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Wireless sensor networks (WSNs) is a large network of small-sized sensor nodes with limited power capacity which monitor specific points (targets) and transmit the collected information wirelessly. Activating every sensor to monitor the targets simultaneously utilizes its limited energy and deteriorates the network's lifetime faster. Thus, monitoring the entire target and increasing network's lifetime are crucial and integral problems of WSN for setting up energy-efficient monitoring in the network. A countermeasure of this issue is portioning the sensor nodes into independent sets with the constraint that each set should monitor the entire targets and activating them one after the other helps us to provide energy-efficient monitoring in the network. This process is collectively termed as set k -cover problem and the independent sensor sets are termed as sensor covers. Thus, identifying maximum number of sensor covers from the considered sensor set is the challenging problem in set k -cover problem. As Graph theory plays a critical role to solve various problems in WSNs, this paper a vertex coloring based sensor scheduling and deployment algorithm is proposed to determine maximum number of sensor covers and optimal sensor positioning. In order to assess the efficiency of the proposed algorithm, the mathematical upper bound is estimated and the maximum number of covers obtained using the proposed algorithm is compared with it. Also, the proposed algorithm is implemented with existing random algorithm, cuckoo search algorithm and genetic algorithm. In both the estimation, the solution reveals that the proposed algorithm provides energy-efficient monitoring. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is a well‐developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error‐free data and run an energy‐efficient network. In this paper, we propose a sling‐shot spider optimization (S2SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)‐based networks. The S2SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1‐hop, 2‐hop, and multi‐hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio‐inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has developed a well-defined infrastructure due to commercializing novel technologies. IoT networks enable smart devices to compile environmental information and transmit it to demanding users through an IoT gateway. The explosive increase of IoT users and sensors causes network bottlenecks, leading to significant energy depletion in IoT devices. The wireless network is a robust, empirically significant, and IoT layer based on progressive characteristics. The development of energy-efficient routing protocols for learning purposes is critical due to environmental volatility, unpredictability, and randomness in the wireless network's weight distribution. To achieve this critical need, learning-based routing systems are emerging as potential candidates due to their high degree of flexibility and accuracy. However, routing becomes more challenging in dynamic IoT networks due to the time-varying characteristics of link connections and access status. Hence, modern learning-based routing systems must be capable of adapting in real-time to network changes. This research presents an intelligent fault detection, energy-efficient, quality-of-service routing technique based on reinforcement learning to find the optimum route with the least amount of end-to-end latency. However, the cluster head selection is dependent on residual energy from the cluster nodes that reduce the entire network's existence. Consequently, it extends the network's lifetime, overcomes the data transmission's energy usage, and improves network robustness. The experimental results indicate that network efficiency has been successfully enhanced by fault-tolerance strategies that include highly trusted computing capabilities, thus decreasing the risk of network failure. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are composed of several sensor nodes which are limited in terms of resources for computing, storage, communication bandwidth, and, most importantly of all, energy. One of the major challenges in these networks is congestion, which is triggered by factors such as packet collision, node buffer overflow, transmission channel contention, transmission rate, and many-to-one data transmission from several nodes to the sink node. Congestion affects energy consumption and various parameters of service quality in sensor nodes. Therefore, it is one of the most critical issues in WSNs that requires developing more advanced techniques for its avoidance, diagnosis, and control. In this paper, different methods of congestion control have been investigated; also, a novel taxonomy has been proposed based on combination of concentration of congestion control policies, different algorithms employed, and the effective parameter in detecting and controlling congestion. [ABSTRACT FROM AUTHOR]

Summary: Packet size optimization is a critical issue in wireless sensor networks (WSNs) for improving many performance metrics (eg, network lifetime, delay, throughput, and reliability). In WSNs, longer packets may experience higher loss rates due to harsh channel conditions. On the other hand, shorter packets may suffer from greater overhead. Hence, the optimal packet size must be chosen to enhance various performance metrics of WSNs. To this end, many approaches have been proposed to determine the optimum packet size in WSNs. In the literature, packet size optimization studies focus on a specific application or deployment environment. However, there is no comprehensive and recent survey paper that categorizes these different approaches. To address this need, in this paper, recent studies and techniques on data packet size optimization for terrestrial WSNs, underwater WSNs, wireless underground sensor networks, and body area sensor networks are reviewed to motivate the research community to further investigate this promising research area. The main objective of this paper is to provide a better understanding of different packet size optimization approaches used in different types of sensor networks and applications as well as introduce open research issues and challenges in this area. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which are used to monitor events or environmental parameters, such as movement, temperature, humidity, etc. Reinforcement learning (RL) has been applied in a wide range of schemes in WSNs, such as cooperative communication, routing and rate control, so that the sensors and sink nodes are able to observe and carry out optimal actions on their respective operating environment for network and application performance enhancements. This article provides an extensive review on the application of RL to WSNs. This covers many components and features of RL, such as state, action and reward. This article presents how most schemes in WSNs have been approached using the traditional and enhanced RL models and algorithms. It also presents performance enhancements brought about by the RL algorithms, and open issues associated with the application of RL in WSNs. This article aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive and applicable to readers outside the specialty of both RL and WSNs. [ABSTRACT FROM AUTHOR]

Task allocation is an important issue in wireless sensor networks (WSNs), and the existing traditional solutions to this problem in high-performance computing cannot be directly implemented in WSNs because of limitations such as resource availability and shared communication medium. In this paper, we address the task allocation problem for a heterogeneous WSN, and a trust dynamic task allocation algorithm is proposed. Firstly, to ensure the nodes in the same coalition are mutually closer in distance, a discrete particle swarm optimization (PSO) is designed to generate a structure of the parallel coalitions. Secondly, in order to minimize the execution time of the tasks, save the energy cost of the nodes and balance the load of the network, we design task strategies and payoff functions by invoking the game theory in WSNs and propose a PSO with the redesigned fitness function to find the Nash equilibrium point for the purpose of improving the effectiveness of scheduling and the reliability of the network. In this step, the sink node will play the role of trust manager, and it will allocate tasks based on the Nash equilibrium point, which is a trust solution to make sure all tasks can be finished. Finally, the extensive experiments are conducted to compare our algorithm with two other algorithms. The experimental results show the feasibility and effectiveness of our algorithm, which can obtain a good balance between local solution and global exploration and achieve superior energy efficiency and network reliability within a short period. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a new method of data authentication and encryption for distributed networks supporting mobile software agents. Software agents are a valuable instrument in wireless distributed monitoring networks, because they can be used to concentrate monitoring efforts in certain areas where an event is taking place. In this way, events can be tracked in a dynamic and efficient way. Mobile agents have to send messages to each other in order to coordinate their actions, and those messages need to be secured by crypto credentials. However, when agents are moved over wireless networks, how can credentials be protected from sniffing by an attacker, besides layer II encryption? Moreover, if a rogue agent is injected in the network, is it possible to limit the damages it can produce? The proposed approach bridges mediated RSA with the trusted platform modules, in order to provide an efficient and secure communication between agents. The communication is secured using indeed Identity based cryptography, while maintaing the compatibility with standard RSA and eliminating the mediator introduced by mediated RSA. We will show that this approach is convenient in terms of traffic overhead, perfectly applicable to existing trusted platform modules specifications and able to limit damages that both external and internal attackers can produce to the network. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Protocols for sensor networks have traditionally been designed using the best effort delivery model. However, there are many specific applications that need reliable transmissions. In event-driven wireless sensor networks, the occurrence of an event may generate a large amount of data in a very short time. Among them, some critical urgent information needs to be transmitted reliably in a timely manner. In this scenario, congestion is inevitable because of the constraints in available resources. How to control the congestion is very important for the reliable transmission of urgent information. To address this problem, we propose a queue-based congestion detection and a multistage rate control mechanism. In our proposed mechanism, not only the current queue length but also the queue fluctuation are adopted as indications of congestion. Each sensor node evaluates its congestion level locally and determines its congestion state with a state machine. We design a multistage rate adjustment mechanism for nodes to adjust their rates depending on their congestion states. We also distinguish high-priority critical traffic from low-priority non-critical traffic. Extensive simulation results confirm the superior performance of our proposed protocol with respect to throughput, loss probability, and delay.Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Relay selection schemes for cooperative communications to achieve full cooperative diversity gains while maintaining spectral and energy efficiency have been extensively studied in a recent research. These schemes select only the best relay from multiple relaying candidates to cooperate with a communication link. In the present paper, we reviewed recently proposed cooperative communication protocols that integrate with relay selection mechanisms. The key design issues for relay selection mechanisms, for example, relaying candidate selection, optimal relay assignment, and cooperative transmission, were identified. We further discussed the challenges of optimal relay assignment in multi-hop wireless sensor networks and presented the potential applications of cooperative communications with a relay selection in such networks. Future research directions were outlined, for example, the issues of service differentiation and system fairness in cooperative communication systems and the joint use of game theory and adaptive learning techniques in relaying candidate selection and optimal-relay assignment mechanisms for efficient allocation of network resources. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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

The inherent temporal connectivity and existence of impairments in wireless channels pose challenges to network performance. Cooperative communication has been proposed as an effective technique to mitigate the imperfections of the wireless medium by exploiting channel diversity and availability of neighboring nodes that can act as relays. Although numerous cooperative communication techniques have been proposed in the literature, most of them do not consider Quality of Service (QoS) issues in a wireless sensor network. In this work, we study how cooperative communication can be applied to achieve differentiated QoS in a sensor network that uses Ultra-Wideband (UWB) as its underlying PHY layer technology. SAUCeR is a slotted-aloha based ultra-wideband medium access control protocol with cooperative retransmissions that provides differentiated QoS in networks with varying traffic classes. Despite the high transmission rates provided by UWB, its impulse-based nature renders many conventional carrier sensing MAC protocols incompatible. Consequently, SAUCeR utilizes slotted-aloha to reduce packet collisions without the need for carrier sensing. Differentiated QoS is provided by allocating different resources (time slots) to varying traffic classes to segregate the contention between them. A QoS-aware cooperative retransmission technique and two distributed relay selection schemes are also introduced to improve overall traffic throughput and reduce end-to-end delay, while preventing the starvation of any traffic class. Copyright © 2010 John Wiley & Sons, Ltd. SAUCeR allocates disjoint resources to different traffic classes to segregate contention between them, and to provide better Quality of Service (QoS) to traffic with higher priority. A Low Priority (LP) node that has overheard a failed transmission from a High Priority (HP) node can also help to cooperatively retransmit data to the destination (dest). This reduces packet delays, increases QoS of higher priority traffic, as well as improves overall transmission quality and network performance. [ABSTRACT FROM AUTHOR]

Summary: In a wireless powered communication network (WPCN), sensor nodes harvest energy to transmit information. By a harvest‐then‐transmit (HT) protocol, nodes can be classified into either energy receiving (ER) or data transmitting (DT) nodes depending on the current level of the harvested energy. Since nodes may join or leave a network any time and energy levels vary, the distribution of ER and DT nodes changes over time. As the number of contending DT nodes is highly dynamic, a quick learning mechanism is required for an access point (AP). We propose a learning AP that learns from experience and adapts the frame size according to the changes in the number of DT nodes. The proposed learning AP is also shown to learn well and react to the situation. We compare the performances of the proposed learning mechanism with a WPCN and conventional HT FSA schemes. The proposed RL scheme outperforms the comparative schemes in terms of success rate and delay. [ABSTRACT FROM AUTHOR]

Summary: Wireless camera sensor networks (WCSNs) possess a powerful physical environment monitoring capability. Camera nodes with adjustable monitoring directions further improve their flexibility. This study focuses on tracking multiple mobile targets to investigate the node scheduling and target location evaluation strategy of WCSNs on the basis of rotating nodes. By referring to existing research, this study improves the camera node monitoring and rotation model and proposes three network performance evaluation indicators. The proposed algorithm schedules nodes and their monitoring directions by using the unutilized energy of the nodes and the number of monitored targets. It also predicts the moving trends of the targets and selects active nodes by using the locations and linear speeds of the targets. Experimental results show that the proposed algorithm has a high target tracking accuracy. Compared with traditional target tracking algorithms, the proposed algorithm can effectively reduce the number of active nodes, balance the energy consumption between nodes, and prolong network lifetime. [ABSTRACT FROM AUTHOR]