Link quality aware and content centric data aggregation in lossy wireless networks

In low-power and lossy wireless networks, message delivery could be jeopardized by factors such as channel conditions, noise, interference etc. This can lead to communication retransmissions, resulting in significant energy depletion and consequently consumes limited on-node energy resources. This paper elaborates on a two-pronged approach to improve energy efficiency and reliability of communications: a) the application of content-centric data aggregation (pre-processing of correlated information) at each node to reduce traffic volume, and b) the use of link quality information to estimate local network lifetime while routing traffic. A novel objective function is proposed which ties the two together and findings from a performance evaluation are presented. It is shown that the proposed mechanism is able to achieve significant energy savings and prolongs the network lifetime in energy constrained multi-hop networks with lossy wireless links.

[1]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[2]  Pramod K. Varshney,et al.  Data-aggregation techniques in sensor networks: a survey , 2006, IEEE Communications Surveys & Tutorials.

[3]  Kiyohito Yoshihara,et al.  DAG based in-network aggregation for sensor network monitoring , 2006, International Symposium on Applications and the Internet (SAINT'06).

[4]  Mahesh Sooriyabandara,et al.  Content centric and Load-balancing Aware Dynamic data Aggregation in Multihop wireless networks , 2012, 2012 IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[5]  Ness B. Shroff,et al.  Constructing Maximum-Lifetime Data-Gathering Forests in Sensor Networks , 2010, IEEE/ACM Transactions on Networking.

[6]  Mohamed A. Sharaf,et al.  Balancing energy efficiency and quality of aggregate data in sensor networks , 2004, The VLDB Journal.

[7]  Siarhei Kuryla,et al.  RPL: IPv6 Routing Protocol for Low power and Lossy Networks , 2010 .

[8]  Krishna M. Sivalingam,et al.  Data Gathering Algorithms in Sensor Networks Using Energy Metrics , 2002, IEEE Trans. Parallel Distributed Syst..

[9]  Adam Dunkels,et al.  A database in every sensor , 2011, SenSys.

[10]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[11]  Katia Obraczka,et al.  The impact of timing in data aggregation for sensor networks , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[12]  Hwa-Chun Lin,et al.  Constructing Maximum-Lifetime Data Gathering Trees in Sensor Networks with Data Aggregation , 2010, 2010 IEEE International Conference on Communications.

[13]  Jörg Widmer,et al.  In-network aggregation techniques for wireless sensor networks: a survey , 2007, IEEE Wireless Communications.

[14]  Rahim Tafazolli,et al.  An Energy-Efficient Clustering Solution for Wireless Sensor Networks , 2011, IEEE Transactions on Wireless Communications.

[15]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[16]  Robert Tappan Morris,et al.  a high-throughput path metric for multi-hop wireless routing , 2003, MobiCom '03.