Data fragmentation across IoT healthcare devices makes identifying and tracing causal health events challenging. In this work, we proposeCasFly, a decentralized, on-the-fly causal chain tracing protocol for healthcare event sequences. We design a specialized data structure,Temporal Probabilistic Health Graph (TPHG), to dynamically trace event dependencies across heterogeneous healthcare devices. When a device detects an anomalous parameter at the edge, it queries its local TPHG to identify the most probable temporal precursors. We propose incremental chain formation at each of the devices usingLaVE Algorithm, a Lag-aware Viterbi Expansion Algorithm. The algorithm transmits data only when new causal paths are identified, thus reducing the latency of the dependency path construction.CasFlyoperates device-to-device by expanding the chain until no further expansion is possible. Finally, the chain is returned to the initiating device for visualization and analysis. We observe that on emulated IoT hardware, TPHGs load in < 0.1 s and full chains develop in ≤5 s using ∼170MB RAM. TPHGs reduce storage by ∼97.6% versus Conditional Probability Tables. As compared to PCMCI+, LaVE improves macro AUPRC by ≈18% and reduces log-loss by ≈8%, with similar AUROC. These results indicate that on-the-fly chain construction supports real-time monitoring and clinician interpretation of probable cross-device event sequences.

This work proposes a marketplace model, EMsaaS , for sharing microservices on the IoT network on edge. Containerized microservices are well-suited for real-time IoT applications on edge because of their lightweight nature, independent lifecycle, and scalability. However, deploying microservices on demand is often failure-prone because of edge bandwidth limitations and resource constraints. Additionally, centralized image registries hosting containerized application images are often far away from the edge site and incur latency for the download of the images. They are also prone to a single point of failure. This work proposes a decentralized mechanism for sharing microservices in the edge network using a peer-to-peer approach. We introduce a component Edge Microservice Tracker and deploy it on the participating edge nodes. This component communicates with the other peer nodes and discovers microservices in the network. When requested, it fetches the requested microservice from the host node and deploys the same on the requesting edge node. We implement the proposed system in an IoT network of edge devices and evaluate the model using several parameters such as the deployment time, CPU and memory consumption, internet upload/download rate, and metadata synchronization time for sharing microservices on the edge. We observe that the system performs better than the cloud or centralized registry based approach with respect to the microservice deployment time with almost a 30% reduction in the deployment time even without any bandwidth limit for internet download from the edge node. It further surpasses the cloud registry-based microservice deployment procedure, with edge nodes having bandwidth limits. Moreover, EMsaaS proposes the concept of microservice sharing in an edge network, which enhances collaboration between edge service providers and fosters faster innovations.

In this work, we propose a smartphone-based system for detecting diabetic foot ulcers. Existing solutions mostly rely on clinical tests or costly imaging systems such as MRI or X-rays. These face challenges in resource-constrained environments and also do not provide immediate results. To address these limitations, we propose IgniSole, an edge-based offline solution for the detection of diabetic foot ulcers. We first train a fine-tuned MobileNetV2 Convolutional Neural Network (CNN) on the plantar thermogram dataset using transfer learning. We also develop an Android application to allow users to test their diabetic foot images using a smartphone. We deploy the trained model on the Android application to reduce dependency on the internet and to secure the user’s data. We evaluate the model’s performance in two phases: the training phase and the inference phase on the smartphone. We observe that our model achieves an accuracy of 96.24%, specificity of 100%, and sensitivity of 92.85% in the training phase. In contrast, the model’s accuracy, specificity, and sensitivity are 96.16%, 98.88%, and 97.96%, respectively, in the inference phase on the smartphone. Moreover, the model predicts the results within a span of 1-2 seconds. Therefore, our work provides a smartphone-based platform to users for the detection of diabetic foot ulcers with affordable, scalable, and accurate diagnostic tools. The source codes are available at https://github.com/anshita510/IgniSole.

Heterogeneous healthcare IoT edge devices operate in isolation. This limits their ability to make reliable collaborative decisions in real-time clinical settings. Existing systems lack lightweight inference-fusion mechanisms for decentralized coordination across sensors. In this work, we propose ZhiSync, a plugand-play, metadata-driven collaborative inference framework for heterogeneous healthcare IoT systems. In this system, we allow the devices to deploy their lightweight neural models for modality-specific prediction. We develop a framework that allows devices to exchange confidence, urgency, and timestamp metadata, known as ZhiTag, over asynchronous User Datagram Protocol (UDP). We deploy an inference algorithm, ZhiAware, which integrates peer ZhiTag with local input to adaptively refine predictions in real-time. We test our system on several parameters using a simulation framework with real datasets that includes three devices, namely, an Electrocardiogram (ECG) monitor, a motion sensor, and a breath analyzer, using embedded neural networks. ZhiSync increases local prediction confidence in 99.6% of decisions, with an average relative gain of 20.6% and stronger effects under high-urgency events and reduces false positives. CPU RAM and runtime is less than 2% as compared to baseline. Communication remains sub-kilobyte per second per node. Overall, lightweight metadata exchange shows that ZhiSync provides a reliable and scalable collaboration across decentralized healthcare IoT.

The constrained availability of resources on an edge analytics platform prompted the need for a trade-off between accuracy and latency by selecting suitable deep neural network (DNN) models on-the-fly. Earlier efforts either used a single powerful multi-core edge computing device or a distributed cluster of edge nodes. While the former has a high cost and power consumption, the latter incur a high communication overhead. In this paper, we propose a quality-of-service (QoS) aware video analytics platform using an edge-cluster made of low-cost devices. The edge nodes, that constitute the cluster, host heterogeneous DNN models having different configurations and number of layers. The nodes cooperate among themselves to jointly process a streaming video to achieve an optimal QoS. We formulate an optimization problem using penalty as the utility function to minimize the long-term average penalty (LTAP). We first design a DNN model recommender algorithm to minimize the LTAP and then compare it with an Oracle to show that it can achieve an LTAP with an error of 1.6 % and 9.88 % for video resolutions of 720p and 2160p respectively. We also show that the bounds on LTAP are lower and tighter for lower resolution videos compared to the higher resolution videos.

This work addresses the problem of ensuring service availability, trust, and profitability in sensor-cloud architecture designed toSensors-as-a-Service(Se-aaS) using IoT generated data. Due to the requirement of geographically distributed wireless sensor networks for Se-aaS, it is not always possible for a single Sensor-cloud Service Provider (SCSP) to meet the end-users requirements. To address this problem, we propose a federated sensor-cloud architecture involving multiple SCSPs for provisioning high-quality Se-aaS. Moreover, for ensuring trust in such a distributed architecture, we propose the use ofconsortium blockchainto keep track of the activities of each SCSP and to automate several functionalities throughSmart Contracts. Additionally, to ensure profitability and end-user satisfaction, we propose a composite scheme, named BRAIN, comprising of two parts. First, we defineminer's scoreto select an optimal subset of SCSPs asminersperiodically. Second, we propose a modifiedmultiple-leaders-multiple-followers Stackelberg game-theoretic approach to decide the association of an optimal subset of SCSPs to each service. Thereafter, we evaluate the performance of BRAIN by comparing with three existing benchmark schemes through simulations. Simulation results depict that BRAIN outperforms existing schemes in terms of profits and resource consumption of SCSPs, and price charged from end-users.

The future social metaverse traffic demands over 5G and beyond (B5G) networks can be supported by efficiently placing the user plane function (UPF). Third-generation partnership project (3GPP) standard mandates that all application traffic in B5G core network must be routed through a UPF before it can reach its destination. In this regard, we propose an optimal UPF placement algorithm, referred to as Decomposed Minimum Power UPF Placement (DeMPUP), which selects the minimum power consumption path from the next-generation node B (gNB) to the edge server on the overlay backbone network. Coupled with the optimal path selection, the proposed algorithm also identifies the optimal node over which the UPF would be placed. We provide a power consumption model of the core network with a corresponding joint non-linear programming (NLP) minimization problem for UPF placement. Owing to the high complexity of the formulated NLP, for enhanced scalability over large B5G cores, we decompose the problem into two sub-problems. The first sub-problem identifies a set of feasible paths commensurate with UPF power requirement, and the subsequent sub-problem selects the optimum path among the feasible paths that minimizes the power consumption. The selected path also uniquely identifies the node to place the UPF for minimum power consumption. Analysis of DeMPUP shows that it executes in linear time and scales linearly with the number of nodes. Experimental results reveal significant power reduction for different network sizes.

The significant increase in mobile phone usage in the past few decades has resulted in various health hazards in individuals due to continuous exposure to radiation for a longer duration of time. To address the health hazards caused by mobile phone radiation, in this paper, we propose a mobile application (app)-based recommendation system to analyze smartphone usage patterns and Generative Artificial Intelligence (GAI)-based alert generation and recommendations to the user. We collect data on user’s mobile phone usage, including screen time, number of near-the-ear long-duration calls, and exposure to external radiation. These collected data offer a deeper understating of radiation exposure through mobile phone usage patterns of the user using the k-means clustering algorithm. As the excessive use of mobile phones leads to various health risks, including adverse physical and mental health conditions besides reduced productivity and social isolation, the designed application tracks the duration of user’s different application usage and generates an alert on excessive mobile usage, including longer near-the-ear calls and screen time as well as exposure to high external radiation. The app-based recommendation system also uses the GAI algorithm to inform users about their mobile usage pattern on a daily basis and the possibility of health hazards to help users take corrective action to reduce mobile usage. From the obtained result, we observe that the designed application is capable of precisely identifying and alerting the users based on their mobile usage.

This work proposes routeNow, a routing algorithm designed for wireless Software Defined Internet of Things by dynamic path relaxation based on real-time heuristic that always gives the best least hop path. This is aimed towards providing an alternative backup network for a smart hospital that functions independently on the failure of the primary hospital network. Given a pair of source and destination nodes, a central controller with a global network view first calculates a minimum hop path greedily by minimizing the remaining distance to destination. Hence, the path so found is relaxed dynamically by substituting certain links with other links keeping the hop count constant if a new path is found with a lesser latency than the previous one. For this, the controller maintains a matrix containing real-time delay between neighboring IoT nodes given the current network state and whose values are periodically refreshed using a heuristic function based on actual delay measurements. This enables the controller to dynamically modify routing paths. Simulation analysis of routeNow shows that it formulates routing paths with significant less time even with large networks and by always maintaining a route with least hops, on average a least latency path is also ensured.

In this work, we propose “SocioLink”, a user-centric blockchain for the management of social media data. The current data management landscape faces various issues, including centralized control, data breaches, and limited data portability, which collectively erode user privacy and control over their personal information. User-centric solutions are potential responses to these issues; however, they often fall short of delivering resolutions to various challenges such as security, transparency, immutability and others. To resolve these issues, we propose SocioLink which seeks to address these issues with a blockchain-based system. By isolating user data from applications, SocioLink not only tackles the problems of centralized control, data breaches, and limited data portability; however, by enhancing immutability, transparency, and data accessibility, So-cioLink promises to elevate data sharing efficiency and scalability, ultimately transforming the landscape of global communications. Experimental results show a 66.6% reduction in mining time and a 53.8% better utilization of CPU.

The shared medium and multihop nature of wireless multimedia sensor networks (WMSNs) involving scalar and camera sensor (CS) nodes poses fundamental challenges to the design of an effective scheme, which can offer, reliable packet delivery, and resource allocation across different network flows. These flows are incurred by multiple events occurring concurrently in the monitored region in presence of shadow-fading environment. In this article, we propose a conjunctive cooperative communication and resource allocation scheme, named cooperative communication and optimal resource allocation (CORA), for Quality of Service (QoS) support in WMSNs. To exploit cooperative communication, we propose coalition formation framework (CFF) along with shortest path routing framework. The shortest path routing framework increases the lifetime of the CS nodes by decreasing hop-by-hop energy consumption. CFF partitions the links (consisting of CS nodes) into disjoint coalitions, which help in concurrent hop-by-hop packet transmission by reducing interference between concurrently scheduled links. This, in turn, increases packet delivery ratio (PDR) and throughput with maximum rate allocations to the competing links. We demonstrate the performance of CORA over the benchmark scheme network flow interaction chart (NFIC), with regard to PDR, throughput, and the lifetime of the CS nodes.

In this work, we propose a reconfigurable blockchain (BC)—“PerBlocks”—for handling data from heterogeneous activities and achieving scalability as well as throughput in the industrial environment. Typically, industries deal with the pervasive deployment of various sensors, resulting in heterogeneous data with varying services. The adoption of conventional BC satisfies transparency, immutability, and security. However, static block size and stringent consensus algorithms are unable to serve the needs of heterogeneous activities. In order to resolve this, we propose reconfigurable service-oriented BC, which dynamically selects the consensus algorithm and block size according to the requirement of heterogeneous activities. Through experimental results, we demonstrate that the proposed method can utilize the resources efficiently and provide user-oriented services as well as scalability. In particular, the proposed method uses 28.2% CPU and 82% memory while reducing response time by 6%, compared to a conventional BC and improving the quality of services by 60%.

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this article, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select vehicle tasks to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least$30.5 \%$and the trust score by at least$20.61 \%$, compared to the benchmark schemes.

In this research work, we propose – UtilityChain –, a method for efficient resource allocation of miners involved in both mining and service-related activities. The aim of this approach is to enhance the miner's utility (overall efficiency) in response to the increasing need for real-time data processing generated by IoT devices. The security of the data is a crucial matter of concern, and the integration of blockchain technology for secure data storage and edge computing for real-time processing is deemed a suitable approach. However, the utilization of edge computing devices adds an extra cost burden. Additionally, fluctuations in resource prices and mining rewards frequently result in miner departures and underutilized resources. To tackle these challenges, UtilityChain employs the resources of miners for mining and providing services to the end-users, without the use of edge computing devices. This is achieved through the utilization of advanced deep reinforcement learning technique to dynamically allocate miner resources for both mining and service tasks. The experimental results demonstrate the efficacy of UtilityChain, with a resource allocation accuracy of 99.2% and a miner allocation accuracy of 98.8%. Additionally, UtilityChain exhibits a resource utilization rate of 8.2% for CPU and 78.2% for memory.

Due to the lack of flexible analytical and traffic measurement modules, the current 6TiSCH network architecture is deficient for use in low-latency and reliable control-loop communications. In this paper, we propose a programmable scheme, PRC6, for use over the 6TiSCH network to support low latency and reliable communications. PRC6 provisions an edge-based programming mechanism, which is capable of (i) programming various application-specific tasks, (ii) reconfiguring the 6TiSCH schedule as per the requirements of tasks, and (iii) scheduling deadline-aware forwarding at the SDN core network. The programmability feature of PRC6 offers flexibility in the execution of decision-making and analytics for improved actuation in the associated control-loops. By reconfiguring 6TiSCH-related parameters, PRC6 establishes low-latency communication between the sensor and actuator nodes. We define a new header type to enable interoperable packet delivery between the Low Power Lossy Networks (LLNs) and the core network.

Stress has become an increasing concern among college students. Passive sensing techniques allow the extraction of stress-related parameters from a student. These techniques use highly resource-intensive machine learning algorithms to predict the stress levels of a student from these parameters. However, the current techniques do not provide any social communication solution for students suffering from stress. In this work, we propose StressAlly, a stress companion recommender system. The system comprises two modules, the stress score predictor and the stress companion recommender. The stress score predictor incorporates edge computing and deploys lightweight in-app inferences. The stress score predictor calculates the stress level in the scale 0–4 in the smartphone using the Artificial Neural Network (ANN) Regressor and sends it to the server. The Stress companion recommender provides similar stress levels of students to each student using the User-User Collaborative Filtering technique. We achieved training MAE and loss of 0.7478 and 1.0298, respectively. We get a test Mean Absolute Error (MAE) of 0.737 on the unseen data. We evaluate the CPU, memory, time delay, and network performance of StressAlly on the server and the Android smartphone. StressAlly utilizes 188 MB memory and 25% CPU on the smartphone.

With the fast growth of the Internet of Medical Things (IoMT), security in IoMT communication remains a critical and significant concern. Edge/fog-based communications for IoMT systems involve the use of wireless communication while connecting the devices to the edge/fog network, which is vulnerable to physical node attacks in which there is always a security risk that allows an attacker to launch an internal attack. In this paper, we address this concern of vulnerability and propose an architecture named — Reputation Aware IoMT Service Provisioning Using SDN (RISP-SDN) architecture, which is a trust model incorporating reputation scores to enhance security and improve latency in low-powered computation scenarios for IoMT. The system architecture uses different subnets of a single network with OpenFlow-enabled software and hardware switches, OpenFlow controller, validator, and reputation nodes. RISP-SDN also includes a mechanism for attack mitigation, enabling the preservation of network integrity and preventing malicious activities. Experimental results show that the proposed model gives enhanced results in terms of latency and use of storage units. Also, the model keeps the percentage of successful attacks such as Denial-of-service (DoS) and Man-in-the-Middle Attacks (MITM) kept under 50%.

In this paper, we propose a mechanism, termed as Opti-Safe, such that the demand of end-users and supply of mobile sensor nodes is fulfilled in a Safety-as-a-Service (Safe-aaS) platform for Social Internet of Vehicles (SIoV) environment. The end-users/customers request for decision parameters and make payment to the Safe-aaS platform. Based on their request, safety-related customized decisions are provided to them. None of the existing mechanisms provide the equilibrium condition between the sensor and vehicle owners, customers, and SSPs. The customers' requests for safety services and the presence of active, mobile sensor nodes present within a particular geographical region fluctuates with time. These mobile sensor nodes attain mobility with the variation in the geographical location of the vehicles, to which they are attached. Moreover, the service region of the SSPs is bounded and may comprise various types of geographical regions such as hilly and plane. To quantify the quality of service provided by SSPs, we define the term satisfaction factor (SF) for the registered users. The demand of customers is computed depending on the SF value. In order to find the equilibrium point, we model the utility of SSPs as an optimization function. We apply Karush-Kuhn-Tucker (KKT) conditions to find the optimal price charged by the SSPs and the number of mobile sensor nodes necessary for decision generation. We extensively simulate our proposed mechanism and observe that the satisfaction factor of users and utility of SSPs fluctuates with time.

The advent of Internet of Things (IoT) has resulted in a massive influx of data from remote networks, with big data originators located at the edge of the Internet. Software-Defined Networking (SDN) has recently emerged as an effective tool for centralized network control, including access nodes, to manage and optimize the flow of data generated by a vast number of IoT devices. However, remote and relay-positioned access nodes are often vulnerable to security attacks. In this paper, we propose DL-IPS, a novel intrusion detection mechanism for Software-Defined IoT (SD-IoT) based on Deep Learning (DL) techniques. Our proposed approach employs a Deep Neural Network (DNN) algorithm to monitor the traffic behaviours generated from IoT devices and predict potential intruders in the network. The proposed mechanism identifies malicious nodes and packets by monitoring incoming traffic behaviours at the local access nodes, thereby providing protection to the switch and flow tables from various attacks. Furthermore, our approach efficiently places flow rules to devices by removing malicious flows, reducing space consumption and overhead, and detecting anomalies effectively.

In this work, we introduce an edge intelligence layer into the traditional Safety-as-a-Service (Safe-aaS) platform for Social Internet of Vehicles (SIoV) networks, to minimize the network latency incurred in delivery of decisions. The prior announcement of safety-related information in social IoV environment, minimizes the rate of accidents to a significant extent. Safe-aaS provides customized safety-related decisions dynamically to the end-users. On the other hand, the timely delivery of accurate decisions to the end-users in a social IoV network is a challenging task. We introduce the concept of edge servers in the edge layer of Safe-aaS, such that the bandwidth required for uploading data is minimized, and the problems associated with processing, storage, and complex analysis of data are eliminated. We apply Artificial Neural Network (ANN) at the edge nodes to select the appropriate edge server and fuzzy logic at the edge server side for the generation of a decision. Here social entities are not humans rather vehicles, distributed edge servers and cloud servers all are acting as intelligent objects. Extensive simulation of our proposed architecture demonstrates that the computing density of edge servers is normally distributed. Additionally, we analyze the classification of the edge servers using training data obtained from the edge nodes and network is tested with the test dataset. We apply fuzzified decision is generated method at the edge sever. Extensive simulation results demonstrate that the delay incurred in delivery of decision is reduced by 90:58%, after introduction of edge intelligence layer.

One of the primary requirements in cellular-enabled multiunmanned aerial vehicle (UAV) Internet of Things (IoT) networks is to preserve data rates according to the IoT users’ (IUs) requirements. The mobility of IUs, 3-D movement of UAVs, environmental conditions, and bandwidth allocation to the IUs increase the challenges to maintain the data rates due to the continual change in the channel state. A constant extraction of channel state is crucial in this regard. We construct a sum-rate maximization problem considering the channel-state information (CSI). Unlike previous work, we propose CSI-driven data rate optimization for multi-UAV IoT networks (CARTEL). First, it allocates optimized bandwidth to IUs and accomplishes UAV–IU associations by adopting the matrix minima method. Subsequently, it maximizes the sum-rate invoking four modules: 1) parameter selector (PS); 2) IU tracker (IT); 3) path-loss estimator (PE); and 4) policy generator (PG). PS, IT, and PE help to extract the CSI by selecting suitable environmental parameters, tracking the IU mobility, and estimating the path loss, respectively. Finally, PG maximizes the data rates by adjusting the 3-D position and transmitting the power of a UAV. Extensive simulation results depict that the sum-rate in CARTEL improves by 26.03% and 65.46% than learn-as-you-fly (LAYF) and random selection (RS), respectively.

LoRa technology endows unprecedented ability to connect isolated geographical landscapes and build community networks that serve specific purposes. As the network grows, coherent routing of messages becomes imperative to meet the network’s objectives and Quality of Service (QoS) requirements. However, despite the recent rise in research and development centered around LoRa networks, not much research addresses the routing mechanisms in LoRa networks. Moreover, the LoRa routing mechanisms must run on low-power and resource-constrained devices, as these networks primarily target far-off locations or volatile environments such as volcanoes. Hence, this work proposes routing mechanisms (LoRaute) for LoRa networks that help route messages considering the messages’ QoS requirements. Also, a multipurpose network hardware is proposed, which serves as a LoRa network base station or a WiFi to LoRa bridge for TCP/IP communication. Additionally, this work furnishes and assesses the implementation of the LoRa network and the routing mechanisms in a real environment. The implementation results indicate a seamless and rapid setup of multihop LoRa networks. Moreover, the implementation achieves a routing table record size of 9 B, 24.45 ms routing latency, and 171 mA peak current consumption by the proposed LoRa node. Finally, the proposed system serves as a backhaul network for essential long-range communications, as demonstrated by the experimental setup.

This article proposes coalitional game-based broadcast proxy re-encryption (P-RE) in IoT—a broadcast P-RE for adding new IoT devices. The P-RE is extended to broadcast P-RE to prevent recalculation of the re-encryption key (ReKey). However, the group of recipients needs to be predetermined before the calculation of the ReKey. If any new IoT device requires the same data, an individual ReKey is generated for him/her. Hence, generating individual ReKey is an overhead for the organization. We propose a ReKey updation for the broadcast P-RE method. If excessive IoT devices want to join the group of the existing recipient, then updation needs to be done learnedly as an excessive number of recipients in a group increases the computation cost of the decryption extremely for all the members of the group. Therefore, we use the coalitional game theory to estimate the optimal number of new recipients from all new recipients. We update the ReKey for the optimal number of members and a separate ReKey is calculated for other recipients. We prove the correctness of the CBP. We prove that if any recipient behaves maliciously, s/he cannot get the secret key of the organization.

In this article, we propose and implement i-AVR, an Internet of Things (IoT)-based critical-aware system for point-of-care recommendation during ambulatory in-transits. The delay due to ambulances stuck in traffic congestion, disruptive roadways, and far-away hospitals restrain the smooth ambulance services. Therefore, in order to assist the time-critical scenario of a hospital-bound patient, we consider a guidance system to address the necessity. Moreover, these patients require continuous vitals monitoring, which may vary with the progress of time, to reduce the response time upon reaching the destination. The implemented i-AVR comprises two units: 1) a portable healthcare unit and 2) an android navigation unit. The healthcare unit aims to compute the criticality index of the en-route patient and recommend the nearest healthcare center while the navigation unit recommends the convenient route in case of any anomaly in vitals. We show the effectiveness of i-AVR regarding network performance while highlighting the response time of the system. We observe the system response time for computation in orders of seconds and interunit communication in milliseconds. Eventually, this analysis indicates the effectiveness of i-AVR in providing quick decisions during the time-critical situations. Our implementation provides essential intervention toward IoT-based healthcare technologies.

In this article, we propose a scheme, named P-VERSE, to prioritize the emergency vehicles (ERVs) in the absence of a traffic signal at a multiway intersection point on a road. We consider the ERVs, such as ambulance, fire trucks, or police vehicles, as prioritized vehicles and provide them safe and quick passage. The traffic signal is a traditional approach to minimize congestion, avoid collision among the vehicles, and reduce accidents. However, this results in the repetitive interruption of vehicles, thereby increasing their waiting time. To address these issues, we formulate a scheme that executes in two stages. In the first stage, we use Markov Chain to predict the future path of each vehicular nodes. Based on the predicted path, the common and ERVs cooperatively form groups to avoid collision and congestion at the intersection point. On the other hand, in the second stage, we apply a cooperative coalition-based game-theoretic approach to design the strategic interactions among the vehicular nodes. These vehicular nodes act as players and dynamically form coalitions among them. Depending upon the utility of the coalition, the vehicular nodes either merge with or split out from a coalition. These nodes form a coalition to provide safe passage to the ERVs. Further, we constrain our optimization problem to the Karush-Kuhn–Tucker (KKT). Extensive simulation-based analysis of our proposed scheme, P-VERSE, demonstrates that the energy consumption is reduced and utility is improved by 31.37% and 42.28% compared to the existing schemes.

In this work, we proposeShadows, a virtual blockchain (VC) for achieving parallel consensus and efficient management of data in industries by utilizing BC. Typically, industrial processes involve heterogeneous activities which require real-time consensus, managed execution, isolation, data sharing, accelerated computation, and efficient utilization of various computational resources such as CPU, RAM, and storage. Achieving these in real-time using a single conventional blockchain (BC) leads to the exertion of computational power. To achieve resource-efficient real-time consensus, we virtualize the nodes of the BC network and create different BC for various activities. Further, to virtualize BC and provide better access to data, we propose smart contracts liable for providing a unified view of a single BC, dynamically creating BCs, allocating resources to these, and making communication between the same. Through lab-scale experiments, we demonstrate thatShadowsis capable of utilizing the resources efficiently and achieving real-time consensus. In particular,Shadowsuses 18% CPU and 92% memory while reducing consensus time by 56%, compared to a single conventional BC.Shadowsalso accesses the data efficiently by utilizing smart contracts and dynamically balances the load by migrating the virtual nodes. Further,Shadowsreduces the number of migrations to make the balance system by 67%.

In social IoMT systems, resource-constrained devices face the challenges of limited computation, bandwidth, and privacy in the deployment of deep learning models. Federated learning (FL) is one of the solutions to user privacy and provides distributed training among several local devices. In addition, it reduces the computation and bandwidth of transferring videos to the central server in camera-based IoMT devices. In this work, we design an edge-based federated framework for such devices. In contrast to traditional methods that drop the resource-constrained stragglers in a federated round, our system provides a methodology to incorporate them. We propose a new phase in the FL algorithm, known as split learning. The stragglers train collaboratively with the nearest edge node using split learning. We test the implementation using heterogeneous computing devices that extract vital signs from videos. The results show a reduction of 3.6 h in the training time of videos using the split learning phase with respect to the traditional approach. We also evaluate the performance of the devices and system with key parameters, CPU utilization, memory consumption, and data rate. Furthermore, we achieve 87.29% and 60.26% test accuracy at the nonstragglers and stragglers, respectively, with a global accuracy of 90.32% at the server. Therefore, FedCare provides a straggler-resistant federated method for a heterogeneous system for social IoMT devices.

Storing data from Industrial-Internet-of-Things (IIoT) sensors in blockchain (BC) for monitoring the applications leads to management issues like bloating. The crux of this work is generating traces (the part of industrial data) using an ARIMA model and storing only the metadata over the network, resulting in reduced delay and managed data. We determine the size of the traces for storing on the store and generate (S&G) blocks (blocks that store traces along with their metadata) by considering principal parameters, such as training time, block size, and error. In general, S&G consists of three phases: 1) categorizing the data into groups based on their sampling rates, 2) storing the trace of data and metadata into the blocks, and 3) retrieving the entire data. We demonstrate the feasibility of S&G with errors and regret in the range of 0.07–0.10 and 0.20–0.25, respectively, using the appropriate ARIMA model.

In this work, we propose SemBox - Semantic interoperability in a Box, to enable wireless on-the-go communication between heterogeneous wearable health monitoring devices. It can connect wirelessly to the health monitoring devices and receive their data packets. It uses a Mamdani-based fuzzy inference system with data pre-processing to classify the received data packet into one of the classes of the vital parameters. It enables semantic interoperability by labelling and annotating the data packets based on the extracted packet information. We implement SemBox using three different health monitoring wearables, with different keywords used for each vital parameter representation in the data packet. SemBox shows a maximum classification accuracy of 85.71%, with a maximum PDR of 1 at the SemBox with varying device parameters. Overall, SemBox is a potential plug-and-play solution to achieve semantic interoperability and collaboration between heterogeneous health monitoring wearable devices, irrespective of their commercial and proprietary specifications. It is customizable for applications that use multiple heterogeneous devices for collaborative monitoring and decision support. SemBox enables interoperability among health monitoring devices, introduces flexibility and ease the inter-device dynamics in the domain of biomedical research.

In this work, we propose Soft-Safe, a Software Defined Safety-as-a-Service (Safe-aaS) model for provisioning safety-related decisions to the registered end-users. In Safe-aaS, the end-users register to the infrastructure, provide their initial and destination location, select certain decision parameters, and make payment through a Web portal. As the safety-related decisions are time-critical in nature, therefore timely delivery of these decisions is essential. Considering these facts and road transportation as the application scenario of Safe-aaS, we address the problem of efficient decision delivery to the end-users in two stages. In the first stage, we propose a Software Defined Safe-aaS platform to address the problems of heterogeneity among the SDN switches present in the edge layer. Further, based on the utility of each of the SDN switches present within the vicinity of the end-users, we optimally select a suitable SDN switch among the available ones, for delivering them decisions in the second stage. To obtain the maximum utility for delivering decisions to the end-users, we map the interactions between the SDN controller and SDN switches as a Non-cooperative Single Leader Multiple Follower game. Then, we estimate the optimal delay incurred by an SDN switch applying the Lagrangian function and Karush-Kuhn-Tucker (KKT) conditions. Exhaustive simulation results illustrate that the energy consumed and delay incurred using our proposed scheme, Soft-Safe, is reduced compared to the existing schemes, Traditional Safe-aaS and MoRule.

One of the crucial challenges in networked Unmanned Aerial Vehicles (UAVs) is to configure them to serve as aerial base stations (BSs) for collecting data from distributed Internet of Things (IoT) devices in a region devoid of backbone connectivity. To address this challenge, it is required to compute optimized trajectories of UAVs to collect data while considering the different activation patterns of IoT devices. We propose a scheme to optimize the trade-off between the number of covered IoT devices and travel time of UAVs. The formulated cost minimization problem is known as the capacitated single depot vehicle routing problem (CSDVRP), which is NP-hard. We propose a solution scheme, named Cost-Effective Data Aggregation for UAV-Enabled IoT Networks (CEDAN), which operates in four steps. First, it determines the optimized hovering locations (HLs) for UAVs. Subsequently, CEDAN determines the optimized route adopting the Christofides's approximation algorithm for Travelling Salesman Problem (TSP). Further, a split function produces the optimized trajectories for all UAVs. Finally, a route adjustment algorithm applies the cost function and rearranges the order of visiting each HL. Extensive simulation results depict that the CEDAN outperforms than Clarke-Wright (CW) savings heuristics, CEDAN without route adjustment (CWRA), and Zhan et al., respectively.

In this work, we propose Timed Loop Gears (TLG), as a distributed method for enablingfragmented learningin Resource-Constrained networked IoT edge devices. TLG identifies atomic operations (gears), such as feed-forward and back-propagation, necessary for training Machine Learning (ML) models. Each of these gears executes on a Fog Node (FN) exclusively for each data point at a time rather than the whole dataset in its entirety. Additionally, the networked Edge Devices (EDs) offload the training data to the fog layer using the Message Queuing Telemetry Transport (MQTT) protocol such that the participating FNs subscribe to incoming training data and store them based on topics, simplifying data sharing. TLG enables the FN to then transfer the partially learned weights to the next suitable FN for further training. This looping of weights is repeated across FNs until the training is complete. Through extensive analysis, we observe that, compared to existing distributed ML training approaches, for$n$devices, TLG reduces the probability of disruption due to device failure by$n^{2}$times. Implementation results of our fragmented learning method demonstrate that, although TLG negligibly increases the memory consumption of the IoT devices by$0.8\%$, it reduces CPU usage by almost$90\%$. The proposed method proves beneficial for developing and hosting ML models, even on constrained IoT devices, in contrast to existing lightweight ML methods.

This work presents an efficient data-centric client selection approach, named DICE, to enable federated learning (FL) over distributed edge networks. Prior research focused on assessing the computation and communication ability of the client devices for selection in FL. On-device data quality, in terms of data volume and heterogeneity, across these distributed devices is largely overlooked. The obvious outcome is the selection of an improper subset of clients with poor-quallity data, which inevitably results in an inefficient trained model. With an aim to address this problem, in this work, we design DICE which prioritizes the data quality of the client devices in the selection phase, in addition to their computation and communication abilities, to improve the accuracy of FL. Additionally, in DICE, we introduce the assistance of vicinal edge devices to account for the lack of computation or communication abilities in certain devices without violating the privacy-preserving guarantees of FL. Towards this aim, we propose a scheme to decide the optimal edge device, in terms of latency and workload, to be selected as the helper device. The experimental results show that DICE improves convergence speed for a given level of model accuracy. Further, the simulation results show that DICE reduces delay by at least 16%, energy consumption by at least 17%, and packet loss by at least 55% compared to the existing benchmarks while prioritizing the on-device data quality across clients.

In this paper, we propose a Quality of Service (QoS)-aware pricing scheme, termed as Q-Safe, for provisioning safety-related decisions to the end-users. A Safe-aaS platform provides customized decisions to the end-users, as per their requirement. In this proposed pricing scheme, we consider the presence of multiple Safety Service Providers (SSPs) in the Safe-aaS platform. Therefore, the end-users possess the opportunity to select a SSP, depending on the price charged by them. The end-users may compromise with the quality of the decision provided through the selection of the available safety services at a low cost. Considering road transportation as the application scenario of Safe-aaS and to address these above-mentioned issues, we propose a dynamic pricing scheme, Q-Safe. We introduce the concept of varying price to be charged by the SSPs for each of the decision parameters, based on the fluctuation in the value of these parameters with time. Each of the end-users selects certain decision parameters, among the ones displayed in the Web portal. Thereafter, the SSPs suggest decision parameters to the end-users depending upon their present geographical location. To model these interactions between the SSPs and the end-users, we map the scenario with Non-Cooperative Multiple Leader Multiple Follower Stackelberg game.

The deployment of software-defined wireless local area networks (SDWLANs) in hostile regions requires solar energy harvesting-enabled battery-powered access points (APs). The aforementioned APs replenish batteries and forward data flows by avoiding the problem of frequent energy outages. In this work, we propose a scheme calledSeamFlowto place APs in energy harvesting mode without disrupting the forwarding of flows. In contrast to previous works, we focus not only on the energy replenishment of battery, but also, seamless forwarding of flows without violating flow constraints. Specifically, we formulate SeamFlow as a two-phase optimization problem – a) optimal selection of APs and b) finding forwarding paths. As solving both optimization problems are NP-hard, we propose two greedy-heuristic schemes to solve them in polynomial time. Extensive simulation results show that proposed scheme reduces flow forwarding cost by 24% and 16%, per-flow energy consumption by 27% and 21%, and flow violations by 92% and 86% compared to the benchmarks using random and grid topologies, respectively.

In this work, we propose an environment-aware electrical safety assistant using smartphones in pervasive domains like industry, homes, and healthcare. Conventional methods involve using eye shields, gloves, finger guards, and safety toe shoes. We depend on IoT-based solutions and propose Magdroid, an autonomous and standalone smartphone application that detects any electrical anomaly around the user and alerts all the users in the network. Magdroid supports edge computing and provides in-app inferences without any dependency on remote servers. It extracts the in-built magnetometer readings to detect any electrical anomaly in the environment. Since the readings vary with the environments, Magdroid first senses and then uses a cascaded deep learning technique to predict the electrical anomaly around the smartphone. We use two Convolution Neural Network (CNN) architectures and cascade the inference of one with the input of another to generate efficient results for detecting electrical anomalies that are particular to that environment. The first model achieves a test accuracy of 98.97% for the prediction of the environment and the cascaded CNN achieves a test accuracy of 81.88% with 7.82% and 37.27% loss, respectively. Additionally, Magdroid is a low resource-consuming application that utilizes 8% CPU and 126.MB memory of the smartphone.

With the growing number of green Internet of Things (IoT) applications, the underlining network and decision services need more Machine Learning (ML) models at the same time while reducing latency and utilized energy. In this paper, we propose HI-SDN, a Heterogeneous fog Intelligence enabled architecture for complex Software-Defined Network (SDN)-based IoT network running applications such as a smart city and healthcare. HI-SDN proposes a fog node and ML algorithm selection mechanism with dynamic traffic forwarding for green IoT to reduce delay and energy consumption. It adopts a reconfigurable approach to the ML method to dynamically change the required prediction policy and flow rules to be executed in a selected edge/fog node for critical computation. The results from the performance analysis show that HI-SDN has a substantial reduction in delay by 32% and consumed energy by 25%, in comparison to the existing state-of-the-art, besides having an increase in packet delivery ratio.

We propose xDIoT, a paradigm for reliable cross-domain communication across separated IoT network domains over some public network such as the Internet. Depending on use-case scenarios, several individual IoT domains, each consisting of heterogeneous end-devices (sensors and actuators) are deployed across geographical regions. When these domains need to communicate with one another they can use an intermediate public network like the Internet. To this end, a standard paradigm is required for such inter-domain communication over public networks to reduce latencies and prevent inconsistencies, which is absent in current deployments. With xDIoT we address this issue to provide a uniform communication paradigm. In xDIoT, each individual domain has an associated gateway access point (AP) acting as the bridge between the intra-domain IoT network and the external public network. These APs perform Domain Information Exchange through JSON format to gain knowledge about each other and use a generalized packet header structure to encapsulate all data flowing between these APs. Through the use of JSON data exchange and the proposed packet header, the APs can perform seamless inter-domain communication. Implementation and analysis show that xDIoT achieves about 10% improvement in total communication latency with 80% improvement in packet processing time at individual gateway APs.

In this work, we propose – "CartelChain" – for the secure communication of blockchains (BCs) and achieving opti-mal throughput. With the advancement of BC technology, many industries are adopting it to maintain a secure, immutable, and decentralized system. Industries such as IoT, supply chain, and finance apply BC technology to maintain a decentralized database and automate different activities using smart contracts. However, storing data of different scenarios from the Industrial Internet of Things (IIoT) in separate BCs (multi-chains) leads to isolated data islands. This results in difficulty for these multi-chains to interact with one another efficiently and credibly. For the seamless operation of industries, it is of significant importance to achieve interoperability among different BCs. Toward achieving this, we propose a solution that utilizes smart contracts for enabling data exchange among various BCs. Further, for secure and reliable communication, we use encryption and an access control mechanism that makes the same more credible and reduces the latency compared with multi-chains sharing the data sequentially. Through experimental results, we demonstrate that the proposed method can utilize the resources more efficiently and reduce CPU as well energy usage by 8% and 6%, respectively. Apart from this, the throughput of the proposed method is 900 tps at 200 requests.

Due to the lack of unified standards in the Internet of Things (IoT), heterogeneity in terms of protocol and packet types exist. In such a case, accurate traffic identification using port-based and payload-based solutions are not suitable for flow processing in Software-Defined IoT (SD-IoT). In this article, we proposeiAcceSD, an intelligent Access Node (SD-Access) and SDN controller (SD-Controller) collaborated traffic identification mechanism for SD-IoT. Concerning the heterogeneous and unknown traffic flows in IoT, iAcceSD uses machine learning (ML)-based traffic identification mechanisms at the access node. The SD-Controller trains a lightweight ML module for a specific SD-Access node dealing with a similar set of traffic flows. By processing flows at the edge, network latency is reduced in the proposed scheme. An optimization model is developed to program the SD-Access nodes considering the heterogeneous and unknown traffic. Thorough performance analysis of iAcceSD shows improvement in latency by 34% and controller overheads by 23.4%, compared to the existing state of the art, with a simultaneous improvement in energy consumption and packet delivery ratio.

This work sketches a location priority-aware service availability scheme for use in a cellular-enabled multiple unmanned aerial vehicle (UAV) networks for Internet-of-Things (IoT) applications. These UAVs have the ability to sense the location-based data employing onboard heterogeneous sensors and send the sensed IoT data to the base station (BS). Existing works treat all locations equally and are inefficient in terms of assigning UAVs to provide on-demand location-based IoT services to the users. To address this issue, we formulate the objective as a optimization problem to maximize service availability. As the formulated objective is NP-hard, we propose a scheme named priority-aware service availability for multi-UAV networks (PRISM) to obtain the solution. PRISM operates in two steps. First, it offers a UAV assignment algorithm to prioritize locations and assign the UAVs to the high-priority locations proactively. Next, it employs a service assignment algorithm that assigns the requested location-based IoT services to UAVs. The results of simulation and real experiments depict the efficacy of PRISM in terms of availability and delay of the incoming IoT service requests. From the results, we observe that PRISM improves the average service availability by 20.61% and reduces average service delay by 29.4%, compared to the benchmark solutions.

The next-generation cyber–physical systems (CPSs) with heterogeneous applications have diverse Quality-of-Service (QoS) requirements in terms of throughput, end-to-end latency, and packet drop reliability. To meet such diverse QoS requirements, in this article, we propose a QoS-aware traffic forwarding scheme in software-defined CPS. The proposed scheme is presented as a two-stage optimization framework to minimize the associated costs in traffic forwarding. In the first stage, we aim to minimize the required number of “candidate” switches for a given network to minimize network deployment costs. In the second stage, we design a comprehensive cost function considering end-to-end delay, flow-rule utilization, and link utilization in the network. Based on the designed cost function, we formulate another optimization problem for optimal traffic forwarding (OTF). As solving OTF is NP-hard, we propose an efficient greedy-heuristic approach to solve the problem while considering application-specific QoS requirements. Further, we propose a packet-tagging method to assist the controller in mitigating rule congestion at the software-defined networking devices, and hence improve the overall network performance. Extensive results show that the proposed scheme minimizes the network delay and QoS-violated flows by up to 50% and 90%, respectively, compared to the state-of-the-art schemes.

The recent advances in Internet of Things (IoT)-based healthcare systems pave the path for the development of body-to-body network (BBN), wherein a group of wireless body area network (WBAN) users collaborates and shares their individual resources. Since these WBAN users have individual decision-making capabilities and are self-centric in nature, they always aim to maximize their own performance while expecting benefits through resource sharing. In this article, we analyze the interaction among participating WBANs in BBN and develop joint data uploading and relaying strategy. In BBN, each WBAN not only utilizes its resources (uplink capacity and battery energy) to upload physiological data but also trades resources with other participating WBANs. Specifically, WBAN users with unused resources trade with other users deprived of Internet connection and low battery for mutual gain. Therefore, we model this interaction as an$N $-person bargaining game and design an efficient incentive mechanism to facilitate user cooperation. The proposed mechanism ensures efficient resource sharing and fair division of mutual benefit among the participating WBAN users. Also, we propose a distributed algorithm for the practical implementation of the proposed mechanism in decentralized BBN. The simulation results demonstrate that the proposed mechanism always improves WBAN user’s individual performance together with the overall BBN performance. Furthermore, the overall performance increases with an increase in participating WBAN users’ resource heterogeneity.

The body-to-body network (BBN), which enables a group of wireless body area network (WBAN) users to collaborate and share their individual network resources, has emerged as a promised technology for the Internet of Things (IoT)-based healthcare system. In BBN, WBAN users with good Internet connectivity act as gateway users and help their nearby WBAN users with poor Internet connectivity to upload their physiological data in exchange for incentives. The WBAN users are heterogeneous in terms of their data priority, which depends on the criticality of medical data and require varying uplink transmission rates for uploading. Designing an incentive mechanism for such a scenario is very challenging because the data priority is a private information to the WBAN user. In this work, we propose an incentive scheme based on contract theory, to model the economic interaction between the gateway and requesting WBAN users and ensure priority-aware data uploading in BBN. First, the requesting WBAN users are categorized into different types based on their data priority. Thereafter, we formulate a contract design problem to maximize the payoff of gateway WBAN user while satisfying the requirements of requesting users. The gateway WBAN user offers a contract to requesting users and each requesting user selects it based on its type. Finally, the simulation results demonstrate that the proposed mechanism improves the payoffs of both the gateway and requesting WBAN users.

In this work, we propose two lightweight access point (AP) localization and selection schemes, AP-Cov and improved AP-Cov (IAP-Cov), in IEEE 802.11-enabled IoT networks for reducing handover delay and maintaining uninterrupted connectivity during movement. The proposed scheme AP-Cov consists of two phases—the localization of APs anda prioriselection of APs in the direction of mobility. In the localization phase, an IoT device exploits the features of neighbor report request (NRR) and beacon report request (BRR) of IEEE 802.11k to get the nearby APs’ information along with their location and uses simple algebra to compute the location of next APs beforehand, instead of using heavyweight processing in resource-constrained IoT networks. In the AP selection phase, a linear optimization model is formulated to select the optimal AP for subsequent association. In IAP-Cov, a broadcast sequence of neighbor IoT is proposed for reducing the number of broadcasts. We implement the schemes in NS-3 and compare performance with the baseline standard, which shows that AP-Cov detects the availability of APs with 98.625% and 96.5% accuracy, improves accuracy over message complexity by 23% and 78% and reduces handover by 36.37% and 67.52% in constant-velocity and random-velocity mobility models, respectively. The evaluation of computational complexity shows that the proposed schemes are lightweight too. IAP-Cov reduces broadcasts and delay by 49%, 77.36% and 6%, 25% compared to AP-Cov using the constant-velocity and random-velocity mobility models, respectively.

In this work, we present a solution toward facilitating dynamic encryption schemes—Constrained Encryption as a Service (CEaaS)—in fog-enabled IoT environments. CEaaS is a two-level fuzzy inference system (FIS) in the fog layer which offers customized encryption algorithm decisions to the IoT user devices based on the current configuration, data size, and network state. Fog nodes use the two-tier FIS system to determine the category of the requesting IoT device at the first level and then the encryption scheme at the second level. Existing research on encryption focuses on developing new lightweight algorithms as a global solution for all devices without considering the heterogeneity and corresponding communication links. The device and network configurations collectively add operational delays, which elevates time and security threats. Under such circumstances, a solution that considers both the conditions (varying) for determining the appropriate encryption scheme and the key is important. Through extensive implementation and deployment of heterogeneous fog nodes, we observe that CEaaS is feasible for both powerful and resource-constrained IoT user devices with CPU and memory usage as low as 0.24% and 0.9%, respectively. CEaaS also incurs delays in the range of 0.7 s and energy consumption of 0.07 Joules while securing data transmission. With the feasibility of CEaaS, the dynamic encryption schemes ensure secure communications irrespective of the device types in a fog-enabled IoT environment.

Underwater Internet of Things (IoT) faces challenges, such as low data rate, high node mobility, high error probability, and propagation delay. Furthermore, underwater multimedia communication is more challenging, as it requires a high data rate and reliable delivery. The next-generation networking paradigm—software-defined network (SDN)—improves flexibility and virtualization through programmability. SDN increases resource utilization, simplifies management, and reduces operating costs. This article proposes ProStream, a reconfigurable SDN-based underwater multihop network for improved topology management, association control, and multimedia transmission. The proposed scheme considers a network with multiple access points (APs) and mobile relays and stations. The SDN controller places flow rules in the relays, and APs are placed as per the location of the nodes. Moreover, we present a programmable station for reducing Tx/Rx complexity in underwater networks. The stations or relays are triggered through programmability to notify their sleep time and current relays and AP for association and data transmission. We evaluate the proposed scheme first through simulation-based experimentation. We also develop a small-scale real testbed prototype of the proposed SDN. The performance results show significant improvement of performance in terms of latency, throughput, and packet delivery ratio.

This article introduces the concept of context-aware attribute learning with cipher policy-attribute-based encryption (CP-ABE) to preserve the privacy of users’ information in IoT-enabled Society 5.0. The concept of Society 5.0 pioneers an abstract system unifying different smart environments (SEs) to provide seamless services to the citizens. While serving different applications, these SEs store users’ information in the cloud engendering users’ privacy. CP-ABE is one of the conventional security systems that preserves privacy with group data accessibility. Contemporary CP-ABE solutions enforce users to manually provide their contextual information, namely, attributes, to encrypt/decrypt data. From these solutions it can be conjectured that incorrect attribute selection by a user raises the issue of unauthenticated access to information. To address these issues, we propose a scheme, named the context-aware attribute learning scheme (CASE), which autonomously learns users’ contextual information, exploiting edge intelligence, generates attributes, and reduces the post-encryption data size using the learned attributes. We examine the performance of CASE with the help of a case study on CP-ABE over smart healthcare systems (SHSs). Extensive experimental results show that CASE outperforms the existing CP-ABE-based security schemes by reducing 32%–33% average network delay, 33%–35% average energy consumption, and 31%–36% average packet loss. Additionally, we analyze the performance of attribute learning schemes using the support vector machine (SVM), decision tree (DT), and naive Bayes (NB) learning models. We observe that DT reports better performance over SVM and NB in prediction accuracy, prediction time, and clock cycles required for execution.

Robust and reliable communication systems, whether based on electromagnetic (EM) waves or light, fail to perform under water due to very high attenuation and changing visibility conditions. The present generation of systems designed for underwater communications relies mostly on acoustic waves, typically in the ultrasonic frequency range. In this work, we develop and evaluate a means of implementing underwater acoustic channel-based severely constrained IoT networks using low-cost, off-the-shelf, open hardware electronics and transducers, which can support direct communication between two nodes at a data rate of 2.4 kbps for over 65 m. These nodes can be deployed over much longer distances through multihop relay topologies. Furthermore, we evaluate the efficacy of our system toward supporting multimedia data transmission and even attempt multimedia streaming through our deployed underwater IoT network using video compression and reduced sampling of the video frames. We observe that the system successfully supports multihop network topologies and undertakes multimedia transmission by compromising the quality of the data. The system has a clear tradeoff between data quality, transmission range, and transmission delays.

In this work, we propose a dynamic decision query mapping mechanism, DQ-Map, for provisioning Safety-as-a-Service (Safe-aaS) (Royet al., 2018). A Safe-aaS infrastructure provides customized safety-related decisions simultaneously to multiple end-users. We consider road transportation as the application scenario of Safe-aaS and termed the safety-related decision to be delivered to the end-users as decision queries (DQs). These DQs are generated according to the decision parameters selected by the end-users. The primary aim of our proposed work is to reduce the total number of sensor nodes required to generate safety-related decisions, which minimizes both energy and time consumption. Further, the requested DQs are processed and a decision is generated in three different stages. First, the DQs are categorized asemergency decision query(EDQ) andnonemergency decision query(NEDQ), depending upon the type of vehicle from where the end-users have requested safety services. The EDQs and NEDQs are mapped with the stored decisions present in the database of the decision virtualization layer during the second level. In case of mismatch with the stored decisions in the database, EDQs are directly executed from the sensor nodes deployed at a particular geographical location or into the vehicles, in the device layer of the Safe-aaS infrastructure. In the third level, the similarity score of NEDQs, which do not match with the parameters of the stored decisions, is computed. Based on the number of similar decision parameters present in them, the similarity score is computed. Extensive simulation results of the proposed scheme, DQ-Map, depict that the amount of energy consumed and time required to generate a decision is reduced by 55.16% and 54.55%, respectively, compared to the traditional Safe-aaS architecture.

In this article, we propose a software-defined fog architecture, named as Soft-Health, to serve various Internet-of-Things (IoT)-based healthcare applications. The health conditions of the patients fluctuate over time. Further, specialized medical care may not always be available in all healthcare facilities. The use of wireless body area network (WBAN) for continuous patient monitoring addresses the issue to a certain extent. However, as the physiological parameters of a patient are time-critical in nature, any delay, packet loss, and network overhead, may result in deterioration of the patient’s health conditions. Considering this, we design a Software-defined fog-enabled IoT platform for various healthcare applications. We consider that the fog layer comprises SDN switches that allocate the packet to the appropriate fog/cloud depending upon the criticality index (CI) of the data packets originating from patients. We mathematically formulate the CI, based on the physiological parameters sensed and transmitted to the switches. Further, we design an optimization function to obtain the maximum utility of a fog node, for an optimal number of processes executed by that node. We apply the Lagrangian method to simplify the optimization function and solve it using Karush–Kuhn–Tucker (KKT) conditions. We apply the auto-regression model to predict the total delay incurred and the total energy consumed by the proposed scheme. Exhaustive analysis of our proposed scheme, Soft-Health, demonstrates that the delay incurred decreases by 24.57% and 40.1% approximately, compared to the existing schemes, Mobi-Flow, and CARE, respectively.

Software-defined IoT (SDIoT) is a promising approach to address the requirements of the Internet of Things (IoT), such as network management, Quality of Service (QoS), and resource utilization. The advantages of SDIoT are facilitated by the separation of the data- and the control-planes usingflow-rules, that allow fine-grained control over individual flows. However, the number of flow-rules that can be placed at the switches is limited, leading to scalability issues in SDIoT. Existing approaches to flow-rule management either do not consider the impact on QoS or are applicable only to a particular topology. In this article, we propose a QoS-aware flow-rule aggregation scheme for generic network topologies, which aims to achieve a satisfactory tradeoff among flow-rule compression and its impact on the QoS of IoT traffic flows. Specifically, the proposed scheme adaptively aggregates flow-rules while considering different QoS requirements of IoT applications in the network, and the flow-rule capacity of the switches. The proposed scheme consists of the following components—1) a path selection heuristic to increase the total number of flow-rules that can be accommodated in the network and 2) a multiarm bandit-based flow-rule aggregation scheme capable of reducing the number of flow-rules, while maintaining adequate performance in terms of QoS. Experimental results using IoT traffic show that, on average, the proposed scheme is capable of reducing the average end-to-end delay and QoS-violated flows in the network by 22% and 30%, respectively, compared to the state-of-the-art schemes.

In this article, we propose a dynamic-consensus-based blockchain system—A-Blocks—for efficiently managing the data produced by the sensors in an Industrial Internet of Things (IIoT) environment. Typically, industries deal with a heterogeneous set of data from a diverse range of sensors. Conventional blockchain adoptions are a popular choice in such scenarios for data security while satisfying both transparency and immutability. However, stringent consensus algorithms are inadequate for managing heterogeneous data, especially due to its implicit constraints. For instance, while PoW provides inevitable security and is highly distributive, it is not scalable and requires more energy. In contrast, PoS is energy efficient but has reduced scalability and PBFT is suitable for faster processing. A-Blocks exploits the features of the available consensus algorithms and dynamically selects the best one in real time. It operates in two phases: 1) categorizing the data into groups based on their traits and then 2) selecting the appropriate consensus algorithm. Extensive experimental results using open industrial data sets demonstrate the effectiveness of A-Blocks with 8% CPU and 78% memory consumptions on resource-constrained devices. Furthermore, compared to the existing methods, although A-Blocks increases energy consumption by 11%, it also reduces mining time by 7%.

The COVID-19 pandemic has resulted in one of the major challenges for humanity in the 21st century. The impact of these challenges has led to a tremendous loss of life, impact on long-term health, well-being as well as personal psychology, and negative societal changes and not to mention its impact on the global economy. Since this is a health issue, similar to other forms of diseases and pandemics, society has largely relied on the fields of medical, virology, immunology, biotechnology, and pharmaceutical science to develop novel therapeutic solutions for treatments. This has resulted in vaccines that have been rolled out to elevate immunity levels that will hopefully allow the majority of the population to reach herd immunity. However, given the technological advancements that we have reached in the 21st century, questions have also risen as to how other disciplines can play a role in solving and obtaining new knowledge of communicable disease pandemics.

We propose the adaptation of vibration-based condition monitoring systems and techniques, popularly used in industrial condition-based maintenance, for identifying the possibility of compromising the privacy of personal computing systems. This work exploits the automated fan-based heat dissipation features and read/write operations of disk-based storage, commonly present in personal computers, to read computing task-specific vibration signatures on the computer’s cabinet/case. These vibration signatures are then used to identify the broad classes of tasks being executed on a separate computer without ever needing to log into the monitored machine. This work builds upon the premise that heterogeneous tasks have distinct computing requirements, which translates to variations in the amount of heat generated by the computer’s processor, eventually leading to variations in the computer’s heat control fan speed. The variations in the fan’s speed and the frequency of read/write operations to disk-based storage create unique vibration signatures, which maps uniquely to the computer’s processing operations, leading to a breach of privacy of the computer. Our work’s preliminary results suggest that computer-based tasks can be mapped from their vibration signatures with an accuracy of at least$70\%$. We additionally study the task identification granularity of such an approach.

In this work, the problem of high-quality energy service provisioning in the presence of competitive prosumers and micro-grids in cloud-enabled smart grid is studied. Oligopolistic prosumers behave non-cooperatively and store the excess generated energy for future use, which increases the load on the main grid and degrades the performance of the smart grid. To address this issue, we propose a dynamic cloud-based pricing scheme, named SmartPrice, to enforce cooperation among the prosumers for ensuring high quality of service provided by the micro-grids. In SmartPrice, using cloud infrastructure, each micro-grid calculates a reward factor for each prosumer based on his/her behavior to enforce cooperation among them. We model the interaction between each micro-grid and the prosumers using a single-leader-multiple-followers Stackelberg game, where the micro-grids and the prosumers act as the leaders and the followers, respectively. Each micro-grid determines the unit energy price to be charged/paid and each prosumer determines the quantity of excess energy to be supplied for ensuring high revenue. Thus, SmartPrice enforces cooperation among the micro-grids and prosumers. Additionally, using SmartPrice, the price for unit energy charged from the prosumers reduces by 23.37-$35.63\%$, thereby ensuring high revenue and the number of prosumers served by the micro-grids increases by 38.19-$53.14\%$.

The presence of dumb nodes in sensor-cloud environment leads to degraded system performance. In this article, we consider the presence of dumb nodes in the sensor-cloud platform, and thereafter, propose a dynamic pricing scheme, while considering the existence of such nodes in the networks. The existing literature addresses the problem of pricing in sensor-cloud with the assumption of an ideal environment with normally functioning sensor nodes. The proposed pricing model considers the realistic existence of dumb nodes in sensor-cloud platforms. Further, the dumb behavior of a sensor node is dynamic in nature, as it is dependent on environmental conditions such as the occurrence of heavy rainfall, high temperature, and the presence of fog. However, in the absence of such adverse environmental conditions, the erstwhile dumb nodes resume normal behavior. The permanent removal of a dumb node from sensor-cloud is not always a feasible solution. When a dumb node is assigned to a virtual sensor, the existing pricing scheme in sensor-cloud charges same as other normal nodes. Thus, in such a situation, a user pays the normal price for a dumb node to the Sensor-Cloud Service Provider (SCSP). Consequently, the sensor owner of dumb node earn same profit as the owner of a normal node. Therefore, we formulate a scheme forDynamic pricing in sensor-cloud environment in the presence of dumb nodes(DISCLOUD). As the presence of dumb nodes in sensor-cloud affects the Quality of Service (QoS), we propose a scheme considering QoS of the sensor-cloud. The proposed scheme, DISCLOUD, enables profit maximization of the SCSP, while considering the price required to be paid by end-user based on QoS.

This article proposes an optimal pricing scheme for provisioning sensors-as-a-service (Se-aaS) for catering to applications with multi-tenancy requirements in a sensor-cloud platform. The scheme orchestrates a trade-off analysis between communication range and price in a sensor-cloud platform with range-reconfigurable nodes. The proposed scheme consists of two phases – (a) selection of a neighbor node of a source node, and determination of optimal price for the selected neighbor node. In the first phase, a source node adjusts its communication range and selects its best possible neighbor node usingselectivity factorof all the neighbor nodes. The selectivity factor considers the determinants such as effective residual energy, effective power consumption, and the number of applications to which the neighbor nodes are associated in the neighbor selection process. In the second phase, we design a utility function to determine the optimal price of the selected neighbor node. We use theLagrangianfunction to model the proposed problem as a mixed-integer linear program (MILP) and obtain the optimal solution using the Karush-Kuhn-Tucker (KKT) conditions. The existing works on pricing in sensor-cloud are deficient in considering the presence of the reconfigurable communication range of sensor nodes. Moreover, based on the value of the communication range, the charged price of the sensor nodes varies. Thus, in this article, we propose a pricing scheme with a trade-off of the reconfigurable communication range of sensor nodes and the charged price incurred in adjusting the communication range. Extensive experimental results show that the proposed scheme performs better compared to the existing pricing schemes for sensor-cloud. In precise, the proposed scheme is capable of increasing the average number of neighbor nodes by at least 1.38 percent. Further, the proposed scheme is capable of reducing the charged price by 10.55 percent, as compared to the existing pricing scheme, DOPH.

In this paper, we study a body-to-body network (BBN) framework, which enables wireless body area network (WBAN) users located in close proximity to cooperate and share their network resources to improve the overall network performance. Our main aim is to design a distributed resource allocation mechanism that encourages each participating WBAN user to participate and upload each other’s data collaboratively. We propose an auction-based mechanism that optimizes data uploading for all participating users and the corresponding reimbursement. In the proposed auction mechanism, each user acts as both auctioneer and bidder. Fist, the auctioneer initiates the auction by announcing the amount of resource it wants to share and its price and each bidder submits their bid to each auctioneer based on its demand. We further propose a distributed algorithm for the auction mechanism that jointly solves both the auctioneers’ and the bidders’ optimization problems and determines the optimal amount of resource users should reserve for their own and the portion they should share. Our theoretical analysis demonstrates that the proposed distributed algorithm converges to the solution that maximizes the aggregated benefit of the users. Finally, the simulation results exhibit that the proposed algorithm always improves WBAN user’s individual performance together with overall BBN performance.

In this article, we propose SEGA, a secured edge gateway microservices architecture for industrial Internet of things-based monitoring of machines in industries. SEGA allows the secured collection, transmission, and temporary storage of data within the edge network. A k-nearest neighbors-based analytics module hosted on the edge gateway processes time-sensitive machine monitoring data on the gateway itself and identifies machines’ operational status. The system predicts the machine state and displays the monitored parameters such as current consumed, power factor, power consumption, and vibrational state of machinery. SEGA also enables secured offloading of data and advanced analytical functions from the edge gateway to the cloud. SEGA's deployment results show negligible changes in the edge gateway's performance due to the inclusion of various security and encryption mechanisms. However, the resource-constrained edge sensor nodes show an increase in wireless packet transmission latencies between them and the gateway by approximately 84.12 ms.

The resource constrained nature of IoT devices set about task offloading over the Internet for robust processing. However, this increases the Turnaround Time (TAT) of the IoT services. High TATs may cause catastrophe in time-sensitive environments such as chemical and steel industries, vehicular networks, healthcare, and others. Moreover, the unreliable Internet in rural parts of underdeveloped and developing countries is unsuitable for time-critical IoT systems. In this work, we propose a framework for continuous delivery of IoT services to address the issue of high latency/TAT with poor/no-internet coverage. The proposed framework guarantees service delivery in such areas. To demonstrate the proposed framework, we implemented an IoT-based mobile patient monitoring system. It predicts the patient's criticality using actual sensor data. When the sensed parameters exceed the pre-set threshold in the rule-base, it initiates data transfer to the fog or cloud server. If fog or the cloud is unreachable, it performs onboard predictions. Thus, the framework ensures essential service delivery to the user at all times. Our test-bed-based evaluation demonstrates edge CPU and RAM load reduction of 16% and 26%, respectively, in the ML model's test phase. Also, the results confirm continuous service delivery, reduced latency, power and computing resource consumption.

In this paper, we investigate the dependency-aware trustworthy task offloading problem (DeTTO), especially in an IoT-enabled vehicular network, where a large computation-intensive task offloaded from a vehicle is fragmented into multiple subtasks and then offloaded to multiple trusted nodes. First, we formulate the task offloading problem as a graph optimization problem intending to find an optimal set of trustworthy nodes for offloading the subtasks. We aim to minimize the task completion delay and energy consumption, while satisfying the dependency relations between the subtasks and the trust requirements of the tasks. We consider three types of dependency structures – fully independent task, fully dependent task, and partially dependent task. For a fully independent task with no dependency between the subtasks, we propose a greedy algorithm to get the optimal set of nodes for task offloading. After showing the NP-hardness of solving the dependent task offloading problem, we propose a two-fold efficient heuristic approach for the tasks with all dependent subtasks. We adopt the solution approaches used by the first two types of tasks for a partially dependent task. Through simulation experiments, we analyze the performance of the proposed algorithms for three types of intra-task dependencies. The experimental results show that the proposed algorithms significantly reduce the delay and energy consumption, when compared to the benchmark schemes.

In this paper, we propose a microservices and deep learning-based scheme, termed as Micro-Safe, for provisioning Safety-as-a-Service (Safe-aaS) in a 6G environment. A Safe-aaS infrastructure provides customized safety-related decisions dynamically to the registered end-users. As the decisions are time-sensitive in nature, the generation of these decisions should incur minimum latency and high accuracy. Further, scalability and extension of the coverage of the entire Safe-aaS platform are also necessary. Considering road transportation as the application scenario, we propose Safe-aaS, which is a microservices- and deep learning-based platform for provisioning ultra-low latency safety services to the end-users in a 6G scenario. We design the proposed solution in two stages. In the first stage, we develop the microservices-enabled application layer to improve the scalability and adaptability of the traditional Safe-aaS platform. Moreover, we apply the state space model to represent the decision parameters requested and the decision delivered to the end-users. During the second stage, we use deep learning models to improve the accuracy in the decisions delivered to the end-users. Additionally, we apply an assortment of activation functions to analyze and compare the accuracy of the decisions generated in the proposed scheme. Extensive simulation of our proposed scheme, Micro-Safe, demonstrates that latency is improved by 26.1 – 31.2%, energy consumption is reduced by 22.1 – 29.9%, throughput is increased by 26.1 – 31.7%, compared to the existing schemes.

In this paper, we present FedServ, a federated task service system for fog-enabled Internet of vehicles (IoV), using which we aim to minimize the delay of vehicle task service. FedServ considers different task service requirements such as delay, computation-intensiveness, processing units required, and energy consumption. The existing literature mainly focuses on the delay requirement of tasks and considers the energy consumption for task processing, whereas FedServ aims to achieve the Quality of Service (QoS) of the task while optimizing different resource requirements for task service. FedServ provisions the federated task service by fragmenting the tasks, which minimizes the task complexity while preserving task fragment dependency. To determine the suitable fog nodes for task fragment service, we formulate the problem as a coalition graph game. Analytical results depict that the proposed scheme minimizes the delay of the task service and the energy consumption while reducing the number of QoS violated tasks by 34.32%, 25.32%, and 22.78% compared to the existing schemes.

In this article, we propose a pricing scheme, named PRIME, for provisioningmobile Sensors-as-a-Service(mSe-aaS) in the mobile sensor-cloud (MSC) architecture, with an aim to optimally distribute the financial profit among different actors of MSC. Unlike traditional sensor-cloud, MSC introduces a new actor as device owner, whose mobile device hosts the physical sensor nodes. On the other hand, the device and sensor owners earn certain revenues, based on the usage of the sensor nodes and the mobile devices, for provisioning mSe-aaS to the end-users. MSC is a contemporary architecture, and therefore, no pricing scheme exists for it. In this work, we consider the presence of the device owner, sensor owner, Sensor-Cloud Service Provider (SCSP), and end-user to determine an optimal pricing strategy. In order to design such a strategy, we use theLagrangian multipliermethod and applyKarush-Kuhn-Tucker(KKT) conditions. On the other hand, an end-user has multiple options to select an SCSP among the available ones. Therefore, based on the reputation of all the available SCSPs, PRIME enables an end-user to select a suitable one. Extensive experimental results report that PRIME increases the profit of sensor and device owners by 25.67% and 29.12%, respectively. We also compare PRIME with an existing pricing scheme for traditional sensor-cloud architecture. We notice that the service return using PRIME increases by 55.31% as compared to the same using the traditional sensor-cloud architecture.

High propagation delay, high error probability, floating node mobility, and low data rates are the key challenges for Underwater Wireless Multimedia Sensor Networks (UMWSNs). In this article, we propose RL-MAC, a Reinforcement Learning (RL)–based Medium Access Control (MAC) protocol for multimedia sensing in an Underwater Acoustic Network (UAN) environment. The proposed scheme uses Transmission Opportunity (TXOP) for relay nodes in a multi-hop network for improved efficiency concerning the mobility of the relays and sensor nodes. The access point (AP) and relay nodes calculate traffic demands from the initial contention of the sensor nodes. Our solution uses Q-learning to enhance the contention mechanism at the initial phase of multimedia transmission. Based on the traffic demands, RL-MAC allocates TXOP duration for the uplink multimedia reception. Further, the Structural Similarity Index Measure (SSIM) and compression techniques are used for calculating the image quality at the receiver end and reducing the image at the destination, respectively. We implement a prototype of the proposed scheme over an off-the-shelf, low-cost hardware setup. Moreover, extensive simulation over NS-3 shows a significant packet delivery ratio and throughput compared with the existing state-of-the-art.

IoT deployments that have limited memories lack sustained computation power and have limited connectivity to the Internet due to intermittent last-mile connectivity, particularly in rural and remote locations. For maintaining congestion-free operations, most of the collected data from these networks are discarded, instead of being transmitted remotely for further processing. In this article, we propose the paradigm Timed Loop Storage to distribute the data and use the underutilized bandwidth of local network links for sequentially queuing packets of computational data that are being operated on in parts in one of the IoT nodes. While the sequenced packets are executed sequentially on the target IoT device, the remaining packets, which are currently not being operated on, distribute and keep looping over the network links until they are required for processing. A time-synchronized packet deflection mechanism on each node handles data transfer and looping of individual packets. In our implementation, although we observe that the proposed approach requires data rates of 6 Mbps, it incurs only 45 Kb usage of primary storage systems even for sizeable data, ensuring scalability of the connected IoT devices' temporary storage capabilities, thereby making it useful for real-life applications.

In this article, we study the problem of quality of service (QoS)-aware cost management of sensor-cloud comprising multiple sensor-cloud service providers (SCSPs) and sensor-owners. The rapid adaptation of the wireless sensor network (WSN) and Internet-of-Things (IoT) technology led to the conceptualization of the sensor-cloud infrastructure which primarily aims to reduce the complexities associated with operating WSN-based applications by rendering Sensors-as-a-Service (Se-aaS). However, the oligopolistic market scenario of sensor-cloud involving multiple SCSPs and sensor-owners significantly impacts its profitability and QoS. Thus, there is a need to explore the dynamics of this market competition elaborately in order to maintain the usability of sensor-cloud. The existing works fail to address the aforementioned issues in sensor-cloud. Hence, in this work, we analyze the interactions among the sensor-owners and the SCSPs using a game-theoretic approach. We propose a QoS-aware dynamic cost management scheme, named QUEST, to determine the optimal strategies of the various actors in sensor-cloud market. Through simulations, we observe that, using QUEST, the price paid by the end-users decreases by 10.31-20.43 percent and the revenue of sensor-owners improves by 66.83-89.94 percent. Moreover, QUEST ensures the service satisfaction of the end-users while optimally distributing the services among the SCSPs and the sensor-owners.

In this paper, the problem of ensuring profitability for multiple sensor-owners in sensor-cloud, while satisfying the service requirements of end-users, is studied. In traditional sensor-cloud, Sensor-Cloud Service Provider (SCSP) solely dictates the service provisioning process. However, the SCSP cannot always ensure high profits for sensor-owners, who incur significant maintenance costs for their sensor-nodes. Contrarily, it is highly essential to meet the Quality-of-Information (QoI) requirements of end-users to ensure their service satisfaction. Existing works proposed a few node allocation schemes which neither consider the cost incurred by sensor-owners nor the QoI of sensed-data in sensor-cloud. To address this problem, a strategic resource allocation scheme, named RACE, is proposed, which introduces the participation of sensor-owners in the node allocation process. First, utility theory is used to calculate the optimum number of nodes to be allocated for a service. Thereafter, single leader multiple followers Stackelberg game is formulated to decide the number of nodes to be contributed by each sensor-owner and the price to be charged. Simulation-based experimental results reveal that, using RACE, the profits of the sensor-owners and those of the SCSP increase by 86.11–89.26 percent and 41.95–80.82 percent, respectively, as compared to the existing benchmark schemes, while considering that each sensor-node is capable of serving multiple applications simultaneously. Moreover, service availability in sensor-cloud increases by 31.70–96.96 percent using RACE.

In this article, we introduce anenergy-efficient decision deliverymechanism,Safe-Serv, in the Safety-as-a-Service (Safe-aaS) infrastructure for the road transportation industry. A Safe-aaS architecture provides safety-related customized dynamic decisions to the registered end-users. Moreover, the concept ofdecision virtualizationenables to deliver the same decision to multiple end-users at the same time. The sensor nodes sense and transmit the data to the edge node/cloud, which is further processed to generate a decision. As the sensor nodes are energy-constrained in nature, energy efficiency is one of the important parameters to be considered for Safe-aaS infrastructure. Safe-Serv reduces energy consumption through the elimination of redundant data transmission from the sensor node to the edge node or cloud. We use the cooperative Nash bargaining approach among different homogeneous sensor nodes, which bargain among themselves to transmit data to the edge node/cloud. Based on the total dissipated energy, effective proportional distance, duty factor, nodal delay, and cost-efficient state, the appropriate sensor node is chosen. Thus, the selected sensor node transmits data to the edge layer or cloud. We incorporate the cost of data transmitted by the sensor node, which leads to cost-effective utilization of the resources. Through extensive simulation, we observe that the energy dissipated by the sensor nodes using the proposed scheme, Safe-Serv, is reduced by 85 and 78 percent approximately compared to the existing schemes – SASPENCE and manoeuvre-based trajectory planning – respectively.

To meet the demands of new healthcare applications with high computation complexity, multi-access edge computing (MEC) is emerging as a key component of modern healthcare systems which provides rich computing services to the users. With the increase in the number of wireless body area network (WBAN) users requesting computing services, the computational load on the MEC servers increases. A major issue related to the operation of these MEC servers is their sustainability in terms of energy consumption and heavy carbon emission. Therefore, in this work, we propose a resource management scheme, which minimizes the energy consumption of the MEC server without compromising on the quality-of-experience (QoE) of the WBAN users. For that, we propose a cooperative framework between the MEC server and WBAN users, where the MEC server motivates WBAN users to opt for partial offloading instead of full offloading of the computing services. More specifically, the MEC server bargains with each WBAN user for the amount of the task it compute locally and the corresponding reimbursement. We model this economic interaction between the MEC server and all the participating WBAN users using the Nash bargaining theory. Thereafter, we derive the closed-form Nash bargaining solutions (NBS) for two different bargaining protocols. Finally, numerical results show the proposed bargaining scheme is capable of improving the MEC server payoff by$ 44.3\%$,$ 51.4\%$, and$ 56.1\%$, respectively, compared to the state-of-the art schemes.

In this paper, we present a traffic engineering scheme for sustainable hybrid Software-Defined Networks (SDN) to reduce the overall energy consumption of the network and increase the amount of programmable traffic. Hybrid SDN involves both legacy and SDN switches because of the migration from a legacy network to an SDN. The primary reason for this migration is to increase the amount of programmable traffic and add flexibility to network management operations such as network monitoring, load distribution, and energy management. The energy management solutions in SDN include dynamic activation or deactivation of network elements and traffic rerouting. However, there exists a trade-off between energy-aware routing and programmable traffic, as unplanned traffic rerouting may transform programmable traffic into a non-programmable one. In this paper, we propose a scheme for dynamic activation of SDN links and optimal route selection of existing flows. In contrast to the previous works, we focus on reducing energy consumption, while maximizing the programmable traffic, as it serves the primary intent of transforming a legacy network into an SDN. The simulation results show that the proposed scheme, ETHoS, increases the energy savings by$28.91\%$compared to SENEtoR, an existing scheme.

In this paper, the problem of ensuring reliable energy distribution in smart grid is studied, while considering that each customer is connected with multiple micro-grids. In the traditional smart grid, each customer is connected with a single micro-grid. Additionally, in the existing literature, some researchers proposed energy distribution schemes considering the presence of multiple micro-grids. However, none of these existing schemes consider that the customers can consume energy from multiple micro-grids simultaneously, which can essentially enhance the quality of service (QoS) in energy distribution, as it aids in reducing the transmission loss and increasing the profit of the micro-grids, while the customers pay less. To address the aforementioned problem, we design a sustainable energy distribution scheme, named SEED, to decide the distributed energy request vector, while ensuring high QoS in terms of energy availability and the price charged by the micro-grids in smart grid. We use an evolutionary game to ensure that the energy load is optimally distributed among the micro-grids and each micro-grid gets an equal opportunity to earn a profit. Through simulation, we observe that using SEED, renewable energy consumption per customer improves by 14.05 percent while reducing the cost by 29.87 percent. In other words, SEED ensures a sustainable environment by reducing the CO$_2$2emission by 14.05 percent, while reducing non-renewable energy consumption from the main grid. Additionally, the profit of each micro-grid increases by 58.32 percent.

Remarkable prevalence of in-car entertainment systems empowers vehicular users to download multimedia-enabled contents in transit and creates a new business opportunity for content providers (CPs). However, the timely delivery of requested content is a major concern for CPs to improve the quality of service (QoS) of their subscribe users. Roadside unit (RSU)-based caching appears as a promising solution for CPs wherein CPs proactively store their content at the RSU to reduce the content delivery time. Since the RSUs are enabled with limited storage capacity, the competition among multiple CPs for storage space is unavoidable. Further, the CPs are connected with RSUs using capacity limited backhaul links. Hence the allocation of RSUs’ storage among CPs becomes a fundamental issue in RSU-based caching networks. In this paper, we design a market scenario in which the set of CPs competes for the storage space of RSUs. In the unavailability of utility and cost functions of the CPs and the RSUs, we introduce a market maker to manage the marketplace. Further, we employ iteration-based double-sided auction mechanism to compute the optimal storage allocation and corresponding payment transfer for CPs which maximizes the social welfare of the networks. The simulation results demonstrate the proposed auction mechanism improves the social welfare of the network by at least 29.3% compared to the benchmark schemes. Further, with the help of both analytical and numerical analysis, we show that the proposed auction mechanism also holds vital economical properties.

This paper introduces a scheme for autonomous service exchange among multiple sensor-cloud service providers (SCSPs) in a sensor-cloud (SC) platform for Internet of Things (IoT) applications. Typically, SC offers Sensors-as-a-Service (SeaaS) using the concept of sensor virtualization for serving different IoT applications seamlessly in real-time. On the other hand, an SC platform reduces the tasks of sensor deployment and management on the user by employing SCSP. In an SC platform, single SCSP may be incapable of serving an entire IoT application requested by an end-user due to the lack of sufficient sensor nodes (SNs) present in the region of interested of an application. However, the presence of multiple SCSPs in an SC platform plays a complementary role in serving an IoT application entirely by implementing the idea of service exchange among them. The proposed scheme, ServEx, enables service exchange among multiple SCSPs in an SC platform. In ServEx, we apply a 2-phase approach. In the first phase, we introduce the use of a data structure to store the service profile of end-users and SCSPs. On the other hand, in the second phase, we design a profile matching mechanism for enabling a SCSP to find desired SNs registered to other SCSPs. Through extensive experiments, we observe that ServEx reduces the average delay by 57% and the average energy consumption by 74% and increases the capability of complete service provisioning for each SCSPs.

In this paper, we present a method for dynamically selecting leaders in a master-slave communication model in a swarm of micro-Unmanned Aerial Vehicles (UAVs). With the growing size of the UAV swarm in complex missions, it becomes a challenge to control them for efficient execution of missions. In a traditional centralized communication model where all UAVs in the swarm are controlled directly through ground control, channel capacity limits the number of UAVs in the swarm which restricts the scalability. In the context of low-power miniature drones, we limit the communication of the ground Base Station (gBS) with only one UAV (leader) which controls the rest of the UAVs (followers). Towards this, we propose a greedy heuristic method for selecting the UAV leader that requires minimal time to communicate with the gBS in real-time. The proposed master-slave model enhances the scalability of the swarm by improving the utilization of channel resources. Simulation results demonstrate that the proposed dynamic leader selection enhances the lifetime of the entire network with a multifold decrease in energy consumption, compared to the state-of-the-art. Additionally, the lifetime of the network also decreases on operating with a single UAV leader. We also observe reductions in delays by almost 60% and an increase in data rate by 50%.

Due to the lack of an integrated communication and computation architecture for Software-Defined Healthcare IoT (SD-HI), provisioning critical services is challenging. In this paper, we propose SD-Health, an edge-based decision making and task allocation (EDT) scheme for SD-HI. The proposed SD-HI network uses Machine Learning (ML)-based approach to predict the criticality of flows and location of mobile devices. Based on the predicted values, the controller delegates the required EDT module to the respective edge node. The controller identifies the future healthcare-related decisions for an edge node and prepares the module accordingly. The ML-based trajectory prediction allows to find the future location of mobile devices in the network. Once the location of the mobile device is predicted, a set of computation tasks is dynamically allocated to the edge node. The results of performance analysis show that SD-Health has a significant improvement in latency by 43.3% and energy consumption by 30%, compared to the existing state-of-the-art, along with a fair improvement in packet delivery ratio.

A social fog-IoV network involves time-critical tasks because it is highly dynamic due to rapid changes in the network topology. Therefore, completing tasks within the allowable delay is a challenge for social fog-IoV networks. In this context, we present a latency-aware task offloading scheme, named LOAN, in a fog-enabled association-free social Internet of Vehicle (IoV) network that aims to minimize the delay of time-critical tasks while saving the starvation of best-effort tasks. Our work considers different priorities for the service of time-critical tasks while efficiently utilizing fog resources. Different from the works in the literature that provide privilege to the time-critical tasks, LOAN handles the service of best-effort tasks efficiently without making them suffer conditions such as starvation. LOAN manages the priority levels of the tasks by incrementing their priorities based on their waiting time for the task service. We formulate the problem of efficient task service by suitable fog node as a coalition formulation game. Numerical results show that the LOAN achieves a reduction in delay compared to Greedy method by 36.5%.

In this paper, we propose SD-Reco, a Software-Defined Network (SDN)-based centralized AP and relay node re-configuration scheme for link recovery in IEEE 802.11ah networks. The proposed scheme identifies the overlapping regions and congestion of a network and re-configures Relays, APs, and SDN core nodes for reliable data transmission. Taking advantage of redundant links, the SDN controller triggers a node to receive/transmit frames using the best relay/AP for reliable and low latency delivery. The stations use redundant available links to relay/AP for any failure, not meeting the Quality of Service (QoS) requirement and congestion. Our solution use a relay placement scheme to know topology of the network for centralized controlling. An AP node determines the congestion status of each relay and itself and updates the same to the SDN controller. Accordingly, the controller selects an AP/relay and places flow-rules from overlapping regions for forwarding the traffic dynamically. SD-Reco improves packet delivery ratio up to 18.7% and latency up to 33.3%, compared to the existing state-of-the-art.

Due to the large-scale deployment of mobile stations, relays, and Access Points (APs), IEEE 802.11ah brings challenges that cannot be effectively addressed in a distributive manner. In this paper, we propose an uplink traffic-aware dynamic association and channel control mechanism for improving multi-hop throughput and delay performances. The proposed software-defined access network estimates throughput for relays and APs considering the stations’ probability of successful transmission. A centralized controller uses the estimated throughput for better decisions in association and channel selection in relays and APs. For improving the capacity of a relay or an AP, a utility function is dynamically measured from the available channels and bandwidth. The proposed association control mechanism allows for seamless handoff throughout the network. Overall, the proposed protocol improves throughput up to 10% and delay up to 13% as compared to the traditional scheme.

The frames captured by the camera should be of sufficient quality, generally measured in pixel density on a target (pixels per foot or pixels per meter (ppm)) to derive information suitable for social behavior analysis. Achieving 24x7 high pixel density coverage incurs a large number of cameras and exorbitant network bandwidth for large scale deployments. In this paper, we introduce additional features to the Camera Network-as-a-Service (CNaaS) concept proposed by Misra et al. to address two aspects pertinent to services related to social behavioral analysis. We present a Quality of Information-Aware (QoI-Aware) CNaaS to address the two aspects. (1) To allow the reuse of cameras which are already available in a locality to minimize the number of additional cameras required to provide the CNaaS service. (2) Inclusion and exclusion of camera node(s) into the CNaaS platform while ensuring fair opportunities to the Camera Network Owners (CNOs) which provide the same Quality of Information (QoI). The simulation results indicate that the proposed scheme is fair to nodes providing the same QoI, reduces the energy consumption, and excludes the cameras which offer lesser QoI. This will lead to an improvement in the usage of network bandwidth, and the profit of the Camera Network Service Provider (CNSP).

Sensor-cloud architecture is a wireless sensor network (WSN)-based Service-Oriented Architecture (SOA), in which a Sensor-Cloud Service Provider (SCSP) obtains WSNs on rental basis from multiple sensor-owners and provides these resources to the users in the form of chargeable units of services, termed as Sensors-as-a-Service (Se-aaS). A fraction of the revenue earned by the SCSP from the users is distributed among the oligopolistic sensor-owners for the usage of their nodes. Due to the inter-dependency among the sensor-owners for Se-aaS provisioning, selfish sensor-owners behave dishonestly to gain higher profits, thereby degrading the overall QoS. Existing works on sensor-cloud fail to address this issue. Hence, in this work, we propose DETER, a dynamic trust enforcing pricing scheme, which enforces trust among the selfish sensor-owners while ensuring profits for the SCSP.

In this article, we present a Q-learning-enabled safe navigation system-S-Nav-that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.

The Internet-of-Things (IoT) environments have hard real-time tasks that need execution within fixed deadlines. As IoT devices consist of a myriad of sensors, each task is composed of multiple interdependent subtasks. Toward this, the cloud and fog computing platforms have the potential of facilitating these IoT sensor nodes (SNs) in accommodating complex operations with minimum delay. To further reduce operational latencies, we breakdown the high-level tasks into smaller subtasks and form a directed acyclic task graph (DATG). Initially, the SNs offload their tasks to a nearby fog node (FN) based on a greedy choice. The greedy formulation helps in selecting the FN in linear time while avoiding combinatorial optimizations at the SN, which saves time as well as energy. IoT environments are highly dynamic, which mandates the need for adaptive solutions. At the chosen FN, depending on the dependencies on the DATGs, its corresponding deadlines, and the varying conditions of the other FNs, we propose an ϵ-greedy nonstationary multiarmed bandit-based scheme (D2CIT) for online task allocation among them. The online learning D2CIT scheme allows the FN to autonomously select a set of FNs for distributing the subtasks among themselves and executes the subtasks in parallel with minimum latency, energy, and resource usage. Simulation results show that D2CIT offers a reduction in latency by 17% compared to traditional fog computing schemes. Additionally, upon comparison with existing online learning-based task offloading solutions in fog environments, D2CIT offers an improved speedup of 59% due to the induced parallelism.

The advent of the Internet of Things (IoT) inspired various new and enhanced sets of applications in multiple domains including agriculture. The recent drive in the adoption of IoT technologies offers a major enhancement for the agricultural sectors in terms of efficiency and scalability. In this article, we investigate the specific issues and challenges associated with IoT, and review various IoT architectures, communication, middleware, and information processing technologies. We, then, discuss few IoT applications for agriculture-presenting various case studies to thoroughly analyze the solutions along with their design and implementation related parameters. Consequently, we provide a comprehensive review of the available simulation tools, data sets, and testbeds which provisions experimentation with IoT in agriculture. We enumerate open issues and challenges present in enabling IoT for agriculture. Finally, this article concludes while giving directions for future research.

The atmospheric buoyancy and intangible nature of fatal communicable viruses lead to rapid transmissions among individuals, resulting in global pandemics. Strategic lockdowns and mandatory social distancing are immediate solutions in such scenarios. However, this leads to operational disruptions in education, manufacturing, economy, transportation, governance, and community. Although technological assistance is beneficial in overcoming such issues, the current Internet of Things (IoT) infrastructure has limitations. In this article, we provide a comprehensive review of the possible IoT-based solutions that have the capacity of combating the COVID-19-like viruses. We highlight the societal impacts due to pandemics and identify the specific lacunae in current IoT solutions. We also provide comprehensive detail on how to overcome the challenges along with directions toward the possible technological trends for future research. Compared to existing reviews, our work offers a holistic view of the cause, effects, and the possible solutions that are existing, along with already existing solutions that can be customized to serve the special needs during the pandemic.

In this work, we propose and develop healthcare device interoperability (HeDI)—a system to enable device interoperability in IoT-enabled in-home healthcare monitoring platforms. The system consists of multiple sensors, each connected wirelessly to an edge device, acting as a wireless communication gateway to a remote server. The system initiates information handshaking between the sensor adapters and edge device at the beginning of the operation, which is later used to detect the sensor settings to process the data received from the sensor. The system is scalable and dynamically accommodates multiple sensors without any predefined ontologies at the edge device. The implementation of our system avoids dependencies on a system’s physical ports. The low form factor and wireless connectivity of the adapter make the system portable and convenient for in-home health monitoring. Additionally, the system allows multiple homogeneous sensors to operate at the same time in the same system. We implement and evaluate our system with a 3-lead ECG, pulse, and temperature sensors against two different network configurations—star and mesh. We use the data set generated from our implemented system for performance analysis. The network-level analysis of our system shows an average packet delivery ratio of 0.92 for star network configuration and 0.98 for mesh network configuration, ensuring the reliability of performance and its suitability for healthcare monitoring systems.

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

In this article, we propose a fog-enabled federated learning framework-FogFL-to facilitate distributed learning for delay-sensitive applications in resource-constrained IoT environments. While federated learning (FL) is a popular distributed learning approach, it suffers from communication overheads and high computational requirements. Moreover, global aggregation in FL relies on a centralized server, prone to malicious attacks, resulting in inefficient training models. We address these issues by introducing geospatially placed fog nodes into the FL framework as local aggregators. These fog nodes are responsible for defined demographics, which help share location-based information for applications with similar environments. Furthermore, we formulate a greedy heuristic approach for selecting an optimal fog node for assuming a global aggregator's role at each round of communication between the edge and cloud, thereby reducing the dependence on the execution at the centralized server. Fog nodes in the FogFL framework reduce communication latency and energy consumption of resource-constrained edge devices without affecting the global model's convergence rate, thereby increasing the system's reliability. Extensive deployment and experimental results corroborate that, in addition to a decrease in global aggregation rounds, FogFL reduces energy consumption and communication latency by 92% and 85%, respectively, as compared to state of the art.

In this article, we present an efficient criticality-aware decision-making system, named Devote, for fog-based Internet of Things (IoT) environment. Devote introduces an intelligent algorithm for the service of data based on the criticality, while considering the current availability of the resources at the fog node (FN). To cope with the dynamic IoT environment, we adopt a reinforcement-learning-based algorithm for the processing of the IoT data based on time-varying conditions. Additionally, we propose an efficient online secretary-based algorithm for choosing the best suitable candidate FN for offloading the data. To show the effectiveness of Devote, we obtained the numerical results for assessing its performance, while collating it with the benchmark schemes. We analyze different performance metrics, such as service delay, economy, and user satisfaction, which show that Devote incurs less service delay, as compared to other systems, while achieving user satisfaction of 88.4%.

We propose “DROPS”, a scheme which dynamically selects radio protocols in an energy-constrained wearable IoT healthcare system. We consider the use of multiple radio protocols, which are capable of transmitting a patient's sensed physiological parameters to the server through Local Processing Units (LPUs). As the health parameters are non-stationary and temporally fluctuating, especially for critical patients, the selection of an appropriate radio protocol is essential to maintain the accuracy and timely delivery of data from the patient to the server. Additionally, the mobility of patients through various locations within the hospital mandates the selection of the best radio protocol among the multiple available ones for each location, to enable data to offload to the remote server. We use single-leader-multiple-follower Stackelberg non-cooperative game to map the strategic interactions between a patient's LPU and the hospital's server. “DROPS” dynamically selects the appropriate radio protocol, based on the criticality index of a patient, the reputation of the radio, the Euclidean distance between the radios and the LPU, and the load on the protocol. Results on real-life data and their large-scale emulation show that the data rate increases by almost 78% and throughput by approximately 7%, as compared to existing schemes.

Node localization is a fundamental requirement in underwater sensor networks (UWSNs) due to the ineptness of GPS and other terrestrial localization techniques in the underwater environment. In any UWSN monitoring application, the sensed information produces a better result when it is tagged with location information. However, the deployed nodes in UWSNs are vulnerable to many attacks, and hence, can be compromised by interested parties to generate incorrect location information. Consequently, using the existing localization schemes, the deployed nodes are unable to autonomously estimate the precise location information. In this regard, similar existing schemes for terrestrial wireless sensor networks are not applicable to UWSNs due to its inherent mobility, limited bandwidth availability, strict energy constraints, and high bit-error rates. In this article, we propose SecRET , a Secure Range-based localization scheme empowered by Evidence Theory for UWSNs. With trust-based computations, the proposed scheme, SecRET , enables the unlocalized nodes to select the most reliable set of anchors with low resource consumption. Thus, the proposed scheme is adaptive to many attacks in UWSN environment. NS-3 based performance evaluation indicates that SecRET maintains energy-efficiency of the deployed nodes while ensuring efficient and secure localization, despite the presence of compromised nodes under various attacks.

The proposed work relates to the development ofBig-Sensor-Cloud Infrastructure(BSCI) that immensely enhances the usability and management of the physical sensor devices. Traditional Wireless Sensor Networks (WSNs) are manufactured in a proprietary, vendor-specific design. Thus, the renderability of WSNs is almost infeasible to people/organizations that do not own a network of their own. Thus, in the existing system, WSN-based applications are inaccessible to the naive-users or common people who do not own physical sensor devices. Recently, sensor-cloud infrastructure has been viewed as a substitute for traditional WSNs. However, with the increasing growth in the velocity, variety, and variability of data, the management becomes a serious concern and difficulty. Thus, existing systems are not able to capture, analyze, and control the present data efficiently, in real-time. BSCI is a distributed framework for “Big” sensor-data storage, processing, virtualization, leveraging, and efficient remote management. The methods of the proposed BSCI are persuasive as they are equipped with the ability to handle “Big” data with enormous heterogeneous data volumes (in zettabyte) generated with tremendous velocity. The framework interfaces between the physical and cyber worlds, thereby acquiring real-time data from the physical WSNs into the cloud platform. This data are processed and delivered to the end-users as a simple service – Sensors-as-a-Service (Se-aaS). BSCI completely maintains and manages the data and the metadata internally within its database. Multiple organizations with heterogeneous demand can be successfully served with Se-aaS through BSCI. From a user-perspective, BSCI is highly convenient as the users are completely abstracted from the underlying complex processing logic. This allows the naive users to envision the typical hardware sensor devices as simple accessible services like electricity, and water.

In this paper, we present a traffic-aware flow migration approach, which reduces data plane load in Software-Defined Networking (SDN) during a network update. SDN update involves rerouting of multiple traffic flows to accommodate new flows. An unplanned flow migration schedule overloads the data plane by burdening the data links and flooding the rule-space of capacity-constrained SDN switches. The overload of data links and switches blocks the update process, and the network fails to address the Quality of Service (QoS) demands of the traffic flows, especially latency-sensitive flows. Prior approaches migrate flows without considering load reduction of the data plane along with QoS demands of the flows. In this work, we propose a load reduction strategy that prioritizes traffic flows based on QoS demands and aims to avoid link congestion and rule-space overflow during flow migration. The proposed scheme significantly reduces the maximum data link bandwidth usage. In particular, the maximum data link bandwidth usage is 13.22% less than the two-phase update approach.

In this paper, we propose a scheme to address the problem of load management in the control plane of Software-Defined Internet of Things (SDIoT) networks. In SDIoT, multiple controllers are deployed to enhance network scalability. With the growth of IoT, the number of devices is increasing rapidly. The management of control plane load is an essential issue for IoT networks because of the dynamic traffic characteristics. IoT traffic is highly dynamic due to the heterogeneity of IoT devices in terms of mobility, activation model, Quality of Service (QoS) demand, and flow generation rate. The challenge is to prevent controller overload and distribute traffic optimally under the consideration of heterogeneous IoT devices. The proposed scheme estimates control plane load based on the mobility and activation model of IoT devices. For mobility prediction, we use Order- m fallback Markov Predictor as it consumes less space and performs efficiently even for small values of m. Based on the prediction results, we implement a traffic-aware rule-caching mechanism and a master controller assignment scheme to reduce the control plane load. Simulation results show that the proposed scheme reduces the peak intensity of the control traffic by 23.08% and 16.67%, as compared to the considered benchmark schemes.

In this article, we propose a lightweight blockchain-inspired framework-Magnum-as a magazine of transfer learning models in blocks. We propose the storage of these blocks on proximal fog nodes to simplify access to pretrained base models by industrial plants to tune them before deployment. We design Magnum for B5G-enabled scenarios to reduce the block transfer time. We formulate a demand-centric distribution scheme to further reduce the search and access time by adopting a nonlinear program model and solving it using the branch-and-bound method. Through extensive experiments and comparison with state-of-the-art solutions, we show that Magnum retains the accuracy of the models and present its feasibility with a maximum CPU and memory usage of 80% and 6%, respectively. Additionally, while Magnum requires a maximum of 10 s for writing models as large as 17 Mb on the blocks, it requires 16 ms for fetching the same.

In this paper, we propose a service handoff scheme, termed as Safe-Passé, for provisioning Safety-as-a-Service (Safe-aaS) to the end-users in a 5G environment. A Safe-aaS architecture provides customized safety-related decisions to the end-users. Practically, the service region of a Safety Service Provider (SSP) is bounded. On the other hand, the distance for which the end-users request for services may cover the service region of multiple SSPs. As a result, the services provided to the end-users are interrupted due to switching from the service region of one SSP to another. However, none of the existing handoff schemes provide customized safety-related decisions to the end-users. Considering road transportation as the application scenario of Safe-aaS in a 5G-enabled Intelligent Transportation System (ITS) environment, we propose a service handoff scheme among the multiple service providers for provisioning safety-related decisions to the end-users. Based on the eminence and profit of a SSP to which the end-users have initially registered for services, we compute the profit of the nearest SSP, who agrees to provide services to that end-user. We map these interactions among the SSPs to a cooperative coalition game, where the SSPs act as players. Extensive simulation-based analysis demonstrates that the ratio of the end-users served to the total number of end-users is improved by 4.19% and 3.65% using Safe-Passé compared to the existing handoff schemes, UCH and VHO.

The functioning of bacterial nanonetworks as a ”drug delivery system” requires the engineered bacteria to track the targets, such as harmful micro-organisms, pathogens, or chemical weapons, to release drug molecules effectively. The coordinated and intelligent movement of energy-constrained engineered bacteria is desired for successful tracking of mobile targets. In this work, first, we analyze the energy consumption by engineered bacteria for releasing molecules and propagating for the tracking process. Then we show that the events of the release of molecules by engineered bacteria and their propagation are interlinked in such a way that the strategy of releasing attractants upon detecting the target is coupled to the energy available with the engineered bacteria. Based on the finding, we propose an energy-aware algorithm, named as EnPoS, which probabilistically selects a group of engineered bacteria among the deployed bacterial population to release signaling molecules over a particular time period in order for engineered bacteria to track the mobile targets. The simulation results show better performance of the proposed algorithm as compared with the basic algorithm incorporating continuous releasing of signaling molecules, concerning the energy expenses, mean displacement over time, and distribution of the engineered bacteria around the targets.

This paper explores the concept of AI-based Communication-as-a-Service (ACUTE) to reduce transmission delay and energy consumption, while transmitting data from end-devices to the cloud in the context of Society 5.0. Society 5.0 revolutionizes connected living with the help of a unified system that provides fully automated and end-to-end services, while addressing the demands of all the citizens or users in a society. On the other hand, 6G is one of the promising communication platforms that offers the communication requirements of Society 5.0 by provisioning dense network deployment and fast data delivery. Building Society 5.0 founded on the 6G architecture enables serialized data transmission in the connected living fabric by allowing a user to connect with an access point and transmit data over a single path. Without concurrent and intelligent data transmission, the communication framework of Society 5.0 increases network delay and overall energy consumption and affects the Quality-of-Service (QoS). To address these issues, we propose a solution founded on the concept of Communication-as-a-Service (CaaS), which offers an architecture to facilitate intelligent access point virtualization for enabling concurrency in data transmission across individual users in a 6G-enabled Society 5.0. In ACUTE, a virtual module (VM) employed at each edge device performs concurrent data transmissions by associating with a virtual access point (VAP), which is a set of access points optimally selected using Fuzzy C-Means. Thereafter, the VM forms a virtual path (VP), which maps to a set of paths between physical access points and VAPs. ACUTE distributes the data through the VAP and associated VP and randomizes data sequence for transmission across VP. Experimental results show that ACUTE outperforms the state-of-the-art while reducing the network delay by 27%, energy consumption by 95%, packet loss by 95%, and service cost by 26%.

Internet of Things (IoT) is a network of heterogeneous physical devices connected over the Internet. Each of the devices is capable of collecting and processing data. Due to the connection with the Internet, the IoT devices become more susceptible to attacks by malicious nodes, which may result in privacy loss and security breaches. Thus, network security is necessary for the privacy of transmitted messages. In this context, we propose a scheme, Service-Oriented game-theoretic Security (SOS), which provides a simple yet robust security solution for IoT networks. Here, we have amalgamated our scheme with Named Data Networking (NDN), which is more of a data content-specific approach, unlike the traditional IP address search. In this scheme, at first, the hop count between the sender and the receiver is used to generate the public key to encrypt the messages by the sender. When the receiver receives this message, it decrypts the message with the help of the decryption function generated by the sender using the hop count between them as the private key. A non-cooperative Stackelberg game-theoretic model is used to model defenders and attackers, which helps to decide strategies to maximize the payoff (profit) of the defenders to protect the network from malicious attacks. The results are further extended for a modified public key encryption technique, which results in the robustness of the security scheme to be used for all real-life network scenarios. Simulation results show that the proposed scheme, SOS, has a better performance compared to the existing state-of-the-art security schemes, UAKMP and CLS, in terms of time complexity, message overhead, throughput, and attack probability.

The proliferation of IoT in various technological realms has resulted in the massive spurt of unsecured data. The use of complex security mechanisms for securing these data is highly restricted owing to the low-power and low-resource nature of most of the IoT devices, especially at the Edge. In this article, we propose to use blockchains for extending security to such IoT implementations. We deploy a Ethereum blockchain consisting of both regular and constrained devices connecting to the blockchain through wired and wireless heterogeneous networks. We additionally implement a secure and encrypted networked clock mechanism to synchronize the non-real-time IoT Edge nodes within the blockchain. Further, we experimentally study the feasibility of such a deployment and the bottlenecks associated with it by running necessary cryptographic operations for blockchains in IoT devices. We study the effects of network latency, increase in constrained blockchain nodes, data size, Ether, and blockchain node mobility during transaction and mining of data within our deployed blockchain. This study serves as a guideline for designing secured solutions for IoT implementations under various operating conditions such as those encountered for static IoT nodes and mobile IoT devices.

In this paper, the problem of provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of competitive sensor-owners, i.e., oligopolistic market, and heterogeneous sensor nodes in service-oriented sensor-cloud is studied. Oligopolistic sensor-owners adopt unfair means to degrade the quality of service provided by other sensor-owners in the sensor-cloud market. In order to address this problem, a dynamic pricing scheme, named DETER, is proposed in this work to enforce trust among the sensor-owners for maintaining the quality of Se-aaS provided by the Sensor-Cloud Service Provider (SCSP). Each sensor node calculates distributed trust opinion for other nodes, while the SCSP calculates centralized trust opinion for each sensor-owner. A Single-Leader-Multiple-Follower Stackelberg Game is formulated in which the SCSP acts as the leader and decides price to be paid to each sensor-owner, while ensuring maximum profit. On the other hand, the sensor-owners act as the followers and decide their strategies for earning maximum profit. Thereby, using DETER, SCSP enforces high trust among the sensor-owners. Additionally, using DETER, energy consumption of sensor nodes in sensor-cloud decreases by 4.69-11.56 percent, and network overhead decreases by 52.6-56.53 percent. The trade-off between price earned by the sensor-owners and profit of the SCSP in service-oriented sensor-cloud is also maintained using DETER.

In this paper, we study the problem of dynamic mapping of virtual sensors in sensor-cloud for provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of multiple sensor-owners and heterogeneous sensor nodes. We divide this problem into two subproblems—optimal dispersed node selection and optimal data-rate distribution, and analyze that these problems are NP-complete. Hence, we propose a game theory-based online scheme, named QADMAP, to solve these two problems in polynomial time. For the optimal node selection problem, we design a dynamic coalition-formation game-based online scheme, while maximizing thedispersion indexof the selected nodes. On the other hand, we propose an evolutionary game theory-based scheme for distributing the data-rate requirements of the services among the selected nodes, optimally. As per our knowledge, none of the existing works on dynamic mapping of virtual sensors considers the stochastic behavior of sensor-cloud for provisioning Se-aaS. From simulations, we observe that, using QADMAP, the energy consumption of the network reduces by 29.88-31.73 percent, thereby improving the QoS in terms of service availability by 11 percent and increasing the profit of the SCSP by 3.63-9.82 percent, compared to the existing benchmark schemes.

Opportunistic Mobile Networks (OMNs) enable communication among the otherwise disconnected devices via intermittent contacts. Based on this premise, the paradigms of Opportunistic Computing (OC) and Opportunistic Service Provisioning (OSP) were proposed, where a user can request remotely available hardware and software services from the relevant nodes that are not in direct contact with the user's device. In this work, we consider the problem of OSP together with ordered chaining. Chaining (or composition) refers to the scenario where two or more services must be availed by a given data object one after the another, for example, translating a text file and converting it to another format. Such a chaining is termed as ordered when the required services must be availed in a specific sequence. We discuss four schemes for achieving OSP with ordered chaining. One of the proposed schemes unicasts the OSP request messages, while another one adapts a popular unicasting algorithm for OMNs. The third scheme is based on flooding, whereas in the final one, nodes replicate requests only to those who provide the concerned services. The results of performance evaluation using real-life traces and synthetic mobility models show that up to about 99 percent of the service requests can be satisfied. The results also indicate that unicasting is rather unsuitable to achieve OSP.

Due to the low data-rate, high propagation delay, floating node mobility, and high error probability, underwater multimedia communication is still challenging. In this paper, we propose an acoustic-based reliable streaming network for resource-constrained underwater communication. The proposed protocol uses a Null Data Packet (NDP)-based contention and acknowledgment mechanism to reduce control overhead and improve reliability and energy efficiency. With the use of a lightweight Traffic Indication Map (TIM) and video compression technique, our system's efficiency for multimedia transmission is further improved. The proposed schemes provide multimedia communication without compromising on the quality of the data transmitted. We experimentally demonstrate the proposed scheme in a hydrodynamic water-tank facility on our campus. The testbed we have built in this facility is capable of running real-time video streaming. The system's performance, which is evaluated using parameters such as coverage, range, latency, and energy consumption, was found to prove the proposed solution's validity.

In this paper, we propose Health-Flow, a criticality-aware traffic forwarding scheme for mobile devices to maximize the efficiency of a software-defined healthcare network. The proposed scheme uses a machine learning-based approach to find the criticality of flows and the location of the mobile device. Concerning the criticality levels in traffic, the proposed protocol dynamically places or removes flow-rules at the edge access points. Consequently, it helps to take adequate actions for the incoming requests adaptively with improved network reconfiguration overhead, latency, and energy consumption. We mathematically formulate Integer Linear Programming for optimally selecting access points. We mathematically formulate the resource reallocation problem in terms of optimization by minimizing the network overhead subject to the packet flows' criticality requirements. The proposed scheme has the potential to reduce latency by 52%, overhead by 19%, and energy consumption by 12% as compared to the existing schemes.

In this paper, we propose a secure programmable fog architecture for future Internet of Things (IoT) applications. The programmable feature in the proposed architecture enables us to achieve additional flexibility and support changes over the fog nodes deployed on a large scale network, where individual fog nodes are managed by the centralized fog controller. Specifically, the exchange of programmable content between the controller and the nodes is secured using blockchain. The performance evaluation of the proposed scheme shows significant improvements in reducing downtime and increasing reliability, as compared to conventional fog computing schemes.

This work proposes an Activity-Aware Data Rate Tuning (A2D) scheme for Wireless Body Area Network (WBAN), while considering the criticality of the physiological sensed data. We consider different physical activities of the patients and thereafter, compute their health criticality. Further, on the basis of the health criticality value, the data rate of these physiological sensors are tuned. Depending on the physical activity of a patient, the value sensed by the physiological sensors may change. Consequently, when a healthy person runs, a particular sensor value may be significantly high, even if it is normal, however, the same data reading may be critical for a person who is sitting or standing. Thus, a WBAN is required to be activity-aware in order to measure the correct criticality values. We implemented in a real hardware platform system to show the effectiveness of the proposed scheme. Experimental results show that the proposed scheme is capable of tuning the data rate of different physiological sensors, based on human activity and critical conditions, while ensuring more than 90% of packet delivery ratio in intra-BAN communication and 93% in inter-BAN communication.

This work proposes an optimal camera selection scheme, OptiCam, for provisioning Camera-Network-as-a-Service (CNaaS) to multiple end-users while ensuring fair participation of the camera-network owners. Typically, in CNaaS, multiple cameras work together to form a virtual camera network and provide the services to the end-users. These cameras are procured and deployed by their respective owners, who receive rents for lending their cameras. In such a situation, the camera selection mechanism must be fair, so that, each of the owners receives an equal opportunity to participate in the CNaaS platform and earn the profit. On the other hand, multiple camera nodes may be available to serve an end-user application. Therefore, it is pertinent to select the suitable camera nodes, among the available ones, and serve the application while ensuring a longer lifetime of the network. The proposed scheme, OptiCam, is capable of selecting suitable camera nodes, optimally, while ensuring the fair participation of the owners, and increasing the network lifetime. To formulate the proposed scheme, we use a market-based auction model. The results of extensive simulations show that the average lifetime is improved in the case of OptiCam as compared to normal camera network (NCN) by 34.4%. Additionally, we observed that the total number of cameras activated per unit time increases by 8.63% in the case of NCN, while it decreases by 70.77%, in case of OptiCam.

In this paper, we propose a channel access scheme for relay-based IEEE 802.11ah network and analyze the feasibility and efficiency of it for multi-hop and large-scale Internet of Things (IoT). A new Restricted Access Window (RAW) scheme is designed to consider the currently active number of stations in a group for channel access. The proposed protocol facilitates multi-hop communication efficiently by utilizing the available channels. Accordingly, the relay node can maintain the best possible frame size for multi-hop communication. Simulation and analysis results show that the proposed protocol improves throughput performance up to 30.7% and the average frame delay up to 41.6% over the traditional scheme.

In this work, we address the problem of efficient utilization of resource-constrained wireless sensor nodes for provisioning Sensors-as-a-Service (Se-aaS) with high quality. In sensor-cloud, the sensor-owners provide their respective sensor nodes to the sensor-cloud service provider (SCSP) on rent. The SCSP utilizes these nodes to create virtual sensors and provisions them as Se-aaS for serving their WSN-dependent applications of the end-users and earns revenue in exchange. To ascertain high quality-of-service (QoS) of Se-aaS while simultaneously ensuring profits for itself and the sensor-owners, the SCSP needs to optimally allocate physical sensor nodes to serve the virtual sensors, while considering their limited capacity and the fair distribution of service load among different sensor-owners. Although a few existing works focused on resource allocation problem in sensor-cloud, none of them considered the possibility of sharing the same physical sensor node among multiple virtual sensors. Hence, in this work, we propose a resource orchestration scheme for sensor-cloud, named SensOrch, which is based on coalition formation game with transferable utility. Using SensOrch, the SCSP ensures the optimal allocation of sensor nodes to form virtual sensors while maintaining high QoS and profitability of Se-aaS. Through simulations, we yield that, using SensOrch, the network lifetime increases by 25.31 - 59.6% along with a simultaneous increase in the profit of the SCSP by 23.64 - 29.49%, compared to the existing schemes. Additionally, SensOrch ensures fair revenue distribution among the sensor-owners.

In this paper, we develop and analyze a smart digital stethoscope - SkopEdge - to provide reliable remote e-health monitoring with a minimum delay while enhancing overall network performance. SkopEdge initially records the heart sounds from individuals and then senses the quality of the network. Depending on the network traffic, SkopEdge converts the audio clip into an appropriate format before transferring it to remote locations for estimating the number of heartbeats and storage. Towards this, we formulate the link quality along with SkopEdge's current configuration as a Markov Decision Process (MDP) with actions as conversion format selection. The remote server then returns the result, which SkopEdge displays on its screen. Real-time implementations show that SkopEdge works efficiently in all network conditions. Further, audio conversions usually degrade the quality of sound, but our proposed system does not change its primary components. Although SkopEdge exhibits an increase in energy consumption by 79% while converting to lower-quality formats, it also reduces the energy consumption by 99% while transmitting the same, which subsequently results in energy savings. Further, we provide an analysis of the estimated heartbeats in an audio clip by SkopEdge.

In this paper, we present a traffic-aware consistent approach for flow migration in Software Defined Networking (SDN). The proposed scheme considers heterogeneous traffic to determine a consistent flow migration schedule. In a large-scale network, majority of traffic flows are latency sensitive. These flows change path frequently to accommodate new traffic flows. The challenge is to reduce the time required to modify the flow-path of latency sensitive flows. Existing solutions do not consider specific flow characteristics to decide a consistent traffic flow migration schedule. In this work, we propose a coalition graph game-based strategy while prioritizing traffic flows based on latency sensitivity. The proposed scheme significantly reduces the migration duration of latency sensitive traffic flows. In particular, the average traffic flow migration duration is 15.43% less than existing timed two-phase update solution.

The development of nanomedical systems through the Internet of Bio-Nano Things (IoBNT) paradigm promotes designing of therapeutic models to facilitate drug transport and delivery. Such systems utilize microbial communities such as bacteria, which act as biosensors for molecular communication. We model the drug transport and delivery system by considering more realistic properties and characteristics of the biosensor community. We devise a Markov Decision Process (MDP) to model the biosensor lifecycle while considering division and death as parameters to regulate the model. This aids in estimating the required number of drug encapsulated biosensors. The proposed model indicates an increase in the number of instances of biosensor-target interactions that would be required for a better understanding of system dynamics. The proposed approach suggests a populace-aware coordination scheme with 3.5% increase in population, along with 20 -50% increase in information delivery. The solution proposed here can be harnessed in designing the number of optimum drug dosages. We show the effectiveness of our model with 90% increase in average biosensor lifetime, while highlighting the increase in the energy utilized in the network.

In this work, we propose Over-The-Air (OTA)-based reconfigurable IoT health-monitoring wearables, which tether wirelessly to a low-power and portable central processing and communication hub (CPH). This hub is responsible for the proper packetization and transmission of the physiological data received from the individual sensors connected to each wearable to a remote server. Each wearable consists of a sensor, a communication adapter, and its power module. We introduce low-power adapters with each sensor, which facilitates the sensor-CPH linkups and on-demand network parameter reconfigurations. The newly introduced adapter supports the interoperability of heterogeneous sensors by eradicating the need for sensor-specific modules through OTA-based reconfiguration. The reconfiguration feature allows for new sensors to connect to an existing adapter, without changing the hardware units or any external interface. The proposed system is scalable and enables multiple sensors to connect in a network and work in synchronization with the CPH to achieve semantic and device interoperability among the sensors. We test the implementation in real-time using three different health-monitoring sensor types - temperature, pulse oximeter, and ECG. The results of our real-time system evaluation depict that the proposed system is reliable and responsive in terms of the achieved packet delivery ratio, received signal strength, and energy consumption.

In this paper, we study the problem of data flow management in the presence of heterogeneous flows — elephant and mice flows — in software-defined networks (SDNs). Most of the researchers considered the homogeneous flows in SDN in the existing literature. The optimal data flow management in the presence of heterogeneous flows is NP-hard. Hence, we propose a game theory-based heterogeneous data flow management scheme, named FlowMan. In FlowMan, initially, we use a generalized Nash bargaining game to obtain a sub-optimal problem, which is NP-complete in nature. By solving it, we get the Pareto optimal solution for data-rate associated with each switch. Thereafter, we use a heuristic method to decide the flow-association with the switches, distributedly, which, in turn, helps to get a Pareto optimal solution. Extensive simulation results depict that FlowMan is capable of ensuring quality-of-service (QoS) for data flow management in the presence of heterogeneous flows. In particular, FlowMan is capable of reducing network delay by 77.8–98.7%, while ensuring 24.6–47.8% increase in network throughput, compared to the existing schemes such as FlowStat and CURE. Additionally, FlowMan ensures that per-flow delay is reduced by 27.7% with balanced load distribution among the SDN switches.

In this paper, we propose a dynamic controller assignment scheme while considering flow-specific requirements, with an aim to minimize controller response time in software-defined networks (SDN). Using the FlowVisor model, we develop a virtualized platform that acts as a manager between the control- and data-planes of SDN architecture. The proposed scheme consists of two phases - adaptive window selection and controller assignment. In the window selection phase, the virtualized manager determines time to wait before incoming flows can be assigned to controllers in adaptive manner. Based on the adaptive window size, the flows are assigned to the controllers in the second phase. We use dynamic stable-matching game to assign flows to controllers, while defining their preference lists to minimize flow-setup delay and associated control overhead. Simulation studies show that the proposed scheme is capable of minimizing controller response time by atleast 31% compared to the existing state-of-the-art. Further, the proposed scheme also reduces the percentage of QoS violated flows in the network.

In this work, we propose an autonomous and onboard image-based agricultural land demarcation and path-planning system—IDeA ( I oT-Based Autonomous Aerial De marcation and Path Planning for Precision A griculture) with Unmanned Aerial Vehicles (UAVs)—using our advanced UAV-based aerial IoT platform. Our work successfully addresses the problem of onboard and autonomous path planning—which conventional UAV-based systems are not capable of—during stand-alone operations and without preloaded Global Positioning SYstem (GPS) markers for flight path waypoints. Our aerial system visually identifies non-electronically and singularly tagged agricultural plots and assesses the enclosing boundaries of the identified plot. Subsequently, an onboard path planning module autonomously generates GPS waypoints for traversing the identified plot with minimal overlaps and maximal coverage. Our proposed system exhibits an area coverage efficiency of 95.39%, performs pixel-to-GPS coordinate conversion with an accuracy of 90.35%, and has high agricultural potential in applications such as surveying crop health conditions and spraying pesticide/herbicides. The proposed system has massive applications in scenarios requiring aerial detection, demarcation, geographical tagging, and coverage of an area.

In this work, we address the problem of Named Data Networking (NDN)-based fake content dissemination in Opportunistic Mobile Networks (OMNs), where nodes have intermittent connectivity and typically lack in end-to-end communication paths. It is important to mitigate the dissemination of such fake contents not only because they waste bandwidth for legitimate communication, but also because such files can be harmful for devices. However, the inherent characteristics of OMNs make such mitigation a challenging task. In this context, we consider a group of nodes, Fake Content Providers (FCPs), who, on receiving content requests, respond with fake contents, rather than the real version. In particular, we consider four different behaviors of the FCPs-referred to as threat scenarios-depending on whether or not they always respond to all requests with fake contents. We analyze these distinct threat scenarios, and characterize the relative performance degradation arising because of them. To mitigate the adverse effects of FCPs, we propose two schemes, wherein the identified FCPs are blacklisted permanently or temporarily, and communication with them is restricted. Results of simulation-based experiments using real-life connection traces show that, compared to the normal scenario with no FCP, the percentage of content requests satisfied decreases by 20-40 percent in the presence of 40 percent FCPs in the OMN. Moreover, in the same scenario, the average latency of content satisfaction relatively increases by up to 176 percent with respect to the normal scenario. However, on using the proposed mitigation schemes, the latency can be reduced by about 13-36 percent together with up to 9 percent improvement in the number of interests satisfied.

In this article, we propose an Energy-aware Collaborative Routing (ECoR) scheme for optimally handling task offloading between source and destination UAVs in a grid-locked UAV swarm. We divide the proposed scheme into two parts - routing path discovery and routing path selection. The scheme selects the most optimal path between a source and destination from a massive set of all possible paths, based on the maximization of residual energy of UAVs along a selected path. This routing path selection ensures balanced energy utilization between members of the UAV swarm and enhances the overall path lifetime without incurring additional delays in doing so. Actual readings from our small-scale UAV swarm testbed are utilized to emulate a large-scale scenario and analyze the behavior of our proposed scheme. Upon comparison of the ECoR scheme with broadcast-based routing and the shortest path based routing, we observe better sustainability regarding the longevity of the UAV lifetimes in the swarm, optimized individual UAV, as well as reduced collective path-based energy consumption, all the while having comparable transmission delays to the shortest path based scheme.

In this paper, we propose a dynamic edge node selection scheme, named as DENSE, for the Safety- as-a-Service (Safe-aaS) architecture [1]. A Safe- aaS infrastructure provisions customized safety- related decisions remotely to the registered end- users. Depending on the time-criticality of data, the static and mobile sensor nodes sense and transmit data to the edge nodes. The number of edge nodes present within the proximity of a mobile sensor node vary with the change in the locations of the vehicle. Moreover, the distance between the mobile sensor node and the edge nodes, within its proximity, change with the variation in the vehicle's location. Therefore, in such a situation, dynamic selection of the appropriate edge node for processing the time-critical data is necessary. To optimally select the edge node, we use cooperative coalition-based game theoretic approach. Further, we apply Karush-Kuhn-Tucker (KKT) conditions to find the existence of equilibrium. The analytical results of our proposed scheme, DENSE, shows that the average utility increases by 11.33% with respect to the available storage space of the edge nodes. Moreover, the average utility increases by 50.43% with respect to the average number of tasks executed per unit time by the edge node.

In this paper, the problem of dynamic topology management in underwater wireless multimedia sensor networks (UWMSNs) in the presence of underwater wireless multimedia sensor nodes is studied using cooperation game theory. In the existing literature, researchers focused on the efficient management of underwater sensor networks and terrestrial wireless multimedia sensor networks. However, in the presence of underwater multimedia wireless sensor nodes, the amount of data to be transmitted increases significantly which deteriorates the overall network performance. Hence, there is a need to design a delay-optimal dynamic topology control scheme for UWMSNs, while maximizing the network throughput and lifetime. In this work, we propose a cooperative game theory-based scheme, named DATUM, for dynamic topology control. In DATUM, initially, we explore the feasible data transmission path available from the source node at seabed to the sink node at the surface of the ocean. Thereafter, using cooperation game theory, we identify the set of optimal paths to be selected. Finally, in DATUM, each underwater wireless multimedia sensor node decides its optimum transmission communication range for maximizing the network lifetime, while ensuring the network connectivity. Through simulation, we observed that using DATUM, network delay reduces by 30.74 percent, while the network lifetime increases by 59.61 percent.

We propose a method for a single ground camera-based visual gesture control of a quadrotor mUAV platform, making its flight more responsive and adaptive to its human controller as compared to a human controller using keypads or joysticks for controls. The proposed camera-based gesture control scheme provides an average accuracy of 100% gestures detected, as compared to accuracies obtained using expensive Kinect-based hardware, or processing intensive CNN-based pose estimation techniques with 97.5% and 83.3% average accuracies, respectively. A fog-based stabilization mechanism is additionally employed, which allows for flight-time stabilization of the mUAV, even in the presence of unbalanced payloads or unbalancing of the mUAV due to minor structural damages. This allows the use of the same mUAV without the need for frequent weight readjustments or mUAV calibration. This approach has been tested in real-time, both indoors as well as outdoors.

In this paper, we propose a scheme, OPTIVE, for obtaining the optimal configuration of a virtual sensor in the mobile sensor-cloud (MSC) architecture. The proposed scheme is capable of selecting the physical sensor nodes to form a virtual sensor (VS), based on the sensing area coverage for an application region. We use Markov Decision Process (MDP) to select the optimal mobile sensor nodes among the available ones, for configuring the VS in the application area. The MSC architecture is a new paradigm in which physical sensor nodes attain mobility by the virtue of mobile devices such as, laptops, cell phones, and vehicles. In MSC, a mobile device may move or exit from the application region at any time instant. Consequently, sensing hole arises in the application area, resulting in undesirable interruption in the end-user services. As multiple sensor nodes may be present in the application region, it is not suitable to allocate any available sensor node, randomly, to re-configure the VS for covering the sensing hole. In such a situation, OPTIVE selects the optimal physical sensor node to allocate in the VS for ensuring uninterrupted services to the end-users. Simulation results show that OPTIVE is capable of providing at least 80 - 90% coverage in the application area. Additionally, in the presence of 2 to 7 sensor nodes, the number of iterations in MDP change by 19.56%.

This paper looks into the problem of a decentralized data offloading within an edge unmanned aerial vehicle (UAV) swarm to mitigate the complexities of a single UAV continually generating and processing large application-specific data. The mobile edge UAVs considered here are multirotor types having constrained energy and processing power, which makes long-term handling of large data volumes impossible for standalone UAVs. The load mitigation is carried out by offloading data from a source UAV to other swarm members with sufficient energy and processing requirements. In this paper, we focus on selecting the most optimal multihop path through the UAVs concerning available energies and processing resources, which can survive the duration of the data offload between the source and a target UAV. We formulate a multiarmed bandit-based offload path selection scheme, which selects the most energy and processing optimized multihop path between a source and a target UAV. Upon comparison of our scheme against the naive shortest path approach, we observe that our approach results in significant savings of collective network energies, even for long operational durations.

Integration of various technologies to an Internet of Things (IoT) framework share the common goals of a consistent and structured data format that can be applied to any device, given the vast application scope of IoT. Additional goals include minimizing channel traffic and system energy consumption. In this paper, we propose to dismiss the requirement of certain seemingly crucial identifier fields from packets arriving through various sensor nodes in an agricultural IoT deployment. The proposed approach reduces packet size, thereby reducing channel traffic and energy consumption, as well as retaining the capability of identifying these originating nodes. We propose a method of a blind agricultural IoT node and sensor identification, which can be sourced and operated from a master node as well as a remote server. Additionally, this scheme has the capability of detecting the radio link quality between the master and slave nodes in a rudimentary form, as well as identifying the sensor nodes. We successfully trained and tested various multilayer perceptron-based models for blind identification, in real-time, using our implemented agricultural IoT implementation. The effect of changes in learning rate and momentum of the optimizer on the accuracy of classification is also studied. The projected cumulative energy savings across the network architecture, of our scheme, in conjunction with TCP/IP header compression techniques, are substantial. For a 100 node deployment using a combination of the proposed blind identification reduced sampling strategies over regular IPv4-based TCP/IP connection, an estimated annual saving of ≈99% is projected.

Virtual sensor provisioning is an essential process in sensor-cloud-based Internet of Things (IoT), and it is responsible for the efficient utilization of physical resources in the system. However, the existing schemes for virtual sensor provisioning do not provide an optimal solution while considering overall demand of multiple users/services. As a result, redundant sensor nodes are provisioned, which leads to increased energy consumption and reduced network lifetime. In this paper, we present a dynamic virtual sensor provisioning scheme for sensor-cloud-based IoT applications to maintain the energy efficiency of the deployed physical sensor nodes while maintaining the quality of service (QoS) of the service requests. We model the interaction between the cloud service provider and the sensor owners using the single-leader multifollower Stackelberg game. The players of the game exploit the spatial correlation among the on-field sensor nodes, and consequently, the oligopoly created between the players is dynamically updated. We show the existence of a Stackelberg-Nash-Cournot equilibrium in the game. We evaluated the performance of the proposed scheme through extensive simulations. The results depict improvement in the energy efficiency of the nodes as well as increase in the lifetime of the deployed on-fields sensors in the proposed scheme compared to benchmark schemes. We also plot the average number of QoS violations in each iteration for the user requests.

In this paper, we propose an adaptive flow-rule placement scheme, FlowStat, in a software-defined network (SDN) with an aim to provide per-flow statistics to SDN controller while enhancing overall network performance. The proposed scheme consists of three phases-forwarding path selection, flow-rule placement, and rule redistribution. In the first phase, we formulate a max-flow-min-cost optimization problem to determine optimal forwarding paths while considering multi-commodity flows with heterogeneous requirements. In the second phase, an integer linear programming problem is formulated to decide forwarding rules for paths computed in the first phase, so that the total number of exact-match is minimized. As finding optimal solution to the problems is NP-hard, we propose two greedy heuristic approaches to solve the problems in polynomial time. Finally, we propose a rule redistribution scheme on detecting rule congestion at a switch, in order to accommodate new flows in the network. Extensive experimental results show that the proposed scheme, FlowStat, is capable of providing per-flow statistics to the SDN controller while enhancing the network performance compared to existing schemes-ReWiFlow and ExactMatch. In particular, FlowStat is capable of reducing end-to-end delay and QoS violation by 46% and 75% (approx.), respectively, compared with the ReWiFlow and ExactMatch schemes, while providing 85% accurate per-flow statistics to the SDN controller.

In this paper, we consider the problem of task offloading in a software-defined access network, where IoT devices are connected to fog computing nodes by multi-hop IoT access-points (APs). The proposed scheme considers the following aspects in a fog-computing-based IoT architecture: 1) optimal decision on local or remote task computation; 2) optimal fog node selection; and 3) optimal path selection for offloading. Accordingly, we formulate the multi-hop task offloading problem as an integer linear program (ILP). Since the feasible set is non-convex, we propose a greedy-heuristic-based approach to efficiently solve the problem. The greedy solution takes into account delay, energy consumption, multi-hop paths, and dynamic network conditions, such as link utilization and SDN rule-capacity. Experimental results show that the proposed scheme is capable of reducing the average delay and energy consumption by 12% and 21%, respectively, compared with the state of the art.

The proposed work concentrates on the networking facets of sensor-cloud infrastructures-one of the first attempts of its kind. In a sensor-cloud, multiple sets of physical sensor nodes that are activated based on an application demand, in turn give rise to multiple distinct virtual sensors (VSs). The VSs are considered to span across multiple geographical regions; thereby, depositing the data (from each of the VS) to the closest cloud data center (DC). Quite obviously, multiple geospatial DCs get involve with an application. However, the principle of sensor-cloud is to store and conglomerate the data from various VSs, before they can be provisioned as Sensors-as-a-Service (Se-aaS). The assortation of data occurs within a single Virtual Machine (VM) (or in some cases multiple VMs) residing inside a particular DC. This work addresses the problem of scheduling a particular DC that congregates data from various VSs, and transmit the same to the end-user application. The work follows the general pairwise choice framework of the Optimal Decision Rule. The scheduling of the DC is performed under several network constraints, such as data migration cost, data delivery cost, and service delay of an application that ensures the preservation of the Quality-of-Service (QoS) and maintenance of the user satisfaction. The work quantifies the effective QoS of Se-aaS and determines an optimal decision rule for electing a particular DC. While arriving at a collective decision, the work incorporates the fallible decision making ability of a DC; thereby, excluding the loss of generality. Experimental results depict that the proposed algorithm for generating the optimal decision rule finds applicability in real-time cloud computing scenarios.

In mobile cloud computing (MCC), optimal utilization of resources (e.g., bandwidth), while maintaining the required level of quality-of-services (QoS), is essential. A user participating in the resource allocation process can provide untruthful information for acquiring undue advantages with respect to the allocated resource amount, and the cost incurred. In this paper, we identify, formulate, and address the problem of such misbehaviour. We formulate the bandwidth distribution as a constrained convex utility maximization problem, and solve it using the proposed cheating-resilient bandwidth distribution (CRAB) scheme. Numerical analysis shows that, in CRAB, the misbehaving user is impelled to behave normally as the misbehaviour increases its own cost while the other users including the cloud service provider (CSP) get benefit in terms of revenue. We investigate the existence of Nash Equilibrium (NE) of the proposed scheme. Both the problem and the solution are extensively analysed theoretically. The maximum and minimum selling prices of bandwidth, and the optimal solution for individual user are computed using the method of Lagrange multiplier.

In mobile cloud networks (MCNs), a mobile user is connected with a cloud server through a network gateway, which is responsible for providing the required quality-of-service (QoS) to the users. If a user increases its service demand, the connecting gateway may fail to provide the requested QoS due to the overloaded demand, while the other gateways remain underloaded. Due to the increase in load in one gateway, the sharing of load among all the gateways is one of the prospective solutions for providing QoS-guaranteed services to the mobile users. Additionally, if a user misbehaves, the situation becomes more challenging. In this paper, we address the problem of QoS-guaranteed secured service provisioning in MCNs. We design a utility maximization problem for quality-assured secured load sharing (QuaLShare) in MCN, and determine its optimal solution using auction theory. In QuaLShare, the overloaded gateway detects the misbehaving gateways, and, then, prevents them from participating in the auction process. Theoretically, we characterize both the problem and the solution approaches in an MCN environment. Finally, we investigate the existence of Nash Equilibrium of the proposed scheme. We extend the solution for the case of multiple users, followed by theoretical analysis. Numerical analysis establishes the correctness of the proposed algorithms.

In this paper, we propose a mobility-aware adaptive flow-rule placement scheme, named as Mobi-Flow, with an aim to maximize the overall performance in a software-defined access network (SDAN). The proposed scheme consists of two components - path estimator and flow-manager. The path estimator predicts future locations of end-users present in the network, depending on their history location sets, and delivers the predicted locations to the flow-manager. We use the order-k Markov predictor to predict the next possible locations of the end-users. Based on the predicted locations, the flow-manager determines access points (APs) in the network, which can be associated with the users to fulfill the requirements of the latter. We formulate an integer linear programming (ILP) to determine optimal number of APs, so that overall cost associated with flow-rule placement is minimized. Further, we propose a greedy approach to determine optimal number of APs, as solving the ILP is NP-hard. Consequently, the flow-manager implements the flow-rules at APs, so that adequate actions for the incoming requests can be taken in an adaptive manner, without querying the controller. We consider a practical scenario of an IoT environment, in which both static and mobile users are present. Therefore, the proposed scheme, Mobi-Flow, can be integrated atop the SDN controller to support the emerging concept of SDN-based IoT networking. Through extensive simulations, we show that Mobi-Flow is beneficial for minimizing the delay, number of activated APs, control overhead, energy consumption, and cost in the network, compared to the existing schemes-open shortest path first (OSPF), minimum occupied rule capacity (MRC), distributed (non-SDN), and MoRule. Particularly, the proposed scheme is capable of reducing the cost by 39, 38, 65, and 11 percent, compared to OSPF, MRC, distributed, and MoRule, respectively.

In this paper, we analyze the superframe structure of the Medium Access Control (MAC) sublayer of IEEE 802.15.4 protocol (ZigBee), designed for Low-Rate Wireless Personal Area Networks (LR-WPANs), and evaluate the effects of the inactive portion of a superframe on average delay, and average power consumption. The four-dimensional Markov chain-based analysis of the slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithm presented in this work considers backoff freezing and acknowledged packet transmission that are not studied in the existing works. The analytical results prove that the performance of LR-WPANs depends significantly on the length of a superframe's active portion. We introduce a variable-Superframe duration-Beacon interval Ratio (SBR), which is utilized by tuning a few MAC parameters to achieve 35 percent reduced delay, on an average, compared to the existing state of the art. The results show that the proposed model also yields improved performance in terms of power consumption, for short and medium contention windows. In addition to the proposed analysis, this work provides optimized superframe order values that achieve trade-offs between delay and power consumption as demanded by user-provided QoS requirements corresponding to different contexts.

In sensor-cloud framework, the concept of virtual sensor provisioning is applied to serve the end- users, who requests sensing information from the deployed sensor network. In a multi-hop sensor- cloud framework, the information collection from the physical sensors to the virtual sensor needs to activate additional nodes for information forwarding to the Cloud Service Provider (CSP). The existing works mainly consider the activation of these nodes from the same sensor owner (SO) and exhibit higher energy consumption. Although, in a sensor-cloud framework, multiple SOs co-exist naturally, and consequently, the service area of these SOs overlap. In this paper, contrasting to the existing works, we argue that the collaboration between the CSP and SOs can improve dynamic virtual sensor provisioning. We propose a scheme named Intelligent Dynamic Virtual Sensor Provisioning (iDVSP) to enable optimal selection of nodes in a multi-hop path with different SOs. We employ multi-unit single-item combinatorial reverse auction to model the interaction between the CSP and SOs. The auction based scheme facilitates the CSP to dynamically negotiate with the SOs, and ensure cost-effective node selection for virtual sensor provisioning. Simulation based results indicate that the proposed scheme is 46.51% energy-efficient compared to existing literature. Furthermore, we observe that the proposed scheme employ fair policy for node selection from different SOs. Therefore, we can argue that the proposed scheme enforces cooperation between the SOs in the sensor-cloud framework.

In this paper, we propose a QoS-aware adaptive flow-rule aggregation scheme in software-defined IoT (SDIoT) network with an aim to address flow-table overflow problem in SDN switches. The proposed scheme uses a key-based mechanism that is capable of fast aggregation and provides sufficient reduction in the number of flow rules, while having minimal impact on the QoS of IoT traffic. Further, we observe that it is necessary to adequately select a QoS path from multiple candidate paths, while considering the flow-table utilization at the switches. Accordingly, we present the Best-fit heuristic which takes into account the number of flow-rule insertions along with the bottleneck rule-capacity switch on a path, in order to minimize the total number of flow-rules in the network. Experimental results show that the proposed scheme is capable of reducing the average delay and packet drop by 35% and 12%, respectively, and improving the average throughput by 20% compared to the existing delay-based flow-aggregation scheme, while having comparable performance in terms of rule-aggregation.

In this work, we propose a dynamic cache-based pricing scheme, named CASH, for service-oriented sensor-cloud. In sensor-cloud, the Sensor-Cloud Service Provider (SCSP) provisions Sensors-as-a-Service (Se-aaS) to multiple end-users based on a pay-per-use model. The service-requests of the end-users have heterogeneous data-rate requirements. In the cache-enabled architecture of sensor-cloud, these service-requests are served by the SCSP using either the Internal or the External cache, which incurs different costs to the SCSP. Thereby, the SCSP tries to maximize its own profit by distributing these service-requests, optimally, among the caches. Additionally, the SCSP ensures that the end-users are minimally charged. Existing literature fails to propose any pricing scheme for service-oriented sensor-cloud, while considering the cost incurred for data caching. In CASH, we propose a dynamic pricing model for sensor-cloud using dynamic coalition formation game with transferable utility. Using CASH, based on the preference relation of the partitions, we determine the optimal internal cache refresh rate, while maximizing the coalition value. Through simulation, we observe that the cost incurred by the SCSP reduces by 34.32–51.15% and the price paid by the end-users decreases by 9.60–17.47% as compared to the existing schemes. Additionally, CASH ensures 9.60–21.85% increase in the profit of the SCSP.

In this paper, we propose the use of a Long Short-Term Memory (LSTM) based server-side sequence prediction algorithm to ease network data-load caused by rapid polling of multiple sensors onboard aerial robotic platforms, which are wirelessly tethered to a remote server for control and coordination. Our scheme reduces the network access time latencies between these platforms and the remote server hosting the control and scheduling mechanisms. Reduction in the TDMA-based access time is achieved by reducing the actual amount of data transmitted over the network, using partial transmission of actual sensor data over the network and server-side sequence prediction of the voluntarily missed sensor values. Our scheme allows the TDMA control of an increased number of networked platforms without change of infrastructure or the network characteristics.

In this work, we propose a scheme for the formation of Dynamic Virtual Sensor for Overlapping Region (DIVISOR) in a IoT-based sensor-cloud platform. Practically, the interest of deployment area of similar sensor nodes by respective sensor owners may be the same, and consequently, the areas of coverage of the deployed sensor nodes of the different owners overlap with one another. Thus, in such a scenario, each of the sensor owners must get equal opportunity to earn profit from the deployment of their sensor nodes. Therefore, in order to provide an equal privilege to all sensor owners, we propose the scheme, DIVISOR, in order to form virtual sensors. This is one of the first attempts for the dynamic formation of virtual sensors, where the overlapping area of deployed sensor nodes by different sensor owners is considered. The experimental results demonstrate that the proposed scheme is energy-efficient and the average number of participating nodes increases with the increase in the total number of nodes in the network. Moreover, each sensor owner gets almost equal opportunity to rent their nodes.

In this paper, we present solution for the development of a novel infrastructure, safety-as-a-service (Safe-aaS) for the road transportation industry. Safe-aaS provides safety related decisions to the registered end-users. The safety decisions are customized as per the end-user types and their requirements. Existing related research work on road safety focus on the development of the safety systems, which are able to assist the driver of the vehicle. However, none of the works serves as a common platform for providing customized decisions dynamically as per user requirements. As per our knowledge, Safe-aaS is one of the first attempts in its domain, where multiple end-users receive safety related decision dynamically. An end-user enjoys the pay-per-use service of Safe-aaS, without concerning about the back-end process. Safe-aaS is based on service oriented architecture, where different business entities such as vehicle owners, sensor owners, safety service provider, and end-users are involved. We introduce the term, decision virtualization, which enables multiple end-users to access the customized decisions remotely. We present possible cost analysis for the entities involved in the system. Analytical results show the cost and profit analysis of the different entities. We observe the profit gain by mobile sensor owner is 19.69% more as compared to static sensor owner. In the presence of 5, 10, and 15 end-users, payable rent varies between 15%-20%. Additionally, we present two case studies to depict a clear view of usage of Safe-aaS.

This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing-one of the first attempts of its kind. With the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide (CO2) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, CO2emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding real-time service. A case study is performed with traffic generated from the 100 highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with 50 percent applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by 50.09 percent. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.

In this paper, we propose a mechanism to ensure the proper functioning of a wireless sensor network (WSN) in the presence of static and proactive jammer within the network. Existing research works have primarily focused on the detection of jammer node within the network and ameliorating its consequent effects on the network. However, these countermeasures to mitigate the effect of the jammer suffer from certain limitations as WSNs are primarily resource constrained and most of the countermeasures are computationally intensive. The objective of this paper is to prevent the disruption of the network in the presence of jamming by bypassing the jammed zone and setting up alternative paths. The alternative paths are chosen with the maximum link quality in order to maintain the quality of service of the network even after jamming. This paper proposes Link-quality Aware Path SElection (LAPSE) algorithm that chooses alternative paths based on the optimal link quality. LAPSE is based on optimal decision rule and its design considers the fallible nature of the nodes while choosing/rejecting a particular link. Finally, the performance of the proposed algorithm, LAPSE, is evaluated in terms of the network parameters-packet delivery rate, network throughput, transmission energy, node lifetime, and network lifetime. Results indicate that the performance of LAPSE is significantly better than the existing jamming avoidance algorithms.

In this paper, we address the issue of rule duplication during network updates in software-defined networking (SDN). In SDN, network update involves the controller in sending update packets to desired set of switches, where the update rules are installed. To ensure update consistency, old flow rules are stored until the total update procedure is complete. Higher consumption of ternary content addressable memories (TCAMs) during update increases the cost of network update and decreases the scalability of SDN. In this paper, we propose an approach for consistent update with redundancy reduction, named CURE, which reduces the TCAM usage during update. CURE prioritizes switches according to their usage pattern and schedules updates based on priority zones. The proposed approach guarantees that highly loaded switches are updated first. CURE also maintains packet-level consistency by implementing a multilevel queuing approach. In this framework, each switch in the current update region stores the incoming packets in individual device queues until the switch completes update. Therefore, after the initiation of an update, packets are processed according to new rules only. The results of performance evaluation depict that the average rule space utilization during update using CURE is 29.954% less than using the two-phase update proposed in the existing literature.

In a post-disaster situation, increased concentration of patients in an area increases the traffic load of the network significantly, which degrades its performance with respect to mapping cost and network throughput. Therefore, to manage the increased traffic load and to provide ubiquitous medical services, we propose a disease-centric health-care management system using wireless body area networks (WBAN) in the presence of multiple health-cloud service providers (H-CSP). The theory of Social Network Analysis (SNA) is adopted to optimize the computational complexity and the traffic load of the network in an area, considering different disease types and the criticality indices of the WBANs. In such a scenario, Disease-centric Patient Group (DPG) formation among coexisting WBANs ensures optimized traffic load and reduced computational complexity. However, the formation of DPG alone is not sufficient to provide Quality-of-Service (QoS) to each WBAN. Therefore, to address these issues, we formulate a pricing model for the efficient mapping of critical WBANs from a DPG to a H-CSP to optimize the expected packet delivery delay and the network throughput. Consequently, to identify the critical WBANs from a DPG, we design a decision parameter based on an assortment of selection parameters. The performance of the Efficient Healthcare Management (HCM) scheme is analyzed based on distinct measures such as cost effectiveness, service delay, and throughput. Simulation results exhibit significant improvement in the network performance over the existing schemes.

Mobility induced by limb/body movements in Wireless Body Area Networks (WBANs) significantly affects the linkquality of intra-BAN and inter-BAN communication units, which, in turn, affects the Quality-of-Service (QoS) of each WBAN, in terms of reliability, efficient data transmission and network throughput guarantees. Further, the variation in link-quality between WBANs and Access Points (APs) makes the WBAN-equipped patients more resource-constrained in nature, which also increases the data dissemination delay. Therefore, to minimize the data dissemination delay of the network, WBANs send patients' physiological data to local servers using the proposed opportunistic transient connectivity establishment algorithm. Additionally, limb/body movements induce dynamic changes to the on-body network topology, which, in turn, increases the network management cost and decreases the life-time of the sensor nodes periodically. Also, mutual and cross technology interference among coexisting WBANs and other radio technologies increases the energy consumption rate of the sensor nodes and also the energy management cost. To address the problem of increased network management cost and data dissemination delay, we propose a network management cost minimization framework to optimize the network throughput and QoS of each WBAN. The proposed framework attempts to minimize the dynamic connectivity, interference management, and data dissemination costs for opportunistic WBAN. We have, theoretically, analyzed the performance of the proposed framework to provide reliable data transmission in opportunistic WBANs. Simulation results show significant improvement in the network performance compared to the existing solutions.

In a hospital environment, the total number of Wireless Body Area Network (WBAN) equipped patients requesting ubiquitous healthcare services in an area increases significantly. Therefore, increased traffic load and group-based mobility of WBANs degrades the performance of each WBAN significantly, concerning service delay and network throughput. In addition, the mobility of WBANs affects connectivity between a WBAN and an Access Point (AP) dynamically, which affects the variation in link quality significantly. To address the connectivity problem and provide Quality of Services (QoS) in the network, we propose a dynamic connectivity establishment and cooperative scheduling scheme, which minimizes the packet delivery delay and maximizes the network throughput. First, to secure the reliable connectivity among WBANs and APs dynamically, we formulate a selection parameter using a price-based approach. Thereafter, we formulate a utility function for the WBANs to offer QoS using a coalition game-theoretic approach. We study the performance of the proposed approach holistically, based on different network parameters. We also compare the performance of the proposed scheme with the existing state-of-the-art.

Wireless Body Area Networks (WBANs) are inherently resource-constrained in nature and each WBAN has different kind of Quality-of-Service (QoS) requirements. Therefore, in the presence of interference and poor link-quality, the resource pool of WBANs depletes significantly, which inherently increases the data dissemination delay and decreases the QoS requirements of WBANs in terms of resource availability. In order to minimize the data dissemination delay and to provide fair resources to WBANs in a link-failure situation, first we propose a fault tolerant mechanism for WBANs. Thereafter, we propose an energy-efficient resource management process to provide fair amount of resources to WBANs and minimize the energy consumption rate. We formulate the proposed scheme mathematically and evaluate through a series of simulations. Results show that the proposed scheme provides significant improvement in terms of delay, fairness and network throughput.

Mobile technology has a major role in every day life. The limitations of the mobile devices cause some serious issues in the performance of an application. The emerging mobile environment needs computational support from the external environment called cloud computing. The mobile devices establish the communication to the cloud through the wireless medium. The property of the mobile devices is not static. So, the link failures occur frequently and this ultimately leads to communication failure. To overcome this issue in the mobile cloud computing (MCC), an ad hoc networking model which uses the available mobile devices within the range is proposed. Virtual Cloud Learning Automata algorithm (VCLA) is proposed for selecting the suitable nodes to create the ad hoc virtual cloud. Some of the nodes are selected as optimal nodes by VCLA on which the computational offloading is performed. The experimental results show the effectiveness of VCLA when compared to the process without LA.

More focus of research is going on routing in VANET as there would be frequent path breaks due to its nodes high mobility. It is better to consider multipath routing instead of single path routing to have the uninterrupted transmission in the networks like VANET. This paper shows the design of an optimized multipath routing which is based on learning automata and leapfrog method (LA-MPRLF). Particle Swarm Optimization (PSO) method is utilized to determine the better available paths. Learning automata is used to determine the number of multiple paths that can be used for transmission. Leapfrog algorithm is used to predetermine the path breaks in the network. The projected graphs demonstrate that LA-MPRLF shows improved performance in comparison with legacy systems with respect to the QoS parameters - packet delivery ratio and throughput.

Internet of things (IoT) facilitates billions of devices to be enabled with network connectivity to collect and exchange real-time information for providing intelligent services. Thus, IoT allows connected devices to be controlled and accessed remotely in the presence of adequate network infrastructure. Unfortunately, traditional network technologies such as enterprise networks and classic timeout-based transport protocols are not capable of handling such requirements of IoT in an efficient, scalable, seamless, and cost-effective manner. Besides, the advent of software-defined networking (SDN) introduces features that allow the network operators and users to control and access the network devices remotely, while leveraging the global view of the network. In this respect, we provide a comprehensive survey of different SDN-based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects-edge, access, core, and data center networking. In these areas, the utility of SDN-based technologies is discussed, while presenting different challenges and requirements of the same in the context of IoT applications. We present a synthesized overview of the current state of IoT development. We also highlight some of the future research directions and open research issues based on the limitations of the existing SDN-based technologies.

Wireless Multimedia Sensor Networks (WMSNs) involving camera and Scalar Sensor (SS) nodes provide precise information of events occurring in the monitored region by transmitting video packets. In WMSNs, it is necessary to provide coverage of events occurring in the monitored region for longer durations of time. The Camera Sensor (CS) nodes provide the coverage of an event and transmit the video data to the Base Station (BS), when these nodes are actuated by the associated SS nodes on occurring of an event. Therefore, in the existing pieces of work, distributed actuation focuses on the coverage of an event and prolongation of the lifetime of the CS nodes. However, for distributed actuation of the CS nodes, the SS nodes play a vital role. When the data sent by the associated SS nodes in an event area exceed the preconfigured threshold, the CS nodes start sensing the event and send the video data to the BS. Therefore, in addition to the lifetime of the CS nodes, the lifetime of the SS nodes and their data reporting latencies are important aspects for distributed actuation of the CS nodes, while sending both the video and scalar data to the BS. In this work, we propose a topology management-based distributed camera actuation scheme, named TADA, to prolong the lifetime of SS nodes, and decrease the data reporting latency in event area only. The increased lifetime of the SS nodes, in turn, increases the event coverage and packet delivery ratio. To increase the lifetime of the SS nodes in an event area, the SS nodes with the most residual energies are selected as the packet aggregators. In addition, the transmission range of these nodes is decreased, and in-network packet aggregation is performed, while reporting the happening of an event to the associated CS nodes. The aggregator selection mechanism helps in balancing energy consumption of the SS nodes. Similarly, the decrease in transmission range and aggregation mechanism help in decreasing energy consumption of these nodes. The transmission range of the SS nodes is decreased using social network analysis and Coalition Formation Game (CFG). CFG also helps in decreasing the data reporting latency of an event by the SS nodes to their associated CS nodes. Performance evaluation results show that the proposed scheme, TADA, which is based on the distributed topology management protocol named T-Must, achieves high performance in terms of the lifetime of the SS nodes, data reporting latency, coverage ratio of the event, event reporting credibility index, and packet delivery ratio in an environment affected by shadow fading.

In this work, the problem of topology control for self-adaptation in stationary Wireless Sensor Networks (WSNs) is revisited, specifically for the case of networks with a subset of nodes having temporary connection impairment between them. This study focuses on misbehaviors arising due to the presence of\enskip “dumb” nodes [Misra et al. 2014; Roy et al. 2014a, 2014b, 2014c; Kar and Misra 2015], which can sense its surroundings but cannot communicate with its neighbors due to shrinkage in its communication range by the environmental effects attributed to change in temperature, rainfall, and fog. However, a dumb node is expected to behave normally on the onset of favorable environmental conditions. Therefore, the presence of such dumb nodes in the network gives rise to impaired connectivity between a subset of nodes and, consequently, results in change in topology. Such phenomena are dynamic in nature and are thus distinct from the phenomena attributed to traditional isolation problems considered in stationary WSNs. Activation of all the sensor nodes simultaneously is not necessarily energy efficient and cost-effective. In order to maintain self-adaptivity of the network, two algorithms, named Connectivity Re-establishment in the presence of Dumb nodes ( CoRD ) and Connectivity Re-establishment in the presence of Dumb nodes Without Applying Constraints ( CoRDWAC ), are designed. The performance of these algorithms is evaluated through simulation-based experiments. Further, it is also observed that the performance of CoRD is better than the existing topology control protocols—LETC and A1—with respect to the number of nodes activated, overhead, and energy consumption.

In cooperative communication, a set of players forming a coalition ensures communal behavior among themselves by helping one another in message forwarding. Opportunistic mobile networks (OMNs) require multihop communications for transferring messages from the source to the destination nodes. However, noncooperative nodes only forward their own messages to others, and drop others' messages upon receiving them. So, the message delivery overhead increases in OMN. For minimizing the overhead and maximizing the delivery rate, we propose two coalition-based cooperative schemes: 1) simple coalition formation (SCF) and 2) overlapping coalition formation (OCF) game. In SCF, we consider the presence of a central information center, whereas OCF is a fully distributed scheme. In SCF, coalitions are disjoint, whereas in OCF, a node may be the member of multiple coalitions at the same time. All nodes in a coalition help each other cooperatively by forwarding group messages to the intermediate or destination nodes. The goal of the nodes is to achieve high success rate in delivering messages. The proposed SCF scheme is cohesive, in which disjoint coalitions always combine to form grand coalition. In OCF, a node reaches a stable grand coalition when all the nodes of the OMN are members of overlapping coalition of the node. No node gains by deviating from the grand coalition in SCF and OCF. Simulation results based on synthetic mobility model and real-life traces show that the message delivery ratio of OMNs increase by up to 67%, as compared to the noncooperative scenario. Moreover, the message overhead ratio using the proposed coalition-based schemes reduces by up to about (1/3)rd of that of the noncooperative communication scheme.

In wireless sensor networks, adaptive tuning of Medium Access Control (MAC) parameters is necessary in order to assure the QoS requirements. In this paper, we propose an adaptive MAC-frame payload tuning mechanism for wireless body area networks (WBANs) to maximize the probability of successful packet delivery or reliability of the associated sensor nodes based on real-time situation. The enabling algorithm, Adaptively Tuned MAC (AT-MAC), has been proposed to tune the MAC-frame payload of a WBAN sensor node, which is compliant with the IEEE 802.15.4 protocol. AT-MAC prioritizes sensor nodes based on the seriousness of the health parameters that are being measured by the respective sensor nodes. Further, we consider a Markov chain-based analytical approach that acknowledges the slotted CSMA/CA backoff mechanism with retry limits, as described in the IEEE 802.15.4 protocol. We derive expressions for reliability, power consumption, and throughput, which are the key metrics to evaluate the network performance of the proposed protocol, and analyze the impact of MAC parameters on them. Finally, results indicate that the low rate and low power IEEE 802.15.4 can be used effectively in case of WBANs if the payload is tuned properly through the proposed algorithm. The proposed AT-MAC algorithm yields around 70 percent increase in reliability of a critical node in a WBAN.

This paper presents a bargaining-based resource allocation and price agreement in an environment of cloud-assisted Wireless Body Area Networks (WBANs). The challenge is to finalize a price agreement between the Cloud Service Providers (CSPs) and the WBANs, followed by a cost-effective mapping among them. Existing solutions primarily focus on profits of the CSPs, while guaranteeing different user satisfaction levels. Such pricing schemes are bias prone, as quantifying user satisfaction is fuzzy in nature and hard to implement. Moreover, such an traditional approach may lead to an unregulated market, where few service providers enjoy the monopoly/oligopoly situation. However, in this work, we try to remove such biasness from the pricing agreements, and envision this challenge from a comparatively fair point of view. In order to do so, we use the concept of bargaining, an interesting approach involving cooperative game theory. We introduce an exposition - multi-stage Nash bargaining solution (MUST-NBS), that unfolds into multiple stages of bargaining, as the name suggests, until we conclude price agreement between the CSPs and the WBANs. In addition, the proposed approach also consummates the final mapping between the CSPs and the WBANs, depending on the cost-effectiveness of the WBANs. Analysis of the proposed algorithms and the inferences of the results validates the usefulness of the proposed mapping technique.

Opportunistic Mobile Networks (OMNs) are characterized by intermittent connectivity among nodes. In many scenarios, the nodes attempt at local decision making based on greedy approaches, which can result in getting trapped at local optimum. Moreover, for efficient routing, the nodes often collect and exchange a lot of information about others. To alleviate such issues, we present SeeR, a simulated annealing-based routing protocol for OMNs. In SeeR, each message is associated with a cost function, which is evaluated by considering its current hop-count and the average aggregated inter-contact time of the node. A node replicates a message to another node, when the latter offers a lower cost. Otherwise, the message is replicated with decreasing probability. Moreover, SeeR works based solely upon local observations. In particular, a node does not track information about other nodes, and, therefore, reduces the risk of privacy leaks unlike many other protocols. We evaluated the performance of SeeR by considering several real-life traces under plausible conditions. Experimental results show that, in the best case, SeeR can reduce the average message delivery latency by about 58 percent, when compared to other popular routing protocols.

This paper proposes a dynamic and optimal pricing scheme for provisioning Sensors-as-a-Service (Se-aaS) [1] within the sensor-cloud infrastructure. Existing cloud pricing models are limited in terms of the homogeneity in service-types, and hence, are not compliant for the heterogeneous service oriented architecture of Se-aaS. We propose a new pricing model comprising of two components, applicable for Se-aaS architecture: pricing attributed to Hardware (pH) and pricing attributed to Infrastructure (pI). pH addresses the problem of pricing the physical sensor nodes subject to variable demand and utility of the end-users. It maximizes the profit incurred by every sensor owner, while keeping in mind the end-users' utility. pI mainly focuses on the pricing incurred due to the virtualization of resources. It takes into account the cost for the usage of the infrastructural resources, inclusive of the cost for maintaining virtualization within sensor-cloud. pI maximizes the profit of the sensor-cloud service provider (SCSP) by considering the user satisfaction. Simulation results depict improved performance of pH in comparison to the traditional hardware pricing algorithms, viz. PPM and Sprite, in terms of the residual energy, proximity to the base station (BS), received signal strength (RSS), overhead, and cumulative energy consumption. The results also show the tendency of the sensor-owners to converge to the end-user utility, but not exceed it. We also analyze the performance of pI. The results show the optimality in the profit incurred by SCSP and the user satisfaction.

In this paper, we propose a mobility-aware flow-table implementation scheme with an aim to maximize overall network performance in software-defined IoT. The proposed scheme consists of two components - path estimator and flow-manager. The path estimator predicts future locations of end devices present in the network, and delivers info to the flow-manager. Based on predicted locations, the flow-manager implements forwarding rules at access devices (ADs) in the network, so that adequate actions for incoming requests can be taken immediately without asking the controller. We use order-k Markov predictor to predict the next possible locations of the end devices. We consider a practical scenario of an IoT environment, in which both static and mobile devices are present. Extensive simulation results show that the proposed scheme is beneficial for improving network performance in terms of energy consumption and message overhead for flow-table implementation, while predicting the future locations of the devices. We show that the proposed scheme is capable of enhancing the overall network performance approximately by 50%.

The very high mobility of the nodes in Vehicular Adhoc Networks (VANET) increases the rate at which the handoff occurs. This motivates researchers to investigate the novel strategies for channel allocation such that the handoff becomes transparent. At the same time the utilization of the channels need to be effective in order to enhance the QoS. Hence this paper proposes a channel reservation procedure based on learning automata and node speed to improve the QoS in VANET. Channel reusability technique is incorporated to make the efficient usage of channels. The percentage of dropped calls and handoff latency are used as metrics to evaluate the proposed method.

In medical emergency situations, fair distribution of resources in a multi-tenant scenario is crucial. In such resource-constrained situations, these organizations may behave in a non-cooperative and selfish manner to maximize their individual incentives at the cost of the overall system welfare. Existing research works on dynamic resource allocation, have mostly assumed that the participating agents always behave truthfully, and place bids in accordance with their actual requirements. In practice, this assumption may not always hold true, as organizations have positive incentives for overstating. We design an algorithm, grounded in the theory of distributed mechanism design, to effectively alleviate untruthful demeanor of the organizations. The proposed resource allocation algorithm allows such organizations to maximize their individual incentives only by acting truthfully, whilst the overall system welfare is also maximized. The mechanism designed is resilient to selfish behavior of the organizations, and ensures voluntary participation of the organizations in the auction. It is also incentive compatible in nature, and dictates a truthful incentive-payment scheme.

The Multichannel assignment in wireless mesh networks is a challenging problem to be solved efficiently by assigning the channels to communicate among the wireless mesh nodes. An algorithm which solves the control channel contention problem and reduces the ripple factor is proposed. The contention problem is reduced by dividing the channel transmission time period and by using the sequential access control factor to make the probability of accessing the channel for all the nodes to be equal. The proposed method, Reservation and Contention Reduced Channel Access (RCR-CA) improves the channel throughput up to 80% with minimum packet loss rate and end-to-end delay. The simulated analysis shows the performance improvement of the proposed method when compared with the existing models.

This work focuses on an automated selection of Cloud Service Provider (CSP) for a naive end-user in an IoT scenario. In traditional cloud computing model, the end-users are knowledgeable about the Virtual Machines (VMs) and are technically aware of their requirements in terms of the computing cores, processing abilities, and storage requirements. In case of IoT, the users are envisioned to be widespread from naive, unsophisticated people to even objects or things who are devoid of the required knowledge and expertise. Further, in IoT technology, multiple Cloud Service Providers (CSPs) may possess the potential of serving an IoT application. Therefore, it is required for the end-user to judiciously select a single CSP based on the maximum obtainable Quality of Service (QoS) from a CSP. This work proposes an algorithm QoS based Automated Selection of CSP (QASeC) for automated selection of a CSP from a set of nominated CSPs based on the maximum achievable QoS. The work identifies and models the QoS parameters for every CSP and defines a QoS utility metric for each CSP. Based on the metric, the work proposes an optimization for selection of the appropriate CSP and the cloud gateway associated with it. From the obtained results, we infer the suitability of QASeC in real-life IoT scenarios.

In this work, we propose a scheme, named CoRAD , for the reestablishment of lost connectivity using sensor nodes with adjustable communication range in stationary wireless sensor networks (WSNs), when “dumb” behavior occurs some of the nodes. Due to the occurrence of such behavior, there may be temporary loss of connectivity between among the nodes. Such a phenomenon is different from the commonly known node isolation problem in stationary WSNs. The mere activation of intermediate sleep nodes cannot guarantee reestablishment of connectivity, because there may not exist neighbor nodes of the isolated nodes. On the contrary, the increase in communication range of a single sensor node may make it die quickly. Including this, a sensor node has maximum limit of increase in communication range that may not be sufficient to reestablish connectivity. Therefore, considering all these factors for self-organization of the network and isolated node re-connection, we propose a price-based scheme, which addresses the issue by activating intermediate sleep nodes or by adjusting the communication range of some of the other nodes in the network. The scheme also deactivates the additional activated nodes and reduces the increased communication range when the dumb nodes resume their normal behavior, upon the return of favorable environmental conditions. To implement the proposed scheme, CoRAD it is required to construct the network using GPS-enabled adjustable communication range sensor nodes. Through simulation we compare our proposed scheme with the existing topology management schemes -- LETC and A1 -- in the same scenario by considering the number of activated nodes, message overhead, and energy consumption. We find that the proposed scheme shows improved performance compared to the existing topology management schemes.

A distributed topology management scheme in wireless multimedia sensor networks (WMSNs) ensures coverage of an event, prolongs network lifetime, and maintains connectivity between camera sensor (CS) nodes. However, the deployment of WMSNs in unattended environments makes the nodes vulnerable to security attacks. Hence, security issues should be considered, along with topology management, in WMSNs. In this work, we propose a trust-based distributed topology management scheme, named TRAST, for use in WMSNs. TRASTexploits the received signal strength of the control packets, which are then used to construct the distributed topology. In a non-secure distributed topology, the use of trust helps in providing coverage of an event, and maintaining connectivity, even in the presence of malicious attacks. The proposed topology management scheme achieves higher average coverage ratio and average packet delivery ratio than those corresponding to the LDTS and T-Must schemes, in the presence of malicious attacks.

Opportunistic mobile networks (OMNs), which are formed by mobile devices carried by human users, present an interesting communication paradigm in the absence of access to global network connectivity or any form of network infrastructure. In this work, we combine thenaturalmobility of the human users—which has been shown to resemble Levy Walk—in OMNs, together with intentionalexplorations. We consider the case where the human users in an OMN undergo explorations, i.e., occasionally visit a set of fixed point of interests (PoI), for example, shopping malls. The objective of this work is two-fold-1) Establishing that limited explorations of the users can help in enhancing the performance of OMNs, and 2) Formulating a method to decide whether or not a user should undergo exploration. In this regard, we propose two schemes based on prospect theory (PT) and expected utility theory (EUT). The results of extensive simulation-based performance evaluation indicate that limited exploration can promote the delivery ratio of messages by large levels—about$7$-$33$percent depending on the number of randomly placed PoI, and about$36$percent depending upon the terrain size. Moreover, the time spent in exploration, on an average, is negligibly small—a typical value is about$0.55$percent of the simulation duration, which indicates its feasibility in real life.

Integration of physical processes with the computing world is driving newer challenges for networking frameworks. Cyber physical social systems (CPSSs) are another upcoming paradigm that encompasses the ever-growing interaction between the physical, social, and cyber worlds. As communication networks form the basis of these interactions, a cognitive evaluation of networks is called for. This CPSS driven network evolution was a direction motivating this paper. With the implementation of the next generation networks, traffic from real-time interactive services, such as video conferencing, is surpassing those of conventional transactional services. As such multimedia data transportation over IP networks has stringent quality constraints in terms of required bandwidth, latency, and jitter, legacy networks with no quality of service face challenges in terms of performance. We attempt to perform a multivariate analysis of video call record data collected from a wide area organizational network over a period of time. Learning-based prediction is attempted by training four classifiers: naïve Bayes, $k$ -nearest neighbor, decision tree, and support vector machine. Two independent set of experiments were conducted with oversights of bandwidth and destination prediction. Both the discrete and continuous valued predictors were involved in the training. Performance evaluation of the generated hypothesis in both the cases was conducted using tenfold cross validation. Combined analysis using the assorted combinations of attributes was conducted, and thereafter, the effect of each feature was evaluated through singular attribute portioning. This paper presents observations, which exhibit deviations from the conventional machine learning paradigms. An attempt to increase the prediction accuracy of the classifiers was made through the boosting ensemble methodology. However, miniscule addition in performance was achieved. A maximum prediction accuracy of 81% for bandwidth and 60% for destination was obtained. Reasons of low accuracy of conventionally better performing algorithm were reasoned with a mathematical comprehension. Divergence of the obtained results from the accepted patterns poses an open research problem, particularly with respect to the nature and peculiarities of the data set. The proposed learning technique can have potential applications in social, tactical, and strategic spheres.

In target tracking applications of wireless sensor networks (WSNs), one of the important but overlooked issues is the estimation of mobility behavior of a target inside a coverage hole. The existing approaches are restricted to networks with effective coverage by wireless sensors. Additionally, those works implicitly considered that a target does not change its mobility pattern inside the entire tracking region. In this paper, we address the above lacunae by designing a stochastic learning weak estimation-based scheme, namely mobility prediction inside a coverage hole (MIRACLE). The objectives of MIRACLE are two fold. First, one should be able to correctly predict the mobility pattern of a target inside a coverage hole with low computational overhead. Second, if a target changes its mobility pattern inside the coverage hole, the proposed estimator should give some estimation about all possible transitions among the mobility models. We use the trajectory extrapolation and fusion techniques for exploring all possible transitions among the mobility models. We validate the results with simulated traces of mobile targets generated using network simulator NS-2. Simulation results show that MIRACLE estimates the mobility patterns inside coverage hole with an accuracy of more than 60% in WSNs.

In recent times, Plug-in Electric Vehicles (PEVs) have emerged as a new alternative to increase the efficiency of smart grids (SGs) in a vehicles-to-grid (V2G) environment. The V2G environment provides a bidirectional power and information flow, so that users can have an optimized usage as per their requirements. However, uncontrolled and unmanaged power distribution may lead to an overall performance degradation in V2G environment. One reason for this uncontrolled and unmanaged flow may be due to the usage of power by unauthorized users. To address this issue, we propose a Bayesian Coalition Negotiation Game (BCNG) as a utility for secure energy management for PEVs in the V2G environment. We have used a BCNG along with Learning Automata (LA), wherein LA are stationed on PEVs and are assumed as the players in the game. To provide an approach based on resilience for any misuse of electricity consumption, a new Secure Payoff Function (SPF) is proposed. The players take actions and update their action probability vector using the SPF. A Nash Equilibrium (NE) is also achieved in the game using convergence theory. Our proposal is evaluated with various metrics. The proposed scheme also provides mutual authentication and resilience against various attacks during power distribution.

In wireless sensor networks (WSNs), sensor observations are spatiotemporally correlated, and that correlation signifies redundancy among the observations. Spatial correlation is primarily employed to estimate the minimum number of event-monitoring nodes. However, an event-monitoring node can intelligently exploit the temporal correlation between its observations to adapt with its dynamic surroundings. This self-adaptation helps resource-constrained nodes to enhance their performance by saving battery power and maintaining the quality of transmitted data. In WSNs, the sensor nodes switch between the active and sleep states to conserve energy. Using temporal correlation, a node can dynamically estimate the appropriate sleep duration, which is an important parameter for a node to adapt with its dynamic surroundings in an energy-efficient manner. In this paper, dynamic Bayesian network and entropy are used to estimate utility of observations. Moreover, a node estimates temporal correlation between its consecutive observations by mutual information. Further, the sensor nodes calculate appropriate sleep duration and control their communications at a particular time instant on the basis of estimated temporal correlation. A reinforcement-learning-based approach is used, in a distributed manner, to calculate the optimum sleep duration. Extensive simulation studies show that the proposed approach performs more efficiently in terms of energy conservation, energy utilization, and data accuracy than the benchmark schemes.

The IEEE 802.11e enhanced distributed channel access (EDCA) protocol follows class-based service differentiation for providing differentiated quality-of-service (QoS). However, its collision avoidance mechanism using backoff algorithm can be inefficient for providing improved performance with respect to throughput and channel access delay, especially in a high network configuration (i.e. number of stations) with imperfect wireless channel. The existing and emerging works have devoted considerable attention on tuning the backoff parameters for achieving optimal throughput only. The prior works do not consider the channel access delay and throughput metrics altogether for performance improvement. Additionally, in most of the cases, the optimal configuration of backoff parameters are performed by a centralized controller. In this paper, we propose a delay-aware distributed dynamic adaptation of contention window scheme, namely D2D, for the cumulative improvement of both the throughput and the channel access delay at runtime. The D2D scheme requires two ad-hoc, distributed, and easy-to-obtain estimates-delay deviation ratio and channel busyness ratio-of the present delay level and channel congestion status of the network, respectively. A key advantage of the D2D scheme is that it is compliant with the IEEE 802.11 standard, and, thus, can be seamlessly integrable with the existing wireless card. We show the integrated model of the medium access control protocol, namely D2D Channel Access (D2DCA), for the IEEE 802.11e networks. We further propose a two-dimensional Markov chain model of the D2DCA protocol for analyzing its theoretical performance in saturated networks with imperfect wireless channel. Theoretical comparison with the benchmark protocols establishes the effectiveness of the D2DCA protocol.

A collection of spatially distributed sensor nodes in a wireless sensor network (WSN) work collaboratively to sense the physical phenomena around them and then send the sensed information to the sink node through single-hop or multihop paths. In this work, we propose a scheme, named ReDAST, for reliable and efficient data acquisition in a stationary WSN in the presence of transfaulty nodes. Due to the transfaulty behavior, a sensor node gets temporarily isolated from the network. Temporary node isolation leads to the formation of dynamic communication holes in the network, which form and disappear dynamically. Furthermore, they may increase or decrease in size dynamically as well. These effects result in loss of information in the radiation-affected area. To prevent information loss in WSN due to transfaulty behavior of sensor nodes, in the proposed scheme, we construct the network using sensor nodes having dual mode of communication-RF and acoustic. To get redundant coverage within a radiation affected area, all the sensor nodes in the area become activated and switch to the acoustic communication mode after detecting themselves to be affected by radiations. In-network data fusion is performed to get actual information from the redundant information received from the radiation-affected area. Simulation results exhibit that the proposed scheme, ReDAST, achieves better energy efficiency and reduced average end-to-end delay than sensor nodes having only acoustic mode of communication.

Reliable message delivery is a challenging task in Vehicular Ad Hoc Networks (VANETs). Current literature on VANETs generalize all messages and use the same strategy to transmit them. In this paper, we model the cooperative service-based message sharing problem in VANETs as a coalition formation game among nodes. Nodes associate with a coalition based on the type of service-message they process. In the proposed model, service-messages are distinguished from one another by their types. Nodes process different types of service-messages and form a coalition based on the type of messages they process at that time. Some nodes within a coalition can work as a relay, which is modeled as a network formation game to select exactly one relay among a group of potential relay nodes to improve efficiency of the network in terms of improved packet reception rate and reducing transmission delay. The nodes form independent disjoint coalitions and tree structure is formed with the relay nodes within a coalition by using the proposed algorithm, COMES. Simulation results show that COMES, which allows nodes to form independent coalitions among themselves, improves the network performance in terms of incentive received by players by at least 40 percent compared to a non-cooperative function.

In a smart grid, micro-grids can exchange energy among themselves in order to provide reliable energy service to customers. Therefore, the micro-grids need to exchange their real-time energy status with other micro-grids, which, in turn, maximizes the energy consumption and CO2emissions to them. In this paper, we propose a cloud-based energy forecasting scheme to minimize the energy consumption and CO2emission towards enabling a green smart grid communication technology. Additionally, we device an optimal strategy for the proposed cloud-based energy forecasting scheme to minimize the energy consumption furthermore. Numerical results show the effectiveness of the proposed scheme over without cloud-based approach in terms of message overhead, energy consumption, and CO2emissions of the micro-grids. We see that the proposed scheme can minimize the energy consumption and the CO2emissions involved in the forecasting process significantly, which supports the green architecture of the smart grid communication technology. Additionally, the message overhead for energy forecasting can also be minimized.

Due to the evolutionary nature of computing and communication technologies, smartness and intelligence have become inevitable requirements for all futuristic systems. How to network these futuristic systems and make them deliver their services effectively is a major challenge. This challenge could be achieved by using wireless communication technologies. But, these smart systems need to be interconnected in seamless manner with facilities such as dynamic connection and disconnection, re-configurability, self-configurability, bandwidth optimization, etc. The existing wireless communication provides few of these facilities for the interconnection of generic computing devices such as server computers, desktop computers and laptop computers. A smart system highly depends on the individual real-time data monitoring for their effective performance. Hence, existing wireless communication technologies need to be customized for heterogeneous, event-driven and proactive smart systems. Hence this paper proposes a learning automata based cross layer framework for wireless networks using context awareness which aids in the reduction of energy consumption and better management of resources. The proposed technique has been tested on a simulated wireless multimedia network.

The work focuses on optimal formation of virtual sensors (VSs) within a sensor-cloud infrastructure. Existing work on sensor-cloud have considered the formation of VS with the maximal set of compatible physical sensor nodes. However, as these underlying nodes are highly resource constrained, inefficient and redundant utilization of the nodes takes a toll on the entire performance of the cloud and the network. In this work, we propose algorithms for efficient virtualization of the physical sensor nodes and optimal composition of VSs - within the same geographic region (CoV-I) and spanning across multiple regions (CoV-II). Experimental results demonstrate that, compared to the existing strategy of maximal composition of VSs, CoV-I improves the cumulative energy consumption and the network lifetime by 34.9% and 61.04%, respectively, and CoV-II enhances the parameters by 68.4% and 29.59%, respectively.

The most sought after bandwidth killer application on networks has been video conference. For a complex network, specifically based within an organization, implementing quality of service (QoS) is administratively not always feasible as the priorities are regulated. Predicting future network traffic in a non-QoS implemented network by using information about the source, destination and application can give preparatory time to make the network ready for unstable and random demands. The paper uses machine learning techniques to predict the bandwidth utilization of an end-to-end video conference session. Experimental results in this paper show that these features work well in detecting the bandwidth utilization. These experiments were done on a corpus of 24,000 video conference connections. The cognition is based on experimenting on features such as time of call, source, destination, call type, expected duration and cause codes. The result is based on combination of all these features which gave an accuracy of more than 78% on real traffic using two of the common classifiers - k-nearest neighbors and tree based classifier. Support vector machine (SVM) and Naive Bayes gave lower learning accuracy. Prediction results were also obtained by varying the combination of features to detect the predominating features in the cognition. It has been established that the bandwidth at which the connection is established is not entirely dependent on the source and destination but the other features also play a role in deciding the bandwidth of the connection. The prediction accuracy further increases if video calls are allowed only at discrete pre-designated bandwidth levels.

In many developing countries, agriculture is one of the primary livelihoods of common people. Agriculture requires various types of technologies for improving crop yields. The attack of animals in the agricultural land and the theft of crops by humans cause heavy loss in cultivation. In this work, we propose a hardware prototype using Wireless Sensor Network (WSN) for intruder detection in an agricultural field. The proposed system is named Agricultural Intrusion Detection (AID). AID helps to generate alarms in the farmer's house and at the same time transmits a text message to the farmer's cell phone when an intruder enters into the field. In order to implement the proposed scheme, we design and deploy Advanced Virtual RISC (AVR) micro-controller-based wireless sensor boards over an outdoor environment and evaluate the performance.

Increased population in an area degrades the performance of Wireless Body Area Networks (WBANs) in terms of throughput and packet delivery latency. WBANs by nature do not get fair amount of resources (bandwidth, time and spectrum). In this work, we consider the WBANs with varying traffic load in an area. In order to minimize the computational complexity of the algorithms executed the WBANs, the latter form different groups named Relational Patient Group (RPG), based on the disease types and the syndromes of the patients who are equipped with WBANs. RPG minimizes the computational complexity but does not minimize the traffic load. To minimize the traffic load the WBANs in the RPG form optimal grouping based on the optimal decision making process, named as Virtual Patient Group (VPG). We have formulated the proposed scheme mathematically and evaluated through a series of simulations. Results show that the proposed scheme provides significant improvement in the traffic load and the packet drop probability.

Wireless Sensor Networks (WSNs) have gained a lot of popularity in recent years because these are being used in wide range of applications. A collection of randomly/planned deployed tiny Sensor Nodes (SNs) can perform the task according to the need of a specific application. Utilization of energy of SNs is one of the key issues in these networks as nodes are battery operated and recharge or replacement of battery is a difficult task to be achieved. To address this issue, we propose a Bayesian Coalition Game-based optimized clustering in WSNs. To formulate the game, we propose a new Hybrid Homogeneous LEACH (HHO-LEACH) protocol for SNs in WSNs. We have used the concepts of Learning Automata (LA), and Bayesian Coalition Game (BCG) in which SNs are assumed as the players in the game with dynamic thresholds-based coalition formation among themselves, i.e., coalition among the nodes are formed using distance-based thresholds which makes a partition of the network field. SNs near to base station use direct communication for data transfer with or without single hop to the Base Station(BS) after interacting with the environment. During this process, each player may get a reward, or a penalty with respect to the finite number of actions performed. Performance of the proposed protocol is evaluated using extensive simulations by selecting various evaluation metrics. The results obtained show that proposed coalition game achieved better stability, and network lifetime in comparison to other existing protocols such as LEACH, and DD.

The evolution of Internet of Things (IoT) leads to the emergence of Internet of Vehicles (IoV). In IoV, nodes/vehicles are connected with one another to form a vehicular ad hoc network (VANET). But, due to constant topological changes, database repository (centralized/distributed) in IoV is of spatio-temporal nature, as it contains traffic-related data, which is dependent on time and location from a large number of inter-connected vehicles. The nature of collected data varies in size, volume, and dimensions with the passage of time, which requires large storage and computation time for processing. So, one of the biggest challenges in IoV is to process this large volume of data and later on deliver to its destination with the help of a set of the intermediate/relay nodes. The intermediate/relay nodes may act either in cooperative or non-cooperative mode for processing the spatio-temporal data. This paper analyze this problem using Bayesian coalition game (BCG) and learning automata (LA). The LA stationed on the vehicles are assumed as the players in the game. For each action performed by an automaton, it may get a reward or a penalty from the environment using which each automaton updates its action probability vector for all the actions to be taken in future. A detailed comparison has been provided by analyzing the cooperative and noncooperative nature of the players in the game. The existence of Nash equilibrium (NE) with respect to the probabilistic belief of the strategies of the other players in the coalition game is also analyzed.

In this paper, energy trading for the distributed smart grid architecture is projected as an incomplete information game —a viewpoint that contrasts from all the existing pieces of literature available on the broader issue of energy management in smart grid. The incomplete information is considered as the real-time demand and price to grid and customers, respectively, due to the packet loss in the communication network. Therefore, the paper addresses a realistic scenario, in which real-time information to the destination may not be guaranteed to be received adequately, due to the packet loss. In the proposed scheme, we introduce two types of intelligent agents— customer-agents and grid-agent . The customer-agents are deployed at the customers׳ end, and are capable of estimating adequately the real-time price decided by the grid. On the contrary, the grid-agent is deployed at the service provider׳s end, and are also capable of estimating adequate real-time energy demand from the customers. Therefore, one of the key advantage of the proposed agent-based scheme is that the customers and the grid are not involved in complex calculations in order to take real-time decisions for cost-effective energy management, while there is information loss in the communication networks. In the proposed game model, the grid-agent and the customers agents are the players, and estimate real-time demand and price based on the probability of belief to each other. We show the existence of Bayesian Nash Equilibrium in the proposed model, where the utility of the players is maximized. We compare the real-time price with and without packet loss as the price with incomplete and complete information, respectively. We observe that the proposed model is beneficial for the grid, as its utility is maximized. The simulation results show that the utility of the grid increases approximately 40% over that of the existing ones under the scenario of information incompleteness.

Recently, the IEEE 802.15.6 Task Group introduced a new wireless communication standard that provides a suitable framework specifically to support the requirements of wireless body area networks (WBANs). The standardization dictates the physical (PHY) layer and medium access control (MAC) layer protocols for WBAN-based communications. Unlike the pre-existing wireless communication standards, IEEE 802.15.6 standardization supports short-range, extremely low power wireless communication with high quality of service and support for high data rates upto 10 Mbps in the vicinity of living tissues. In this work, we construct a discrete-time Markov chain (DTMC) that efficiently depicts the states of an IEEE 802.15.6 CSMA/CA-based WBAN. Following this, we put forward a thorough analysis of the standard in terms of reliability, throughput, average delay, and power consumption. The work concerns non-ideal channel characteristics and a saturated network traffic regime. The major shortcoming of the existing literature on Markov chain-based analysis of IEEE 802.15.6 is that the authors did not take into consideration the time spent by a node awaiting the acknowledgement frame after transmission of a packet, until time-out occurs. Also, most of the work assume that ideal channel characteristics persist for the network which is hardly the case in practice. This work remains distinctive as we take into account the waiting time of a node after it transmits a packet while constructing the DTMC. Based on the DTMC, we perform a user priority (UP)-wise analysis, and justify the importance of the standard from a medical perspective.

In critical medical emergency situations, wireless body area network (WBAN) equipped health monitoring systems treat data packets with critical information regarding patients' health in the same way as data packets bearing regular healthcare information. This snag results in a higher average waiting time for the local data processing units (LDPUs) transmitting data packets of higher importance. In this paper, we formulate an algorithm for Priority-based Allocation of Time Slots (PATS) that considers a fitness parameter characterizing the criticality of health data that a packet carries, energy consumption rate for a transmitting LDPU, and other crucial LDPU properties. Based on this fitness parameter, we design the constant model hawk-dove game that ensures prioritizing the LDPUs based on crucial properties. In comparison with the existing works on priority-based wireless transmission, we measure and take into consideration the urgency, seriousness, and criticality associated with an LDPU and, thus, allocate transmission time slots proportionately. We show that the number of transmitting LDPUs in medical emergency situations can be reduced by 25.97%, in comparison with the existing time-division-based techniques.

Collision is imminent in wireless networks (WNs) capacitated with randomized distributed channel access. In such networks, the probability of frame drop (Pdrop) is greater than zero due to successive collisions with a finite retry limit m. The IEEE 802.11 DCF is one such widely accepted protocol used in wireless local area networks (WLANs). The objective of the work reported in this paper is to provide guaranteed channel access to colliding frames for avoiding successive collisions for any loss-sensitive application, and thus, increase network throughput under finite m. We propose the semi-distributed backoff (SDB) algorithm, which operates in two modes: S-mode and R-mode. The key idea of the SDB scheme is to perform receiver-side backoff, if a mobile station encounters collision even after performing the existing sender-side backoff procedures. Using the SDB scheme, we design Semi-DCF, a MAC protocol for WLANs, which performs opportunistic migration from random to deterministic backoff. Semi-DCF functions independent of m. This protocol exploits the collision detection capability of receivers for disseminating information on optimal backoffs to the contenders using signature vectors. An analysis of Semi-DCF using the 2D Markov chain, coupled with results of network simulation establishes its superior performance in WLANs.

In this paper, we propose a localization scheme named Opportunistic Localization by Topology Control (OLTC), specifically for sparse Underwater Sensor Networks (UWSNs). In a UWSN, an unlocalized sensor node finds its location by utilizing the spatio-temporal relation with the reference nodes. Generally, UWSNs are sparsely deployed because of the high implementation cost, and unfortunately, the network topology experiences partitioning due to the effect of passive node mobility. Consequently, most of the underwater sensor nodes lack the required number of reference nodes for localization in underwater environments. The existing literature is deficient in addressing the problem of node localization in the above mentioned scenario. Antagonistically, however, we promote that even in such sparse UWSN context, it is possible to localize the nodes by exploiting their available opportunities. We formulate a game-theoretic model based on theSingle-Leader-Multi-Follower Stackelberg gamefor topology control of the unlocalized and localized nodes. We also prove that both the players choose strategies to reach asocially optimal Stackelberg-Nash-Cournot Equilibrium. NS-3 based simulation results indicate that the localization coverage of the network increases upto 1.5 times compared to the existing state-of-the-art. The energy-efficiency of OLTC has also been established.

The fast-paced development of power systems necessitates smart grids to facilitate real-time control and monitoring with bidirectional communication and electricity flows. Future smart grids are expected to have reliable, efficient, secured, and cost-effective power management with the implementation of distributed architecture. To focus on these requirements, we provide a comprehensive survey on different cloud computing applications for the smart grid architecture, in three different areas-energy management, information management, and security. In these areas, the utility of cloud computing applications is discussed, while giving directions on future opportunities for the development of the smart grid. We also highlight different challenges existing in the conventional smart grid (without cloud application) that can be overcome using cloud. In this survey, we present a synthesized overview of the current state of research on smart grid development. We also identify the current research problems in the areas of cloud-based energy management, information management, and security in smart grid.

Future large-scale deployment of plug-in hybrid electric vehicles (PHEVs) will render massive energy demand on the electric grid during peak-hours. We propose an intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture-an effort towards optimizing the energy consumption profile of PHEVs users. Each micro-grid decides realtime dynamic price as home-price and roaming-price, depending on the supply-demand curve, to optimize its revenue. Consequently, two types of energy services are considered-home micro-grid energy, and foreign micro-grid energy. After designing the PHEVs' mobility and battery models, the pricing policies for the home-price and the roaming-price are presented. A decision making process to implement a cost-effective charging and discharging method for PHEVs is also demonstrated based on the real-time price decided by the micro-grids. We evaluate and compare the results of distributed pricing policy with other existing centralized/distributed ones. Simulation results show that using the proposed architecture, the utility corresponding to the PHEVs increases by approximately 34 percent over that of the existing ones for optimal charging of PHEVs.

Cooperation among nodes is a fundamental necessity in opportunistic mobile networks (OMNs), where the messages are transferred using the store-carry-and-forward mechanism, due to sporadic inter-node wireless connectivity. While multiple works have addressed this issue, they are often constrained in their assumptions on solutions (e.g., requirement of central authority, and tracing the recipient nodes for providing reward or punishment). In this work, we address this research lacuna by taking an evolutionary theory-based approach. In evolutionary theory, the players analyze alternative strategies and select the best one to survive in a population. Inspired by this, in this work, we propose a Distributed Information-Based Cooperation Ushering Scheme (DISCUSS) to promote cooperation in message forwarding between nodes. In this scheme, the nodes maintain and exchange information with one another during contacts about the messages created and delivered in the network. Based on this, the nodes evaluate their own performance and compare that with the approximated network performance to adapt the most successful forwarding strategy. Simulation results show that the message delivery ratio in the network improves upto 31 percent, when the nodes dynamically switch their strategies, as compared to the case when they do not. Furthermore, the DISCUSS scheme fared closely to its variant with the nodes having complete knowledge about the network-wide performance.

In this paper, we propose a cooperative game theoretic approach for data-rate tuning among sensors in a Wireless Body Area Network (WBAN). In a WBAN, the body sensor nodes implanted on a human body typically communicate through a capacity-constrained single channel. This is a serious concern because most applications in WBANs involve real-time data streaming and providing useful notifications and efficient feedback to the patients or other users according to their health conditions. To increase the Quality of Service (QoS), we need an efficient data-rate tuning mechanism, which tunes the data-rate of a sensor based on the criticality of health parameter measured through it. Our approach considers the unique features typical of WBAN applications, and provides a generalized solution for the problem. We propose a cooperative game theoretic approach, based on the Nash Bargaining Solution (NBS), which does not only provide priority-based tuning, but also maintains the fairness axioms of game theory. The proposed approach yields 10% average increase in data-rates for the sensor nodes that have critical physiological data to transmit. We also validate the approach through real system implementation with the help of real sensor devices such as heart rate sensor, and pulse oximeter.

In this paper, the problem of energy distribution using virtual energy-cloud to the plug-in hybrid electric vehicles (PHEVs) is studied as a single leader multiple follower non-cooperative Stackelberg game. In this game, the energy-cloud service provider acts as the leader, and decides the price to be paid by each PHEV according to its usage. On the other hand, the PHEVs act as the followers, and need to decide the amount of energy to be consumed based on their requirements. Using variational inequality, it is shown that the proposed scheme, virtual energy cloud topology control (VELD), has a generalized Nash equilibrium, which is also socially optimal. The proposed scheme, VELD, which enables the energy-cloud service provider and the PHEVs to reach the equilibrium state, is evaluated theoretically as well as through simulations. Using the proposed scheme, VELD, the PHEVs consume up to 47.49-52.96% higher amount of energy, while paying 5.52% less per unit energy, which, in turn, increases the utilization of the generated energy by the micro-grids.

In this paper, we propose a dynamic duty scheduling scheme for minimizing the energy consumption of the on-field sensor networks in a sensor-cloud application framework. The conjugation of cloud framework with Wireless Sensor Networks (WSNs) adds enhanced processing and storage capacity to the on-field WSN applications. However, the WSN applications performing periodic information update to the cloud exhibit low network lifetime, low resource utilization, and high cost. In this regard, the advent of the sensor-cloud technology facilitates dynamic duty scheduling of the on-field WSNs. As a result, the on-field WSNs attain improved energy-efficiency and cost-effectiveness. The simulation results show the effectiveness of the proposed scheme over the traditional scenarios.

In this work, we consider the presence of dumb nodes in the underlying physical networks of Sensor-Cloud infrastructure. The dumb nodes get isolated from the network and degrade the network performance. Consequently, a Cloud Service Provider (CSP) is unable to use Wireless Sensor Networks (WSNs) in an efficient manner, as the dumb sensor nodes cannot use as a virtual sensor. Thus, in this work, we propose a scheme, Connectivity Re-establishment in the Presence of Dumb Nodes in Sensor-Cloud Infrastructure (CoRDS), that facilitates the reestablishment of the connectivity between dumb nodes and the sink. Using the proposed scheme, CoRDS, the CSP is able to provide the services efficiently as per end-users requirements. In order to re-establish connectivity between the dumb nodes and the sink, we use a single leader multiple follower Stackelberg game. Additionally, theoretical characterization of CoRDS has been shown.

In a smart grid, smart meters are expected to be the key technology to support bi-directional information exchange between service providers and end-users. The on-growing demand of smart meters (residential customers and plug-in hybrid electric vehicles) would result in huge energy consumption, while communicating with the entities in the smart grid. Therefore, green wireless communication technologies are expected to help in reducing their impact on environment. Therefore, it is important to design energy-efficient schemes that can reduce CO2emissions and cost-effective energy management in the smart grid. In this paper, an energy-efficient smart metering scheme is proposed - an effort towards minimizing the energy consumption by the smart meters for green smart grid communication. We incorporate the use of coalition game to form multiple coalitions among smart meters to communicate with the service provider. We show that there exists a stable condition of the coalitions for which the payoff values of the smart meters are maximized. The simulation results show that using the proposed approach, energy consumption by the smart meters can be reduced, which, in turn, would enable green wireless communication in the smart grid.

This paper presents a priority-based MAC-frame payload tuning mechanism with reduced energy consumption for healthcare systems that use Wireless Body Area Networks (WBANs). A fundamental problem in WBANs is to prioritize the physiological sensors depending on several health and external criteria. The challenge is to design a dynamic decision making model that can optimize the energy consumption of each physiological sensor. To address this problem we employ the concept of Fuzzy Inference System (FIS) in order to calculate Criticality Index (CI), which signifies the severity or the priority of the physiological data sensed by each sensor. Considering the obtained CI value we proceed with designing a Markov Decision Process (MDP) based dynamic decision making model in order to tune MAC-frame payload by optimizing the energy consumption of each sensor node. We achieve around 25% decrease in the overall energy consumption using our proposed mechanism.

The standardization of the IEEE 802.15.6 protocol for wireless body area networks (WBANs) dictates the physical layer and medium access control layer standards from the communication perspective. The standard supports short-range, extremely low power wireless communication with high quality of service and data rates upto 10 Mbps in the vicinity of any living tissue. In this paper, we develop a discrete-time Markov model for the accurate analysis of reliability and throughput of an IEEE 802.15.6 CSMA/CA-based WBAN under saturation condition. Existing literature on Markov chain-based analysis of IEEE 802.15.6, however, do not take into consideration the time a node spends waiting for the immediate acknowledgement frame after transmission of a packet, until time-out occurs. In this work, we take into consideration the waiting time for a node after its transmission, and accordingly modified the structure of the discrete-time Markov chain (DTMC). We also show that as the payload length increases, the reliability of a node decreases; whereas its throughput sharply increases.

Routing issues in the existing Smart Grid (SG) literature are focused on Home Area Networks (HANs), Neighborhood Area Networks (NANs), or Wide Area Networks (WANs). Among these, routing in NANs is the most challenging as it entails construction and maintenance of backhaul having various Mesh Routers (MRs). Wireless networks are generally used for communication between backhaul and centralized controller for power distribution. This triggers increased chances of congestion due to scarce resources of available bandwidth and number of channels. Keeping in view of the same, in this paper, we propose a new Efficient Routing Scheme (ERS) as a Bayesian Coalition Game (BCG). The solution strategy integrates the concepts of Learning Automata (LA) in NANs. LA are assumed to be the players in the game, which are deployed at the MRs in NANs. Coalition among the players of the game is scaffolded upon the concepts of Bayesian Networks. Each player in the game is allowed to move from one coalition to another depending upon the payoff function. Corresponding to each move of the player in the game, its action may be rewarded or penalized from the environment. Based upon reward/penalty from the environment, each player updates its action probability vector. The proposed scheme is evaluated with respect to various performance evaluation metrics such as load utilization factor, user satisfaction levels, delay and probability of transmission.

ABSTRACT With the rapid evolution of the Internet, security has become a major area of concern and, consequently, an interesting research area. Different applications transmit sensitive information over the Internet, which creates increased chances for attackers to look into every piece of data, unless it is secured using secure socket layer (SSL) certificate. However, the present SSL certificates too face challenges because of various attacks, and these certificates need to be verified before transmitting information. In this paper, we show how the concepts of learning automata (LA) can be used to verify SSL certificates. The proposed LA‐based system can detect safe or unsafe SSL certificates. The LA reward/penalty scheme is used to build the trust value for SSL certificates. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile cloud computing (MCC) improves the computational capabilities of resource-constrained mobile devices. On the other hand, the mobile users demand a certain level of quality-of-service (QoS) provisioning while they use services from the cloud, even if the interfacing gateway changes due to the mobility of the users. In this paper, we identify, formulate, and address the problem of QoS-guaranteed bandwidth shifting and redistribution among the interfacing gateways for maximizing their utility. Due to node mobility, bandwidth shifting is required for providing QoS-guarantee to the mobile nodes. However, shifting alone is not always sufficient for maintaining QoS-guarantee because of varying spectral efficiency across the associated channels, coupled with the corresponding protocol overhead involved with the computation of utility. We formulate bandwidth redistribution as a utility maximization problem, and solve it using a modified descending bid auction. In the proposed scheme, named as AQUM, each gateway aggregates the demands of all the connecting mobile nodes and makes a bid for the required amount of bandwidth. We investigate the existence of Nash equilibrium (NE) in the proposed solution. Theoretically, we deduce the maximum and minimum selling prices of bandwidth, and prove the convergence of AQUM. Simulation results establish the correctness of the proposed algorithm.

In this paper, we envisage the architecture of Green Wireless Body Area Nanonetwork (GBAN) as a collection of nanodevices, in which each device is capable of communicating in both the molecular and wireless electromagnetic communication modes. The term green refers to the fact that the nanodevices in such a network can harvest energy from their surrounding environment, so that no nanodevice gets old solely due to the reasons attributed to energy depletion. However, the residual energy of a nanodevice can deplete substantially with the lapse of time, if the rate of energy consumption is not comparable with the rate of energy harvesting. It is observed that the rate of energy harvesting is nonlinear and sporadic in nature. So, the management of energy of the nanodevices is fundamentally important. We specifically address this problem in a ubiquitous healthcare monitoring scenario and formulate it as a cooperative Nash Bargaining game. The optimal strategy obtained from the Nash equilibrium solution provides improved network performance in terms of throughput and delay.

This paper presents a Learning Automata (LA)-based QoS (LAQ) framework capable of addressing some of the challenges and demands of various cloud applications. The proposed LAQ framework ensures that the computing resources are used in an efficient manner and are not over- or under-utilized by the consumer applications. Service provisioning can only be guaranteed by continuously monitoring the resource and quantifying various QoS metrics, so that services can be delivered in an on-demand basis with certain levels of guarantee. The proposed framework helps in ensuring guarantees with these metrics in order to provide QoS-enabled cloud services. The performance of the proposed system is evaluated with and without LA, and it is shown that the LA-based solution improves the performance of the system in terms of response time and speed up.

Body-sensors such as accelerometers, oximeters and arm cuff based monitoring systems are used to sense patients' health conditions. Any abnormal behavior of patient's data triggers an alert signal to the health-centers to take action. In this paper, we address the problem of mobile patients' health monitoring using Health-Cloud. When a patient changes his/her location from one place to another, the associated default gateway changes. With the change in the gateway connected to the health-cloud, the optimum mapping between the server and the mobile node also changes. We propose an optimal resource allocation framework for health-cloud to monitor patients' health conditions when they change locations. To optimize the resource allocation problem and provide an optimum mapping between the mobile node and the server, we use an auction theory based solution approach. We evaluate the performance of the proposed scheme numerically. The experimental results show that we receive 20%, 58%, and 61% more utility for the three different cases with respect to the default mapping.

With the elderly population growing around the world, falls increase the risk progressively with age. Those falls can origin injuries that may cause a great dependence and debilitation to the elderly, and even death in extreme cases. This paper reviews the related literature about this topic and introduces a mobile solution for falls prevention, detection, and biofeedback monitoring. The falls prevention system uses collected data from sensors in order to control and advice the patient or even to give instructions to treat an abnormal condition to reduce the falls risk. In cases of prolonged symptoms it can even detect a possible disease. The signal processing algorithms play a key role in a fall prevention system. In real time, based on biofeedback data collection, these algorithms analyses bio-signals to thereby warn the user, when needed. Monitoring and processing data from sensors is performed by a smartphone that will issue warnings to the user and, in gravity situations, send them to a caretaker. The proposed solution for falls prevention and detection is evaluated and validated through a prototype and it is ready for use.

A Wireless Bio-nanosensor Network (WB2N) is a collection of bio-nanodevices having applications in e-health. In this paper, we address the issue of catastrophic collision - a phenomenon which exhibits recurrent collisions of femtosecond-long pulse symbols emanating from the nanodevices in a WB2N. Such type of collision is very serious in these networks due to unique properties of the terahertz band (0.1-10 THz). The existing sate-of-the-art on the issue of coordination for medium access by nano-devices is based on asynchronous exchange of pulses by assigning different symbol rates. The existing method of choosing the symbol rate does not completely avoid catastrophic collision. The severity of collision is further pronounced when molecular absorption noise gets compounded. In essence, a few number of collisions, in turn, invites huge number of such events for the subsequent transmission and eventually degrades the whole network performance. So, it is important to handle such collisions for successful execution of protocols of the higher layers. In present work, we analyze the catastrophic collision in detail and model the collision. The preliminary results exhibit the severity of such collisions in the network. It requires immediate attention in order to accept WB2Ns to be successful e-health system.

Traditional distributed flow scheduling for wireless body sensor networks (WBSNs) is designed based on perfect control channels where the instantaneous control information from the neighbors is available. However, in practice it is very difficult, sometimes even impossible, to obtain this information especially for dynamic WBSNs. This motivates us in this paper to study the distributed flow scheduling with heterogeneous delayed control information (DCI). First, we translate this scheduling problem into a stochastic optimization problem that opens up a new methodology to exploit in a tractable framework. Subsequently, we investigate the relationship between the DCI and scheduling performance, and derive a general performance property bound for any distributed scheduling. Importantly, a class of asynchronous flow scheduling scheme is proposed to achieve the performance bound by making use of the correlation among the time-scale control information.

Due to various operational constraints on the unmanned autonomous vehicle (AUxV) networks operating in an adversarial environment, a fault-tolerant routing scheme is an imperative need. Looking at the risk involved in their applications such as search and rescue, threat surveillance, chemical and biohazard sampling, even a fault of minor nature in the system software/hardware may result in destructive consequences. The AUxV network member nodes vary in architecture, capability, application and power of their internal systems. In such a case it is important that the fault-tolerant scheme should take into consideration the kind of heterogeneity involved and should be able to perform in such a scenario as well. Therefore, to address these issues, in this paper we propose a cross-layer and learning automata (LA) based fault-tolerant routing algorithm for AUxVs, named as ULARC (Unmanned Vehicle Network with LA based Routing using Cross Layer Design). We use the theory of LA for the selection of optimal path for routing between source and destination. In this paper, we also focus on making our proposed strategy an energy-efficient one by using a cross-layer architecture for sleep scheduling of nodes. Further, we have devised an α-based scheduling scheme which further adds to the energy efficiency of our protocol by reducing the overhead on the network.

Shallow water environments exhibit significantly prominent spatio-temporal variability; compared to the ones corresponding to deep oceans. An acoustic signal propagating through a shallow water region faces multiple reflections with the ocean surface as well as with the ocean bottom. So, rendering wireless acoustic communication in such type of unpredictable environment is challenging, as the acoustic signal is subjected to different losses such as multipath fading and absorption. In this paper, we analyze the near ocean surface anomalous behavior of acoustic signals, typically observed in Underwater Wireless Acoustic Communication Networks (UWACNs), due to damping in the presence of sub-surface bubble plumes. Bubbles have profound impact on the acoustical properties in the oceanic environment. The injection of bubble plumes near ocean surface renders the subsurface acoustic signal to behave anomalously.

In this paper, we propose a community detection scheme in an integrated Internet of Things (IoT) and Social Network (SN) architecture. The paper takes a graph mining approach to solve the problem in complex network of IoT and SN. A number of pieces of research literature exist on community detection in SNs; however, no work specifically on integrated IoT and SN architecture addresses this issue. The existing community detection approaches have not considered things into account. We propose the scheme, Community Detection in an Integrated IoT and SN (CDIISN) in which we divide the nodes/actors in complex networks into basic nodes and IoT nodes, and execute the community detection algorithm. We consider two nodes to be in a community, only if the nodes are at most one hop apart and have at least two mutual friends. The smallest community in our case is a subgraph with a cycle of length four. In our approach, a node can be part of multiple communities, and it works well for weighted graphs. Once communities are extracted, we use an access control scheme, based on which access to nodes is provided. This approach of community detection in an integrated environment would find tremendous use in the future, because in the case of any search operation performed by any node, the results obtained intra-community are more relevant than inter-community.

Internet of Things (IOT) is a wireless ad-hoc network of everyday objects collaborating and cooperating with one other in order to accomplish some shared objectives. The envisioned high degrees of association of humans with IOT nodes require equally high degrees of reliability of the network. In order to render this reliability to IOT networks, it is necessary to make them tolerant to faults. In this paper, we propose mixed cross-layered and learning automata (LA)-based fault-tolerant routing protocol for IOTs, which assures successful delivery of packets even in the presence of faults between a pair of source and destination nodes. As this work concerns IOT, the algorithm designed should be highly scalable and should be able to deliver high degrees of performance in a heterogeneous environment. The LA and cross-layer concepts adopted in the proposed approach endow this flexibility to the algorithm so that the same standard can be used across the network. It dynamically adopts itself to the changing environment and, hence, chooses the optimal action. Since energy is a major concern in IOTs, the algorithm performs energy-aware fault-tolerant routing. To save on energy, all the nodes lying in the unused path are put to sleep. Again this sleep scheduling is dynamic and adaptive. The simulation results of the proposed strategy shows an increase in the overall energy-efficiency of the network and decrease in overhead, as compared to the existing protocols we have considered as benchmarks in this study.

Underwater sensor networks (UWSNs) can be deployed for sensing the environment in oceanographic columns and other water bodies in which they are deployed. The peculiar characteristic of the underwater medium, coupled with the queer nature of the sound waves in water, poses an enigmatic problem for UWSN researchers. In this study, the authors focus on the problem of UWSN jamming, which is a popular type of denial-of-service attack. The existing jamming detection solutions for sensor networks are primarily targeted towards the terrestrial ones. In this work, the authors study the unique characteristics of jamming in UWSN, and propose a protocol, known as underwater jamming detection protocol (UWJDP), to detect and mitigate jamming in underwater environments. The results show that if the packet delivery ratio (PDR) is less than or equal to 0.8, the authors have the maximum probability of detecting jamming. The jamming detection ratio is around 2–11% more for the said PDR.

Wireless Sensor Networks (WSN)-based surveillance applications necessitate tracking a target's trajectory with a high degree of precision. Further, target tracking schemes should consider energy consumption in these resource-constrained networks. In this work, we propose an energy-efficient target tracking algorithm, which minimizes the number of nodes in the network that should be activated for tracking the movement of the target. We model the movement of a target based on the Gauss Markov Mobility Model [Camp et al. 2002]. On detecting a target, the cluster head which detects it activates an optimal number of nodes within its cluster, so that these nodes start sensing the target. A Markov Decision Process (MDP)-based framework is designed to adaptively determine the optimal policy for selecting the nodes localized with each cluster. As the distance between the node and the target decreases, the Received Signal Strength (RSS) increases, thereby increasing the precision of the readings of sensing the target at each node. Simulations show that our proposed algorithm is energy-efficient. Also, the accuracy of the tracked trajectory varies between 50% to 1% over time.

This study provides a mechanism to divide the complete geographical extent of wireless sensor networks (WSNs) under attack of a jammer into different zones as per the severity of jamming experienced by various nodes of the network. There are some existing methods such as, ‘Localised Edge Detection in Sensor Field’, ‘Robust Edge Detection in Wireless Sensor Networks’ and ‘JAM: A Jammed Area Mapping Service for Sensor Networks’, that solve similar problems; but all of them are able to map the geographical extent into only two zones – ‘jammed’ and ‘not jammed’, and they all are vulnerable to information warfare as they all require to communicate even while under a jamming attack. The proposed method for zonalisation of the geographical extent of WSNs based on the effects of jamming on various nodes follows the centralised approach, where the mapping is done by the base station through hull tracing of jammed nodes as per their pre-calculated jamming indices thus enforcing the economy of scale, and making it one of the most energy-efficient and fastest-known mapping systems. The method is procedure-centric, as against almost all of the known systems that are protocol-centric, wherein the proposed method dispenses with the need of inter-nodal communications during moments of jamming. The system has no inherent inaccuracies.

Wireless network technologies are undergoing rapid advancements. Researchers are currently envisioning different attractive properties of wireless systems such as the ability to self-organize, self-configure, self-heal, self-manage, and self-maintain. Different wireless networks having the potential to offer cost-effective home and enterprise access networking solutions are being researched. Concepts such as dynamic spectrum access, convergence, unified network architectures, and seamless service access in heterogeneous networks are gaining widespread popularity. Technologies such as Wireless Mesh Networks (WMNs), WiFi, WiMAX, LTE, Bluetooth, ZigBee, RFID, IEEE 802.20, IEEE 802.22, and software defined radio are becoming increasingly popular. Even though these technologies hold great promises for our future, there are several research challenges that need to be addressed. A significant portion of these research challenges are attributed to security and privacy issues in these kinds of networks. This Special Issue has been launched with the aim to publish a few high quality research papers related to the recent advances in the security and privacy of different emerging and next-generation network technologies. We have received a large number of submissions for this Special Issue. However, only a few of papers that have been adjudged to be of relatively high quality as per the results of an independent peer review process could be accepted. They are summarized below. In WLAN security policy management, the standard IP-based access control mechanisms are not sufficient due to dynamic changes in network topology and access control states. The role-based access control (RBAC) models may be appropriate to strengthen the security perimeter over the network resources. Bera et al. have proposed in this paper a WLAN (wireless local area network) security policy management framework based on a formal spatio-temporal RBAC (STRBAC) model. The present work primarily focuses on dynamic computation of security policies based on various control states, its formal representation using STRBAC model and security property verification of the proposed STRBAC model. The proposed policy management framework logically partitions the WLAN topology into various security policy zones named as Central Authentication and Role Server (CARS) and a Global Policy Server (GPS). Each policy zone consists of a policy zone controller (WPZ con) which dynamically computes the low level access configurations. Finally, a SAT based verification procedure has been presented for verifying the security properties of the proposed STRBAC model. Khurana and Gupta have presented an end-to-end algorithm. According to them this is more efficient than the existing one in spatio-temporal way. This algorithm does not require clock synchronization as it is independent of space and time. They have proved that their algorithm is able to detect wormholes with tunnel length greater than or equal to ()rmax where p = , where rmin = minimum communication range and rmax = maximum communication range. They also studied the effect of error in the positions of the node on the wormhole detection capability. With the help of simulations they have shown that detection mechanism is also possible when tunnel length is less than or equal to ()rmax. Shrisat and Bhargava have presented a local, distributed hole detection algorithm for sensor network that identifies the geographical boundary of voids in the network assuming the relative geographic information of only 2-hop neighbors. This algorithm is distributed, O (k) per node computation (for k 2-hop neighbors) and requires synchronization between nodes that are not more than 2-hops away. They have verified it for both uniform and non-uniform distributions. The algorithm takes a local best-effort approach and does not verify if the nodes indeed form a closed polygonal loop. They also discuss the security implications of the hole detection framework in the context of sensor networks. Pathan et al. have proposed a new deployment model of distributed sensor network termed as HDSN(Heterogeneous Distributed Sensor Network). Based on the novel deployment model, they have proposed a secure group association management scheme that could be employed alongside other supplementary security mechanisms for HDSN. They have also presented an efficient pair wise key derivation scheme between two sensor nodes to resist any adversary's attempt. They also discuss the characteristics of HDSN, its scopes. Aparna and Amberker have studied the numerous applications relied upon secure group communication. In some applications many users join and leave the group at the same time known as bursty behavior. They have proposed a scheme for handling all the bursty behavior scenarios and analyzing the communication and computation costs for the worst cases. They have also shown that in comparison to the scheme proposed by Wong et al., their scheme is efficient in terms of encryption and cost of generation. WLM is one of the most prevalent ubiquitous Instant Messaging application programs that dramatically changes the way of communication for human beings in the past decade in all aspects. Few researches have formally incorporated the DF of WLM into generic guidelines for the associate personnel to follow. Cheng Chu et al. have provided the system architecture of the experiment accompanied with their proposed Check Point methodology trying to disclose the possible digital evidences that could be explicitly collected and scientifically presented as probative evidences with respect to the persistently mushrooming information security incidents in the next generation wireless communication networks. As vehicular networks approach deployment phases, there is wide recognition for challenges and pressing needs for solutions with respect to the areas of security, privacy, and performance. One of the stringent requirements in this area is that of protecting the privacy of vehicle owners (i.e., their anonymity and their vehicle's location unlinkability) during their participation in a vehicular network, such as in traffic safety applications. In this paper the authors have presented novel models of concrete anonymity and unlinkability requirements for vehicular networks. One key aspect of their modeling consists of recognizing the existence and impact of additional certification authorities managed by vehicle manufacturers. The resulting vehicular-network key infrastructures satisfy desirable combinations of anonymity, unlinkability, bad actor detection, and performance. One of the main drawbacks of Slotted ALOHA is its throughput collapse at higher traffic load condition due to excessive collisions and known as stability problem. The maximum throughput of Slotted ALOHA can be achieved by the knowledge of the number of active mobile nodes and the average rate of the attacking. Jahangir and Hussein have presented in this paper a self-stabilized slotted ALOHA system against the random packet destruction attacking noise packets. Results show that the system provides nearly optimal stable throughput without the knowledge of current active number of mobile nodes and current attacking packets arrival rate. The proposed system is truly distributive in nature and can be easily implemented in wireless access systems without requiring any centralized control and can defend against random packet destruction Denial of Service (DoS) attack. We are thankful to all those authors who considered submitting their work to this Special Issue, irrespective of whether their papers could be accepted or not. We are thankful to all the Referees, who painstakingly reviewed the papers. Without their hard work and dedication, it would not have been possible to select these high quality papers within the time limits of this Special Issue. We are extremely grateful to the Editor-in-Chief, Professor Hsiao-Hwa Chen, and the Editorial Staff of this Journal for supporting the launch of this Special Issue and providing help whenever it was required.

Abstract A number of security concerns are associated with vehicularad hocnetworks (VANET) – some primarily related with the transmission issues between the vehicles and the base stations, while others related with the privacy of the end‐users. Security in VANET is of significant importance, considering the scale of the possible deployment of VANET and their role as a traffic manager. In this paper, we propose an intrusion detection system (IDS) for a typical VANET scenario. Our solution approach is underlain on the concepts of learning automata (LA). To the best of our knowledge, no attempts have been made so far to develop any LA‐based solution for VANETs. We have designed this system considering the privacy issues involved with the identification of each vehicle. We have evaluated the performance of our proposed solution by conducting a variety of experiments and have found our solution approach to be effective in detecting malicious packets in the system. Specifically, the proposed solution is capable of detecting up to around 90–95% of the malicious packets in the system. Copyright © 2010 John Wiley & Sons, Ltd.

One of the major challenges in the research of mobile ad hoc networks is designing dynamic, scalable, and low cost (in terms of utilization of resources) routing protocols usable in real-world applications. Routing in ad hoc networks has been explored to a large extent over the past decade and different protocols have been proposed. They are based on a two-dimensional view of the ad hoc network geographical region, and are not always realistic. In this article, we propose a bird flight-inspired, highly scalable, dynamic, energy-efficient, and position-based routing protocol called Bird Flight-Inspired Routing Protocol (BFIRP). The proposed protocol is inspired by the navigation of birds over long distances following the great circle arc, the shortest arc connecting two points on the surface of a sphere. This sheds light on how birds save their energy while navigating over thousands of miles. The proposed algorithm can be readily applied in many real-world applications, as it is designed with a realistic three-dimensional view of the network’s geographic region. In the proposed algorithm, each node obtains its location coordinates (X, Y, Z), and speed from the GPS (Global Positioning System); whereas, the destination’s location coordinates (X, Y, Z), and speed are obtained from any other distributed localized service. Based on the location information, the source and each intermediate node choose their immediate neighbor as the next hop that has the maximum priority. The priority is calculated by taking into consideration the energy of the node, the distance between the node and the destination and the degree of closeness of the node to the trajectory of the great circle arc between the current node and the destination. The proposed algorithm is simulated in J-SIM and compared with the algorithms of Ad Hoc On Demand Distance Vector (AODV), and Most Forward Within Distance R (MFR) routing protocols. The results of the simulations show that the proposed BFIRP algorithm is highly scalable, and has low end-to-end delay compared to AODV. The algorithm is also simulated in various scenarios, and the results demonstrate that BFIRP is more efficient than AODV in energy and throughput by 20% and 15% respectively. It also shows satisfactory improvement over MFR in terms of throughput and routing overhead.

Abstract Key revocation involves secure and efficient managing of the information about compromised keys. Spreading the information of revoked keys to the receivers of the key is a challenging task in public key infrastructure (PKI). PKI is more suitable for wired Internet infrastructure and lacks any tailor‐made protocols for extension over anad hocnetwork. The paper presents a MobileAd hocKey Revocation Server (MAKeRS) scheme which proposes to improve the performance and reliability of the system. Simulation shows that the concept presented in the paper is more reliable, faster, and scalable than the existing usage of PKI overAd hocnetworks. It proposes auto‐creation of zone of network availability (ZoNA) by each MAKeRS, which holds the revocation list and is the best service provider in its zone. A node automatically updates the identity of the key revocation server when it enters a new ZoNA. Each node maintains a list of identities of the key revocation servers sorted in order of their communication overhead. This list is regularly updated based on the broadcast from the servers and also gets modified based on the mobility of nodes and servers. The various scenarios of mobility of nodes and servers are considered and the scheme is designed to suit such scenarios in an optimum way. It reduces the time to gain information about the revocation list and ensures availability and, thus, improvement of the system as a whole. Hence, the proposed system results in scalable, reliable, and faster PKI infrastructure and will be attractive for the mobileAd hocnetwork (MANET) users who frequently connect to the Internet for secured transactions. We discuss the architecture as well as the performance of our scheme compared to the popular existing scheme. However, our scheme does not call for the entire change in PKI, but is compatible with the existing scheme. Our simulations show that the proposed scheme is better for key revocation. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The protocols in designed for Wireless Sensor Networks (WSN) have a unique requirement for being of low complexity and energy‐efficient. Due to their possible deployment in remote locations for civil, educational, scientific, and military purposes, security, which includes intrusion detection and intrusion prevention, is of utmost importance. In this paper, we propose a simple, low complexity, and energy‐aware protocol for intrusion detection in WSN. The protocol is self‐learning and distributed in nature. The distributed nature avoids all other nodes being sacrificed when a single node is compromised. The protocol juxtaposes the concept of stochastic learning automata on packet sampling mechanism to achieve an energy aware intrusion detection system. We have rigorously evaluated the performance of our proposed solution by performing a variety of experiments and have found our solution approach to be promising. In the experiments performed the highest achieved packet sapling efficiency was 97%. Copyright © 2010 John Wiley & Sons, Ltd.

Inter-vehicular communication involves the exchange of data between two mobile devices in an ad hoc network. Since the devices are not stationary and the topology is wide, the passage of messages between source and destination nodes involves various intermediate nodes that act as links between the two. The more the number of nodes involved in a network at a time, the more is the power consumed by them, thereby adding to the average power consumption of the network and the transmission time. The authors aim to develop an efficient routing protocol, which finds the minimum possible path length between a source and a destination involving minimum nodes to transmit data. Information regarding the angular position of the nodes is exploited in selecting the most suitable node for transmission, thereby achieving proper network connectivity among nodes with minimum power consumption. The proposed protocol has been compared with dynamic source routing (DSR) and DSR with stale route removed (DSR-SRR). The results achieved by implementing the proposed protocol establish the fact that our protocol is better than DSR and DSR-SRR in terms of the following: (i) average power consumption during transmission, (ii) throughput of transmission and (iii) number of control packets used. The proposed protocol proves to work relatively efficiently even under dense traffic conditions.

Security in wireless mesh networks (WMNs) has always been a major concern ever since the existence of these networks. The open medium and the lack of physical security make the WMNs susceptible to various kinds of attacks. This study addresses the problem of intrusion detection in WMNs. The authors propose a routing protocol that is capable of detecting intrusions, while undertaking the tasks of routing in WMNs. The authors base the routing tasks in the existing protocol on the existing optimised link-state routing protocol. This protocol uses the sampling mechanism for the detection of malicious information in the network. Concepts of learning automata have been introduced to optimise the sampling process. Two new frame formats and its associated handling procedures have been developed. The authors evaluated the performance of our protocol using network simulator 3. In the experiments performed, the highest achieved intrusion detection rate with the proposed protocol was observed to be 94%.

In this paper, we consider a sensor network with either node replacement or battery replacement as the maintenance operation. We address the problem of how the failed nodes are to be replaced, in order to obtain a desirable tradeoff between maintenance cost and network performance, in the management of the network. Since node replacement and battery replacement are analytically identical, we solve this problem only for the network, where the maintenance operation is node replacement. We do this by converting performance loss into cost terms and minimizing the summation of node replacement costs and performance loss costs. We use Markov decision processes (MDP) to develop a probabilistic approach in order to estimate the longrun cost of the network. For this we use statistical data based on the past behaviour of the network. We also propose an algorithm to determine the optimal node-replacement policy. The longrun node replacement cost and the longrun performance loss cost of the simulated network are found to be theoretically consistent.

In this paper, we present a learning-automata-like The reason why the mechanism is not a pure LA, but rather why it yet mimics one, will be clarified in the body of this paper. (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as LAL Random Early Detection (LALRED), is founded on the principles of the operations of existing RED congestion-avoidance mechanisms, augmented with a LAL philosophy. The primary objective of LALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In LALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED methods which were chosen as the benchmarks for performance comparison purposes.

The six papers in this special issue represent both the theoretical and application flavors of adaptive and learning systems.

Wireless sensor networks suffer from the problems of congestion, which lead to packet loss and excessive energy consumption. In this paper, we address both node-level and link-level congestion and propose a new routing protocol namely ant based routing with congestion control (ARCC), for wireless sensor networks, which takes into account the congestion of the network at a given instance and proposes to reduce it and then finds the optimum paths. Also, a comparison of simulation with a few existing works highlights the edge that ARCC has over its contemporaries in terms of various network quality parameters.

The Gnutella protocol of peer-to-peer (P2P) networks has undergone several changes since its inception in the beginning of this century. However, despite the large number of revisions to the original version of the protocol, Gnutella suffers from serious problems of dead searches, complexity in study of network topology and network overloading. In this paper, we report the development of a new P2P simulator, PeerNS, which was built to study different problems of P2P networks and Gnutella, including those mentioned above. PeerNS works on actual P2P network statistics and, hence, it is very close to the real scenario. Moreover, we also discuss the implementation and the integration issues involved in using PeerNS to simulate our crawling-based algorithm, which could minimize the number of dead searches in the network and enhance the availability of information across the network.

A P2P file sharing system implementation on mobile ad-hoc networks is quite tricky to implement as compared to that on a wired network. With the use of Swarm Intelligence, the P2P file Sharing methodology not only has an optimized search process involving a more selective node tracing but also provides a far more time efficient and robust sharing mechanism. A P2P File sharing system implementation poses (a) percentage network area scanned and (b) selective file retrieval from a set of file bearing nodes as the biggest challenge. In this paper, we propose to use another Swarm Intelligence Technique Bees Algorithm - P2PBA (Peer to Peer file sharing - Bees Algorithm) to tackle these issues. Based on the lines of food search behavior of Honey Bees, it optimizes the search process by selectively going to more promising honey sources and scan through a sizeable area. Following a description of the algorithm, the paper gives simulation results for the network against specified parameters that our algorithm proposes to make file sharing technique more efficient.

This paper presents a technique called as Virtual Back off Algorithm (VBA), based on the sequencing technique for efficient media access control. The proposed method minimizes the number of collisions as well as reduces delays during back off periods. We present an analytical study on MAC layer issues that are very important while accessing channel over wireless networks. The VBA method uses fair distributed mechanisms to access channel. We introduce a counter at each node to maintain the discipline of the nodes. The performance of the proposed method is evaluated under various conditions and results are very promising.

Key management involves two aspects: key distribution and key revocation. This paper presents the geographic server distributed model for key revocation which concerns about the security and performance of the system. The concept presented in this paper is more reliable, faster and scalable than the existing revocation techniques used in public key infrastructure (PKI) framework in various countries, as it optimises key authentication in a network. It proposes auto-seeking of a geographically distributed certifying authority's key revocation server, which holds the revocation lists by the client, based on the best service availability. The network is divided itself into the strongest availability zones (SAZ), which automatically allows the new receiver to update the address of the authentication server and replace the old address with the new address of the SAZ, in case it moves to another location in the zone, or in case the server becomes unavailable in the same zone. Our scheme eases out the revocation mechanism and enables key revocation in the legacy systems.

In this paper, we present an adaptive learning (specifically, learning automata) Like (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as learning automata like random early detection (LALRED), is founded on the principles of operations of the existing random early detection (RED) congestion avoidance mechanisms, augmented with a LAL philosophy. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED, which was chosen as the benchmark for performance comparison purposes.

One of the major challenges in wireless sensor network (WSN) research is to curb down congestion in the network's traffic, without compromising with the energy of the sensor nodes. In this work, we address the problem of congestion in the nodes of a WSN using Learning Automata (LA)-based adaptive learning approach. Our primary objective, using this approach, is to adaptively make the processing rate (data packet arrival rate) in the nodes equal to the transmitting rate (packet service rate), so that the occurrence of congestion in the nodes is seamlessly avoided. We maintain that the proposed algorithm, named as Learning Automata-Based Congestion Avoidance Algorithm in Sensor Networks (LACAS), can counter the congestion problem in WSNs effectively. The results obtained through the experiments with respect to important performance criteria showed that the proposed algorithm is capable of successfully avoiding congestion in typical WSNs requiring a reliable congestion control mechanism.

This paper addresses the survivability in existing ATM mesh networks with the goal to (1) compare the network survivability for link and path restorations, (2) to determine the effect of spare capacity distribution schemes on the restoration ratio, and (3) to determine the effect of the choice of candidate paths per node pair on the restoration ratio. It is observed that our results can contribute to enhance the design decisions when dealing with survivable ATM mesh-based network designs, for the predefined restoration objective.

It is possible for vehicles moving on a highway to communicate with each other, if they are equipped with wireless interfaces. These vehicles, equipped with wireless connectivity, are referred to as nodes in a Vehicular Ad Hoc Network (VANET). The more the number of nodes involved in a network at a time, the more is the power consumed by them, thereby adding to the average power consumption of the network and the transmission time. In this paper, we propose an efficient routing protocol, named as Efficient Angular Routing (EAR), which finds the minimum possible path length between a source and a destination involving minimum nodes to transmit data. Information regarding the angular position of the nodes is exploited in selecting the most suitable node for transmission, thereby, achieving proper network connectivity among nodes with minimum power consumption. The proposed protocol has been compared with Dynamic Source Routing (DSR) and DSR-with stale route removed (DSR-SRR). The results achieved establish the fact that the proposed protocol, EAR, outperforms DSR and DSR-SRR in terms of the average power consumption during transmission and the number of control packets used. The proposed protocol proves to work relatively better even under dense traffic conditions.

Routing in ad-hoc networks can consume considerable amount of battery power. However, as the nodes in these networks have limited power, routing is very much energy-constrained. Continuous drainage of energy degrades battery performance as well. If a battery is allowed to intermittently remain in an idle state, it recovers some of its lost charge due to the charge recovery effect, which, in turn, results in prolonged battery life. In this paper, we use the ideas of naturally occurring ants' foraging behavior and based on those ideas we design an energy-aware routing protocol, which not only incorporates the effect of power consumption in routing a packet, but also exploits the multi-path transmission properties of ant swarms and, hence, increases the battery life of a node. The efficiency of the protocol with respect to some of the existing ones has been established through simulations.

In this paper, we address some of the problems such as dead searches, complexity in the study of network topology and network overloading that are associated with Gnutella and Gnutella-like peer-to-peer (P2P) networks. We use advanced heuristic parameters with information shuffling as a solution for them. We propose an advancement of Gnutella using the above-mentioned schemes. At a panoramic level, our work is founded on the following concepts: (a) Crawling the P2P networks to shuffle information, so that the knowledge is distributed over the whole network, and (b) Bringing the information within searchable hops of each network. These have been verified on a self-built P2P simulator, named PeerNS, which works on actual P2P network statistics and is, hence, very close to the actual scenario. The results obtained through simulation affirm that the nodes with extremely large number of dead searches benefit the most and are observed to have a sharp decrease in their dead search count after crawling a small part of the overall network.

The fault-prone nodes in mobile ad-hoc networks (MANETs) degrade the performance of any routing protocol. Using greedy routing mechanisms that tend to choose a single path every time, may cause major data losses, if there is a breakdown of such a path in a fault-prone environment. On the other hand, using all the available paths causes an undesirable amount of overhead on the system. Designing an effective and efficient fault-tolerant routing protocol is inherently hard, since the problem is NP-complete, due to the unavailability of precise path information in adversarial environments. To address the challenges of effective fault-tolerant routing, we present a fault- tolerant routing algorithm (FTAR), based on the ideas of how swarms of natural ants operate. The algorithm is divided into various stages namely initialization, path selection, pheromone deposition, confidence calculation, evaporation and negative reinforcement. Simulation results show that FTAR achieves high packet delivery ratio and throughput as compared to some of the key protocols which do not do fault-tolerance at all. Most importantly, FTAR beats the best fault-tolerant MANET routing algorithm known currently, with respect to the amount of routing overhead incurred, which is an important consideration.

A mobile ad hoc network (MANET) is an infrastructure-less system having no designated access points or routers and it has a dynamic topology. MANETs follow a distributed architecture, in which each node can move randomly in an area of operation. MANETs are vulnerable to various attacks. Security services in these kinds of networks are more complex than in traditional networks. In this paper, we implement a new RSA-threshold cryptography-based scheme for MANETs using verifiable secret sharing (VSS) scheme (Feldman, 1987). Threshold cryptography (TC) provides a promise of securing these networks. The proposed scheme is based on the Chinese remainder theorem (CRT) under the consideration of Asmuth-Bloom secret sharing scheme (Kaya and Selcuk, 2008). To the best of our knowledge, such a work does not exist in MANETs. The proposed scheme is efficient in terms of computational security.

Utilising the battery life and the limited bandwidth available in mobile ad hoc networks (MANETs) in the most efficient manner is an important issue, along with providing security at the network layer. The authors propose, design and describe E2-SCAN, an energy-efficient network layered security solution for MANETs, which protects both routing and packet forwarding functionalities in the context of the on demand distance vector protocol. E2-SCAN is an advanced approach that builds on and improves upon some of the state-of-the-art results available in the literature. The proposed E2-SCAN algorithm protects the routing and data forwarding operations through the same reactive approach, as is provided by the SCAN algorithm. It also enhances the security of the network by detecting and reacting to the malicious nodes. In E2-SCAN, the immediate one-hop neighbour nodes collaboratively monitor. E2-SCAN adopts a modified novel credit strategy to decrease its overhead as the time evolves. Through both analysis and simulation results, the authors demonstrate the effectiveness of E2-SCAN over SCAN in a hostile environment.

Ad hoc bio-sensor networks have a very characteristic structure with three types of nodes: the command centre, the sensor nodes (animals such as rats) and the relaying nodes. We have taken up such networks and measured the throughput of such systems and suggest ways in which the throughput can be increased. It was also found that to increase the throughput of such systems, no sophisticated routing techniques or expensive transmission techniques are needed. This can be achieved by simply adopting the appropriate motion trajectories of the nodes. We have also explained the structure of these networks in detail and the routing issues involved in these networks. A Hot-Spot problem at the command centre has also been discussed. The suggestions of appropriate motions target this problem and show how an even distribution of nodes can alleviate this problem to a large extent. In addition to this, a constraint on the number of messages the sensor node can send per unit time can also make the network more efficient.

One of the major challenges in wireless sensor network (WSN) research is to curb down congestion in the network's traffic, without compromising with the energy of the sensor nodes. Congestion affects the continuous flow of data, loss of information, delay in the arrival of data to the destination and unwanted consumption of significant amount of the very limited amount of energy in the nodes. Obviously, in healthcare WSN applications, particularly in the ones that cater to medical emergencies or in the ones that closely monitor critically ailing patients, it is desirable in the first place to avoid congestion from occurring and even if it occurs, to reduce the loss of data due to congestion. In this work, we address the problem of congestion in the nodes of healthcare WSN using a learning automata (LA)-based approach. Our primary objective in using this approach is to adaptively make the processing rate (data packet arrival rate) in the nodes equal to the transmitting rate (packet service rate), so that the occurrence of congestion in the nodes is seamlessly avoided. We maintain that the proposed algorithm, named as learning automata-based congestion avoidance algorithm in sensor networks (LACAS), can counter the congestion problem in healthcare WSNs effectively. An important feature of LACAS is that it intelligently' learns' from the past and improves its performance significantly as time progresses. Our proposed LA based model was evaluated using simulations representing healthcare WSNs. The results obtained through the experiments with respect to performance criteria having important implications in the healthcare domain, for example, the number of collisions, the energy consumption at the nodes, the network throughput, the number of unicast packets delivered, the number of packets delivered to each node, the signals received and forwarded to the medium access control (MAC) layer, and the change in energy consumption with variation in transmission range, have shown that the proposed algorithm is capable of successfully avoiding congestion in typical healthcare WSNs requiring a reliable congestion control mechanism.

Abstract In wireless sensor networks (WSN), message security is an important concern. The protection of integrity and confidentiality of information and the protection from unauthorized access are important issues. However, due to factors such as resource limitations, absence of centralized access points, open wireless medium and small size of the sensor nodes, the implementation of security in WSN is a challenging task. In this paper, we propose a protocol, quality‐based distance vector routing (QDV), for securing WSN using concepts based on Ant colony optimization ACO [1]. Two fundamental parameters—quality‐of‐service (QoS) and reputation [2]—are used. The high value of reputation of a node signifies that the node is trusted and is more reliable for data communication purposes. As a node shows signs of misbehavior, its reputation decreases, which, in turn, affects its quality‐of‐security QSec [2], thereby disabling the malicious nodes from gaining access to the network. By incorporating these two factors, we are able to distinguish the nodes present in the network. We, then, present a method to achieve “equilibrium” where the node is able to guarantee that its neighbors are secure. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract In this paper, we address the problem of intrusion detection in wireless sensor networks (WSNs) using a learning automata (LA)‐based approach. We are not aware of any LA‐based intrusion detection systems (IDSs) for WSN. Additionally, the S‐model approach that we have taken to solve the problem, wherein the feedback of the environment to the automaton can not only be completely favorable or completely unfavorable, but also be any continuous value within these extremities, makes it one of the attractive solution approaches in LA. We have rigorously evaluated the performance of our proposed solution by performing a variety of experiments and have found our solution approach to be promising. Copyright © 2008 John Wiley & Sons, Ltd.

In wireless sensor networks (WSNs), message security is an important concern. The protection of integrity and confidentiality of information and the protection from unauthorized access are important issues. However, due to factors such as resource limitations, absence of centralized access points, open wireless medium and small size of the sensor nodes, the implementation of security in WSNs is a challenging task. In this paper, we propose a protocol, quality-based distance vector routing (QDV), for securing WSNs using concepts based on ant colony optimization (ACO). Two fundamental parameters: quality-of-service (QoS) and reputation are used. The high value of reputation of a node signifies that the node is trusted and is more reliable for data communication purposes. As a node shows signs of misbehavior, its reputation decreases, which, in turn, affect its quality-of-security (QSec), thereby disabling the malicious nodes from gaining access to the network. By incorporating these two factors, we are able to distinguish the nodes present in the network. We, then, present a method to achieve "equilibrium" where the node is able to guarantee that its neighbors are secure.

In this paper, we address the problem of intrusion detection in wireless sensor networks (WSNs) using a learning automata (LA)-based approach. We are not aware of any LA-based intrusion detection systems (IDSs) solutions for WSNs. Additionally, the S-model approach that we have taken to solve the problem, where in the feedback of the environment to the automaton can not only be completely favourable or completely unfavourable, but also be any continuous value within these extremities, makes it one of the attractive solution approaches in LA. We have rigorously evaluated the performance of our proposed solution by performing a variety of experiments and have found our solution approach to be promising.

Owing to the growing demand for low-cost ‘networkable’ sensors in conjunction with recent developments of micro-electro mechanical system (MEMS) and radio frequency (RF) technology, new sensors come with advanced functionalities for processing and communication. Since these nodes are normally very small and powered with irreplaceable batteries, efficient use of energy is paramount and one of the most challenging tasks in designing wireless sensor networks (WSN). A new energy-aware WSN routing protocol, reliable and energy efficient protocol (REEP), which is proposed, makes sensor nodes establish more reliable and energy-efficient paths for data transmission. The performance of REEP has been evaluated under different scenarios, and has been found to be superior to the popular data-centric routing protocol, directed-diffusion (DD) (discussed by Intanagonwiwat et al. in ‘Directed diffusion for wireless sensor networking’ IEEE/ACM Trans. Netw., 2003, 11(1), pp. 2–16), used as the benchmark.

This paper presents an efficient adaptive online routing algorithm for the computation of bandwidth-guaranteed paths in multiprotocol label switching witching (MPLS)-based networks by using a learning scheme that computes an optimal ordering of routes. The contribution of this work is twofold. The first is that we propose a new class of solutions other than those available in the literature, incorporating the family of stochastic random races (RR) algorithms. The most popular previously proposed MPLS-based traffic engineering (TE) solutions attempt to find a superior path to route an incoming setup request. Our algorithm, on the other hand, tries to learn an optimal ordering of the paths through which requests can be routed according to the rank of the paths in the order learned by the algorithm. The second contribution of our work is that we have proposed a routing algorithm that has a performance superior to the important algorithms in the literature. Our conclusions are based on three important performance criteria: 1) the rejection ratio, 2) the percentage of accepted bandwidth, and 3) the average route computation time per request. Although some of the previously proposed algorithms were designed to achieve low rejection and high throughput of route requests, they are unreasonably slow. Our algorithm, on the other hand, in general attempts to reject the least number of requests, achieves the highest throughput, and computes routes in the fastest possible time when compared to the algorithms that we used as benchmarks for comparison.

Designing routing schemes that would successfully operate in the presence of adversarial environments in mobile ad hoc networks (MANETs) is a challenging issue. In this paper we discuss fault-tolerant routing schemes where there are malfunctioning nodes in the network. Most existing MANET protocols were postulated considering scenarios where all the mobile nodes in the ad hoc network function properly, and in an idealistic manner. However, adversarial environments are common in MANET environments, and there are misbehaving nodes that degrade the performance of these routing protocols. The need for fault tolerant routing protocols was identified to address routing in adversarial environments in the presence of faulty nodes by exploring network redundancies in networks. In this paper, we present a new fault-tolerant routing scheme using a stochastic learning-based weak estimation procedure. The superiority of our algorithm, as compared to the existing algorithms, was experimentally established

This paper presents a new solution to the dynamic all-pairs shortest path routing problem, using a linear reinforcement learning scheme. The particular instance of the problem that we have investigated concerns finding the all-pairs shortest paths in a stochastic graph, where there are continuous probabilistically-based updates in edge-weights. We present the details of the algorithm with an illustrative example. The algorithm can be used to find the all-pairs shortest paths for the "statistical" average graph, and the solution converges irrespective of whether there are new changes in edge-weights or not. On the other hand, the existing algorithms will fail to exhibit such a behavior and would recalculate the affected shortest paths after each edge-weight update. There are two important contributions of the proposed algorithm. The first contribution is that not all the edges in a stochastic graph are probed and, even if they are, they are not all probed equally often. Indeed, the algorithm attempts to almost always probe only those edges that will be included in the final list involving all pairs of nodes in the graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. The second contribution is designing a data-structure, the elements of which represent the probability that a particular edge in the graph lies in the shortest path between a pair of nodes in the graph. All the algorithms were tested in environments where edge-weights change stochastically and where the graph topologies undergo multiple simultaneous edge-weight updates. Its superiority in terms of the average number of processed nodes, scanned edges, and the time per update operation, when compared with the existing algorithms, was experimentally established.

This paper presents a new solution to the dynamic all-pairs shortest path routing problem, using a linear reinforcement learning scheme. It involves finding the shortest path in a stochastic network, where there are continuous probabilistically-based updates in link-costs. In this paper we present the details of the algorithm and also provide an example to illustrate how the algorithm would function. The initial experimental results of the algorithm show that the algorithm is few orders of magnitude superior to the algorithms available in the literature. It can be used to find the shortest path (between all pairs of nodes in a network) within the "statistical" average network, which converges irrespective of whether there are new changes in link-costs or not. On the other hand, the existing algorithms fails to exhibit such a behavior and would recalculate the affected shortest paths after each link-cost update.

This paper presents the first Learning Automaton-based solution to the dynamic single source shortest path problem. It involves finding the shortest path in a single-source stochastic graph topology where there are continuous probabilistic updates in the edge-weights. The algorithm is significantly more efficient than the existing solutions, and can be used to find the "statistical" shortest path tree in the "average" graph topology. It converges to this solution irrespective of whether there are new changes in edge-weights taking place or not. In such random settings, the proposed learning automata solution converges to the set of shortest paths. On the other hand, the existing algorithms will fail to exhibit such a behavior, and would recalculate the affected shortest paths after each weight-change. The important contribution of the proposed algorithm is that all the edges in a stochastic graph are not probed, and even if they are, they are not all probed equally often. Indeed, the algorithm attempts to almost always probe only those edges that will be included in the shortest path graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. All the algorithms were tested in environments where edge-weights change stochastically, and where the graph topologies undergo multiple simultaneous edge-weight updates. Its superiority in terms of the average number of processed nodes, scanned edges and the time per update operation, when compared with the existing algorithms, was experimentally established. The algorithm can be applicable in domains ranging from ground transportation to aerospace, from civilian applications to military, from spatial database applications to telecommunications networking.

This paper presents a new efficient solution to the dynamic single source shortest path routing problem, using the principles of generalized pursuit learning. It involves finding the shortest path in a stochastic network, where there are continuous probabilistically based updates in link-costs. The algorithm has been rigorously experimentally evaluated and has been found to be a few orders of magnitude superior to the algorithms available in the literature. It can be used to find the shortest path within the "statistical" average network, which converges irrespective of whether there are new changes in link-costs or not. On the other hand, the existing algorithms would fail to exhibit such a behavior and would recalculate the affected shortest paths after each link-cost update.