Adaptive protocols for information dissemination in wireless sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Issues in data stream management
ACM SIGMOD Record
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Sensor Information Networking Architecture
ICPP '00 Proceedings of the 2000 International Workshop on Parallel Processing
Adaptive Power-Fidelity in Energy-Aware Wireless Embedded Systems
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Simulation of scale-free networks
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
| Hi-index | 0.00 |
Algorithms for data processing in sensor nodes of wireless sensor networks should be able to handle the resource limitations the nodes face (i.e. energy and memory). An important issue to be considered is that the materialization of large amounts of data in sensor nodes may cause memory overflows and consequently data losses. On the other hand, the excessive sending of data packets to other nodes may result in unacceptable energy consumption levels. Some alternatives to save energy and also avoid memory overflows involve in-network aggregation and reductions in sensor activities (when sensor nodes experience resource constraints). In this paper we study energy-memory tradeoffs in sensor nodes and how they affect the accuracy of query results in wireless sensor networks. We propose an adaptive data processing strategy to balance energy consumption and memory usage at sensor node level. Our goal is to maximize sensor lifetime while also maintaining the accuracy of query results. We implemented our approach by means of an algorithm called ADAGA (ADaptive AGgregation Algorithm for sensor networks), which process in-network aggregation in sensor nodes. ADAGA is able to adapt its behavior according to energy and memory availabilities by dynamically adjusting data collection and data sending intervals. The results on the efficiency of our approach are also presented at the end of the paper.