Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Elements of information theory
Elements of information theory
Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
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
Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Sensor deployment strategy for detection of targets traversing a region
Mobile Networks and Applications
Gathering correlated data in sensor networks
Proceedings of the 2004 joint workshop on Foundations of mobile computing
Optimal Resource Allocation in Wireless Ad Hoc Networks: A Price-Based Approach
IEEE Transactions on Mobile Computing
The capacity of wireless networks
IEEE Transactions on Information Theory
A hop-bounded single-actor selection algorithm for wireless sensor and actor networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Efficient routing from multiple sources to multiple sinks in wireless sensor networks
EWSN'07 Proceedings of the 4th European conference on Wireless sensor networks
Relay-Bounded single-actor selection algorithms for wireless sensor and actor networks
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
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In this paper, we propose an effective distributed algorithm to solve the minimum energy data gathering (MEDG) problem in sensor networks with multiple sinks. The problem objective is to find a rate allocation on the sensor nodes and a transmission structure on the network graph, such that the data collected by the sink nodes can reproduce the field of observation, and the total energy consumed by the sensor nodes is minimized. We formulate the problem as a linear optimization problem. The formulation exploits data correlation among the sensor nodes and considers the effect of wireless channel interference. We apply Lagrangian dualization technique on this formulation to obtain a subgradient algorithm for computing the optimal solution. The subgradient algorithm is asynchronous and amenable to fully distributed implementations, which corresponds to the decentralized nature of sensor networks.