Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things

Future Generation Computer Systems

Volumn 92, Issue , 2019, Pages 1142-1156

  Liu, Xiao ^a   Zhao, Shaona ^a   Liu, Anfeng ^a   Xiong, Naixue ^b   Vasilakos, Athanasios V ^c  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b NORTHEASTERN STATE UNIVERSITY   (United States)

^c LULEÅ UNIVERSITY OF TECHNOLOGY   (Sweden)

Author keywords

Energy efficient; Network lifetime; Proactive nodes selection system; Target tracking

Indexed keywords

CLUTTER (INFORMATION THEORY); FORECASTING; INTERNET OF THINGS; TARGET TRACKING;

ANALYSIS AND SIMULATION; DIFFERENT DOMAINS; ENERGY EFFICIENT; ENGINEERING APPLICATIONS; MONITORING PERFORMANCE; NETWORK LIFETIME; NODES SELECTIONS; PREDICTION ACCURACY;

ENERGY EFFICIENCY;

EID: 85028345951     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2017.07.022     Document Type: Article

References (48)

1. Xiong, N., Vasilakos, A.V., Yang, L.T., Song, L., Pan, Y., Kannan, R., Li, Y., Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE J. Sel. Areas Commun. 4:27 (2009), 495–509.
2. Liu, X., Dong, M., Ota, K., et al. Trace malicious source to guarantee cyber security for mass monitor critical infrastructure. J. Comput. System Sci., 2016, 10.1016/j.jcss.2016.09.008.
3. He, S., Chen, J., Li, X., et al. Mobility and intruder prior information improving the barrier coverage of sparse sensor networks. IEEE Trans. Mob. Comput. 13:6 (2015), 1268–1282.
4. Zheng, J., Bhuiyan, M.Z.A., Liang, S., et al. Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks. Future Gener. Comput. Syst. 39:1 (2014), 88–99.
5. Liu, Y., Liu, A., Li, Y., Li, Z., Choi, Y., Sekiya, H., Li, J., APMD: A fast data transmission protocol with reliability guarantee for pervasive sensing data communication. Pervasive Mob. Comput., 2017, 10.1016/j.pmcj.2017.03.012.
6. Jiang, B., Ravindran, B., Cho, H., Probability-based prediction and sleep scheduling for energy-efficient target tracking in sensor networks. IEEE Trans. Mob. Comput. 12:4 (2013), 735–747.
7. Vasudevan, M., Tian, Y.C., Tang, M., et al. Profile-based application assignment for greener and more energy-efficient data centers. Future Gener. Comput. Syst. 67 (2016), 94–108.
8. Chen, J., Li, J., Lai, T., Trapping mobile targets in wireless sensor networks: An energy-efficient perspective. IEEE Trans. Veh. Technol. 62:7 (2013), 3287–3300.
9. Hu, Y., Dong, M., Ota, K., Liu, A., Guo, M., Mobile target detection in wireless sensor networks with adjustable sensing frequency. IEEE Syst. J. 10:3 (2016), 1160–1171.
10. C. Gui, P. Mohapatra, Power conservation and quality of surveillance in target tracking sensor network, in: Proceedings of the 10th annual international conference on Mobile computing and networking, 2004, pp. 129–143.
11. Fuemmeler, J.A., Veeravalli, V.V., Energy efficient multi-object tracking in sensor networks. IEEE Trans. Signal Process 58:7 (2010), 3742–3750.
12. Liu, Y., Liu, A., Guo, S., Li, Z., Choi, Y., Sekiy, H., Context-aware collect data with energy efficient in Cyber-physical cloud systems. Future Gener. Comput. Syst., 2017, 10.1016/j.future.2017.05.029.
13. Minaeian, S., Liu, J., Son, Y.J., Vision-based target detection and localization via a team of cooperative UAV and UGVs. IEEE Trans. Syst. Man Cybern. Syst. 46:7 (2016), 1005–1016.
14. Liu, A., Liu, X., Tang, Z., Yang, Laurence., et al. Preserving smart sink location privacy with delay guaranteed routing scheme for WSNs. ACM Trans. Embedded Comput. Syst., 16(3), 2017, 68.
15. Liu, T., Chen, F., Ma, Y., et al. An energy-efficient task scheduling for mobile devices based on cloud assistant. Future Gener. Comput. Syst. 61 (2016), 1–12.
16. Liu, Y., Dong, M., Ota, K., Liu, A., ActiveTrust: Secure and trustable routing in wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 11:9 (2016), 2013–2027.
17. Xiong, N., Jia, X., Yang, L.T., Vasilakos, A.V., Li, Y., Pan, Y., A distributed efficient flow control scheme for multirate multicast networks. IEEE Trans. Parallel Distrib. Syst. 9:21 (2010), 1254–1266.
18. Zhao, S., Liu, A., High performance target tracking scheme with low prediction precision requirement in WSNs. Int. J. Ad Hoc Ubiquitous Comput., 2017 http://www.inderscience.com/info/ingeneral/forthcoming.php?jcode =ijahuc.
19. Liu, X., Li, G., Zhang, S., Liu, A., Big program code dissemination scheme for emergency software-define wireless sensor networks. Peer-to-Peer Netw. Appl., 2017, 10.1007/s12083-017-0565-5.
20. Y. Tao, C. Faloutsos, D. Papadias, B. Liu, Prediction and indexing of moving objects with unknown motion patterns, in: Proceedings of the 2004 ACM SIGMOD international conference on Management of data, 2014, pp. 611–622.
21. Liu, A., Liu, X., Wei, T., Yang, L., Rho, S., Paul, A., Distributed multi-representative re-fusion approach for heterogeneous sensing data collection. ACM Trans. Embedded Comput. Syst., 16(3), 2017, 73.
22. H. Jeung, Q. Liu, H. Shen, et al. A hybrid prediction model for moving objects, in: ICDE, 2008.
23. Rao, Y.S., A secure and efficient Ciphertext-Policy Attribute-Based Signcryption for Personal Health Records sharing in cloud computing. Future Gener. Comput. Syst. 67 (2017), 133–151.
24. G. He, J. Hou, Tracking targets with quality in wireless sensor networks, in: 13th IEEE International Conference, ICNP’05, 2005, pp. 65–74.
25. K. Tsukamoto, H. Ueda, et al. Design of wireless sensor network for multi-point surveillance of a moving target based on the relationship between tracking probability and sensor density, in: 6th IEEE Consumer Communications and Networking Conference, 2009, pp. 1–5.
26. Suganya, S.A., A cluster-based approach for collaborative target tracking in wireless sensor networks. Emerg. Trends Eng. Technol., 2008, 276–281.
27. A. Alaybeyoglu, O. Dagdeviren, K. Erciyes, et al. Performance evaluation of cluster-based target tracking protocols for wireless sensor networks, in: 24th International Symposium on Computer and Information Sciences, 2009, pp. 357–362.
28. Yang, W., Fu, Z., et al. An adaptive dynamic cluster-based protocol for target tracking in wireless sensor networks. Adv. Data Web Manag., 2007, 157–167.
29. D. Liu, N. Wang, Y. An, Dynamic cluster based object tracking algorithm in WSN, in: Proceedings of the 2010 Second WRI Global Congress on Intelligent Systems, 2010, pp. 397–399.
30. R.H. Jamali, M. Azarafrooz, et al. A new adaptive power optimization scheme for target tracking wireless sensor networks, in: The IEEE Symposium on Industrial Electronics & Applications, 2009, pp. 307–312.
31. Zhang, W., Cao, G., DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks. IEEE Trans. Wireless Commun. 3:5 (2004), 1689–1701.
32. An, Y.K., Yoo, S.M., An, C., Wells, B.E., Rule-based multiple-target tracking in acoustic wireless sensor networks. Comput. Commun. 51:5 (2014), 81–94.
33. Mahfouz, S., Mourad-Chehade, F., Honeine, P., et al. Target tracking using machine learning and Kalman filter in wireless sensor networks. IEEE Sens. J. 14:10 (2014), 3715–3725.
34. F. Deldar, M.H. Yaghmaee, Designing a prediction-based clustering algorithm for target tracking in wireless sensor networks, in: International Symposium on Computer Networks and Distributed Systems, 2011, pp. 199–203.
35. Huang, C., Tseng, Y., Lo, L.C., The coverage problem in three-dimensional wireless sensor networks. J. Interconnect. Netw. 8:3 (2007), 209–227.
36. Akan, O., Karli, O., Ergul, O., Cognitive radio sensor networks. IEEE Netw. 23:4 (2009), 34–40.
37. Bukhari, S., Rehmani, M., Siraj, S., A survey of channel bonding for wireless networks and guidelines of channel bonding for futuristic cognitive radio sensor networks. IEEE Commun. Surv. Tutor. 18:2 (2016), 924–948.
38. Rehmani, M., Viana, A., Khalife, H., et al. SURF: A distributed channel selection strategy for data dissemination in multi-hop cognitive radio networks. Comput. Commun. 36:10–11 (2013), 1172–1185.
39. Akhtar, F., Rehmani, M., Energy replenishment using renewable and traditional energy resources for sustainable wireless sensor networks: A review. Renew. Sustainable Energy Rev. 45 (2015), 769–784.
40. M. Saxena, P. Gupta, B.N. Jain, Experimental analysis of rssi-based location estimation in wireless sensor networks. Communication Systems Software and Middleware and Workshops, 2008, pp. 503–510.
41. Wang, G., Alam, M.Z., Cao, J., Wu, J., Detecting movements of a target using face tracking in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 25:4 (2014), 939–949.
42. Zhang, H., Hou, J., Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc Sens. Wirel. Netw. 1:1/2 (2005), 89–124.
43. Malik, Z., Bouguettaya, A., Reputation bootstrapping for trust establishment among Web services. IEEE Internet Comput. 13:1 (2009), 40–47.
44. Cooke, P.J., Bounds for coverage probabilities with applications to sequential coverage problems. J. Appl. Probab. 11:2 (1974), 281–293.
45. Liu, Q., Liu, A., On the hybrid using of unicast-broadcast in wireless sensor networks. Comput. Electr. Eng., 2017, 10.1016/j.compeleceng.2017.03.004.
46. OMNeT++ Network Simulation Framework. http://www.omnetpp.org.
47. Zhou, F., Trajcevski, G., Ghica, O., et al. Deflection-aware tracking-principal selection in active wireless sensor networks. IEEE Trans. Veh. Technol. 61:7 (2012), 3240–3254.
48. Zorbas, D., Razafindralambo, T., Prolonging network lifetime under probabilistic target coverage in wireless mobile sensor networks. Comput. Commun. 36:9 (2013), 1039–1053.