A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks

The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of studies have been carried out over the last decade in this regard. However, no comprehensive survey exists to compile the state-of-the-art literature and provide insight into future research directions. To fill this gap, we put forward a detailed survey on mobile charging techniques (MCTs) in WRSNs. In particular, we first describe the network model, various WPT techniques with empirical models, system design issues and performance metrics concerning the MCTs. Next, we introduce an exhaustive taxonomy of the MCTs based on various design attributes and then review the literature by categorizing it into periodic and on-demand charging techniques. In addition, we compare the state-of-the-art MCTs in terms of objectives, constraints, solution approaches, charging options, design issues, performance metrics, evaluation methods, and limitations. Finally, we highlight some potential directions for future research

A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes\u27 optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets

Location Error Minimization with the Help of Run Time Coordinates Estimation Method

The energy is the limited resource of communication in Wireless Sensor Network (WSN). The nodes proper functions in WSN are depend on the battery power. The each node in network are mobile and having different mobility speed. The topology in WSN is forming completely dynamic and change according to time instance. The signal strength of node/s is varying according to power capacity of nodes. The less energy of sensor nodes is shows weak signal strength that means having weak Received Signal Strength (RSS). If the signal strength of nodes are reduced that means the nodes have insufficient energy. In this research we proposed the Location based RSS scheme to improve energy utilization.  In this research we compare the performance of protocols like existing AIES-RSS and proposed Location based RSS. The performance of proposed scheme is better than AIES-RSS and the performance of proposed scheme is provides better routing performance in WSN as compare to AIES-RSS. If the RSS of any node in network is weak that means the nodes energy level is down. If the node/s having sufficient amount of energy then their signal strength is high. The Location records of sensor nodes are provides the information of location that’s why routing efficiency is improves and also the energy consumption is reduced. The proposed method is improves the energy utilization and also the residual energy cost is maximum after complete simulation. The proposed scheme is provides the strong connection by that the packet dropping and overhead is minimized. Keywords:- RSS, Routing, Location, AIES-RSS, Energy, proposed RSS, WSN

Mitigating the Event and Effect of Energy Holes in Multi-hop Wireless Sensor Networks Using an Ultra-Low Power Wake-up Receiver and an Energy Scheduling Technique

This research work presents an algorithm for extending network lifetime in multi-hop wireless sensor networks (WSN). WSNs face energy gap issues around sink nodes due to the transmission of large amounts of data through nearby sensor nodes. The limited power supply to the nodes limits the lifetime of the network, which makes energy efficiency crucial. Multi-hop communication has been proposed as an efficient strategy, but its power consumption remains a research challenge. In this study, an algorithm is developed to mitigate energy holes around the sink nodes by using a modified ultra-low-power wake-up receiver and an energy scheduling technique. Efficient power scheduling reduces the power consumption of the relay node, and when the residual power of the sensor node falls below a defined threshold, the power emitters charge the nodes to eliminate energy-hole problems. The modified wake-up receiver improves sensor sensitivity while staying within the micro-power budget. This study's simulations showed that the developed RF energy harvesting algorithm outperformed previous work, achieving a 30% improvement in average charged energy (AEC), a 0.41% improvement in average energy (AEH), an 8.39% improvement in the number of energy transmitters, an 8.59% improvement in throughput, and a 0.19 decrease in outage probability compared to the existing network lifetime enhancement of multi-hop wireless sensor networks by RF Energy Harvesting algorithm. Overall, the enhanced power efficiency technique significantly improves the performance of WSNs

An Efficient Wireless Recharging Mechanism for Achieving Perpetual Lifetime of Wireless Sensor Networks

[[abstract]]Energy recharging has received much attention in recent years. Several recharging mechanisms were proposed for achieving perpetual lifetime of a given Wireless Sensor Network (WSN). However, most of them require a mobile recharger to visit each sensor and then perform the recharging task, which increases the length of the recharging path. Another common weakness of these works is the requirement for the mobile recharger to stop at the location of each sensor. As a result, it is impossible for recharger to move with a constant speed, leading to inefficient movement. To improve the recharging efficiency, this paper takes “recharging while moving” into consideration when constructing the recharging path. We propose a Recharging Path Construction (RPC) mechanism, which enables the mobile recharger to recharge all sensors using a constant speed, aiming to minimize the length of recharging path and improve the recharging efficiency while achieving the requirement of perpetual network lifetime of a given WSN. Performance studies reveal that the proposed RPC outperforms existing proposals in terms of path length and energy utilization index, as well as visiting cycle.[[notice]]補正完

A Virtual Infrastructure for Mitigating Typical Challenges in Sensor Networks

Sensor networks have their own distinguishing characteristics that set them apart from other types of networks. Typically, the sensors are deployed in large numbers and in random fashion and the resulting sensor network is expected to self-organize in support of the mission for which it was deployed. Because of the random deployment of sensors that are often scattered from an overflying aircraft, the resulting network is not easy to manage since the sensors do not know their location, do not know how to aggregate their sensory data and where and how to route the aggregated data. The limited energy budget available to sensors makes things much worse. To save their energy, sensors have to sleep and wake up asynchronously. However, while promoting energy awareness, these actions continually change the underlying network topology and make the basic network protocols more complex. Several techniques have been proposed in different areas of sensor networks. Most of these techniques attempt to solve one problem in isolation from the others, hence protocol designers have to face the same common challenges again and again. This, in turn, has a direct impact on the complexity of the proposed protocols and on energy consumption. Instead of using this approach we propose to construct a lightweight backbone that can help mitigate many of the typical challenges in sensor networks and allow the development of simpler network protocols. Our backbone construction protocol starts by tiling the area around each sink using identical regular hexagons. After that, the closest sensor to the center of each of these hexagons is determined—we refer to these sensors as backbone sensors. We define a ternary coordinate system to refer to hexagons. The resulting system provides a complete set of communication paths that can be used by any geographic routing technique to simplify data communication across the network. We show how the constructed backbone can help mitigate many of the typical challenges inherent to sensor networks. In addition to sensor localization, the network backbone provides an implicit clustering mechanism in which each hexagon represents a cluster mud the backbone sensor around its center represents the cluster head. As cluster heads, backbone sensors can be used to coordinate task assignment, workforce selection, and data aggregation for different sensing tasks. They also can be used to locally synchronize and adjust the duty cycle of non-backbone sensors in their neighborhood. Finally, we propose “Backbone Switching”, a technique that creates alternative backbones and periodically switches between them in order to balance energy consumption among sensors by distributing the additional load of being part of the backbone over larger number of sensors

Quality-Aware Scheduling Algorithms in Renewable Sensor

Wireless sensor network has emerged as a key technology for various applications such as environmental sensing, structural health monitoring, and area surveillance. Energy is by far one of the most critical design hurdles that hinders the deployment of wireless sensor networks. The lifetime of traditional battery-powered sensor networks is limited by the capacities of batteries. Even many energy conservation schemes were proposed to address this constraint, the network lifetime is still inherently restrained, as the consumed energy cannot be replenished easily. Fully addressing this issue requires energy to be replenished quite often in sensor networks (renewable sensor networks). One viable solution to energy shortages is enabling each sensor to harvest renewable energy from its surroundings such as solar energy, wind energy, and so on. In comparison with their conventional counterparts, the network lifetime in renewable sensor networks is no longer a main issue, since sensors can be recharged repeatedly. This results in a research focus shift from the network lifetime maximization in traditional sensor networks to the network performance optimization (e.g., monitoring quality). This thesis focuses on these issues and tackles important problems in renewable sensor networks as follows. We first study the target coverage optimization in renewable sensor networks via sensor duty cycle scheduling, where a renewable sensor network consisting of a set of heterogeneous sensors and a stationary base station need to be scheduled to monitor a set of targets in a monitoring area (e.g., some critical facilities) for a specified period, by transmitting their sensing data to the base station through multihop relays in a real-time manner. We formulate a coverage maximization problem in a renewable sensor network which is to schedule sensor activities such that the monitoring quality is maximized, subject to that the communication network induced by the activated sensors and the base station at each time moment is connected. We approach the problem for a given monitoring period by adopting a general strategy. That is, we divide the entire monitoring period into equal numbers of time slots and perform sensor activation or inactivation scheduling in the beginning of each time slot. As the problem is NP-hard, we devise efficient offline centralized and distributed algorithms for it, provided that the amount of harvested energy of each sensor for a given monitoring period can be predicted accurately. Otherwise, we propose an online adaptive framework to handle energy prediction fluctuation for this monitoring period. We conduct extensive experiments, and the experimental results show that the proposed solutions are very promising. We then investigate the data collection optimization in renewable sensor networks by exploiting sink mobility, where a mobile sink travels around the sensing field to collect data from sensors through one-hop transmission. With one-hop transmission, each sensor could send data directly to the mobile sink without any relay, and thus no energy are consumed on forwarding packets for others which is more energy efficient in comparison with multi-hop relays. Moreover, one-hop transmission particularly is very useful for a disconnected network, which may be due to the error-prone nature of wireless communication or the physical limit (e.g., some sensors are physically isolated), while multi-hop transmission is not applicable. In particular, we investigate two different kinds of mobile sinks, and formulate optimization problems under different scenarios, for which both centralized and distributed solutions are proposed accordingly. We study the performance of the proposed solutions and validate their effectiveness in improving the data quality. Since the energy harvested often varies over time, we also consider the scenario of renewable sensor networks by utilizing wireless energy transfer technology, where a mobile charging vehicle periodically travels inside the sensing field and charges sensors without any plugs or wires. Specifically, we propose a novel charging paradigm and formulate an optimization problem with an objective of maximizing the number of sensors charged per tour. We devise an offline approximation algorithm which runs in quasi-polynomial time and develop efficient online sensor charging algorithms, by considering the dynamic behaviors of sensors’ various sensing and transmission activities. To study the efficiency of the proposed algorithms, we conduct extensive experiments and the experimental results demonstrate that the proposed algorithms are very efficient. We finally conclude our work and discuss potential research topics which derive from the studies of this thesis