Abstract
A multi-representative re-fusion (MRRF) approximate data collection approach is proposed in which multiple nodes with similar readings form a data coverage set (DCS). The reading value of the DCS is represented by an R-node. The set near the Sink is smaller, while the set far from the Sink is larger, which can reduce the energy consumption in hotspot areas. Then, a distributed data-aggregation strategy is proposed that can re-fuse the value of R-nodes that are far from each other but have similar readings. Both comprehensive theoretical and experimental results indicate that the MRRF approach increases lifetime and energy efficiency.
- H. Attar, L. Stankovic, and V. Stankovic. 2012. Cooperative network-coding system for wireless sensor networks. IET Communications 6, 3, 344--352.Google Scholar
Cross Ref
- Y. Cai, Y. Mo, K. Ota, C. Luo, M. Dong, and L. T. Yang. 2014. Optimal data fusion of collaborative spectrum sensing under attack in cognitive radio networks. IEEE Network 28, 1, 17--23.Google Scholar
Cross Ref
- L. W. Chang, Y. M. Huang, and C. C. Lin. 2013. Performance analysis of S‐MAC protocol. International Journal of Communication Systems 26, 9, 1129--1142.Google Scholar
Cross Ref
- Z. Chen, A. Liu, and Z. Li. 2017. Distributed duty cycle control for delay improvement in wireless sensor networks. Peer-to-Peer Networking and Applications 10, 3, 559--578.Google Scholar
Cross Ref
- R. Cristescu, B. Beferull-Lozano, and M. Vetterli. 2005. Networked Slepian-Wolf: Theory, algorithms, and scaling laws, IEEE Transactions on Information Theory 51, 12, 4057--4073. Google Scholar
Digital Library
- M. Dong, K. Ota, and A. Liu. 2016. RMER: Reliable and energy efficient data collection for large-scale wireless sensor networks. IEEE Internet of Things Journal 3, 4, 511--519.Google Scholar
Cross Ref
- M. Dong, X. Liu, Z. Qian. 2015. QoE-ensured price competition model for emerging mobile networks. IEEE Wireless Communications 22, 4, 50--57.Google Scholar
Digital Library
- Y. Hu, M. Dong, K. Ota, A. Liu, and M. Guo. 2016. Mobile target detection in wireless sensor networks with adjustable sensing frequency, IEEE System Journal 10, 3, 1160--1171.Google Scholar
Cross Ref
- C. C. Hung, W. C. Peng, and W. C. Lee. 2012. Energy-aware set-covering approaches for approximate data collection in wireless sensor networks. IEEE Transactions on Knowledge and Data Engineering 24, 11, 1993--2007. Google Scholar
Digital Library
- S. Hussain, J. H. Park, A. K. Dey, L. T. Yang, and P. K. Biswas. 2011. Information fusion in future generation communication environments. Information Fusion 12, 3, 148--149. Google Scholar
Digital Library
- C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva. 2003. Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking 11, 1, 2--16. Google Scholar
Digital Library
- C. Joo and N. B. Shroff. 2014. On the delay performance of in-network aggregation in lossy wireless sensor networks. IEEE/ACM Transactions on Networking 22, 2, 662--673. Google Scholar
Digital Library
- Y. Kotidis. 2015. Snapshot queries: towards data-centric sensor networks. In Proceedings of the 21st International Conference on Data Engineering (ICDE’15). 131--142. Google Scholar
Digital Library
- S. Lindsey, C. Raghavendra, and K. M. Sivalingam, 2002. Data gathering algorithms in sensor networks using energy metrics. IEEE Transactions on Parallel and Distributed Systems 13, 9, 924--935. Google Scholar
Digital Library
- Y. Liu, A. Liu, and S. He. 2015. A novel joint logging and migrating traceback scheme for achieving low storage requirement and long lifetime in WSNs. AEU-International Journal of Electronics and Communications 69, 10, 1464--1482.Google Scholar
Cross Ref
- C. Liu, K. Wu, and J. Pei. 2007. An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Transactions on Parallel and Distributed Systems 18, 7, 1010--1023. Google Scholar
Digital Library
- Y. Liu, M. Dong, K. Ota, and A. Liu. 2016. ActiveTrust: Secure and trustable routing in wireless sensor networks. IEEE Transactions on Information Forensics and Security 11, 9, 2013--2027. Google Scholar
Digital Library
- X. Liu, M. Dong, K. Ota, L. T. Yang, and A. Liu. 2016a. Trace malicious source to guarantee cyber security for mass monitor critical infrastructure. Journal of Computer and System Sciences. 2016.Google Scholar
- X. Liu, M. Dong, K. Ota, P. Hung, and A. Liu. 2016b. Service pricing decision in cyber-physical systems: Insights from game theory. IEEE Transactions on Services Computing 9, 2, 186--198. Google Scholar
Digital Library
- A. Liu, Q. Zhang, Z. Li, etc. al. 2016a. A Green and Reliable Communication Modeling for Industrial Internet of Things. Computers 8 Electrical Engineering. 2016. Google Scholar
Digital Library
- H. Luo, Y. Liu, and S. K. Das. 2007. Routing correlated data in wireless sensor networks: A survey. IEEE Network 21, 6, 40--47. Google Scholar
Digital Library
- S. Madden, M. J. Franklin, J.M. Hellerstein, and W. Hong. 2002. TAG: A tiny aggregation service for ad-hoc sensor networks. ACM SIGOPS Operating Systems Review 36, SI, 131--146. Google Scholar
Digital Library
- A. Mehmood, S. Khan, B. Shams, and J. Lloret. 2015. Energy efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems 28, 5, 972--989.Google Scholar
Cross Ref
- J. K. Min, R. T. Ng, and K. Shim. 2015. Aggregate query processing in the presence of duplicates in wireless sensor networks. Information Sciences, 297, 1--20. Google Scholar
Digital Library
- W. Niklas and S. Mikael. 2009. Nonlinear coding and estimation for correlated data in wireless sensor networks. IEEE Transactions on Communications 57, 10, 2932--2939. Google Scholar
Digital Library
- H. Ning, H. Liu, and L. T. Yang. 2015. Aggregated-proof based hierarchical authentication scheme for the Internet of Things. IEEE Transactions on Parallel and Distributed Systems. 26, 3, 657--667.Google Scholar
Cross Ref
- OMNet++. 2015. Network Simulation Framework. Retrieved March 28, 2017 from http://www.omnetpp.org/.Google Scholar
- M. Rezvani, A. Ignatovic, and E. Bertino. 2013. Secure data aggregation technique for wireless sensor networks in the presence of collusion attacks. IEEE Transactions on Dependable and Secure Computing 12, 1, 98--110.Google Scholar
Cross Ref
- P. Sayyah, M. T. Lazarescu, and S. Bocchio. 2015. Virtual platform-based design space exploration of power-efficient distributed embedded applications. ACM Transactions on Embedded Computing Systems 14, 3, 49. Google Scholar
Digital Library
- A. Scaglione and S. D. Servetto. 2005. On the interdependence of routing and data compression in multi-hop sensor networks. Wireless Networks 11, 1--2, 149--160. Google Scholar
Digital Library
- T. Wang, V. Azadeh, H. Wendi, and S. Alireza. 2012. Maximizing gathered samples in wireless sensor networks with Slepian-Wolf coding. IEEE Transactions on Wireless Communications 11, 2, 751--761.Google Scholar
Cross Ref
- J. Z. Wang, Z. Yan, L. T. Yang, and B. X. Huang. 2015. An approach to rank reviews by fusing and mining opinions based on review pertinence. Information fusion. 25, 3--15. Google Scholar
Digital Library
- J. Zhang, X. Shen, H. Zeng, G. Dai, C. Bo, F. Chen, and C. Lv. 2013. Energy-efficient and localized lossy data aggregation in asynchronous sensor networks. International Journal of Communication Systems 26, 8, 989--1010.Google Scholar
Cross Ref
Index Terms
Distributed Multi-Representative Re-Fusion Approach for Heterogeneous Sensing Data Collection
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