Quality based drip drag match data collection in wireless sensor network
Keywords:Wireless Sensor Network, Data Collection, Equidistant, Neighbourhood.
Although data collection has received much attention by effectively minimizing delay, computational complexity and increasing the total data transmitted, the transience of sensor nodes for multiple data collection of sensed node in wireless sensor network (WSN) renders quality of service a great challenge. To circumvent transience of sensor nodes for multiple data collection, Quality based Drip-Drag-Match Data Collection (QDDM-DC) scheme have been proposed. In Drip-Drag-Match data collection scheme, initially dripping of data is done on the sink by applying Equidistant-based Optimum Communication Path from the sensor nodes which reduces the data loss. Next the drag operation pulls out the required sensed data using Neighbourhood-based model from multiple locations to reduce the delay for storage. Finally, the matching operation, compares the sensed data received by the dragging operation to that of the corresponding sender sensor node (drip stage) and stores the sensed data accurately which in turn improves the throughput and quality of data collection. Simulation is carried for the QDDM-DC scheme with multiple scenarios (size of data, number of sinks, storage capacity) in WSN with both random and deterministic models. Simulation results show that QDDM-DC provides better performance than other data collection schemes, especially with high throughput, ensuring minimum delay and data loss for effective multiple data collection of sensed data in WSN.
 Liu X, Cao J, Song WZ, Guo P & He Z, â€œDistributed Sensing for High-Quality Structural Health Monitoring Using WSNsâ€, IEEE Transactions on Parallel and Distributed Systems, Vol.26, No.3, (2015), pp.738-747. https://doi.org/10.1109/TPDS.2014.2312911.
 Ji S, Beyah R & Cai Z, â€œSnapshot and Continuous Data Collection in Probabilistic Wireless Sensor Networksâ€, IEEE Transactions on Mobile Computing, Vol.13, No.3, (2014), pp.626-637. https://doi.org/10.1109/TMC.2013.30.
 He L, Pan J & Xu J, â€œA Progressive Approach to Reducing Data Collection Latency in Wireless Sensor Networks with Mobile Elementsâ€, IEEE Transactions on Mobile Computing, Vol.12, No.7, (2013), pp.1308-1320. https://doi.org/10.1109/TMC.2012.105.
 Gao Y, Bu J, Dong W, Chen C, Rao L & Liu X, â€œExploiting Concurrency for Efficient Dissemination in Wireless Sensor Networksâ€, IEEE Transactions on Parallel and Distributed Systems, Vol.24, No.4, (2013), pp.691-670. https://doi.org/10.1109/TPDS.2012.195.
 Ozdemir S & Ã‡am H, â€œIntegration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networksâ€, IEEE/ACM Transactions on Networking, Vol.18, No.3, (2010), pp.736-749. https://doi.org/10.1109/TNET.2009.2032910.
 Fang J & Li H, â€œHyperplane-Based Vector Quantization for Distributed Estimation in Wireless Sensor Networksâ€, IEEE Transactions on Information Theory, Vol.55, No.12, (2009), pp.5682-5699. https://doi.org/10.1109/TIT.2009.2032856.
 Zhu YH, Wu WD, Pan J & Tang YP, â€œAn energy-efficient data gathering algorithm to prolong lifetime of wireless sensor networksâ€, Computer Communications, Vol.33, No.5, (2010), pp.639-647. https://doi.org/10.1016/j.comcom.2009.11.008.
 Jiang H, Jin S & Wang C, â€œPrediction or Not? An Energy-Efficient Framework for Clustering-based Data Collection in Wireless Sensor Networksâ€, IEEE Transactions on Parallel and Distributed Systems, Vol.22, No.6, (2011), pp.1064-1071. https://doi.org/10.1109/TPDS.2010.174.
 Li H, Lin K & Li K, â€œEnergy-efficient and high-accuracy secure data aggregation in wireless sensor networksâ€, Elsevier, Computer Communications, Vol.34, No.4, (2011), pp.591â€“597. https://doi.org/10.1016/j.comcom.2010.02.026.
 Rohankar R, Katti CP & Kumar S, â€œComparison of Energy Efficient Data Collection Techniques in Wireless Sensor Networkâ€, Elsevier, Procedia Computer Science, Vol.57, (2015), pp.146â€“151. https://doi.org/10.1016/j.procs.2015.07.399.
 Rohankara R, â€œAgent Based Predictive Data Collection in Opportunistic Wireless Sensor Networkâ€, Elsevier, Procedia Computer Science, Vol.57, (2015), pp.33â€“40. https://doi.org/10.1016/j.procs.2015.07.361.
 Liu Y, Zeng QA & Wang YH, â€œEnergy-Efficient Data Fusion Technique and Applications in Wireless Sensor Networksâ€, Hindawi Publishing Corporation, Journal of Sensors, (2015), pp.1-3.
 Liu A, Cai LX, Luan TH & Ranabahu A, â€œQoS-Aware Data Collection in Wireless Sensor Networksâ€, Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks, (2015), pp.1-4.
 Alhmiedat T, â€œAn Adaptive Energy-Efficient Data Collection System for Zig Bee Wireless Sensor Networksâ€, Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks, (2015), pp.1-13.
 Palanisamy T & Krishnasamy KN, â€œBayes Node Energy Polynomial Distribution to Improve Routing in Wireless Sensor Networkâ€, Plos One, Vol.10, No.10, (2015), pp.1-15. https://doi.org/10.1371/journal.pone.0138932.
 Di Francesco M, Shah K, Kumar M & Anastasi G, â€œAn adaptive strategy for energy-efficient data collection in sparse wireless sensor networksâ€, European Conference on Wireless Sensor Networks, (2010), pp.322-337. https://doi.org/10.1007/978-3-642-11917-0_21.
 Gao S, Zhang H & Das SK, â€œEfficient Data Collection in Wireless Sensor Networks With Path-constrained Mobile Sinksâ€, IEEE Transactions on Mobile Computing, Vol.10, No.4, (2011), pp.592-608. https://doi.org/10.1109/TMC.2010.193.
 Liu CX, Liu Y & Zhang ZJ, â€œImproved reliable trust-based and energy-efficient data aggregation for wireless sensor networksâ€, International Journal of Distributed Sensor Networks, Vol.9, No.5, (2013). https://doi.org/10.1155/2013/652495.
 Almiâ€™ani K, Almiâ€™ani M, Al-ghonmein A & Al-Moghrabi K, â€œData Collection Scheme for Wireless Sensor Network With Mobile Collectorâ€, International Journal of Wireless & Mobile Networks, Vol.6, No.4, (2014), pp.19-25. https://doi.org/10.5121/ijwmn.2014.6402.