The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Convex Optimization
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Lossy network correlated data gathering with high-resolution coding
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Data compression algorithms for energy-constrained devices in delay tolerant networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Data Gathering with Tunable Compression in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
IEEE Journal on Selected Areas in Communications
Computer Networks: The International Journal of Computer and Telecommunications Networking
International Journal of Ad Hoc and Ubiquitous Computing
| Hi-index | 0.00 |
We address the issue of network lifetime maximization for a special class of wireless sensor networks namely, wireless multi-media sensor networks. High data rates, in these networks at the sensor nodes, compared to the conventional sensor networks and the presence of high temporal correlation in the sampled data make them a suitable candidate for the in-network processing, primarily at the sensor node itself. Using these distinguishing features of wireless multi-media sensor networks to our advantage, we have proposed a framework achieving an optimal tradeoff between communication and computation power consumption leading to network lifetime maximization under the delay quality of service constraints. The distributed implementation of the algorithm realizing the proposed framework is achieved using duality theory. A max-min fairness index based measure of network lifetime maximization is studied as a function of end-to-end delay thresholds. Numerical results show how the total network power consumption is distributed between the communication and the computation power consumption components. The results also provide an insight about the maximum and minimum nodal power consumptions. Our results show that the superior performance in terms of max-min fairness index at higher end-to-end delay thresholds is mainly attributed to the relative lower computation cost compared to the communication cost.