Coverage And Connectivity Aware Energy Charging Mechanism Using The Mobile Charger For Wrsns

Wireless rechargeable sensor networks (WRSNs) have become an important issue in recent years due to their important roles in many monitoring applications. The WRSNs usually consist of many small sensors powered by rechargeable batteries. Since the sensor is small, the capacity of its battery is limited. As a result, the lifetime of WRSNs is limited which obstructs the large-scale deployment of WRSNs. To prolong the lifetime of WRSNs, extensive efforts for energy recharging have been taken in recent years. Wireless energy transfer (WET) technology as a revolutionized energy supply technology provides an alternative solution to prolong the lifetime of the WRSNs. Wireless charging of sensors using a mobile charger (MC) has been widely discussed in recent years. In literature, a lot of recharging algorithms have been proposed to construct recharging paths and determine the stopping points for mobile chargers to recharge the sensors. Most studies treated all sensors as equally important and aimed to maximize the number of recharged sensors. However, different sensors have different contributions, especially for network connectivity and coverage. The sensor closer to the base station has a larger contribution to network connectivity since its failure can block more data transmissions. On the other hand, a sensor that has a small overlapped sensing coverage with others, has a larger contribution to network coverage, since few or no neighboring sensors can replace its sensing coverage if the energy is exhausted. This study proposes an energy recharging mechanism, called ERSQ, which partitions the monitoring region into several equal-sized grids and considers the important factors, including coverage contribution, network connectivity contribution, and the remaining energy, aiming to maximize surveillance quality for a given WRSNs. 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. The above ERSQ does not consider the data quality of the recharging requested sensors. A sensor in a sparse region has a larger contribution to data quality because few or no neighboring sensors can execute the sensing operation instead of the sensor if it is energy exhaustion. This study proposes an energy recharging mechanism, called JDCC, which partitions the monitoring region into grids and considers the contribution of each grid in terms of network connectivity, data quality as well as path cost, aiming to maximize the surveillance quality. Performance studies reveal that the proposed JDCC outperforms existing recharging mechanisms in terms of the data quality, the number of working sensors as well as the effectiveness index of working sensors.

Wireless sensor networks (WSNs) are used in many real-life applications in recent days. WSNs consists of tiny sensors with limited battery. Therefore, energy recharging to the sensors has been a promising issue and received much attention recently. 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). In literature, many recharging path construction algorithms have been proposed. Most of them considered that all sensors are equally important and designed algorithms to increase the number of recharged sensors or decrease the path length of the MC. However, different sensors have different coverage contributions. Recharging the sensors with a larger coverage contribution can achieve better surveillance quality. The proposed recharging scheduling algorithm is divided into three phases, including the Initialization, Recharging Scheduling and Path Construction Phases. In the second phase, this study proposed two recharging scheduling algorithms, namely the Cost-Effective (CE) algorithm and Cost-Effective with Considerations of Coverage and Fairness (C^2 F) algorithm. The proposed two algorithms construct paths for MC and select the recharging sensors based on the higher weight in terms of larger coverage contribution and smaller path cost. Performance results show that the CE and C^2 F algorithms yield better performance in terms of the fairness of recharging, recharging stability and coverage ratio, as compared with the existing studies. The above CE and C^2 F does not consider the spatial and temporal qualities of the recharging requested sensors. This study proposes a Recharge Scheduling Algorithm for Maximizing Spatial and Temporal Data Accuracy called RS-STQ, aiming to maximize the data accuracy of the given network. 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 as well as recharged sensor's spatial quality contributions. Since the RS-STQ is not suitable for the dense sensor networks, this study proposes a work-load aware recharge scheduling algorithm (WLARS) which aims to maximize the surveillance quality of the network by considering the spatial and temporal surveillance qualities of the recharging requested sensors and schedules the MC for recharging. The proposed WLARS firstly partitions the network into many sub-regions by considering the routing load of the sensors, aiming to evenly distribute the charging load of the MCs. Then each sensor determines its threshold value based on the queuing theory. In addition, the adaptive buffer of the MC and cooperation between the neighbouring MCs is also considered in this study. Performance results show that the WLARS outperforms the existing mechanism in terms of spatial and temporal surveillance qualities, average waiting time as well as uncharged sensors spatial quality loss.

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 thesis proposes two energy recharging path planning schemes, including Recharging Path Construction (RPC) mechanism and Coverage Aware Energy Replenish Mechanism (CAERM). The RPG mechanism 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. Finally, the CAERM dynamically adjusts the recharging path according to the recharging requests of sensors, aiming to minimize the coverage loss for a given WSN. Theoretical analyses and performance evaluations show that the proposed mechanisms can significantly improve the performance of existing energy recharging techniques.

The use of a mobile charger (MC) is a popular method to restore energy in wireless rechargeable sensor networks(WRSN), whose effectiveness depends critically on the recharging strategy employed by the MC. In this thesis, we propose a novel on-line recharging mechanism strategy, called Continuous Local Learning (CLL), which predicts the current energy level of the sensor nodes and dynamically updates the schedule to visit the nodes before their batteries get depleted. The strategy is based on simple computations done by the MC with little memory requirements, and the communication is strictly local (between the MC and neighbouring nodes). In spite of its simplicity, this strategy was experimentally shown to be highly effective in keeping the network perpetually operating, ensuring that the number of sensing holes (i.e., non-operational sensors due to battery depletion) and their duration are very small at any time, and achieving immortality (i.e., no node ever becoming nonoperational) under many settings even in large networks. We also studied the flexibility of CLL under a variety of network parameters, showing its applicability in various contexts. We particularly focused on network size, data rate, sensors' battery-capacity, and speed of the MC, and studied their impact on operational size and disconnection time under a wide range of values. The experiments indicate the fact that the effectiveness of CLL holds under all considered settings. We then compared the proposed solution with the popular class of static strategies since they share with CLL the features of simplicity, strict local communication and small memory and computational requirements. Experimental results showed that CLL outperforms these strategies in effectiveness. Not only is the number of sensors that are operational at any time higher under CLL, but the average duration of a sensing hole is also significantly lower. Finally, we studied the adaptability of CLL to a network's sudden changes, in particular changes in data rate, which we call spikes. We studied the impact of spikes parameters on the performance of CLL. Experimental results showed that CLL is capable of reacting and adapting to these sudden changes with only a slight increase in non-operational size and disconnection time.

The escalating demand for ubiquitous computing along with the complementary and flexible natures of Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSNs) have sparked an increase in the integration of these two dynamic technologies. Although a variety of applications can be observed under development and in practical use, there

This book is the definitive guide to the techniques and applications of position location, covering both terrestrial and satellite systems. It gives all the techniques, theoretical models, and algorithms that engineers need to improve their current location schemes and to develop future location algorithms and systems. Comprehensive coverage is given to system design trade-offs, complexity issues, and the design of efficient positioning algorithms to enable the creation of high-performance location positioning systems. Traditional methods are also reexamined in the context of the challenges posed by reconfigurable and multihop networks. Applications discussed include wireless networks (WiFi, ZigBee, UMTS, and DVB networks), cognitive radio, sensor networks and multihop networks. Features Contains a complete guide to models, techniques, and applications of position location Includes applications to wireless networks, demonstrating the relevance of location positioning to these "hot" areas in research and development Covers system design trade-offs and the design of efficient positioning algorithms, enabling the creation of future location positioning systems Provides a theoretical underpinning for understanding current position location algorithms, giving researchers a foundation to develop future algorithms David Muñoz is Director and César Vargas is a member of the Center for Electronics and Telecommunications, Tecnológico de Monterrey, Mexico. Frantz Bouchereau is a senior communications software developer at The MathWorks Inc. in Natick, MA. Rogerio Enríquez-Caldera is at Instituto Nacional de Atrofisica, Optica y Electronica (INAOE), Puebla, Mexico. Contains a complete guide to models, techniques and applications of position location Includes applications to wireless networks (WiFi, ZigBee, DVB networks), cognitive radio, sensor networks and reconfigurable and multi-hop networks, demonstrating the relevance of location positioning to these ‘hot’ areas in research and development Covers system design trade-offs, and the design of efficient positioning algorithms enables the creation of future location positioning systems Provides a theoretical underpinning for understanding current position location algorithms, giving researchers a foundation to develop future algorithms

Trade School was a non-traditional learning space where students bartered with teachers. Anyone could teach a class. Students signed up for classes by agreeing to bring a barter item that the teacher requested. From 2009-2019, Trade School became an international network of local, self-organized chapters that reached over 22,000 people globally. Each chapter coordinated the exchange of knowledge for barter items and services.

Wireless sensor networks, due to their various applications in many fields and limited power consumption, have attracted much attention recently. Most previous methods have focused on providing energy-saving strategies to elevate the lifetime of sensor networks. Another aggressive but different approach is to wirelessly recharge sensor nodes to increase the lifetime of the sensor networks. This book collects articles that address state-of-the-art technologies and new developments for wireless rechargeable sensor networks (WRSNs), including the latest hot topics such as charger deployment, charger scheduling, wireless energy transfer, mobile charger design, energy-harvesting technique, and energy provisioning. We believe that the accepted articles present the most up-to-date progress in algorithms and theory for robust wireless sensor networks with respect to different networking problems.

A comprehensive introduction to architecture design, protocol optimization, and application development.