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

WIRELESS SENSOR BASED MAXIMIZING SENSOR LIFETIME USING ADAPTIVE ALGORITHM

Remote energy move innovation dependent on dazzling reverberating coupling has arisen as a cheerful innovation for remote sensor organizations, by on condition that controllable yet successive energy to sensors. The utilization of a portable charger to remotely charge sensors in a battery-powered sensor organization so the amount of sensor lifetimes is boost even as the go on an outing distance of the versatile charger is limit. Differentiating existing investigations that implicit a versatile charger should charge a sensor to its full energy ability prior to moving to charge the following sensor, we here accept that every sensor can be halfway charged so more sensors can be charged before their energy exhaustions. Under this new energy charging model, we initially plan two novel enhancement issues of booking a portable charger to charge a bunch of sensors, with the goals to augment the amount of sensor lifetimes and to limit the movement distance of the versatile charger while accomplishing the greatest amount of sensor lifetimes, individually. We at that point propose effective calculations for the issues. We at long last gauge the introduction of the proposed calculations through investigational reenactments. Proliferation results make clear that the proposed calculations are very guarantee. Particularly, the normal energy termination length per sensor by the proposed calculation for amplifying the amount of sensor lifetimes is just 9% of that by the best in class calculation while the movement distance of the portable charger constantly proposed calculation is just about from 1% to 15% longer than that by the cutting edge benchmark

Improving sensor network performance with wireless energy transfer

Through recent technology advances in the field of wireless energy transmission Wireless Rechargeable Sensor Networks have emerged. In this new paradigm for wireless sensor networks a mobile entity called mobile charger (MC) traverses the network and replenishes the dissipated energy of sensors. In this work we first provide a formal definition of the charging dispatch decision problem and prove its computational hardness. We then investigate how to optimise the trade-offs of several critical aspects of the charging process such as: a) the trajectory of the charger; b) the different charging policies; c) the impact of the ratio of the energy the Mobile Charger may deliver to the sensors over the total available energy in the network. In the light of these optimisations, we then study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols; a Greedy protocol, a clustering protocol and an energy balancing protocol. Finally, we propose a mobile charging protocol that locally adapts the circular trajectory of the MC to the energy dissipation rate of each sub-region of the network. We compare this protocol against several MC trajectories for all three routing families by a detailed experimental evaluation. The derived findings demonstrate significant performance gains, both with respect to the no charger case as well as the different charging alternatives; in particular, the performance improvements include the network lifetime, as well as connectivity, coverage and energy balance properties

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

Wireless Power Charging Control in Multiuser Broadband Networks

Recent advances in wireless power transfer (WPT) technology provide a cost-effective solution to charge wireless devices remotely without disruption to the use. In this paper, we propose an efficient wireless charging control method for exploiting the frequency diversity in multiuser broadband wireless networks, to reduce energy outage and keep the system operating in an efficient and sustainable state. In particular, we first analyze the impact of charging control method to the operating lifetime of a WPT-enabled broadband system. Based on the analysis, we then propose a multi-criteria charging control policy that optimizes the transmit power allocation over frequency by jointly considering the channel state information (CSI) and the battery state information (BSI) of wireless devices. For practical implementation, the proposed scheme is realized by a novel limited CSI estimation mechanism embedded with partial BSI, which significantly reduces the energy cost of CSI and BSI feedback. Simulation results show that the proposed method could significantly increase the network lifetime under stringent transmit power constraint. Reciprocally, it also consumes lower transmit power to achieve near-perpetual network operation than other single-criterion based charging control methods.Comment: This paper had been accepted by IEEE ICC 2015, Workshop on Green Communications and Networks with Energy Harvesting, Smart Grids, and Renewable Energie

Towards self-powered wireless sensor networks

Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that continuously collect, process, share and transport information. The impact of such technologies in our everyday life is expected to be massive, as it will enable innovative applications that will profoundly change the world around us. Remotely monitoring the conditions of patients and elderly people inside hospitals and at home, preventing catastrophic failures of buildings and critical structures, realizing smart cities with sustainable management of traffic and automatic monitoring of pollution levels, early detecting earthquake and forest fires, monitoring water quality and detecting water leakages, preventing landslides and avalanches are just some examples of life-enhancing applications made possible by smart ubiquitous computing systems. To turn this vision into a reality, however, new raising challenges have to be addressed, overcoming the limits that currently prevent the pervasive deployment of smart devices that are long lasting, trusted, and fully autonomous. In particular, the most critical factor currently limiting the realization of ubiquitous computing is energy provisioning. In fact, embedded devices are typically powered by short-lived batteries that severely affect their lifespan and reliability, often requiring expensive and invasive maintenance. In this PhD thesis, we investigate the use of energy-harvesting techniques to overcome the energy bottleneck problem suffered by embedded devices, particularly focusing on Wireless Sensor Networks (WSNs), which are one of the key enablers of pervasive computing systems. Energy harvesting allows to use energy readily available from the environment (e.g., from solar light, wind, body movements, etc.) to significantly extend the typical lifetime of low-power devices, enabling ubiquitous computing systems that can last virtually forever. However, the design challenges posed both at the hardware and at the software levels by the design of energy-autonomous devices are many. This thesis addresses some of the most challenging problems of this emerging research area, such as devising mechanisms for energy prediction and management, improving the efficiency of the energy scavenging process, developing protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support. %, including the design of mechanisms for energy prediction and management, improving the efficiency of the energy harvesting process, the develop of protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support

Analysis and optimal design of micro-energy harvesting systems for wireless sensor nodes

Presently, wireless sensor nodes are widely used and the lifetime of the system is becoming the biggest problem with using this technology. As more and more low power products have been used in WSN, energy harvesting technologies, based on their own characteristics, attract more and more attention in this area. But in order to design high energy efficiency, low cost and nearly perpetual lifetime micro energy harvesting system is still challenging. This thesis proposes a new way, by applying three factors of the system, which are the energy generation, the energy consumption and the power management strategy, into a theoretical model, to optimally design a highly efficient micro energy harvesting system in a real environment. In order to achieve this goal, three aspects of contributions, which are theoretically analysis an energy harvesting system, practically enhancing the system efficiency, and real system implementation, have been made. For the theoretically analysis, the generic architecture and the system design procedure have been proposed to guide system design. Based on the proposed system architecture, the theoretical analytical models of solar and thermal energy harvesting systems have been developed to evaluate the performance of the system before it being designed and implemented. Based on the model’s findings, two approaches (MPPT based power conversion circuit and the power management subsystem) have been considered to practically increase the system efficiency. As this research has been funded by the two public projects, two energy harvesting systems (solar and thermal) powered wireless sensor nodes have been developed and implemented in the real environments based on the proposed work, although other energy sources are given passing treatment. The experimental results show that the two systems have been efficiently designed with the optimization of the system parameters by using the simulation model. The further experimental results, tested in the real environments, show that both systems can have nearly perpetual lifetime with high energy efficiency

Direct and Non-Invasive Monitoring of Battery Internal State Via Novel GMI-IDT Magnetic Sensor

Efficient battery management systems (BMSs) in rechargeable battery-based systems require precise measurements of various battery parameters including state of charge (SOC), state of health (SOH) and charge capacity. Presently, SOC, charge capacity and SOH can only be indirectly inferred from long-term measurement of current, open circuit voltage (OCV), and temperature using multiple sensors. These techniques can only give an approximation of SOC and often require knowledge of the recent battery history to prevent excessive inaccuracy.To improve the performance of the BMS, an alternative method of monitoring the internal state of Li-ion batteries is presented here. Theoretical analysis of Li-ion batteries has indicated that the concentration of active lithium ions in the cathode is directly related to the magnetic susceptibility of the electrode materials. While charging/discharging, due to the change in the oxidation and/or spin state of metal atoms, the magnetic moment in the cathode varies. This indicates the potential for directly probing the internal state of the Li-ion batteries during charging/discharging by monitoring the changes in magnetic susceptibility via an appropriately designed magnetic sensor. In this research, a highly sensitive micromagnetic sensor design is investigated consisting of a single interdigital transducer (IDT) shunt-loaded with a magnetically sensitive Giant Magnetoimpedance (GMI) microwire. This design takes advantage of the coupling of the impedance characteristics of the GMI microwire to the IDT transduction process. The initial GMI-IDT sensor design is further developed and modified to maximize sensitivity and linearity. The sensor can detect magnetic field in the range of 900 nT and minute changes less than 1 μT when operated at or near its peak sensitivity. In addition, an appropriate procedure for preconditioning the GMI wire is developed to achieve sensor repeatability. Furthermore, using the identified optimum geometry of the experimental setup, the proposed sensor is implemented in monitoring the internal state of two types of Li-ion cells used in electric vehicles (EVs). The initial characterization results confirm that the GMI-IDT sensor can be used to directly monitor the charge capacity of the investigated Li-ion batteries. Other possible applications also include energy storage for renewable energy sources, and portable electronic devices