Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Power-aware routing in mobile ad hoc networks
MobiCom '98 Proceedings of the 4th 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
Rate-Distortion Based Video Compression: Optimal Video Frame Compression and Object Boundary Encoding
Computer Networks
Introduction to Algorithms
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
Siphon: overload traffic management using multi-radio virtual sinks in sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Image compression using binary space partitioning trees
IEEE Transactions on Image Processing
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The inherent many-to-one flow of traffic in wireless sensor networks (WSNs) produces a skewed distribution of energy consumption rates, leading to the early demise of those sensors that are critical to the ability of surviving nodes to communicate their measurements to the base station. Numerous previous approaches aimed at balancing the consumption of energy in wireless networks are either too complex or do not address problems unique to the flow of traffic in WSNs. In this article, we propose the use of a dynamic programming algorithm (DPA), an operational, low-complexity algorithm, used in conjunction with four different route discovery algorithms. We perform complexity analysis, statistical evaluation of changes in power consumption rates effected, and verify spatial redistribution of energy consumption of sensors in the network. Our results on multihop networks of 100 randomly placed nodes show that, on average, the two best performing variants of DPA yield a reduction of up to 28% and 36% in power consumption rate variance at the cost of raising average power consumption by 15% and 21%, respectively. Computational complexities of DPA variants range from O(N3) to O(N4), which is significantly lower than linear search of the solution space of O(N!Ni). Analysis by diffusion plots shows that DPA reduces power consumption of sensors that experience the highest power consumption under the shortest path routes.