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With the help of wireless power transfer (WPT) technology, the mobile charger (MC) can transfer energy to the sensor nodes. This technology provides a new solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). However, most of the existing studies focused on improving the charging efficiency or minimizing the latency, while very few studies improved the data accuracy of the network. This article proposes an efficient-energy recharging schedule for MC, aiming to maximize the data accuracy of the given network. Initially, the data accuracy of each sensor is measured by considering the spatial and temporal qualities. The proposed recharging schedule considers the spatial quality contribution of each recharging requested sensor. In addition, an energy management strategy is proposed for each requested sensor to locally adjust the sensing time sequence, aiming to improve the temporal quality. Each sensor might have a different energy consumption rate; therefore, this study also formulates an adaptive recharging request threshold for the sensor nodes, which is suitable for real applications. The experimental study shows that the proposed algorithm outperforms the literature in terms of data accuracy and recharged sensor’s spatial quality contributions. [ABSTRACT FROM AUTHOR]

The vital dependence of peer to peer (P2P) energy trading frameworks on creative Internet of Things (IoT) has been making it more vulnerable against a wide scope of attacks and performance bottlenecks like low throughput, high latency, high CPU, memory use, etc. This hence compromises the energy exchanging information to store, share, oversee, and access. Blockchain innovation as a feasible solution, works with the rule of untrusted members. To alleviate this threat and performance issues, this paper presents a Blockchain based Confidential Consortium (CoCo) P2P energy trading system that works on the trust issues among the energy exchanging networks and limits performance parameters. It reduces the duplicate validation by creating a trusted network on nodes, where participants identities are known and controlled. A Java-script-based smart contract is sent over the Microsoft CoCo system with Proof of Elapsed Time (PoET) consensus protocol. Also, a functional model is designed for the proposed framework and the performance bench-marking has been done considering about latency, throughput, transaction rate control, success and fail transaction, CPU and memory usage, network traffic. Additionally, it is shown that PoET's performance is superior to proof of work (PoW) for multi-hosting conditions. The measured throughput and latency moving toward database speeds with more flexible, business-specific confidentiality models, network policy management through distributed governance, support for non-deterministic transactions, and reduced energy consumption. [ABSTRACT FROM AUTHOR]

Wireless recharging using a mobile charger has been widely discussed in recent years. Most of them considered that all sensors were equally important and aimed to maximize the number of recharged sensors. The purpose of energy recharging is to extend the lifetime of sensors whose major work is to maximize the surveillance quality. In a randomly deployed wireless rechargeable sensor network, the surveillance quality highly depends on the contributions of coverage and network connectivity of each sensor. Instead of considering maximizing the number of recharged sensors, this article further takes into consideration the contributions of coverage and network connectivity of each sensor when making the decision of recharging schedule, aiming to maximize the surveillance quality and improve the number of data collected from sensors to the sink node. This article proposes an energy recharging mechanism, called an energy recharging mechanism for maximizing the surveillance quality of a given WRSNs (ERSQ), which partitions the monitoring region into several equal-sized grids and considers the important factors, including coverage contribution, network connectivity contribution, the remaining energy as well as the path length cost of each grid, aiming to maximize surveillance quality for a given wireless sensor network. Performance studies reveal that the proposed ERSQ outperforms existing recharging mechanisms in terms of the coverage, the number of working sensors as well as the effectiveness index of working sensors. [ABSTRACT FROM AUTHOR]

Data collection using mobile sink has received much attention in the past years. In the literature, most studies assumed that the mobile sink was controllable and the proposed algorithm made decisions to control the path, speed, and stop locations. However, there still exists some challenges in using the controllable mobile sink, including limited energy and the adaptive capability for overcoming the obstacles. This article considers the bus as the uncontrollable mobile sink to collect data from a given set of deployed static sensors. A distributed data collection mechanism, called energy balanced multihop data collection (EBMDC), is proposed that aims at maximizing the transmitted data volume from sensors to the bus while prolonging the network lifetime of the wireless sensor networks (WSNs). To prolong the network lifetime, each sensor node distributes its data to its multiple parents in trees according to their remaining energies. Then, each anchor node locally schedules its transmission slots based on its and its neighbor's data volumes and remaining energies such that all data can be forwarded to the bus without collision and the lifetime of each anchor can be balanced. Experimental studies reveal that the proposed EBMDC algorithm outperforms related studies in terms of throughput, network lifetime, slot utilization, data loss ratio, and fairness. [ABSTRACT FROM AUTHOR]

Data collection is one of the most important issues in Wireless Sensor Networks (WSNs). Many data collection algorithms have been proposed for collecting data using multiple mobile sinks. However, the anchor selection strategy, which significantly impacts the network lifetime, still can be improved. In addition, the collaboration between multiple mobile sinks is still rare, resulting in the long latency of each round of data collection. This paper proposes an Exploring Cooperative Opportunities, Reducing Latency and Prolong Network Lifetime algorithm for Data Collection, called CORL2, aiming to improve the network lifetime and reduce the latency of data collection. Initially, the proposed CORL2 partitions the monitoring region into several equal-sized subregions and assigns each mobile sink to each subregion. Then CORL2 further partitions each subregion into several equal-sized grids and constructs a path passing through all grids in that subregion. To reduce the energy consumption for packet forwarding, the proposed CORL2 further partitions each grid into several small grids and adjusts the path such that it can pass through high-density small grids, aiming to maximize the number of anchors and prolong the network lifetime. Then the cooperative strategy is applied to cooperatively reduce the maximal latency of data collection in all subregions and balance the data collection workload between neighboring mobile sinks. Performance evaluations reveal that the proposed CORL2 outperforms existing data collection algorithms in terms of latency, energy consumptions, the workload balancing improving index(WBII), and network lifetime for data collection in the given WSNs. [ABSTRACT FROM AUTHOR]

Data collection is one of the most fundamental and important operations in applications of wireless sensor networks (WSNs). Recent research has shown that using mobile sink to collect data brings many advantages to WSNs, such as improving energy efficiency of sensor nodes and prolonging network lifetime. However, the limited speed of mobile sink leads to larger latency, which cause that the data is not fresh or the buffer overflow problem of static sensor nodes. Based on the adaptive rate control, this paper proposes a data collection algorithm which aims to reduce the length of path along which mobile sink collects data from static sensor nodes. The proposed algorithm not only guarantees the completeness of data collection from each static node but also reduces path length by adopting appreciate transmission rate. Through extensive simulations, experimental results show that our approach can notably reduce the path length of mobile sink, while the data of every sensor can be collected completely and efficiently. [ABSTRACT FROM AUTHOR]

With the emergence of Internet of Things (IoT) and allied applications for smart cities, sustainability goals have seen a prominent emphasis. This paper focuses on the energy efficiency aspect of such sustainable smart city goals. Although energy efficiency has been studied at different levels of a smart city's Information and Communication Technology (ICT) infrastructure, this paper specially focuses on device level energy minimization strategy by means of modelling the energy consumption while accounting for the Clusterheads (CluH) and duty cycling and thereby using evolutionary algorithms. In this paper, a Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO) has been used to solve the energy minimization problem. Simulation experiments carried out for different scenarios with varying configuration demonstrate the efficacy of the hybrid neuro evolutionary scheme. [Display omitted] • IoT-based energy optimization model for smart city scenarios. • Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO). • Several output metrics, such as the number of alive nodes, load, residual energy, and cost function, were used to pick the best cluster head nodes in IoT network clusters. • The proposed method enacts an intelligent duty cycling by predicting sleep–wake cycles. [ABSTRACT FROM AUTHOR]