Voronoi图驱动的SDSN覆盖优化算法：节能与效率提升

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在"软件定义的传感器网络中基于Voronoi图的覆盖优化算法"这篇研究论文中，作者Min Tang、Feng Yan、Shuguang Deng（*为通讯作者）、Lianfeng Shen、Sufeng Kuang和Song Xing探讨了如何通过利用Voronoi图这一数学概念来优化软件定义传感器网络（SDSN）的能源效率。Voronoi图是一种几何学工具，它将空间划分为一系列区域，每个区域对应于网络中一个节点的最接近区域，被称为该节点的Voronoi细胞。首先，论文提出了"最小化半径算法"（Minimax Radius Algorithm, MRA），其核心目标是在保证原始网络覆盖范围的前提下，尽可能地减小节点的感知范围。通过精确计算和优化节点的位置，MRA旨在减少能量消耗，延长网络的整体生存期。这种方法侧重于全局视角，力求在最小化节点活动带来的能量成本的同时，确保整个网络的连通性和数据采集能力。其次，"睡眠模式算法"（Sleeping-based Algorithm, SLA）是另一种优化策略。SLA旨在最大化节能，通过智能地让部分节点进入休眠状态，避免因它们的感知范围空缺而产生的新的覆盖盲区。这种算法依赖于对网络动态行为的理解，通过动态调整节点的工作模式，使网络能够在保持有效覆盖的同时，最大程度地节省能源。通过模拟实验，论文展示了这两种算法的有效性，它们能够显著降低SDSN的能量消耗，提高网络的能效比和整体性能。这篇研究论文对于软件定义的传感器网络中的能量管理问题提供了创新的解决方案，展示了Voronoi图在覆盖优化中的应用潜力。通过MRA和SLA，研究者们不仅改进了节点的能耗策略，还可能为未来无线传感器网络的设计和优化提供有价值的经验和理论支持。对于那些关注能源效率、网络维护和可持续性的研究人员和工程师来说，这篇论文是一个重要的参考资源。

Coverage Optimization Algorithms Based on Voronoi

Diagram in Software-Defined Sensor Networks

Min Tang

, Feng Yan

, Shuguang Deng

, Lianfeng Shen

, Sufeng Kuang

, Song Xing

(1. National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China;

2. College of Communications and Electronics Engineering, Hunan City University, Yiyang 413000, China;

3. Dept. of Information Systems, California State University, Los Angeles, CA 90032, USA)

E-mail: {mtang2014, feng.yan, lfshen, sfkuang}@seu.edu.cn, shuguangdeng@163.com, sxing@calstatela.edu

Abstract—In this paper, two coverage optimization

algorithms based on Voronoi diagram are proposed to reduce the

energy consumption of software-defined sensor networks

(SDSNs). In the first algorithm named Minimax Radius

Algorithm (MRA), we try to reduce the sensing radii of nodes as

much as possible while preserving the original network coverage.

In the second algorithm named Sleeping-based Algorithm (SLA),

we try to set as many nodes as possible into sleep mode without

generating new coverage holes. Simulation results show that both

algorithms can reduce the energy consumption significantly and

the energy reduction rate increases with nodes’ sensing

radius and the number of nodes.

Keywords-software-defined sensor networks; Voronoi diagram;

coverage optimization

I. INTRODUCTION

The rapid development of wireless communications and

electronic technology has greatly promoted the research works

of wireless sensor networks (WSNs) [1]. For many

applications in WSNs, the target area is usually over-covered

in order to achieve real-time monitoring and reliable

information transmission. It is thus very important to optimize

the coverage of the monitoring area in order to reduce the

energy consumption of the entire network.

Software-Defined Network (SDN) [2] is a new network

architecture, in which the control plane and data plane are

separated to ensure the dynamic structure, low cost, high

adaptability, and a programmable network. It is naturally to

introduce the SDN architecture into the application-specific

WANs, which results in the software-defined sensor networks

(SDSNs) [3].

A SDSN consists of the central controllers and software-

defined sensor nodes. The network can be reconfigured by

reprogramming the nodes even after the network deployment

is complete. This renders the SDSN as the primary structure in

our problem solving of the coverage optimization in WSNs

due to its network reconfiguration ability.

There are some related works for the coverage

optimization problem in WSNs in recent years. In [4], for

example, the target area is fully covered in the sense that the

sensing perimeters of all sensor nodes are covered by other

nodes. However, the number of coverage holes, the hole

repairing methods, and the coverage optimization are not

considered in [4]. Similar to [4], the authors in [5-6] propose

to detect the coverage holes by checking whether the sensing

perimeter of each node is covered by its neighbors, then they

use the sensing radius to estimate the diameter of the coverage

holes roughly. The authors in [7-8] propose a coverage

optimization method based on Delaunay triangulation, in

which the coverage holes are repaired by moving nodes [7] or

by adding a number of new nodes [8]. But the network

redundancy might be generated by these two methods. [9]

proposes a k-coverage algorithm to achieve the minimum

connected area and keep the minimum number of sensor nodes

under the k-coverage and 1-connectivity conditions. However,

the robustness of the network is not good enough to consider

the change of network topologies. A dynamic k-coverage

scheduling (DkCS) algorithm is proposed in [10] to extend the

lifetime of the network while keeping the network k-coverage

and energy-efficiency by minimizing the average number of

working sensors. The algorithm solves the k-coverage

problems in moving and stationary scenarios. However, DkCS

generates the extra costs on node communication and

movement. The homology theory is used in coverage holes

detection, and the number of holes is detected by the use of

Rips complex [11]. However, removing the redundancy of the

target area is not taken into consideration in [11]. In [12], the

authors propose the KGS and DKEA algorithms to reduce the

coverage redundancy and the network cost with the progress

of building groups which starts from one node to the whole

target area. When the target area is getting bigger, however,

the time of grouping becomes larger and there are also some

failures in this grouping progress.

It is worth noting that, in recent years Voronoi diagram is

applied into the network coverage in WSNs. For example, two

localized algorithms are proposed in [13-14] based on Voronoi

diagram for coverage boundary detection, which only need the

minimal position information of the neighbors and result in a

lower computational complexity. However, the healing of

coverage holes and removing the added network redundancy

have not been considered. A localized method to build 1-order

Voronoi diagram is proposed in [15]. Based on Voronoi

diagram, the authors in [16-18] use the movement of sensor

nodes to repair the coverage holes. However, more energy will

be consumed during the nodes movement and the coverage

optimization is not taken into consideration for the full

coverage of the target area.

The work is supported

in part by the National Natural Science Foundation of

China (No.61471164)

and the Research Fund of National Mobile

Communications Research Laboratory, Southeast University (No. 2016B02).

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