Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

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

Restricting Barrier and Finding the Shortest Path in Wireless Sensor Network Using Mobile Sink

Wireless Sensor Network (WSN) is a collection of spatially deployed in wireless sensors. In general, sensing field could contain various barriers which cause loss of information transferring towards the destination. As a remedy, this proposed work presents an energy-efficient routing mechanism based on cluster in mobile sink. The scope of this work is to provide a mobile sink in a single mobile node which begins data-gathering from starting stage, then immediately collects facts from cluster heads in single-hop range and subsequently, it returns to the starting stage. During the movement of the mobile sink if the barrier exists in the sensing field it can be detected using Spanning graph and Grid based techniques. The possible locations for the mobile sink movement can be reduced easily by Spanning graph. At last, Barrier avoidance-shortest route was established for mobile sink using Dijkstra algorithm. The Distributed location information is collected using a Timer Bloom Filter Aggregation (TBFA) scheme. In the TBFA scheme, the location information of Mobile node (MNs) is maintained by Bloom filters by each Mobile agent (MA). Since the propagation of the whole Bloom filter for every Mobile node (MN) movement leads to high signaling overhead, each Mobile agent (MA) only propagates changed indexes in the Bloom filter when a pre-defined timer expires. To verify the performance of the TBFA scheme, an analytical model is developed on the signaling overhead and the latency and devise an algorithm to select an appropriate timer value. Extensive simulation and Network Simulator 2(NS2) results are given to show the accuracy of analytical models and effectiveness of the proposed method

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Delay-Constrained Mobile Energy Charging in Wireless Sensor Networks

为了延长无线传感网的生存期,基于可充电的移动设备,研究设计了一种无线传感网中移动式能量补充的方法,移动节点可以在为传感器节点补充能量的同时收集数; 据.首先,通过将无线传感器网络监测区域分割为大小相同的子区域,该子区域内的节点组成一个簇;其次,以一个簇内的总能量为计算依据,设计移动节点的路径; 生成算法以确定能量高效的移动路线;最后,使用10种不同的随机网络拓扑图进行了仿真实验,以节点移动速度和时延为限制条件分别得到了对比数据.结果表明; ,本文提出的算法与NJNP( nearest-job-next with preemption)算法相比在时延相同的条件下( 800; s),生存期提升了6 000 s左右,在节点速度5 m/s条件下生存期提升了将近14 000; s.证明本文所提方法有效地提高了充电效率,延长了网络的生存期,可用于大规模的无线传感器网络.In order to prolong the lifetime of wireless sensor networks by using; energy-rechargeable mobile devices,this paper designs a mobile energy; replenishment method wherein a mobile element gathers data and recharges; sensors simultaneously. Firstly,the whole sensor network is divided into; several sub-regions equally and the sensors in each sub-region are; formed into a cluster. Secondly, considering the energy in a whole; cluster,the mobility path is designed to find the energy-efficient; mobile trace of the mobile element. Finally,in the simulation; experiment,we used ten different random network topologies to show the; comparisons with extensive simulation experiments under different; velocities and deadlines. The results indicate that the proposed; algorithm increases lifetime by approximately 6 000 s compared with; Nearest-Job-Next with Pre-emption( NJNP) under the deadline of 800 s.; Moreover,the proposed algorithm increases lifetime by approximately 14; 000 s compared with NJNP at velocity of 5 m/s. Thus,the proposed; algorithm can improve recharging efficiency and prolong the lifetime of; wireless sensor networks,which can be used in large-scale sensor; networks.国家自然科学基金资助项目; 福建省高等学校杰出青年科研人才培育计划资助项

Radio Frequency Energy Harvesting and Management for Wireless Sensor Networks

Radio Frequency (RF) Energy Harvesting holds a promising future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. Reducing power consumption has become a major challenge in wireless sensor networks. As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks is an emerging and active research area. This chapter presents a practical approach for RF Energy harvesting and management of the harvested and available energy for wireless sensor networks using the Improved Energy Efficient Ant Based Routing Algorithm (IEEABR) as our proposed algorithm. The chapter looks at measurement of the RF power density, calculation of the received power, storage of the harvested power, and management of the power in wireless sensor networks. The routing uses IEEABR technique for energy management. Practical and real-time implementations of the RF Energy using Powercast harvesters and simulations using the energy model of our Libelium Waspmote to verify the approach were performed. The chapter concludes with performance analysis of the harvested energy, comparison of IEEABR and other traditional energy management techniques, while also looking at open research areas of energy harvesting and management for wireless sensor networks.Comment: 40 pages, 9 figures, 5 tables, Book chapte

Techniques to Enhance Lifetime of Wireless Sensor Networks: A Survey

Increasing lifetime in wireless sensor networks is a major challenge because the nodes are equipped with low power battery. For increasing the lifetime of the sensor nodes energy efficient routing is one solution which minimizes maintenance cost and maximizes the overall performance of the nodes. In this paper, different energy efficient routing techniques are discussed. Here, photovoltaic cell for efficient power management in wireless sensor networks is also discussed which are developed to increase the lifetime of the nodes. Efficient battery usage techniques and discharge characteristics are then described which enhance the operational battery lifetime