Adaptive Energy Aware Data Aggregation Tree for Wireless Sensor Networks

To meet the demands of wireless sensor networks (WSNs) where data are usually aggregated at a single source prior to transmitting to any distant user, there is a need to establish a tree structure inside to aggregate data. In this paper, an adaptive energy aware data aggregation tree (AEDT) is proposed. The proposed tree uses the maximum energy available node as the data aggregator node. The tree incorporates sleep and awake technology where the communicating node and the parent node are only in awake state rest all the nodes go to sleep state saving the network energy and enhancing the network lifetime. When the traffic load crosses the threshold value, then the packets are accepted adaptively according to the communication capacity of the parent node. The proposed tree maintains a memory table which stores the value of each selected path. Path selection is based on shortest path algorithm where the node with highest available energy is always selected as forwarding node. By simulation results, we show that our proposed tree enhances network lifetime minimizes energy consumption and achieves good delivery ratio with reduced delay.

[1]  Attila Vidács,et al.  Distributed Data Agregation with Geographical Routing in Wireless Sensor Networks , 2007, IEEE International Conference on Pervasive Services.

[2]  Prasun Sinha,et al.  Scalable data aggregation for dynamic events in sensor networks , 2006, SenSys '06.

[3]  Jukka Kohonen,et al.  Data Gathering in Sensor Networks ∗ , .

[4]  Attila Vidács,et al.  Distributed Data Aggregation with Geographical Routing in Wireless Sensor Networks , 2007 .

[5]  Deborah Estrin,et al.  The impact of data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems Workshops.

[6]  Won-Sik Yoon,et al.  Data Aggregation with Error Correction for Wireless Sensor Networks , 2005 .

[7]  Foreword and Editorial International Journal of Hybrid Information Technology , 2022 .

[8]  Prasun Sinha,et al.  Structure-Free Data Aggregation in Sensor Networks , 2007, IEEE Transactions on Mobile Computing.

[9]  Lalit M. Patnaik,et al.  Tree-on-DAG for Data Aggregation in Sensor Networks , 2009 .

[10]  Tian He,et al.  Feedback control of data aggregation in sensor networks , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[11]  Ludmila Scharf Data Gathering in Sensor Networks , 2007, Algorithms for Sensor and Ad Hoc Networks.

[12]  Jun Sun,et al.  Compressive data gathering for large-scale wireless sensor networks , 2009, MobiCom '09.