Simultaneous optimization for concave costs: single sink aggregation or single source buy-at-bulk
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Energy aware lossless data compression
Proceedings of the 1st international conference on Mobile systems, applications and services
Online Data Gathering for Maximizing Network Lifetime in Sensor Networks
IEEE Transactions on Mobile Computing
Efficient gathering of correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Data Gathering with Tunable Compression in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
An optimal data propagation algorithm for maximizing the lifespan of sensor networks
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
An adaptive blind algorithm for energy balanced data propagation in wireless sensors networks
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Hi-index | 0.00 |
Wireless sensor networks consist of nodes with severe energy constraints and thus, they must use energy saving algorithms in different layers. Despite the prior work, the computational cost in a wireless sensor network is usually neglected compared to the communication cost; it may not admit to in which large volumes of data packets are being transmitted, such as those cases in which This packets are video frames captured by video sensor nodes. In this paper, we consider the problem of minimizing the communication needed to send readings from a set of sensors to a single destination with obtaining a trade-off between computation and communication energy. We propose a centralized as well as a distributed algorithm. In the former, the sink node and in the latter, each node endeavors to reduce network energy consumption adaptively, making a trade-off between data compression at the appropriate level and sending row data. The data compression technique used is dependent on the application type and considers spatial correlation. Comparing the simulation results of our approach with similar work, without consideration of trade-off between computation and comparison, shows significant improvement in energy saving of the network.