Query Sensitive Storage for Wireless Sensor Networks

Storage management in wireless sensor networks is an area that has started to attract significant attention, and several methods have been proposed, such as Local Storage (LS), Data-Centric Storage (DCS) and more recently Location-Centric Storage (LCS). Several modern applications, like context-dependent information dissemination for pervasive computing, on-demand warning in surveillance sensor networks and roadway safety warning, require that each originating event is stored around its point of origin. LCS is a suitable approach for such applications. Though, LCS does not take into consideration the origin of the queries,which is equally important to the storage method, because it has immediate influence on the experienced latency. This paper proposes a simple yet effective way of reducing the network latency, namely the Query Sensitive Storage (QSS) protocol. QSS makes certain that not only will the queries be answered, but all subsequent queries that originated in the same area will be answered faster. The experimental evaluation using the J-Sim simulator attests that with the proposed QSS protocol we can achieve smaller network latency at a minimum storage cost as compared to its state-of-the-art competitor, namely LCS.

[1]  Prashant J. Shenoy,et al.  Rethinking Data Management for Storage-centric Sensor Networks , 2007, CIDR.

[2]  Xin Chen,et al.  Analyzing Object Detection Quality Under Probabilistic Coverage in Sensor Networks , 2005, IWQoS.

[3]  Ahmed Helmy,et al.  Rendezvous regions: a scalable architecture for service location and data-centric storage in large-scale wireless networks , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[4]  Daniela Rus,et al.  Reactive Behavior in Self-reconfiguring Sensor Networks , 2002 .

[5]  Scott Shenker,et al.  Practical Data-Centric Storage , 2006, NSDI.

[6]  Dong Xuan,et al.  A dynamic geographic hash table for data-centric storage in sensor networks , 2006, IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006..

[7]  Peter Desnoyers,et al.  Ultra-low power data storage for sensor networks , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[8]  Qun Li,et al.  MobiCom poster: reactive behavior in self-reconfiguring sensor networks , 2003, MOCO.

[9]  Pedro José Marrón,et al.  An Efficient Resilience Mechanism for Data Centric Storage in Mobile Ad Hoc Networks , 2006, 7th International Conference on Mobile Data Management (MDM'06).

[10]  Jiang Li,et al.  On the Performance of Location-Centric Storage in Sensor Networks , 2007, International Conference on Wireless Algorithms, Systems and Applications (WASA 2007).

[11]  Deborah Estrin,et al.  Data-Centric Storage in Sensornets with GHT, a Geographic Hash Table , 2003, Mob. Networks Appl..

[12]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[13]  Hyuk Lim,et al.  J-Sim: a simulation and emulation environment for wireless sensor networks , 2006, IEEE Wireless Communications.

[14]  Holger Karl,et al.  Using energy where it counts: protecting important messages in the link layer , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[15]  Jiang Li,et al.  Location-centric storage for sensor networks , 2005, IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005..

[16]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[17]  Xiuzhen Cheng,et al.  Fault-tolerant target detection in sensor networks , 2005, IEEE Wireless Communications and Networking Conference, 2005.