secrecy throughput in the SWIPT system over fading wiretap channel. Firstly, we formulate the effective secrecy throughput maximization problem under the reliability constraint in the on-off and adaptive transmission modes, and provide the optimal target secrecy rate. Furthermore, we formulate the secrecy outage probability minimization problem and the average harvested energy maximization problem, respectively. By solving the two optimization problems, we propose the corresponding optimal power allocation policy. Finally, we provide numerical results to evaluate the performance of the proposed policy in effective secrecy throughput.

Recent years have witnessed a growing interest in localization algorithms for wireless ad hoc networks. In most localization algorithms, increasing the density of anchor nodes is one of the main strategies to improve the localization accuracy in dense networks. In this paper, based on the number of reference nodes, we propose a distributed localization algorithm, i.e., history based multi-node collaborative localization algorithm (HMCL), which provides a potential approach for localization in sparse ad hoc wireless networks. In the proposed HMCL algorithm, we exploit a new motion model to filter the imprecise estimation values based on the historical position information of nodes, which can improve the localization accuracy and reduce the computation overhead and energy consumption. Moreover, we utilize different strategies to achieve the localization of nodes with different priorities measured by the distance information between neighbor nodes. We verify through experiment that the proposed algorithm provides better performance in terms of localization precision and energy consumption. Besides, we also analyze the effect of the number of neighbor nodes, node density and moving speed of nodes on localization precision.

Cooperative cognitive radio (CCR) is a novel paradigm for improving both radio spectrum efficiency and communication quality. However, for the CCR networks with energy harvesting, how to achieve specific relay selection is still an open issue. In this paper, we consider a CCR network with energy harvesting in which multiple secondary transmitters are able to harvest energy from the received signals to serve their own receivers and primary transmitters. Furthermore, we propose two relay selection schemes, i.e., single relay selection and multiple relay selection, by considering harvested energy and analyze the outage probability for both schemes over Nakagami-m fading channels. Simulation results validate our analysis on outage performance and compare the effects of the number of selected relays, energy harvesting ratio and transmission phase division ratios on two schemes in terms of outage probability. different parameters.

The redundancy of sensing data in wireless sensor networks (WSNs) gives rise to longer transmission delays and more energy consumption. In this paper, we focus on the energy-efficient data redundancy elimination and compression with the objective of recovering the original data. To balance aggregation load of a large-scale WSN, we propose a novel energy-efficient dynamic clustering algorithm by utilizing spatial correlation, which can achieve a distributed compressive data aggregation in each cluster head. Furthermore, we propose a distributed fast data compression approach based on eliminable lifting wavelet to reduce the amount of raw data. Also, it offers high fidelity recovery for the raw data. Extensive experimental results demonstrate that our clustering method based on data correlation clustering (CDSC) for data aggregation outperforms other methods on prolonging network lifetime and reducing the amount of data transmitted. In particular, our data compression aggregation algorithm can achieve 98.4% recovery accuracy when the compression ratio equals 1.3333.

In wireless sensor networks (WSNs), there usually exist spatial overlap and data correlation among sensors. Reducing data redundancy is crucial for prolonging network lifetime inWSNs. Source coding is an effective method for data aggregation to reduce data redundancy. However, source coding was regarded as an independent problem in previous work. Little work pays attention to optimal coding rate and associates it with underlying protocols. In this paper, we adopt Slepian-Wolf theorem to achieve the boundary of coding rate, and propose a cross-layer optimization framework to give the optimal source coding rate and flow allocation. We seek to establish a structure-free, multipath transmission model. To the best of our knowledge, this is the first work to solve the optimal source coding rate allocation problem in WSNs. Our extensive simulation results demonstrate that the proposed framework can reduce network traffic and extend network lifetime significantly.

HyperLogLog Counting is widely used in cardinality estimation. It is the foundation of many algorithms in data analysis, commodity recommendation and database optimization. Facing the large scale internet business like electronic commerce, internet companies have an urgent requirement of distributed real-time cardinality estimation with high accuracy and low time cost. In this paper, we propose a distributed real-time cardinality estimation algorithm named Hermes. Hermes adjusts the estimated cardinality dynamically according to the result of HyperLogLog Counting and also optimizes the data distribution strategy of existing distributed cardinality estimation algorithms. Experiments have been carried out and the results show that Hermes has lower estimation error and time cost compared with existing algorithms.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

Predictive modeling is the art of building statistical models that forecast probabilities and trends of future events. It has broad applications in industry across different domains. Some popular examples include user intention predictions, lead scoring, churn analysis, etc. In this tutorial, we will focus on the best practice of predictive modeling in the big data era and its applications in industry, with motivating examples across a range of business tasks and relevance products. We will start with an overview of how predictive modeling helps power and drive various key business use cases. We will introduce the essential concepts and state of the art in building end-to-end predictive modeling solutions, and discuss the challenges, key technologies, and lessons learned from our practice, including case studies of LinkedIn feed relevance and a platform for email response prediction. Moreover, we will discuss some practical solutions of building predictive modeling platform to scale the modeling efforts for data scientists and analysts, along with an overview of popular tools and platforms used across the industry.

In wireless sensor networks (WSNs), numerous sensors can produce a significant portion of the big data. It remains an open issue how to timely gather and transmit such large amount of data while minimizing data latency through wireless sensor networks (WSNs). On the other hand, spatially correlated sensor observations lead to considerable data redundancy in the network. To efficiently eliminate data redundancy and improve energy efficiency, in this paper, based on the fact that the more similar the measure data are, the smaller the amount of data after aggregation is, we first develop a new distributed clustering algorithm which can categorize sensor nodes with high similarity into a cluster for data aggregation, while ensuring uniform energy consumption within the cluster. Then, we propose a data aggregation algorithm based on principal component analysis (PCA) which can be executed in the cluster head (CH). Finally, our experimental results demonstrate that the amount of data transmission can be significantly reduced based on our proposed clustering and data aggregation algorithm.

Nowadays, a modern e-commerce company may have both online sales and offline sales departments. Normally, online sales attempt to sell in small quantities to individual customers through broadcasting a large amount of emails or promotion codes, which heavily rely on the designed backend algorithms. Offline sales, on the other hand, try to sell in much larger quantities to enterprise customers through contacts initiated by sales representatives, which are more costly compared to online sales. Unlike many previous research works focusing on machine learning algorithms to support online sales, this paper introduces an approach that utilizes heterogenous social networks to improve the effectiveness of offline sales. More specifically, we propose a two-phase framework, HeteroSales, which first constructs a company-to-company graph, a.k.a. Company Homophily Graph (CHG), from semantics based meta-path learning, and then adopts label propagation on the graph to predict promising companies that we may successfully close an offline deal with. Based on the statistical analysis on the world's largest professional social network, LinkedIn, we demonstrate interesting discoveries showing that not all the social connections in a heterogeneous social network are useful in this task. In other words, some proper data preprocessing is essential to ensure the effectiveness of offline sales. Finally, through the experiments on LinkedIn social network data and third-party offline sales records, we demonstrate the power of HereroSales to identify potential enterprise customers in offline sales.

Data uploading time constitutes a large portion of mobile data gathering time in wireless sensor networks. By equipping multiple antennas on the mobile collector, data uploading time can be greatly shortened. However, previous works only treated wireless link capacity as a constant and ignored power control on sensors, which would significantly deviate from the real wireless environments. To overcome this problem, in this paper we propose a new data gathering cost minimization framework for mobile data gathering in wireless sensor networks by considering dynamic wireless link capacity and power control jointly. Our new framework not only allows concurrent data uploading from sensors to the mobile collector, but also determines transmission power under elastic link capacities. We study the problem under constraints of flow conservation, energy consumption, elastic link capacity, transmission compatibility, and Sojourn time. We employ the subgradient iteration algorithm to solve the minimization problem. We first relax the problem with Lagrangian dualization, then decompose the original problem into several subproblems, and present distributed algorithms to derive data rate, link flow and routing, power control, and transmission compatibility. For the mobile collector, we also propose a sub-algorithm to determine sojourn time at different stopping locations. Finally, we provide extensive simulation results to demonstrate the convergence and robustness of proposed algorithms. The results reveal 20 percent shorter data collection latency on average with lower energy consumptions compared to previous works as well as lower data gathering cost and robustness in case of node failures.

Recent advances in environmental energy harvesting technologies have provided great potentials for traditional battery powered sensor networks to achieve perpetual operations. Due to dynamics from the temporal profiles of ambient energy sources, most of the studies so far have focused on designing and optimizing energy management schemes on single sensor node, but overlooked the impact of spatial variations of energy distribution when sensors work together at different locations. To design a robust sensor network, in this paper, we use mobility to circumvent communication bottlenecks caused by spatial energy variations. We employ a mobile collector, called SenCar, to collect data from designated sensors and balance energy consumptions in the network. To show spatial-temporal energy variations, we first conduct a case study in a solar-powered network and analyze possible impact on network performance. Next, we present a two-step approach for mobile data collection. First, we adaptively select a subset of sensor locations where the SenCar stops to collect data packets in a multi-hop fashion. We develop an adaptive algorithm to search for nodes based on their energy and guarantee data collection tour length is bounded. Second, we focus on designing distributed algorithms to achieve maximum network utility by adjusting data rates, link scheduling, and flow routing that adapts to the spatial-temporal environmental energy fluctuations. Finally, our numerical results indicate the distributed algorithms can converge to optimality very fast and validate its convergence in case of node failure. We also show advantages of our framework such as it can adapt to spatial-temporal energy variations and demonstrate its superiority compared to the network with static data sink.

This paper considers applying simultaneously wireless information and power transfer (SWIPT) technique to cooperative clustered wireless sensor networks, aiming at prolonging the lifetime of relay nodes and maximizing the energy efficiency of data transmission. To this end, we first formulate the energy-efficient cooperative transmission (eCotrans) problem for SWIPT as a non-convex optimization problem. By exploiting fractional programming and dual decomposition, we design a distributed iteration algorithm for power allocation, power splitting and relay selection to solve the non-convex optimization problem. Our simulation results illustrate that the proposed algorithm can converge within a few iterations and provide practical insights into the effect of the number of relay nodes and the maximum transmission power allowance on energy efficiency.

The indoor location fingerprint technique that infers the location based on the received signal strength (RSS) has been adopted in many localization applications, due to its high accuracy and low cost. However, there still lacks an analytical model that can be used to reduce the amount of fingerprints and improve the design of indoor localization system. In this paper, we propose a Voronoi analytical model based on graph theory, and apply this model to analyze the fingerprint structure, yield proximity information and compute the centroid of the Voronoi vertex in the Voronoi region. Furthermore, we compare the measured location and the actual location. Based on the comparison results, we select the smallest Euclidean distance between the two locations as the approximation of the actual location. In order to validate the performance of the analytical model on efficiency and reliability, we conduct an extensive experiment in an indoor parking lot, where it is convenient to deploy the access points (APs). The simulation results illustrate that the mean distance error decreases with the number of access points and collected samples.

Adopting mobile data gathering in wireless sensor networks (WSNs) can reduce the energy consumption on data forwarding thus achieve more uniform energy consumption among sensor nodes. However, the data delivery latency inevitable increases in mobile data gathering due to the travel of the mobile sink. In this paper, we consider a delivery latency minimization problem (DLMP) in a randomly deployed WSN. To solve this problem, we first select the traversed anchor points on the border of the communication range of sensor nodes to shorten the travel route, and then let the mobile sink move and collect data at the same time to reduce the travel time. In addition, we also employ the time division approach to traverse the sensor nodes whose signals cover the same travel segments. We formulate the DLMP as an integer programming problem which subjects to the direct access constraint, the data transmission constraint and the route traverse constraint. We prove that the DLMP is an NP-Complete (NPC) problem. To solve the NPC problem, we propose a substitution heuristic algorithm, a traveling salesman problem (TSP) heuristic algorithm and a random heuristic algorithm. We conduct extensive simulations to evaluate the performance of the proposed algorithms, and the results show that all the three algorithms can shorten the data delivery latency in mobile data gathering, with the substitution heuristic algorithm being the most effective one.

This paper addresses the problem of joint relay and power splitting ratio selection along with power allocation for an energy harvesting (EH) cooperative network, where the source and the relays can harvest energy from natural sources (e.g., solar) and radio frequency (RF) signals, respectively. To effectively use the harvested energy from the source, the relays employ the power splitting technique to scavenge energy from RF signals radiated by the source. We formulate this problem into a non-convex constrained optimization problem with the objective of maximizing system payoff, which is defined as the difference between system transmission benefit and system energy cost, and meanwhile minimizing system outage probability in both offline and online settings. In particular, we consider both direct transmission and relay transmission in this paper. Relay transmission is selected dynamically based on network channel conditions and available energy of EH nodes. Our simulation results reveal that considering direct transmission and selecting relay transmission and power splitting ratio dynamically can greatly improve system performance.

Wireless sensor networks (WSNs) are often deployed in hostile environments where an adversary may physically capture some of the nodes in WSNs, and replicate them in a large number of clones, easily taking control of networks. A few solutions have been proposed to cope with this problem. However, these solutions cannot adapt to the change of the network size and have low detection efficiency for clone nodes. In order to discover the clone nodes fast, in this paper, we propose an improved LEACH (NI-LEACH) protocol to reduce the scale of the cluster by considering the residual energy of nodes and the optimal number of clusters. Furthermore, we design an intrusion detection algorithm to detect the replication attacks by introducing monitor nodes in the network so as to greatly reduce the occurrence of tampering with the information. Simulation results show that our proposed algorithm is simple yet efficient. An attacker can be detected with high probability while achieving approximately optimal throughput. The network's ability against the attack from clone nodes is greatly improved.

Energy harvesting is a promising solution to prolong the lifetime of energy-constrained wireless sensor networks. In particular, scavenging energy from ambient radio frequency (RF) signals has drawn a lot of attention recently. In this paper, we apply simultaneous wireless information and power transfer (SWIPT) to a clustered sensor network such that a cluster head node harvests the wireless energy of received RF signals from its cluster members and then employs the harvested energy to compensate the energy consumed by data aggregating and forwarding. In such a network, how to achieve high energy efficiency through trading off between energy harvesting and information decoding is a critical issue. To this end, we formulate the rate and power resource allocation problem in a clustered WSN with SWIPT as a non-convex constrained energy efficiency maximization problem. By exploiting fractional programming and dual decomposition, we further propose a cross-layer resource allocation (ResAll) algorithm consisting of subalgorithms of rate control, power allocation and power splitting to solve the problem efficiently and optimally. Our simulation results reveal that the proposed ResAll algorithm converges within a small number of iterations, and achieves optimal system energy efficiency by balancing energy efficiency, data rate, transmit power and power splitting ratio.

We study the problem of group linkage: linking records that refer to multiple entities in the same group. Applications for group linkage include finding businesses in the same chain, finding social network users from the same organization, and so on. Group linkage faces new challenges compared to traditional entity resolution. First, although different members in the same group can share some similar global values of an attribute, they represent different entities so can also have distinct local values for the same or different attributes, requiring a high tolerance for value diversity. Second, we need to be able to distinguish local values from erroneous values.

Summary Distributed data‐centric storage in wireless sensor networks (WSNs) is considered as a promising big data storage approach, because it contributes to reducing the communication overhead inside the networks. However, most of the existing distributed methods rely on locating systems, which consume more energy, and assume that sensors are uniformly distributed, which is clearly not applicable for the scenarios with nonuniform sensor distribution. To address these issues, in this paper, we propose a big data storage and retrieval algorithm for WSNs with nonuniform node distribution, which aims at estimating the real distribution and the addresses of sensor nodes. In particular, we consider the data redundancy among neighbor nodes in the proposed algorithm and exploit a simple routing based on the algorithm. Experimental results show that our approach outperforms other approaches in terms of data querying efficiency and data loss rate. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we consider lossy mobile ad hoc networks where the data rate of a given flow becomes lower and lower along its routing path. One of the main challenges in lossy mobile ad hoc networks is how to achieve the conflicting goal of increased network utility and reduced power consumption, while without following the instantaneous state of a fading channel. To address this problem, we propose a cross-layer rate-effective network utility maximization (RENUM) framework by taking into account the lossy nature of wireless links and the constraints of rate outage probability and average delay. In the proposed framework, the utility is associated with the effective rate received at the destination node of each flow instead of the injection rate at the source of the flow. We then present a distributed joint transmission rate, link power and average delay control algorithm, in which explicit broadcast message passing is required for power allocation algorithm. Motivated by the desire of power control devoid of message passing, we give a near-optimal power-allocation scheme that makes use of autonomous SINR measurements at each link and enjoys a fast convergence rate. The proposed algorithm is shown through numerical simulations to outperform other network utility maximization algorithms without rate outage probability/average delay constraints, leading to a higher effective rate, lower power consumption and delay. Furthermore, we conduct extensive network-wide simulations in NS-2 simulator to evaluate the performance of the algorithm in terms of throughput, delay, packet delivery ratio and fairness.

This paper considers applying simultaneous wireless information and power transfer (SWIPT) technique to cooperative clustered wireless sensor networks, where energy-constrained relay nodes harvest the ambient radio-frequency (RF) signal and use the harvested energy to forward the packets from sources to destinations. To this end, we first formulate the energy-efficient cooperative transmission (eCotrans) problem for SWIPT in clustered wireless sensor networks as a non-convex constrained optimization problem. Then, by exploiting fractional programming and dual decomposition, we develop a distributed iteration algorithm for power allocation, power splitting and relay selection to solve the non-convex optimization problem. We find that power splitting ratio plays an imperative role in relay selection. Our simulation results illustrate that the proposed algorithm can converge within a few iterations and the numerical analysis provides practical insights into the effect of various system parameters, such as the number of relay nodes, the inter-cluster distance and the maximum transmission power allowance, on energy efficiency and average harvested power.

In this paper, we propose a joint subcarrier pairing and power allocation (JS2PA) scheme with fairness based on the Intelligent Water Drop (IWD) optimization method for Orthogonal Frequency Division Multiple Access (OFDMA) cooperative relay networks. The proposed scheme consists of a subcarrier pairing and selection algorithm and a power allocation algorithm. We first formulate the JS2PA problem as a mixed integer programming problem aiming to maximize the total network utility under the constraints of the total and individual power, subcarrier fairness requirement and pairing. To solve the non-convex JS2PA problem, firstly, we propose a subcarrier pairing and selection algorithm based on Hungarian method so as to select the appropriate subcarrier pairs for relaying. Secondly, we provide a power allocation algorithm based on the IWD method (PA-MIWD) in which water drops act as the agents to find the optimal power allocation for each node. Finally, we conduct simulations to validate the proposed algorithms and the results show that the proposed JS2PA scheme outperforms the existing methods in terms of convergence and total network utility.

Environmental energy harvesting technologies have provided potential for battery-powered wireless sensor networks to have perpetual network operations. To design a robust network that can adapt to not only temporal but also spatial variations of ambient energy sources, in this paper, we utilize mobility to circumvent communication bottlenecks, by employing a mobile data collector, called SenCar. We propose a two-stage approach for mobile data collection. In the first stage, SenCar makes stops at a subset of selected sensor locations to collect data packets in a multi-hop fashion. We provide a selection algorithm to search for sensor locations with most residual energy while guaranteeing a bounded tour length. Then we design a distributed data gathering algorithm to achieve maximum network utility by adjusting data rates, link scheduling and flow routing that adapts to spatial temporal environmental energy variations. The effectiveness and efficiency of the proposed algorithms are validated by extensive numerical results.

The emerging wireless energy transfer technology enables charging sensor batteries in a wireless sensor network (WSN) and maintaining perpetual operation of the network. Recent breakthrough in this area has opened up a new dimension to the design of sensor network protocols. In the meanwhile, mobile data gathering has been considered as an efficient alternative to data relaying in WSNs. However, time variation of recharging rates in wireless rechargeable sensor networks imposes a great challenge in obtaining an optimal data gathering strategy. In this paper, we propose a framework of joint wireless energy replenishment and anchor-point based mobile data gathering (WerMDG) in WSNs by considering various sources of energy consumption and time-varying nature of energy replenishment. To that end, we first determine the anchor point selection strategy and the sequence to visit the anchor points. We then formulate the WerMDG problem into a network utility maximization problem which is constrained by flow, energy balance, link and battery capacity and the bounded sojourn time of the mobile collector. Furthermore, we present a distributed algorithm composed of cross-layer data control, scheduling and routing subalgorithms for each sensor node, and sojourn time allocation subalgorithm for the mobile collector at different anchor points. We also provide the convergence analysis of these subalgorithms. Finally, we implement the WerMDG algorithm in a distributed manner in the NS-2 simulator and give extensive numerical results to verify the convergence of the proposed algorithm and the impact of utility weight, link capacity and recharging rate on network performance.

The emerging wireless energy transfer technology enables charging sensor batteries in a wireless sensor network (WSN) and maintaining perpetual operation of the network. Recent breakthrough in this area has opened up a new dimension to the design of sensor network protocols. In the meanwhile, mobile data gathering has been considered as an efficient alternative to data relaying in WSNs. However, time variation of recharging rates in wireless rechargeable sensor networks imposes a great challenge in obtaining an optimal data gathering strategy. In this paper, we propose a framework of joint Wireless Energy Replenishment and anchor-point based Mobile Data Gathering (WerMDG) in WSNs by considering various sources of energy consumption and time-varying nature of energy replenishment. To that end, we first determine the anchor point selection and the sequence to visit the anchor points. We then formulate the WerMDG problem into a network utility maximization problem which is constrained by flow conversation, energy balance, link and battery capacity and the bounded sojourn time of the mobile collector. Furthermore, we present a distributed algorithm composed of cross-layer data control, scheduling and routing subalgorithms for each sensor node, and sojourn time allocation subalgorithm for the mobile collector at different anchor points. Finally, we give extensive numerical results to verify the convergence of the proposed algorithm and the impact of utility weight on network performance.

The various kinds of booming social media not only provide a platform where people can communicate with each other, but also spread useful domain information, such as career and job market information. For example, LinkedIn publishes a large amount of messages either about people who want to seek jobs or companies who want to recruit new members. By collecting information, we can have a better understanding of the job market and provide insights to job-seekers, companies and even decision makers. In this paper, we analyze the job information from the social network point of view. We first collect the job-related information from various social media sources. Then we construct an inter-company job-hopping network, with the vertices denoting companies and the edges denoting flow of personnel between companies. We subsequently employ graphmining techniques to mine influential companies and related company groups based on the job-hopping network model. Demonstration on LinkedIn data shows that our system JobMiner can provide a better understanding of the dynamic processes and a more accurate identification of important entities in the job market.

Nonuniform energy consumption is an inherent problem in wireless sensor networks characterized by multi-hop routing and many-to-one traffic pattern. Such unbalanced energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of prolonging network lifetime in large scale wireless sensor networks where a mobile sink gathers data periodically along the predefined path and each sensor node uploads its data to the mobile sink over a multi-hop communication path. For this problem, we propose a heuristic topology control algorithm with time complexity O(n(m + n log n)), where n and m are the number of nodes and edges in the network, respectively, and further discuss how to refine our algorithm to satisfy practical requirements such as distributed computing and transmission timeliness. Theoretical analysis and experimental results show that our algorithm is superior to several earlier algorithms for extending network lifetime.

In this paper, we consider lossy mobile ad hoc networks where the data rate of a given flow becomes lower and lower along its routing path, and propose a cross-layer rate-effective network utility maximization (RENUM) framework by taking into account the lossy nature of wireless links and the constraints of rate outage probability and average delay. In the proposed framework, the utility is associated with the effective rate received at the destination node of each flow instead of the injection rate at the source of the flow. We then present a distributed joint transmission rate, link power and average delay control algorithm, in which explicit broadcast message passing is required for power allocation algorithm. The proposed algorithm is shown through numerical simulations to outperform other network utility maximization algorithms without rate outage probability/average delay constraints, leading to a higher effective rate, lower power consumption and delay.

Traditional topology control designs of wireless sensor networks (WSNs) usually assume reliable links between two sensors if their distance is less than a threshold. In real environments, however, relaxing the reliable threshold to utilize lossy links is beneficial for saving node transmission power so that the network lifecycle can be prolonged. Since transmission over lossy links is unreliable, data retransmission is required. But retransmission results in extra energy consumption and remarkable communication delay. In view of this, we define an enhanced version of restricted shortest path (RSP) problem, called end-to-end delay-constrained topology control problem in lossy WSNs (Lossy-RSP), which takes into account retransmission costs in terms of energy and time. The difficulty in the Lossy-RSP problem is to deal with parallel edges collision phenomenon, i.e., different power levels between two adjacent nodes are suited for constructing constrained shortest paths between different sources and destinations. The Lossy-RSP problem is NP-complete in strong sense due to parallel edges collision phenomenon. We present two independent heuristic algorithms to defuse this phenomenon while retaining energy-efficient links. Simulation results show that our algorithms can reduce the energy consumption significantly even under rather strict delay constraint.

In this paper, we consider mobile data gathering in wireless sensor networks (WSNs) by using a mobile collector with multiple antennas. By taking into account the elastic nature of wireless link capacity and the power control for each sensor, we first propose a data gathering cost minimization (DaGCM) framework with concurrent data uploading, which is constrained by flow conservation, energy consumption, link capacity, compatibility among sensors and the bound on total sojourn time of the mobile collector at all anchor points. One of the main features of this framework is that it allows concurrent data uploading from sensors to the mobile collector to sharply shorten data gathering latency and significantly reduce energy consumption due to the use of multiple antennas and space-division multiple access technique. We then relax the DaGCM problem with Lagrangian dualization and solve it with the subgradient iteration algorithm. Furthermore, we present a distributed algorithm composed of cross-layer data control, routing, power control and compatibility determination subalgorithms with explicit message passing. We also give the subalgorithm for finding the optimal sojourn time of the mobile collector at different anchor points. Finally, we provide numerical results to show the convergence of the proposed DaGCM algorithm and its advantages over the algorithm without concurrent data uploading and power control in terms of data gathering latency and energy consumption.

In this paper, we propose a distributed resource allocation framework for cognitive radio networks by using the orthogonal frequency division multiple access (OFDMA) modulation. We jointly consider the constraints of quality of service (QoS), maximum power, and minimum rates in the network to obtain optimal available subcarrier sets and transmission power. The fairness of resource allocation is guaranteed by incorporating the probability that a subcarrier is occupied into the link capacity expression. We present a distributed subcarrier selection and power allocation algorithm and evaluate the algorithm through simulations. Our results confirm that the proposed algorithm outperforms the existing algorithms in terms of throughput, the number of secondary links admitted, and the fairness of resource allocation.

In this demo, we design a monitoring system based on sensor network for zoologists to research the activity budget of Rhinopithecus roxellana. We designed the hardware platform of the system for wild deployment and presented an analysis of the relationship between environmental factors and survival conditions of Rhinopithecus roxellana. The system can locate and track Rhinopithecus roxellana by individual identification. The prototype has been deployed in Wildlife Reserve of Qiling Mountain, China.

Many data-management applications require integrating data from a variety of sources, where different sources may refer to the same real-world entity in different ways and some may even provide erroneous data. An important task in this process is to recognize and merge the various references that refer to the same entity. In practice, some attributes satisfy a uniqueness constraint---each real-world entity (or most entities) has a unique value for the attribute ( e.g. , business contact phone, address, and email). Traditional techniques tackle this case by first linking records that are likely to refer to the same real-world entity, and then fusing the linked records and resolving conflicts if any. Such methods can fall short for three reasons: first, erroneous values from sources may prevent correct linking; second, the real world may contain exceptions to the uniqueness constraints and always enforcing uniqueness can miss correct values; third, locally resolving conflicts for linked records may overlook important global evidence. This paper proposes a novel technique to solve this problem. The key component of our solution is to reduce the problem into a k -partite graph clustering problem and consider in clustering both similarity of attribute values and the sources that associate a pair of values in the same record. Thus, we perform global linkage and fusion simultaneously, and can identify incorrect values and differentiate them from alternative representations of the correct value from the beginning. In addition, we extend our algorithm to be tolerant to a few violations of the uniqueness constraints. Experimental results show accuracy and scalability of our technique.