Adaptive Energy Balanced Routing Strategy for Wireless Rechargeable Sensor Networks

Tang, Liangrui; Chen, Zhiyi; Cai, Jinqi; Guo, Haobo; Wu, Runze; Guo, Jinghong

doi:10.3390/app9102133

Cited by 18 publications

(14 citation statements)

References 34 publications

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“…To determine the best charging strategy of the MC and the energy that it transmits to the sensor-nodes, we adapt the MC charging model and strategy in [22,46], where the full charging strategy is compared against our adaptive partial charging strategy. The model ≈…”

Section: Partial Vs Full Chargingmentioning

confidence: 99%

Swarm Intelligence Techniques for Mobile Wireless Charging

2022

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This paper proposes energy-efficient swarm intelligence (SI)-based approaches for efficient mobile wireless charging in a distributed large-scale wireless sensor network (LS-WSN). This approach considers the use of special multiple mobile elements, which traverse the network for the purpose of energy replenishment. Recent techniques have shown the advantages inherent to the use of a single mobile charger (MC) which periodically visits the network to replenish the sensor-nodes. However, the single MC technique is currently limited and is not feasible for LS-WSN scenarios. Other approaches have overlooked the need to comprehensively discuss some critical tradeoffs associated with mobile wireless charging, which include: (1) determining the efficient coordination and charging strategies for the MCs, and (2) determining the optimal amount of energy available for the MCs, given the overall available network energy. These important tradeoffs are investigated in this study. Thus, this paper aims to investigate some of the critical issues affecting efficient mobile wireless charging for large-scale WSN scenarios; consequently, the network can then be operated without limitations. We first formulate the multiple charger recharge optimization problem (MCROP) and show that it is N-P hard. To solve the complex problem of scheduling multiple MCs in LS-WSN scenarios, we propose the node-partition algorithm based on cluster centroids, which adaptively partitions the whole network into several clusters and regions and distributes an MC to each region. Finally, we provide detailed simulation experiments using SI-based routing protocols. The results show the performance of the proposed scheme in terms of different evaluation metrics, where SI-based techniques are presented as a veritable state-of-the-art approach for improved energy-efficient mobile wireless charging to extend the network operational lifetime. The investigation also reveals the efficacy of the partial charging, over the full charging, strategies of the MCs.

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Section: Partial Vs Full Chargingmentioning

confidence: 99%

Swarm Intelligence Techniques for Mobile Wireless Charging

2022

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“…Aslam [19] attempted an approach based on the shortest path algorithm and grid clustering to save and renew power in a way that minimized energy consumption and prolonged the overall network lifetime of WSNs. Tang [20] proposed an optimization algorithm from the aspects of both charging and routing processes. Furthermore, a joint energy supply and routing path selection algorithm was proposed to extend the network lifetime based on an initiative power supply [21].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

Tang

Guo

et al. 2020

Applied Sciences

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Great improvement recently appeared in terms of efficient service delivery in wireless sensor networks (WSNs) for Internet of things (IoT). The IoT is mainly dependent on optimal routing of energy-aware WSNs for gathering data. In addition, as the wireless charging technology develops in leaps and bounds, the performance of rechargeable wireless sensor networks (RWSNs) is greatly ameliorated. Many researches integrated wireless energy transfer into data gathering to prolong network lifetime. However, the mobile collector cannot visit all nodes under the constraints of charging efficiency and gathering delay. Thus, energy consumption differences caused by different upload distances to collectors impose a great challenge in balancing energy. In this paper, we propose an adaptive dual-mode routing-based mobile data gathering algorithm (ADRMDGA) in RWSNs for IoT. The energy replenishment capability is reasonably allocated to low-energy nodes according to our objective function. Furthermore, the innovative adaptive dual-mode routing allows nodes to choose direct or multi-hop upload modes according to their relative upload distances. The empirical study confirms that ADRMDGA has excellent energy equilibrium and effectively extends the network lifetime.

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“…The choice of an architecture is most often dictated by the nature of the application to be deployed but also by the nature of the sensors to be used. Moreover, the single capture level architecture is the most widely used in many works [27][28][29]. It is usually a network of homogeneous sensors with distributed job processing and centralized storage.…”

Section: Architectures For Large-scale Wireless Sensor Network Deploymentsmentioning

confidence: 99%

A Three-Tier Architecture of Large-Scale Wireless Sensor Networks for Big Data Collection

et al. 2020

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In recent years, technological advances and the ever-increasing power of embedded systems have seen the emergence of so-called smart cities. In these cities, application needs are increasingly calling for Large-Scale Wireless Sensor Networks (LS-WSN). However, the design and implementation of such networks pose several important and interesting challenges. These low-cost, low-power devices are characterized by limited computing, memory storage, communication, and battery power capabilities. Moreover, sensors are often required to cooperate in order to route the collected data to a single central node (or sink). The many-to-one communication model that governs dense and widely deployed Wireless Sensor Networks (WSNs) most often leads to problems of network overload and congestion. Indeed, it is easy to show that the closer a node is geographical to the sink, the more data sources it has to relay. This leads to several problems including overloading of nodes close to the sink, high loss rate in the area close to the sink, and poor distribution of power consumption that directly affects the lives of these networks. In this context, we propose a contribution to the problem of LS-WSN energy consumption. We designed a hierarchical 3-tier architecture of LS-WSNs coupled with a modeling of the activities of the different sensors in the network. This architecture that is based on clustering also includes a redeployment function to maintain the topology in case of coverage gaps. The results of the performed simulations show that our architecture maximizes the lifetime than compared solutions.

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Adaptive Energy Balanced Routing Strategy for Wireless Rechargeable Sensor Networks

Cited by 18 publications

References 34 publications

Swarm Intelligence Techniques for Mobile Wireless Charging

Swarm Intelligence Techniques for Mobile Wireless Charging

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

A Three-Tier Architecture of Large-Scale Wireless Sensor Networks for Big Data Collection

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