Quantum-inspired algorithm for cyber-physical visual surveillance deployment systems

被引:20
作者
Kuo, Shu-Yu [1 ]
Chou, Yao-Hsin [1 ]
Chen, Chi-Yuan [2 ]
机构
[1] Natl Chi Nan Univ, Dept Comp Sci & Informat Engn, Puli 54561, Taiwan
[2] Natl Ilan Univ, Dept Comp Sci & Informat Engn, Yilan, Taiwan
关键词
Cyber-physical systems; Visual surveillance; Quantum-inspired tabu search; Entanglement; Deployment; TABU SEARCH ALGORITHM; UNCERTAINTY-AWARE; WSN; COVERAGE;
D O I
10.1016/j.comnet.2016.11.013
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With recent advances in camera and visual computing technology, visual surveillance is playing a significant role in wireless sensor networks (WSNs) and cyber-physical systems (CPSs). It can be used in civilian areas for traffic control and security monitoring. Deployment is an important and fundamental issue in a WSN/CPS. Many issues, such as the quality of service, energy efficiency, and lifetime, are based on the placement of sensors. Different heuristic and deterministic methods have been proposed to achieve optimal deployment. In this paper, we propose a novel algorithm, called the Quantum-inspired Tabu Search algorithm with Entanglement (QTSwE), which is based on both the Quantum-inspired Tabu Search (QTS) algorithm and quantum entanglement feature. QTSwE is applied to a deployment problem to determine the minimum number of sensors required and their locations. This paper analyzes the property of the deployment problem and calls this phenomenon dependency. It uses the concept of quantum entanglement to build the initial positions of sensors to tackle the dependency of variables. The QTS is then used to find better solutions iteratively. Moreover, we use local search to enhance the search capability of QTS and to avoid being trapped in local optima. The experiment results showed that QTSwE outperformed other deployment approaches and used the least number of sensors to satisfy the monitoring requirement and topology connectivity. With QTSwE, the performance of surveillance system deployment has improved further. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:5 / 18
页数:14
相关论文
共 39 条
[1]  
Ab Aziz NAB, 2007, ICIAS 2007: INTERNATIONAL CONFERENCE ON INTELLIGENT & ADVANCED SYSTEMS, VOLS 1-3, PROCEEDINGS, P961
[2]  
Ab Aziz NAB, 2009, IEEE INT C NETW SENS, P596
[3]   Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey [J].
Adnan, Md Akhtaruzzaman ;
Razzaque, Mohammd Abdur ;
Ahmed, Ishtiaque ;
Isnin, Ismail Fauzi .
SENSORS, 2014, 14 (01) :299-345
[4]  
Aitsaadi N, 2007, OCEANS 2007 - EUROPE, VOLS 1-3, P1410
[5]   Artificial potential field approach in WSN deployment: Cost, QoM, connectivity, and lifetime constraints [J].
Aitsaadi, Nadjib ;
Achir, Nadjib ;
Boussetta, Khaled ;
Pujolle, Guy .
COMPUTER NETWORKS, 2011, 55 (01) :84-105
[6]  
Aitsaadi N, 2009, IEEE ICC, P279
[7]   A Tabu Search approach for differentiated sensor network deployment [J].
Aitsaadi, Nadjib ;
Achir, Nadjib ;
Boussetta, Khaled ;
Pujolle, Guy .
2008 5TH IEEE CONSUMER COMMUNICATIONS AND NETWORKING CONFERENCE, VOLS 1-3, 2008, :163-+
[8]   A Tabu Search WSN Deployment Method for Monitoring Geographically Irregular Distributed Events [J].
Aitsaadi, Nadjib ;
Achir, Nadjib ;
Boussetta, Khaled ;
Pujolle, Guy .
SENSORS, 2009, 9 (03) :1625-1643
[9]   A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems [J].
Chiang, Hua-Pei ;
Chou, Yao-Hsin ;
Chiu, Chia-Hui ;
Kuo, Shu-Yu ;
Huang, Yueh-Min .
SOFT COMPUTING, 2014, 18 (09) :1771-1781
[10]  
Chiu C., 2011, Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, P55