Mobility Aware Charger Deployment For Wireless Rechargeable Sensor Networks

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.

This book constitutes the refereed post-conference proceedings of the Fourth International Conference on IoT as a Service, IoTaaS 2018, which took place in Xi’an, China, in November 2018. The 50 revised full papers were carefully reviewed and selected from 83 submissions. The technical track present IoT-based services in various applications. In addition, there are three workshops: international workshop on edge computing for 5G/IoT, international workshop on green communications for internet of things, and international workshop on space-based internet of things.

The three-volume set constitutes the proceedings of the 16th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2021, which was held during June 25-27, 2021. The conference took place in Nanjing, China.The 103 full and 57 short papers presented in these proceedings were carefully reviewed and selected from 315 submissions. The contributions in Part II of the set are subdivided into the following topical sections: Scheduling & Optimization II; Security; Data Center Networks and Cloud Computing; Privacy-Aware Computing; Internet of Vehicles; Visual Computing for IoT; Mobile Ad-Hoc Networks.

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.

This new book brings together the most recent trends related to AI, machine learning, and network security. The chapters cover diverse topics on machine learning algorithms and security analytics, AI and machine learning, and ntework security applications. The volume presents a survey of speculative parallelism techniques, performance reviews, and efficient power consumption. The book also covers the concepts of IoT, security early detection for COVID-19, multimetric geoprahpical routing in VANETs, V2X communication in VANET, and optimization of congestion control scheme for VANETs. This book is a comprehensive take on recent applications and advancement in the field of computer science and will be of value to scientists, researchers, faculty, and students involved in research in the area of AI, machine learning, and network security.

The three volume set LNCS 13155, 13156, and 13157 constitutes the refereed proceedings of the 21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021, which was held online during December 3-5, 2021. The total of 145 full papers included in these proceedings were carefully reviewed and selected from 403 submissions. They cover the many dimensions of parallel algorithms and architectures including fundamental theoretical approaches, practical experimental projects, and commercial components and systems. The papers were organized in topical sections as follows: Part I, LNCS 13155: Deep learning models and applications; software systems and efficient algorithms; edge computing and edge intelligence; service dependability and security algorithms; data science; Part II, LNCS 13156: Software systems and efficient algorithms; parallel and distributed algorithms and applications; data science; edge computing and edge intelligence; blockchain systems; deept learning models and applications; IoT; Part III, LNCS 13157: Blockchain systems; data science; distributed and network-based computing; edge computing and edge intelligence; service dependability and security algorithms; software systems and efficient algorithms.

This SpringerBrief provides a concise guide to applying wireless energy transfer techniques in traditional battery-powered sensor networks. It examines the benefits and challenges of wireless power including efficiency and reliability. The authors build a wireless rechargeable sensor networks from scratch and aim to provide perpetual network operation. Chapters cover a wide range of topics from the collection of energy information and recharge scheduling to joint design with typical sensing applications such as data gathering. Problems are approached using a natural combination of probability theory, optimization, algorithm and protocol designs. All proposed mechanisms are evaluated by extensive simulations. Wireless Rechargeable Sensor Networks targets professionals and researchers working in networks, wireless communications, energy technology and information technology. Advanced-level students studying electrical engineering and computer science will also find this material useful as a study guide.

The harvesting of energy from ambient energy sources to power electronic devices has been recognized as a promising solution to the issue of powering the ever-growing number of mobile devices around us.Key technologies in the rapidly growing field of energy harvesting focus on developing solutions to capture ambient energy surrounding the mobile devices and convert it into usable electrical energy for the purpose of recharging said devices. Achieving a sustainable network lifetime via battery-aware designs brings forth a new frontier for energy optimization techniques. These techniques had, in their early stages, resulted in the development of low-power hardware designs. Today, they have evolved into power-aware designs and even battery-aware designs.This book covers recent results in the field of rechargeable sensor networks, including technologies and protocol designs to enable harvesting energy from alternative energy sources such as vibrations, temperature variations, wind, solar, and biochemical energy and passive human power.