Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications
Author(s)
Mohammad Abdul Azim*,
Zeyar Aung,
Sofiane Moad,
Nizar Bouabdallah,
Mario E. Rivero-Angeles,
Israel Leyva-Mayorga
Affiliation(s)
Masdar Institute of Science and Technology, Abu Dhabi, UAE. National Polytechnique Institute, CIC-IPN, Mexico City, Mexico. DYONISOS, INRIA, Rennes, France. National Polytechnique Institute, SEPI ESCOM-IPN, Mexico City, Mexico.
Masdar Institute of Science and Technology, Abu Dhabi, UAE. National Polytechnique Institute, CIC-IPN, Mexico City, Mexico. DYONISOS, INRIA, Rennes, France. National Polytechnique Institute, SEPI ESCOM-IPN, Mexico City, Mexico.
ABSTRACT
Continuous-monitoring (CM) of natural phenomenon is one of the major streams of applications in wireless sensor networks (WSNs), where aggregation and clustering techniques are beneficial as correlation dominates in both spatial and temporal aspects of sensed phenomenon. Conversely, in Event Driven Reporting (EDR), the efficient transmission of sensitive data related to some predefined alarm cases is of major importance. As such, reporting latency is a more important performance parameter. However, in some applications, the transmission of both CM and EDR data is encouraged or even required. For either CM or EDR applications, system performance can be greatly improved when both the number of packets to be transmitted as well as the packet size is reduced. This is especially true for highly dense sensor networks where many nodes detect the same values for the sensed phenomenon. Building on this, this paper focuses on studying and proposing compression techniques to improve the system performance in terms of energy consumption and reporting latency in both CM and EDR applications. Furthermore, we extend our analysis to hybrid networks where CM and EDR are required simultaneously. Specifically, this paper presents a simple aggregation technique named smart aggregation (SAG) for the CM applications and an event driven scheme named compression cluster scheme in spatial correlated region (CC_SCR). The proposed SAG exploits both spatial and temporal correlations where CC_SCR exploits the spatial correlation of such networks by data compression. Rationalizing the developments is attained by simulations that compare energy efficiency of the proposed SAG with k-hop aggregation and CM based event driven reporting (CMEDR) schemes. Results of CC_SCR show that the technique may reduce the energy consumption drastically. In some specific cases the reduction becomes more than 10 times compared to a classical clustering scheme. Two different strategies for the transmission of event reports through the CM infrastructure are incorporated: PER and NPER protocols. Both strategies take advantage of the cluster-based architecture which assigns a TDMA schedule for the CM data transmission while using NP/CSMA for the transmission of the event information. Consequently, no extra energy is consumed for separate event clusters. As such, the number of packets to be transmitted is greatly reduced.
Continuous-monitoring (CM) of natural phenomenon is one of the major streams of applications in wireless sensor networks (WSNs), where aggregation and clustering techniques are beneficial as correlation dominates in both spatial and temporal aspects of sensed phenomenon. Conversely, in Event Driven Reporting (EDR), the efficient transmission of sensitive data related to some predefined alarm cases is of major importance. As such, reporting latency is a more important performance parameter. However, in some applications, the transmission of both CM and EDR data is encouraged or even required. For either CM or EDR applications, system performance can be greatly improved when both the number of packets to be transmitted as well as the packet size is reduced. This is especially true for highly dense sensor networks where many nodes detect the same values for the sensed phenomenon. Building on this, this paper focuses on studying and proposing compression techniques to improve the system performance in terms of energy consumption and reporting latency in both CM and EDR applications. Furthermore, we extend our analysis to hybrid networks where CM and EDR are required simultaneously. Specifically, this paper presents a simple aggregation technique named smart aggregation (SAG) for the CM applications and an event driven scheme named compression cluster scheme in spatial correlated region (CC_SCR). The proposed SAG exploits both spatial and temporal correlations where CC_SCR exploits the spatial correlation of such networks by data compression. Rationalizing the developments is attained by simulations that compare energy efficiency of the proposed SAG with k-hop aggregation and CM based event driven reporting (CMEDR) schemes. Results of CC_SCR show that the technique may reduce the energy consumption drastically. In some specific cases the reduction becomes more than 10 times compared to a classical clustering scheme. Two different strategies for the transmission of event reports through the CM infrastructure are incorporated: PER and NPER protocols. Both strategies take advantage of the cluster-based architecture which assigns a TDMA schedule for the CM data transmission while using NP/CSMA for the transmission of the event information. Consequently, no extra energy is consumed for separate event clusters. As such, the number of packets to be transmitted is greatly reduced.
KEYWORDS
Wireless Sensor Networks, Aggregation, Continuous-Monitoring, Event Driven Reporting, Data Compression, Clustering
Wireless Sensor Networks, Aggregation, Continuous-Monitoring, Event Driven Reporting, Data Compression, Clustering
Cite this paper
Abdul Azim, M. , Aung, Z. , Moad, S. , Bouabdallah, N. , E. Rivero-Angeles, M. and Leyva-Mayorga, I. (2014) Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications. Wireless Sensor Network, 6, 67-92. doi: 10.4236/wsn.2014.65009.
Abdul Azim, M. , Aung, Z. , Moad, S. , Bouabdallah, N. , E. Rivero-Angeles, M. and Leyva-Mayorga, I. (2014) Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications. Wireless Sensor Network, 6, 67-92. doi: 10.4236/wsn.2014.65009.
References
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http://dx.doi.org/10.1016/j.ins.2010.01.027 [26] Petrovic, D., Shah, R.C., Ramchandran, K. and Rabaey, J. (2003) Data Funneling: Routing with Aggregation and Compression for Wireless Sensor Networks. IEEE International Workshop on Sensor Network Protocols and Applications (SNPA), Anchorage, 11 May 2003, 156-162. [27] Arici, T., Gedik, B., Altunbasak, Y. and Liu, L. (2003) PINCO: A Pipelined In-Network Compression Scheme for Sata Collection in Wireless Sensor Networks. IEEE International Conference on Computer Communications and Networks (ICCCN), Dallas, 20-22 October 2003, 539-544. [28] Pradhan, S.S., Kusuma, J. and Ramchandran, K. (2002) Distributed Compression in a Dense Micro-Sensor Network. IEEE Signal Processing Magazine, 19, 51-60. http://dx.doi.org/10.1109/79.985684 [29] Wei, D., Kaplan, S. and Chan, H.A. (2008) Energy Efficient Clustering Algorithms for Wireless Sensor Networks. IEEE International Conference on Communications (ICC) Workshops, Bejing, 19-23 May 2008, 236-240. [30] Younis, O., Krunz, M. and Ramasubramanian, S. (2006) Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges. IEEE Network, 20, 20-25.
http://dx.doi.org/10.1109/MNET.2006.1637928 [31] Abbasi, A.A. and Younis, M. (2007) A Survey on Clustering Algorithms for Wireless Sensor Networks. Computer Communications, 30, 2826-2841. http://dx.doi.org/10.1016/j.comcom.2007.05.024 [32] Amaxilatis, D., Chatzigiannakis, I., Koninis, C. and Pyrgelis, A. (2011) Component Based Clustering in Wireless Sensor Networks. ACM Computing Research Repository. [33] Younis, O. and Fahmy, S. (2004) HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks. IEEE Transactions on Mobile Computing, 3, 366-379.
http://dx.doi.org/10.1109/TMC.2004.41 [34] Lindsey, S. and Raghavendra, C.S. (2002) PEGASIS: Power-Efficient Gathering in Sensor Information Systems. IEEE Aerospace Conference, Big Sky, 9-16 March 2002, 1125-1130. [35] Ye, M., Li, C., Chen, G. and Wu, J. (2007) EECS: An Energy Efficient Clustering Scheme in Wireless Sensor Networks. IEEE International Performance, Computing, and Communications Conference (IPCCC), New Orleans, 7-9 April 2005, 535-540. [36] Rivero-Angeles, M.E. and Boubdallah, N. (2009) Event Reporting on Continuous Monitoring Wireless Sensor Networks. IEEE Global Telecommunications Conference (GLOBECOM), Honolulu, 30 November-4 December 2009, 1-6. [37] Jurdak, R., Baldi, P. and Lopes, C.V. (2007) Adaptive Low Power Listening for Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 6, 988-1004.
http://dx.doi.org/10.1109/TMC.2007.1037 [38] Bouabdallah, N., Rivero-Angeles, M.E. and Sericola, B. (2009) Continuous Monitoring Using Event-Driven Reporting for Cluster-Based Wireless Sensor Networks. IEEE Transactions on Vehicular Technology, 58, 3460-3479.
http://dx.doi.org/10.1109/TVT.2009.2015330 [39] Hohlt, B.A. (2005) The Design and Evaluation of Network Power Scheduling for Sensor Networks. Ph.D. Thesis, University of California, Berkeley. [40] Polastre, J., Hill, J. and Culler, D. (2004) Versatile Low Power Media Access for Wireless Sensor Networks. ACM Conference on Embedded Networked Sensor Systems (SenSys), Baltimore, 3-5 November 2004, 95-107. [41] Akyildiz, I.F., Su, W., Sankarasubramaniam, Y. and Cayirci, E. (2002) A Survey on Sensor Networks. IEEE Commu- nications Magazine, 40, 102-114. http://dx.doi.org/10.1109/MCOM.2002.1024422 [42] Jindal, A. and Psounis, K. (2004) Modeling Spatially-Correlated Sensor Network Data. IEEE International Conference on Sensor and Ad Hoc Communications and Networks (SECON), Santa Clara, 4-7 October 2004, 162-171. [43] Faruque, J. and Helmy, A. (2004) Rugged: Routing on Fingerprint Gradient in Sensor Network. IEEE International Conference on Pervasive Service (ICPS), Beirut, 19-23 July 2004, 179-188.
http://dx.doi.org/10.1109/PERSER.2004.27 [44] Levis, P., Lee, N., Welsh, M. and Culler, D. (2003) TOSSIM: Accurate and Scalable Simulation of Entire TinyOS Applications. ACM Conference on Embedded Networked Sensor Systems (SenSys), Los Angeles, 5-7 November 2003, 126-137. [45] Intel Lab Data. http://db.csail.mit.edu/labdata/labdata.html
[1] Al-Karaki, J.N. and Kamal, A.E. (2005) Routing Techniques in Wireless Sensor Networks: A Survey. IEEE Wireless Communications, 11, 6-28. http://dx.doi.org/10.1109/MWC.2004.1368893 [2] Heinzelman, W., Chandrakasan, A. and Balakrishanan, H. (2002) An Application-Specific Protocol Architecture for Wireless Microsensor Networks. IEEE Transactions on Wireless Communications, 1, 660-670.
http://dx.doi.org/10.1109/TWC.2002.804190 [3] Manjeshwar, A. and Agarwal, D.P. (2001) TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks. IEEE International Parallel and Distributed Processing Symposium (IPDPS), San Francisco, 23-27 April 2000, 2009-2015. [4] Manjeshwar, A. and Agarwal, D.P. (2002) APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless Sensor Networks. IEEE International Parallel and Distributed Processing Symposium (IPDPS), Miami, 15-19 April 2001, 195-202.
http://dx.doi.org/10.1109/IPDPS.2002.1016600 [5] Manjeshwar, A., Zeng, Q. and Agrawal, D.P. (2002) An Analytical Model for Information Retrieval in Wireless Sensor Networks Using Enhanced APTEEN Protocol. IEEE Transactions on Parallel and Distributed Systems, 13, 1290-1302. http://dx.doi.org/10.1109/TPDS.2002.1158266 [6] Polastre, J., Szewczyk, R. and Culler, D. (2005) Telos: Enabling Ultra-Low Power Wireless Research. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Los Angeles, 25-27 April 2005, 364-369. [7] Demirkol, I., Ersoy, C. and Alagoz, F. (2006) MAC Protocols for Wireless Sensor Networks: A Survey. IEEE Communications Magazine, 44, 115-121. http://dx.doi.org/10.1109/MCOM.2006.1632658 [8] Azim, M.A., Moad, S. and Bouabdallah, N. (2010) SAG: Smart Aggregation Technique for Continuous-Monitoring in Wireless Sensor Networks. IEEE International Conference on Communications (ICC), Cape Town, 23-27 May 2010, 1-6. [9] Moad, S., Rivero, M., Bouabdallah, N. and Langar, R. (2011) CC_SCR: A Compression Cluster-Based Scheme in a Spatial Correlated Region for Wireless Sensor Networks. IEEE International Conference on Communications (ICC), Kyoto, 5-9 June 2011, 1-6. [10] Moad, S., Rivero-Angeles, M.E., Bouabdallah, N., Sericola, B. and Aoul, Y.H. (2012) Performance Analysis of a Compression Scheme for Highly Dense Cluster-Based Wireless Sensor Network. In: Matin, M.A., Ed., Wireless Sensor Network—Technology and Applications, InTech Press, Rijeka, Croatia, 207-230. http://dx.doi.org/10.5772/48370 [11] Fasolo, E., Rossi, M., Widmer, J. and Zorzi, M. (2007) In-Network Aggregation Techniques for Wireless Sensor Networks: A Survey. IEEE Wireless Communications, 14, 70-87.
http://dx.doi.org/10.1109/MWC.2007.358967 [12] Rajagopalan, R. and Varshney, P.K. (2006) Data-Aggregation Techniques in Sensor Networks: A Survey. IEEE Communications Surveys and Tutorials, 8, 48-63. http://dx.doi.org/10.1109/COMST.2006.283821 [13] Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., Silva, F., et al. (2003) Directed Diffusion for Wireless Sensor Networking. IEEE/ACM Transactions on Networking, 11, 2-16.
http://dx.doi.org/10.1109/TNET.2002.808417 [14] Madden, S.R., Franklin, M.J., Hellerstein, J.M. and Hong, W. (2005) TinyDB: An Acquisitional Query Processing System for Sensor Networks. ACM Transactions on Database Systems, 30, 122-173.
http://dx.doi.org/10.1145/1061318.1061322 [15] Madden, S., Franklin, M.J., Hellerstein, J.M. and Hong, W. (2002) TAG: A Tiny AGgregation Service for Ad-Hoc Sensor Networks. Proceedings of the 5th Symposium on Operating Systems Design and Implementation, Boston, 9-11 December 2002, 131-146. [16] Motegi, S., Yoshihara, K. and Horiuchi, H. (2006) DAG Based in-Network Aggregation for Sensor Network Monitoring. IEEE/IPSJ International Symposium on Applications and the Internet (SAINT), Phoenix, 23-27 January 2006, 292-299. http://dx.doi.org/10.1109/SAINT.2006.20 [17] Sharaf, M.A., Beaver, J., Labrinidis, A. and Chrysanthis, P.K. (2003) TiNA: A Scheme for Temporal Coherency- Aware In-Network Aggregation. ACM Data Engineering for Wireless and Mobile Access, San Diego, 19 September 2003, 69-76. [18] Yoon, S. and Shahabi, C. (2007) The Clustered AGgregation (CAG) Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks. ACM Transactions on Sensor Networks, 3, Article No. 3. [19] Solis, I. and Obraczka, K. (2005) Efficient Continuous Mapping in Sensor Networks Using Isolines. IEEE International Conference on Mobile and Ubiquitous Systems (MobiQuitous): Networking and Services, San Diego, 17-21 July 2005, 325-332. [20] Mirian, F. and Sabaei, M. (2009) A Delay and Accuracy Sensitive Data Aggregation Structure in Wireless Sensor Networks. IEEE International Conference on Information Management and Engineering (ICIME), Kuala Lumpur, 3-5 April 2009, 231-235. [21] Weerasinghe, H., Elhajj, I.H., Krsteva, A. and Najm, M.A. (2007) Data Centric Adaptive In-Network Aggregation for Wireless Sensor Networks. IEEE International Conference on Advance Intelligent Mechatronics (AIM), Zurich, 4-7 September 2007, 1-6. [22] Yang, X.F., Wu, X.B. and Huang, J.A. (2009) TAGPP: A Tiny Aggregation Algorithm with Preprocessing in Local Cluster. IEEE International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC), Wuhan, 25-26 April 2009, 390-393. [23] Barr, K.C. and Asanovic, K. (2006) Energy-Aware Lossless Data Compression. ACM Transactions on Computer Systems, 24, 250-291. http://dx.doi.org/10.1145/1151690.1151692 [24] Sadler, C.M. and Martonosi, M. (2006) Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks. ACM International Conference on Embedded Networked Sensor Systems (SenSys), Boulder, 1-3 November 2006, 265-278. [25] Marcelloni, F. and Vecchio, M. (2010) Enabling Energy-Efficient and Lossy-Aware Data Compression in Wireless Sensor Networks by Multi-Objective Evolutionary Optimization. Information Sciences, 180, 1924-1941.
http://dx.doi.org/10.1016/j.ins.2010.01.027 [26] Petrovic, D., Shah, R.C., Ramchandran, K. and Rabaey, J. (2003) Data Funneling: Routing with Aggregation and Compression for Wireless Sensor Networks. IEEE International Workshop on Sensor Network Protocols and Applications (SNPA), Anchorage, 11 May 2003, 156-162. [27] Arici, T., Gedik, B., Altunbasak, Y. and Liu, L. (2003) PINCO: A Pipelined In-Network Compression Scheme for Sata Collection in Wireless Sensor Networks. IEEE International Conference on Computer Communications and Networks (ICCCN), Dallas, 20-22 October 2003, 539-544. [28] Pradhan, S.S., Kusuma, J. and Ramchandran, K. (2002) Distributed Compression in a Dense Micro-Sensor Network. IEEE Signal Processing Magazine, 19, 51-60. http://dx.doi.org/10.1109/79.985684 [29] Wei, D., Kaplan, S. and Chan, H.A. (2008) Energy Efficient Clustering Algorithms for Wireless Sensor Networks. IEEE International Conference on Communications (ICC) Workshops, Bejing, 19-23 May 2008, 236-240. [30] Younis, O., Krunz, M. and Ramasubramanian, S. (2006) Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges. IEEE Network, 20, 20-25.
http://dx.doi.org/10.1109/MNET.2006.1637928 [31] Abbasi, A.A. and Younis, M. (2007) A Survey on Clustering Algorithms for Wireless Sensor Networks. Computer Communications, 30, 2826-2841. http://dx.doi.org/10.1016/j.comcom.2007.05.024 [32] Amaxilatis, D., Chatzigiannakis, I., Koninis, C. and Pyrgelis, A. (2011) Component Based Clustering in Wireless Sensor Networks. ACM Computing Research Repository. [33] Younis, O. and Fahmy, S. (2004) HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks. IEEE Transactions on Mobile Computing, 3, 366-379.
http://dx.doi.org/10.1109/TMC.2004.41 [34] Lindsey, S. and Raghavendra, C.S. (2002) PEGASIS: Power-Efficient Gathering in Sensor Information Systems. IEEE Aerospace Conference, Big Sky, 9-16 March 2002, 1125-1130. [35] Ye, M., Li, C., Chen, G. and Wu, J. (2007) EECS: An Energy Efficient Clustering Scheme in Wireless Sensor Networks. IEEE International Performance, Computing, and Communications Conference (IPCCC), New Orleans, 7-9 April 2005, 535-540. [36] Rivero-Angeles, M.E. and Boubdallah, N. (2009) Event Reporting on Continuous Monitoring Wireless Sensor Networks. IEEE Global Telecommunications Conference (GLOBECOM), Honolulu, 30 November-4 December 2009, 1-6. [37] Jurdak, R., Baldi, P. and Lopes, C.V. (2007) Adaptive Low Power Listening for Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 6, 988-1004.
http://dx.doi.org/10.1109/TMC.2007.1037 [38] Bouabdallah, N., Rivero-Angeles, M.E. and Sericola, B. (2009) Continuous Monitoring Using Event-Driven Reporting for Cluster-Based Wireless Sensor Networks. IEEE Transactions on Vehicular Technology, 58, 3460-3479.
http://dx.doi.org/10.1109/TVT.2009.2015330 [39] Hohlt, B.A. (2005) The Design and Evaluation of Network Power Scheduling for Sensor Networks. Ph.D. Thesis, University of California, Berkeley. [40] Polastre, J., Hill, J. and Culler, D. (2004) Versatile Low Power Media Access for Wireless Sensor Networks. ACM Conference on Embedded Networked Sensor Systems (SenSys), Baltimore, 3-5 November 2004, 95-107. [41] Akyildiz, I.F., Su, W., Sankarasubramaniam, Y. and Cayirci, E. (2002) A Survey on Sensor Networks. IEEE Commu- nications Magazine, 40, 102-114. http://dx.doi.org/10.1109/MCOM.2002.1024422 [42] Jindal, A. and Psounis, K. (2004) Modeling Spatially-Correlated Sensor Network Data. IEEE International Conference on Sensor and Ad Hoc Communications and Networks (SECON), Santa Clara, 4-7 October 2004, 162-171. [43] Faruque, J. and Helmy, A. (2004) Rugged: Routing on Fingerprint Gradient in Sensor Network. IEEE International Conference on Pervasive Service (ICPS), Beirut, 19-23 July 2004, 179-188.
http://dx.doi.org/10.1109/PERSER.2004.27 [44] Levis, P., Lee, N., Welsh, M. and Culler, D. (2003) TOSSIM: Accurate and Scalable Simulation of Entire TinyOS Applications. ACM Conference on Embedded Networked Sensor Systems (SenSys), Los Angeles, 5-7 November 2003, 126-137. [45] Intel Lab Data. http://db.csail.mit.edu/labdata/labdata.html