软件定义传感器网络中功率约束下的定位算法与节点选择

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"这篇研究论文探讨了一种在软件定义传感器网络(SDSNs)中的定位算法，该算法在全局网络功率约束下采用了节点选择策略。基于软件定义网络(SDN)技术，该算法提供集中式网络控制，以提高定位精度并优化能源效率。作者还引用了Cramer-Rao下界作为性能评估标准。" 正文: 在当前的物联网(IoT)环境中，软件定义传感器网络(SDSNs)已成为一种重要的基础设施，它们通过集中式的网络管理和控制能力，提高了网络的灵活性和效率。然而，由于传感器节点通常由有限的电池供电，因此在网络运行过程中，能量效率和节点寿命是至关重要的考虑因素。这篇研究论文"Localisation algorithm with node selection under power constraint in software-defined sensor networks"针对这一问题提出了一个新的解决方案。论文首先介绍了背景，强调了在SDSNs中实现高效定位算法的需求，尤其是在考虑到节点功率限制的情况下。传统的传感器网络定位方法往往忽视了网络整体的功率消耗，导致能源浪费和网络寿命缩短。因此，该研究旨在设计一个能够平衡定位精度和能源消耗的算法。论文的核心贡献是一种新的节点选择策略，它利用SDN的集中控制特性来决定哪些节点参与定位过程。这种策略考虑了整个网络的功率约束，确保在满足定位精度要求的同时，最大化网络的能源效率。通过这种方式，可以减少不必要的通信开销，延长网络的生存时间。为了评估算法的性能，作者引用了Cramer-Rao下界(CRLB)。CRLB是估计理论中的一个重要概念，它给出了在一定条件下，任何无偏估计器的方差的理论下限。在定位问题中，CRLB提供了估计位置误差的理论最低界限，因此可以用来衡量算法的最优性。论文通过比较所提出的算法与CRLB，展示了新算法在定位精度上的表现，并分析了在不同功率约束条件下的性能变化。此外，论文还可能涉及实验设计和结果分析，通过模拟和实地测试验证了新算法的有效性和节能特性。可能的研究方法包括仿真工具如NS-3，以及对实际传感器网络环境的实验设置，以验证算法在真实世界条件下的性能。这篇研究论文为SDSNs中的节点定位问题提供了一个创新的解决途径，不仅考虑了定位精度，还兼顾了网络的功率效率。通过采用SDN的集中控制和智能节点选择，该算法有望在未来的物联网应用中发挥重要作用，尤其是在能源受限的场景下。

IET Communications

Research Article

Localisation algorithm with node selection

under power constraint in software-defined

sensor networks

ISSN 1751-8628

Received on 21st January 2017

Revised 8th April 2017

Accepted on 13th June 2017

E-First on 19th September 2017

doi: 10.1049/iet-com.2017.0077

www.ietdl.org

Yaping Zhu

, Song Xing

, Yueyue Zhang

, Feng Yan

1,3

, Lianfeng Shen

National Mobile Communications Research Laboratory, Southeast University, Nanjing, People's Republic of China

Department of Information Systems, California State University, Los Angeles, CA 90032, USA

State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, People's Republic

of China

E-mail: lfshen@seu.edu.cn

Abstract: In this study, the authors propose an improved localisation algorithm in the software-defined sensor networks

(SDSNs). This algorithm includes a node-selection strategy under the whole network power constraint, based on the software-

defined networking (SDN) technique for providing the centralised control of the network. The analogous Cramer-Rao lower

bound (A-CRLB) value is derived for each participating node, which represents a fundamental bound on the variance of the

position estimator and is used to evaluate the contribution of each node to localisation accuracy. On the basis of A-CRLB

values, the most helpful nodes for localisation are selected to maximise the sum of the nodes' contributory values to the

localisation accuracy. With the global network knowledge provided by the SDN controller in the SDSN, the node-selection

strategy is formulated into a 0-1 programming problem on the premise of power satisfaction of each node. Furthermore, the

proposed node-selection -based localisation algorithm is applied to both noncooperative and cooperative localisation scenarios.

Simulation results show that the proposed algorithms provide efficient and effective localisation schemes in SDSNs, and can

improve the performance in terms of both the selection convergence speed and the localisation accuracy.

1 Introduction

With the upsurge of sensor applications and the advancement of

sensor technologies, wireless sensor networks (WSNs) are

envisioning a rich variety of promising services in many fields

such as search-and-rescue operations, environmental monitoring,

and precision agriculture. For these applications, the wireless

sensor nodes localisation has attracted considerable attentions over

the past decades in WSNs [1, 2]. In node localisation for WSNs,

the positions of the agent nodes can be estimated through two

methods named non-cooperative localisation and cooperative

localisation. In non-cooperative localisation, the position

estimation only uses the knowledge of a few anchor nodes with

known locations. While in cooperative localisation, the inter-node

range measurements between agent nodes are also used. The

transmitted signals from the source nodes (i.e. the anchor nodes in

non-cooperative localisation scenario, the anchor nodes and agent

nodes in cooperative localisation scenario) are generally used to

provide the position-related range measurements for the target node

(i.e. the agent node). Such range-based localisation techniques can

be categorised into the time-of-arrival (TOA) [3–5], time-

difference-of-arrival [6, 7], angle-of-arrival [8], received signal

strength (RSS) [9, 10], and some hybrid schemes [11–13]. No

matter which range measurement method is adopted, the power of

transmitting signal plays a critical role in the accuracy of position

estimate. In addition, it is related to the throughput and lifetime of

the network. Owing to the energy-limited nature of WSNs,

research of power allocation algorithm for WSN localisation is of

vital importance, since it affects not only the localisation

performance but also the network lifetime [14, 15].

In recent years, various power allocation strategies to improve

the node localisation accuracy and reduce the entire network power

consumption have been investigated. For example, an optimisation

framework for robust power allocation schemes in node

localisation with imperfect knowledge of network parameters is

presented in [16], which is formulated to minimise the localisation

errors for a given power budget. A ranging energy optimisation

method for an unsynchronised positioning system is introduced in

[17], which features a robust sensor positioning in the sense that a

specific accuracy requirement is fulfilled within a prescribed

service area. In [18], a performance-driven resource allocation

scheme for multiple radar systems is proposed and it minimises the

total transmitted power for a predefined mean squares error (MSE)

of localisation estimation. The proposed scheme uses the Cramer–

Rao (CR) bound as an optimisation metric and limits the

transmitted power at each station within an acceptable range.

Another typical strategy to conserve the network power

consumption is to manage and select nodes in an optimal way

during the localisation, which can reduce the number of actively

participating nodes. However, due to the decentralised and

dynamic nature of WSN architecture, it is difficult to manage the

sensor nodes over the entire network. Although developing a

network management system (NMS) for distributed WSNs is a first

demanding task in many applications, NMS is usually scheduled as

the second phase in practical project planning. These render the

WSN prone to be less versatile in node management. To our best

knowledge, existing approaches for node selection in WSN

localisation are mostly distributed. Distributed approaches are

more favorable in implementation due to the ad hoc nature of

WSNs, nevertheless they are generally suboptimal for the lack of

entire network knowledge compared with the centralised ones.

Recently, an emerged software-defined SN (SDSN)

architecture, which introduces the fundamental idea of SD

networking (SDN) [19] into WSNs, has become more attractive for

application-specific wireless communications. In SD networks,

there are two communication planes: the physical data plane and

the abstracted control plane which has a centralised control and

view of the whole network [20, 21]. The separation of data and

control planes makes SDSN programmable and thus the structure

of network becomes dynamic. It is also worth noting that the SD

technique in SDSNs provides a chance to break the limitation in

designing node-selection strategies and seek for an optimal

solution from a global perspective [22]. To benefit from SDSN's

high adaptability nature, it is worth exploring the adaptive

localisation algorithms that take the full advantage of node

management in SDSNs. The authors in [23] investigate localisation

IET Commun., 2017, Vol. 11 Iss. 13, pp. 2035-2041

2035

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