Information-aware traffic reduction for wireless sensor networks

Environmental monitoring is one of the most popular applications in wireless sensor networks. Although it is important to obtain a continuous record of the environment, users may gain enough information without receiving every routine sensor measurement. Reducing unnecessary traffic allows better utilization of the network resources. However, it is uneasy to decide on when and what kind of traffic to reduce in a dynamically changing environment. In this paper, we study the problem of information-aware traffic reduction for wireless sensor networks. We propose a two-step information-aware traffic reduction algorithm to address this problem. First, we provide a distributed and real-time algorithm for sensors to classify their measurements and report them selectively based on the importance of information. Then, we propose a bandwidth allocation algorithm to assign different forwarding probabilities to packets considering both the information quality and the network load. Our algorithm can be implemented and integrated easily with existing routing protocols to maximize the quality of information to the users, while reducing the amount of network traffic. We evaluate our approach based on the real sensing measurements to demonstrate the quality of information achieved. Simulations are also conducted to evaluate the performance of our proposed scheme in a larger network.

[1]  David E. Culler,et al.  A transmission control scheme for media access in sensor networks , 2001, MobiCom '01.

[2]  François Ingelrest,et al.  SensorScope: Out-of-the-Box Environmental Monitoring , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[3]  Chieh-Yih Wan,et al.  CODA: congestion detection and avoidance in sensor networks , 2003, SenSys '03.

[4]  Erol Gelenbe,et al.  Adaptive Random Re-Routing for Differentiated QoS in Sensor Networks , 2008, BCS Int. Acad. Conf..

[5]  Chenyang Lu,et al.  SPEED: A Real-Time Routing Protocol for Sensor Networks , 2002 .

[6]  Nicholas R. Jennings,et al.  Decentralised Adaptive Sampling of Wireless Sensor Networks , 2007 .

[7]  Ruzena Bajcsy,et al.  Congestion control and fairness for many-to-one routing in sensor networks , 2004, SenSys '04.

[8]  Chatschik Bisdikian,et al.  On Sensor Sampling and Quality of Information: A Starting Point , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07).

[9]  Erol Gelenbe,et al.  Adaptive QoS routing for significant events in wireless sensor networks , 2008, 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[10]  Chang-Gun Lee,et al.  MMSPEED: multipath Multi-SPEED protocol for QoS guarantee of reliability and. Timeliness in wireless sensor networks , 2006, IEEE Transactions on Mobile Computing.

[11]  H. Balakrishnan,et al.  Mitigating congestion in wireless sensor networks , 2004, SenSys '04.

[12]  Philip S. Yu,et al.  ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks , 2007, IEEE Transactions on Parallel and Distributed Systems.

[13]  R. Nowak,et al.  Backcasting: adaptive sampling for sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[14]  Michael R. Lyu,et al.  Reliable Reporting of Delay-Sensitive Events in Wireless Sensor-Actuator Networks , 2006, 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[15]  Sanjay Shakkottai,et al.  Hop-by-Hop Congestion Control Over a Wireless Multi-Hop Network , 2004, IEEE/ACM Transactions on Networking.

[16]  E. Gelenbe,et al.  Information Quality aware sensor network services , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.