Comparison of Different Data Aggregation Techniques in Distributed Sensor Networks

Wireless sensor networks (WSNs) are almost everywhere, they are exploited for thousands of applications in a densely distributed manner. Such deployment makes WSNs one of the highly anticipated key contributors of the big data nowadays. Hence, data aggregation is attracting much attention from researchers as efficient way to reduce the huge volume of data generated in WSNs by eliminating the redundancy among sensing data. In this paper, we propose an efficient data aggregation technique for clustering-based periodic wireless sensor networks. Further to a local aggregation at sensor node level, our technique allows cluster-head to eliminate redundant data sets generated by neighbouring nodes by applying three data aggregation methods. These proposed methods are based on the sets similarity functions, the one-way Anova model with statistical tests and the distance functions, respectively. Based on real sensor data, we have analyed their performances according to the energy consumption and the data latency and accuracy, and we show how these methods can significantly improve the performance of sensor networks.

[1]  Qilian Liang,et al.  Optimum Cluster Size for Underwater Acoustic Sensor Networks , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[2]  Yan Sun,et al.  Reliability of Wireless Sensor Networks with Tree Topology , 2012 .

[3]  S. Sivasubramaniam.  A Minimum Cost Effective Cluster AlgorithmUsing UWSN , 2014 .

[4]  Jianzhong Li,et al.  Critical data points retrieving method for big sensory data in wireless sensor networks , 2016, EURASIP J. Wirel. Commun. Netw..

[5]  Abin Abraham Oommen.  DESIGN OF FACE RECOGNITION SYSTEM USING PRINCIPAL COMPONENT ANALYSIS , 2014 .

[6]  Chih-Min Chao,et al.  Design of Structure-Free and Energy-Balanced Data Aggregation in Wireless Sensor Networks , 2009, 2009 11th IEEE International Conference on High Performance Computing and Communications.

[7]  Ali Norouzi,et al.  A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA , 2012 .

[8]  M. Shanmukhi,et al.  Cluster-based comb-needle model for energy-efficient data aggregation in wireless sensor networks , 2015, 2015 Applications and Innovations in Mobile Computing (AIMoC).

[9]  R. K. Krishna,et al.  An Energy-efficient Grid based Clustering Topology for a Wireless Sensor Network , 2012 .

[10]  Jun Ye,et al.  Cosine similarity measures for intuitionistic fuzzy sets and their applications , 2011, Math. Comput. Model..

[11]  Oussama Bazzi,et al.  An Analysis of Variance-Based Methods for Data Aggregation in Periodic Sensor Networks , 2015, Trans. Large Scale Data Knowl. Centered Syst..

[12]  Renfa Li,et al.  Virtual Cluster Model in Clustered Wireless Sensor Network Using Cuckoo Inspired Metaheuristic Algorithm , 2015 .

[13]  Shamik Sural,et al.  Similarity between Euclidean and cosine angle distance for nearest neighbor queries , 2004, SAC '04.

[14]  Hassan Harb,et al.  An Enhanced K-Means and ANOVA-Based Clustering Approach for Similarity Aggregation in Underwater Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[15]  David Laiymani,et al.  K-means based clustering approach for data aggregation in periodic sensor networks , 2014, 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[16]  H. Wolkowicz,et al.  Euclidean distance matrices, semidefinite programming and sensor network localization , 2011 .

[17]  Jiannong Cao,et al.  Energy Balanced Clustering Data Collection Based on Dominating Set in Wireless Sensor Networks , 2015, Ad Hoc Sens. Wirel. Networks.

[18]  Hemavathi Natarajan,et al.  A Fuzzy Based Predictive Cluster Head Selection Scheme for Wireless Sensor Networks , 2014, International Journal on Smart Sensing and Intelligent Systems.

[19]  Jing Zhang,et al.  A Cluster-Head Selection Scheme for Underwater Acoustic Sensor Networks , 2010, 2010 International Conference on Communications and Mobile Computing.

[20]  Chih-Min Chao,et al.  Design of Structure-Free and Energy-Balanced Data Aggregation in Wireless Sensor Networks , 2009, HPCC.

[21]  Jianzhong Li,et al.  Drawing dominant dataset from big sensory data in wireless sensor networks , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[22]  Elena Deza,et al.  Encyclopedia of Distances , 2014 .

[23]  Junhai Luo,et al.  A Dynamic Virtual Force-Based Data Aggregation Algorithm for Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[24]  Yingshu Li,et al.  Approximate Holistic Aggregation in Wireless Sensor Networks , 2015, ICDCS.

[25]  Nadeem Javaid,et al.  Chain-Based Communication in Cylindrical Underwater Wireless Sensor Networks , 2015, Sensors.

[26]  Jacques M. Bahi,et al.  A Two Tiers Data Aggregation Scheme for Periodic Sensor Networks , 2014, Ad Hoc Sens. Wirel. Networks.

[27]  Jacques M. Bahi,et al.  An Optimized In-Network Aggregation Scheme for Data Collection in Periodic Sensor Networks , 2012, ADHOC-NOW.

[28]  Hassan Harb,et al.  A suffix-based enhanced technique for data aggregation in periodic sensor networks , 2014, 2014 International Wireless Communications and Mobile Computing Conference (IWCMC).

[29]  Guoliang Li,et al.  Can we beat the prefix filtering?: an adaptive framework for similarity join and search , 2012, SIGMOD Conference.

[30]  Pierre Kuonen,et al.  Dynamic data aggregation protocol based on multiple objective tree in Wireless Sensor Networks , 2015, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[31]  Shouning Qu,et al.  A High Efficient and Real Time Data Aggregation Scheme for WSNs , 2015, Int. J. Distributed Sens. Networks.

[32]  Majid Sarrafzadeh,et al.  Cluster size optimization in sensor networks with decentralized cluster-based protocols , 2012, Comput. Commun..

[33]  Massudi Mahmuddin,et al.  Chain-based routing protocols in wireless sensor networks: A survey , 2015 .

[34]  Raghav Kaushik,et al.  Efficient exact set-similarity joins , 2006, VLDB.

[35]  Dilip Kumar,et al.  Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks , 2013, IET Wirel. Sens. Syst..

[36]  Nick Savage,et al.  Cluster protocols in Underwater Sensor Networks: a Research Review , 2014 .

[37]  Yun Liu,et al.  A Data-aggregation Scheme for WSN based on Optimal Weight Allocation , 2014, J. Networks.

[38]  Jianzhong Li,et al.  Sampling Based (epsilon, delta)-Approximate Aggregation Algorithm in Sensor Networks , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems.

[39]  Yung-Kuei Chiang,et al.  A Cycle-Based Data Aggregation Scheme for Grid-Based Wireless Sensor Networks , 2014, Sensors.

[40]  Ben Power.  Big data: Big brother or big opportunity? , 2014 .

[41]  Yong-Hwan Lee,et al.  Scalable network joining mechanism in wireless sensor networks , 2012, 2012 IEEE Topical Conference on Wireless Sensors and Sensor Networks.

[42]  Raphaël Couturier,et al.  ATP: An Aggregation and Transmission Protocol for Conserving Energy in Periodic Sensor Networks , 2015, 2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[43]  Ibrahima Faye,et al.  An efficient Dynamic Addressing based routing protocol for Underwater Wireless Sensor Networks , 2012, Comput. Commun..

[44]  Abraham Kandel,et al.  Anomaly detection in web documents using crisp and fuzzy-based cosine clustering methodology , 2007, Inf. Sci..

[45]  E. Ekici,et al.  On Multihop Distances in Wireless Sensor Networks with Random Node Locations , 2010, IEEE Transactions on Mobile Computing.