大规模无线传感器网络中基于充电范围的多节点可再生能源算法

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本文主要探讨了在大规模无线传感器网络（Large-scale Wireless Sensor Networks, WSNs）中，通过利用无线能量传输技术解决节点电源约束问题的一种创新方法。研究者们提出了一种名为"Multi-node Renewable Algorithm Based on Charging Range"（MRCR）的优化算法。该算法的核心是通过移动充电车辆（Mobile Charging Vehicle, MCV）对多个传感器节点进行分组式、同时性的充电。 MRCR算法的关键在于智能选择充电站点，这些站点基于MCV的充电范围来确定，旨在最大化充电覆盖范围，同时减少MCV的停靠次数和行驶距离，从而提高能源效率。算法的设计巧妙地将MCV的工作流程组织成多轮操作，每一轮又细分为判断、充电和休息三个阶段。在判断阶段，算法会评估节点的能量需求和当前状态，决定是否需要充电；充电阶段，MCV负责为选定的节点提供能量补给；休息阶段则是为了确保MCV的可持续运行。目标是通过这个优化算法，不仅显著提升WSN的整体网络寿命，即传感器节点能够持续工作的平均时间，而且还降低了计算复杂度，使得整个系统的管理和维护更为高效。通过在大规模环境中应用这种新颖的充电策略，MRCR有望为无线传感器网络的能源管理提供一个全新的解决方案，为未来物联网(IoT)和智能环境监测等领域带来实质性的进步。

A Multi-node Renewable Algorithm Based on

Charging Range in Large-scale Wireless Sensor

Network

Guowei Wu, Chi Lin, Ying Li, Lin Yao, Ailun Chen

School of Software

Dalian University of Technology

Dalian, China

Email:wgwdut@dlut.edu.cn

Abstract—Recently, wireless energy transfer technologies have

emerged as a promising approach to address the power constraint

problem in Wireless Sensor Networks(WSNs). In this paper, we

propose an optimized algorithm, Multi-node Renewable based on

Charging Range (MRCR), for large-scale WSNs, where multiple

sensor nodes are charging simultaneously. A mobile charging

vehicle (MCV) is responsible for energy supplement of these

nodes group by group at speciﬁed docking spots. These spots

are selected based on charging range of a MCV, which can

not only maximum the charging coverage, but also improve

the energy efﬁciency as the minimum number of stops and the

shortest travel path. We organize MCV schedule into rounds

and each round is divided into slots: judgment, charging and

rest. Then, we provide the objective output to maximize the

network lifetime and the computation complexity of our MRCR

algorithm. Finally, extensive experimental results show MRCR

algorithm can guarantee a short TSP length in every round and

all sensor nodes live immorally.

Index Terms—multi-node charging; docking spot selection;

large-scale wireless sensor network;

I. INTRODUCTION

In a Wireless Sensor Network (WSN), the constrained

energy storage in batteries limits the network lifetime or

conﬁnes its short-term application. Thus, the limited battery

issue has become a big challenge in WSNs. To solve this

problem, energy-efﬁciency has been widely studied in the liter-

ature where duty-cycling and various energy-efﬁcient medium

access and routing protocols have been proposed. Existing en-

ergy conservation schemes can slow down energy consumption

rate, but cannot compensate energy depletion. To address the

problem of energy decay, harvesting energy from surrounding

energy sources including solar [1], vibration [2], wind [3],

biochemical process [4] or passive human movement [5] has

been proposed. However, the drawback of these schemes lies

in those high reliance on unpredictable and uncontrollable

ambient conditions. For instance, it is impossible to harvest

energy for some sensor nodes deployed in shadow areas or

cloudy weather at a satisﬁed level.

Wireless energy transfer technology can be adopted to

increase the lifetime of a new class WSN, called wireless

rechargeable sensor network. With this ever-lasting energy

replenishment, we have found two particular breakthroughs in

the areas of wireless energy transfer [6], [7]and rechargeable

lithium batteries [8].It means power can be transferred from

one energy storage device to another without any plugs or

wires. Kurs et al. also have developed an enhanced technology

to transfer energy towards multiple receiving nodes simultane-

ously [9]. Delightfully, they have proved that the overall output

efﬁciency of charging each device individually is inferior to

that charging multiple devices. And what’s more, wireless

energy transfer is not subject to the objective neighboring

environment and it does not require any mediums between

the mobile charger and the receiver.

Recent advances in charging sensors dispatch a mobile

charging vehicle (MCV) carrying certain amount of energy

to move around the network [10], [11], [12], [13]. A MCV’s

capacity is high enough to maintain the eternal network

lifetime before it returns to the base station. Different from

these works, we also consider about the docking spot selection

to balance the power consumption of a MCV’s movement and

the distance between a MCV and every sensor node. We adopt

Traveling Salesman Problem (TSP) [14], [15] to balance them.

TSP aims to ﬁnd the shortest Hamiltonian cycle during visiting

every vertex.

In this paper, we propose an optimized algorithm Multi-

node Renewable based on Charging Range (MRCR) in the

large-scale WSN, where a mobile charging vehicle is allowed

to charge a group of sensor nodes. Every MCV only needs

to visit the speciﬁed docking spots to charge those nodes at

one time. Though the selection of docking spots with length-

objective is a NP-hard problem, it can certainly be transformed

to the cover-objective problem. We also formulate how to

schedule MCV’s charging sensor nodes. The whole process

is organized into rounds and a round is divided into slots: 1)

judgment, 2) charging, 3) rest. To make a WSN’s lifetime as

long as possible even immortal, we develop a provable solution

combining the number of data packets transmitted over the link

and the total charging time at docking spot.

The remainder of this paper is organized as follows. We

survey the related work in Section II. Section III introduces

the basal information including parameters and system models.

Then in Section IV we investigate the problem formulation and

2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

DOI 10.1109/IMIS.2015.19

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