Energy-aware selective compression scheme for solar energy based wireless sensor networks

Data compression involves a trade-off between delay time and data size. More the delay time, lesser the data size and vice versa. There have been many studies performed in the field of wireless sensor networks about increasing network life cycle durations that involve minimizing energy consumption by reducing data size; however, reducing data size results in increased delay time due to the added processing time required for data compression. Meanwhile, as energy generation occurs periodically in solar energy based wireless sensor networks, the redundant energy is often generated that is sufficient to run a node. In this study, the excess energy is used to reduce the delay time between nodes in a sensor network consisting of solar energy based nodes. The energy threshold value is determined by a formula based on the residual energy and charging speed. Nodes with residual energy less than the threshold, transfer data with compression in order to reduce energy consumption, and nodes with residual energy over the threshold transfer data without compression to reduce the delay time between nodes. Simulation based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay time compared with traditional methods.

[1]  Purushottam Kulkarni,et al.  Energy Harvesting Sensor Nodes: Survey and Implications , 2011, IEEE Communications Surveys & Tutorials.

[2]  Margaret Martonosi,et al.  Data compression algorithms for energy-constrained devices in delay tolerant networks , 2006, SenSys '06.

[3]  Cesare Alippi,et al.  An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[4]  Francesco Marcelloni,et al.  A Simple Algorithm for Data Compression in Wireless Sensor Networks , 2008, IEEE Communications Letters.

[5]  Renyong Wu,et al.  A Novel Location-Based Routing Algorithm for Energy Balance in Wireless Sensor Networks , 2009, 2009 WRI International Conference on Communications and Mobile Computing.

[6]  Wei Hong,et al.  A macroscope in the redwoods , 2005, SenSys '05.

[7]  Tossaporn Srisooksai,et al.  Practical data compression in wireless sensor networks: A survey , 2012, J. Netw. Comput. Appl..

[8]  Heonshik Shin,et al.  Low-Latency Geographic Routing for Asynchronous Energy-Harvesting WSNs , 2008, J. Networks.

[9]  Dong Kun Noh,et al.  SolarStore: enhancing data reliability in solar-powered storage-centric sensor networks , 2009, MobiSys '09.

[10]  Jan M. Rabaey,et al.  Data funneling: routing with aggregation and compression for wireless sensor networks , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..

[11]  Dong Kun Noh,et al.  Using a dynamic backbone for efficient data delivery in solar-powered WSNs , 2012, J. Netw. Comput. Appl..

[12]  David E. Culler,et al.  Design, Modeling, and Capacity Planning for Micro-solar Power Sensor Networks , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[13]  HyungJune Lee,et al.  CPAC: Energy-Efficient Data Collection through Adaptive Selection of Compression Algorithms for Sensor Networks , 2014, Sensors.

[14]  David E. Culler,et al.  Perpetual environmentally powered sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[15]  Jan M. Rabaey,et al.  A 1.9GHz RF Transmit Beacon using Environmentally Scavenged Energy , 2003 .

[16]  Mani B. Srivastava,et al.  Harvesting aware power management for sensor networks , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[17]  Yücel Altunbasak,et al.  PINCO: a pipelined in-network compression scheme for data collection in wireless sensor networks , 2003, Proceedings. 12th International Conference on Computer Communications and Networks (IEEE Cat. No.03EX712).

[18]  Heonshik Shin,et al.  QoS-Aware Geographic Routing for Solar-Powered Wireless Sensor Networks , 2007, IEICE Trans. Commun..

[19]  Margaret Martonosi,et al.  Implementing software on resource-constrained mobile sensors: experiences with Impala and ZebraNet , 2004, MobiSys '04.

[20]  Andrew G. Barto,et al.  Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks , 2007, 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[21]  Hartmut Ritter,et al.  Utilizing solar power in wireless sensor networks , 2003, 28th Annual IEEE International Conference on Local Computer Networks, 2003. LCN '03. Proceedings..