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

Computer Networks

Volumn 117, Issue , 2017, Pages 5-18

  Kuo, Shu Yu ^a   Chou, Yao Hsin ^a   Chen, Chi Yuan ^b  

^a NATIONAL CHI NAN UNIVERSITY   (Taiwan)

^b NATIONAL ILAN UNIVERSITY   (Taiwan)

Author keywords

Cyber physical systems; Deployment; Entanglement; Quantum inspired tabu search; Visual surveillance

Indexed keywords

EMBEDDED SYSTEMS; ENERGY EFFICIENCY; HEURISTIC METHODS; ITERATIVE METHODS; MONITORING; OPTIMIZATION; QUALITY OF SERVICE; SECURITY SYSTEMS; TABU SEARCH; TRAFFIC CONTROL; WIRELESS SENSOR NETWORKS;

CYBER PHYSICAL SYSTEMS (CPSS); DEPLOYMENT; DETERMINISTIC METHODS; ENTANGLEMENT; SURVEILLANCE SYSTEMS; TABU SEARCH ALGORITHMS; VISUAL SURVEILLANCE; WIRELESS SENSOR NETWORK (WSNS);

QUANTUM ENTANGLEMENT;

EID: 85007427328     PISSN: 13891286     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.comnet.2016.11.013     Document Type: Article

References (39)

1. [1] Adnan, M.A., Razzaque, M.A., Ahmed, I., Isnin, I.F., Bio-mimic optimization strategies in wireless sensor networks: a survey. Sensors 14:1 (2014), 299–345.
2. [2] Deif, D.S., Gadallah, Y., Classification of wireless sensor networks deployment techniques. IEEE Commun. Surv. Tut. 16:2 (2014), 834–855 Second Quarter.
3. [3] Tsai, C.W., Tsai, P.W., Pan, J.S., Chao, H.C., Metaheuristics for the deployment problem of WSN: a review. Microprocess. Microsyst. 39:8 (2015), 1305–1317.
4. [4] Han, X., Hong-xu, M., Maximum lifetime data aggregation in distributed intelligent robot network based on ACO. IEEE Congr. Evol. Comput., 2008, 50–55.
5. [5] Dhillon, S., Chakrabarty, K., Iyengar, S., Sensor placement for grid coverage under imprecise detections. Proceedings of International Conference on Information Fusion, 2002, 1581–1587.
6. [6] Chou, Y.H., Yang, Y.J., Chiu, C.H., Classical and quantum-inspired tabu search for solving 0/1 knapsack problem. IEEE International Conference on Systems, Man, and Cybernetics, 2011, 1364–1369.
7. [7] Chou, Y.H., Chiu, C.H., Yang, Y.J., Quantum-inspired tabu search algorithm for solving 0/1 knapsack problems. Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, 2011, 55–56.
8. [8] Chiang, H.P., Chou, Y.H., Chiu, C.H., Kuo, S.Y., Huang, Y.M., A quantum-inspired tabu search algorithm for solving combinatorial optimization problems. Soft Comput. 18:9 (2014), 1771–1781.
9. [9] Yang, Y.J., Kuo, S.Y., Lin, F.J., Liu, I.I., Chou, Y.H., Improved quantum-inspired tabu search algorithm for solving function optimization problem. IEEE International Conference on Systems, Man, and Cybernetics, 2013, 823–828.
10. [10] Kuo, S.Y., Kuo, C., Chou, Y.H., Dynamic stock trading system based on quantum-inspired tabu search algorithm. IEEE Congress on Evolutionary Computation, 2013, 1029–1036.
11. [11] Chou, Y.-H., Kuo, S.-Y., Kuo, C., Tsai, Y.-C., Intelligent stock trading system based on QTS algorithm in japan's stock market. IEEE International Conference on Systems, Man, and Cybernetics, 2013, 977–982.
12. [12] Chou, Y.H., Kuo, S.Y., Chen, C.Y., Chao, H.C., A rule-based dynamic decision-making stock trading system based on quantum-inspired tabu search algorithm. IEEE Access, volume 2, 2014, 883–896.
13. [13] Wang, W.H., Chiu, C.H., Kuo, S.Y., Huang, S.F., Chou, Y.H., Quantum-inspired tabu search algorithm for reversible logic circuit synthesis. IEEE International Conference on Systems, Man, and Cybernetics, 2012, 709–714.
14. [14] Dhillon, S., Chakrabarty, K., Sensor placement for effective coverage and surveillance in distributed sensor networks. IEEE Wireless Communications and Networking Conference, 2003, 1609–1614.
15. [15] Zou, Y., Chakrabarty, K., Uncertainty-aware and coverage-oriented deployment for sensor networks. J. Parallel Distrib. Comput. 64:7 (2004), 788–798.
16. [16] Zou, Y., Chakrabarty, K., Uncertainty-aware sensor deployment algorithms for surveillance applications. IEEE Global Telecommunications Conference, volume 5, 2003, 2972–2976.
17. [17] Zhang, J., Yan, T., Son, S.H., Deployment strategies for differentiated detection in wireless sensor networks. Third Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, 2006, 316–325.
18. [18] Aitsaadi, N., Achir, N., Boussetta, K., Pujolle, G., Differentiated underwater sensor network deployment. 2007, OCEANS, Europe, 1–6.
19. [19] Senouci, M.R., Mellouk, A., Oukhellou, L., Aissani, A., Uncertainty-aware sensor network deployment. IEEE Global Telecommunications Conference (GLOBECOM), 2011, 1–5.
20. [20] Reda, S.M., Abdelhamid, M., Latifa, O., Amar, A., Efficient uncertainty-aware deployment algorithms for wireless sensor networks. IEEE Wireless Communications and Networking Conference (WCNC), 2012, 2163–2167.
21. [21] Senouci, M.R., Mellouk, A., Oukhellou, L., Aissani, A., WSNS deployment framework based on the theory of belief functions. Comput. Netw. 88 (2015), 12–26.
22. [22] Li, Y., Gao, W., Wu, C., Wang, Y., Deployment of sensors in WSN: an efficient approach based on dynamic programming. Chin. J. Electron. 24:1 (2015), 33–36.
23. [23] Aitsaadi, N., Achir, N., Boussetta, K., Pujolle, G., A tabu search approach for differentiated sensor network deployment. IEEE Consumer Communications and Networking Conference, 2008, 163–167.
24. [24] Aitsaadi, N., Achir, N., Boussetta, K., Pujolle, G., A tabu search WSN deployment method for monitoring geographically irregular distributed events. Sensors 9:3 (2009), 1625–1643.
25. [25] Aitsaadi, N., Achir, N., Boussetta, K., Pujolle, G., Potential field approach to ensure connectivity and differentiated detection in WSN deployment. IEEE International Conference on Communications, 2009, 1–6.
26. [26] Aitsaadi, N., Achir, N., Boussetta, K., Pujolle, G., Artificial potential field approach in WSN deployment: cost, qom, connectivity, and lifetime constraints. Comput. Netw. 55:1 (2011), 84–105.
27. [27] Yoon, Y., Kim, Y.-H., An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Transactions on Cybernetics, volume 43, 2013, 1473–1483.
28. [28] Zorlu, O., Sahingoz, O.K., Increasing the coverage of homogeneous wireless sensor network by genetic algorithm based deployment. International Conference on Digital Information and Communication Technology and its Applications (DICTAP), 2016, 109–114.
29. [29] Aziz, N.A.B.A., Mohemmed, A.W., Sagar, B.S.D., Particle swarm optimization and voronoi diagram for wireless sensor networks coverage optimization. International Conference on Intelligent and Advanced Systems (ICIAS), 2007, 961–965.
30. [30] Aziz, N.A.B.A., Mohemmed, A.W., Alias, M.Y., A wireless sensor network coverage optimization algorithm based on particle swarm optimization and voronoi diagram. International Conference on Networking Sensing and Control, 2009, 602–607.
31. [31] Safa, H., El-Hajj, W., Zoubian, H., Particle swarm optimization based approach to solve the multiple sink placement problem in WSNs. International Conference on Communications (ICC), 2012, 5445–5450.
32. [32] De, C., Rane, A.D., Prabhakar, N., A comparative study on performances of sensor deployment algorithms in WSN. National Systems Conference (NSC), 2015, 1–6.
33. [33] Tsai, C.W., An effective WSN deployment algorithm via search economics. Comput. Netw. 101:4 (2016), 178–191.
34. [34] Han, K.H., Kim, J.H., Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6:6 (2002), 580–593.
35. [35] Han, K.H., Kim, J.H., Quantum-inspired evolutionary algorithms with a new termination criterion, h gate, and two-phase scheme. IEEE Transactions on Evolutionary Computation, volume 8, 2004, 156–169.
36. [36] Chou, Y.H., Chen, C.Y., Chiu, C.H., Chao, H.C., Classical and quantum-inspired electromagnetism-like mechanism and its applications. IET Control Theor. Appl.s 6:10 (2012), 1424–1433.
37. [37] Glassner, A., Quantum computing. 1. IEEE Computer Graphics and Applications, volume 21, 2001, 84–92.
38. [38] Glassner, A., Quantum computing. 2. IEEE Comput. Graph. Appl. 21:6 (2001), 72–82.
39. [39] Glassner, A., Quantum computing. 3. IEEE Comput. Graph. Appl. 21:5 (2001), 86–95.