A Storage Centric Approach to Scalable Sensor Networks

With the advent of Machine to Machine (M2M) communication, we are witnessing an increased interest towards technologies that will enable efficient and reliable operation of Wireless Sensor Networks (WSN). Such networks are expected to include a large number of sensor devices which will generate large body of M2M traffic. To reduce the impact of this M2M traffic, efficient storage and retrieval methods should be employed in a distributed manner for the successful deployment of this technology. In this paper, a distributed storage solution is presented with the aim of reducing the impact of M2M traffic on data centres and the network backbone. The reliable and efficient storage of the sensor data is established by taking advantage of codes with Maximum Distance Separable (MDS) properties. The solution is implanted in Contiki OS using RPL protocol [1] and its performance is evaluated through simulations. Furthermore, to realise such a system, a centralised clique finding algorithm is demonstrated and benchmarked against a solution that uses a brute force approach.

[1]  Ken Barker,et al.  A Cost Model for Storing and Retrieving Data in Wireless Sensor Networks , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[2]  Philip Levis,et al.  An empirical study of low-power wireless , 2010, TOSN.

[3]  C. Bron,et al.  Algorithm 457: finding all cliques of an undirected graph , 1973 .

[4]  Michael R. Fellows,et al.  FIXED-PARAMETER TRACTABILITY AND COMPLETENESS , 2022 .

[5]  Thomas Narten,et al.  Neighbor Discovery for IP Version 6 (IPv6) , 1996, RFC.

[6]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[7]  Zsolt Alfred Polgar,et al.  Network coding solution for improving transmission reliability in wireless sensor networks employed in industrial monitoring , 2012, 2012 35th International Conference on Telecommunications and Signal Processing (TSP).

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

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

[10]  Emil Jovanov,et al.  Guest Editorial Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-Care Connectivity , 2004, IEEE Transactions on Information Technology in Biomedicine.

[11]  Filippo Tosato,et al.  Irregular MDS Array Codes , 2014, IEEE Transactions on Information Theory.

[12]  Anno Accademico,et al.  Smart Grid Communications: Overview of research challenges, solutions and standardization activities , 2013 .

[13]  Alexandros G. Dimakis,et al.  Network Coding for Distributed Storage Systems , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[14]  Gianluigi Ferrari,et al.  Data storage and retrieval with RPL routing , 2013, 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC).