Data Gathering with Tunable Compression in Sensor Networks

We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computationally intensive applications, where traditional "maximum" in-network compression may result in significant computation energy. We investigate a tunable data compression technique that enables effective trade-offs between the computation and communication costs. We derive the optimal compression strategy for a given data gathering tree and then investigate the performance of different tree structures for networks deployed on a grid topology, as well as general graphs. Our analytical results pertaining to the grid topology and simulation results pertaining to the general graphs indicate that the performance of a simple greedy approximation to the Minimal Steiner Tree (MST) provides a constant-factor approximation for the grid topology and good average performance on the general graphs. Although, theoretically, a more complicated randomized algorithm offers a polylogarithmic performance bound, the simple greedy approximation of MST is attractive for practical implementation.

[1]  Baltasar Beferull-Lozano,et al.  On network correlated data gathering , 2004, IEEE INFOCOM 2004.

[2]  Satish Rao,et al.  A tight bound on approximating arbitrary metrics by tree metrics , 2003, STOC '03.

[3]  Ramesh Govindan,et al.  The impact of spatial correlation on routing with compression in wireless sensor networks , 2008, TOSN.

[4]  Yossi Azar,et al.  Buy-at-bulk network design , 1997, Proceedings 38th Annual Symposium on Foundations of Computer Science.

[5]  Katia Obraczka,et al.  Energy-efficient collision-free medium access control for wireless sensor networks , 2003, SenSys '03.

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

[7]  Mingyan Liu,et al.  On the Many-to-One Transport Capacity of a Dense Wireless Sensor Network and the Compressibility of Its Data , 2003, IPSN.

[8]  Viktor K. Prasanna,et al.  Optimizing a class of in-network processing applications in networked sensor systems , 2004, 2004 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE Cat. No.04EX975).

[9]  Yair Bartal,et al.  Probabilistic approximation of metric spaces and its algorithmic applications , 1996, Proceedings of 37th Conference on Foundations of Computer Science.

[10]  Sajal K. Das,et al.  Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Networks , 2006, IEEE Transactions on Computers.

[11]  Mani Srivastava,et al.  Energy-aware wireless microsensor networks , 2002, IEEE Signal Process. Mag..

[12]  José D. P. Rolim,et al.  An Optimal Data Propagation Algorithm for Maximizing the Lifespan of Sensor Networks , 2006, DCOSS.

[13]  Mahadev Satyanarayanan,et al.  Agile application-aware adaptation for mobility , 1997, SOSP.

[14]  Viktor K. Prasanna,et al.  Energy-latency tradeoffs for data gathering in wireless sensor networks , 2004, IEEE INFOCOM 2004.

[15]  Jack K. Wolf,et al.  Noiseless coding of correlated information sources , 1973, IEEE Trans. Inf. Theory.

[16]  Cathy H. Xia,et al.  Distributed source coding in dense sensor networks , 2005, Data Compression Conference.

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

[18]  Deborah Estrin,et al.  Simultaneous Optimization for Concave Costs: Single Sink Aggregation or Single Source Buy-at-Bulk , 2003, SODA '03.

[19]  Krste Asanovic,et al.  Energy-aware lossless data compression , 2006, TOCS.

[20]  J. Acimovic,et al.  Adaptive distributed algorithms for power-efficient data gathering in sensor networks , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[21]  José D. P. Rolim,et al.  An Adaptive Blind Algorithm for Energy Balanced Data Propagation in Wireless Sensors Networks , 2005, DCOSS.

[22]  Sajal K. Das,et al.  A framework for energy-saving data gathering using two-phase clustering in wireless sensor networks , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[23]  Kunal Talwar,et al.  A tight bound on approximating arbitrary metrics by tree metrics , 2004, J. Comput. Syst. Sci..

[24]  Mark D. Corner,et al.  Fugue: time scales of adaptation in mobile video , 2000, IS&T/SPIE Electronic Imaging.

[25]  S. Sitharama Iyengar,et al.  Game-theoretic models for reliable path-length and energy-constrained routing with data aggregation in wireless sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[26]  Sajal K. Das,et al.  A novel framework for energy - conserving data gathering in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..