Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
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
Determining DNA Sequence Similarity Using Maximum Independent Set Algorithms for Interval Graphs
SWAT '92 Proceedings of the Third Scandinavian Workshop on Algorithm Theory
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Reliable bursty convergecast in wireless sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
SIAM Journal on Computing
Gathering with Minimum Delay in Tree Sensor Networks
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
Sensor-mission assignment in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Delay performance of scheduling with data aggregation in wireless sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Approximate Dynamic Programming for Communication-Constrained Sensor Network Management
IEEE Transactions on Signal Processing
On the Use of Binary Programming for Sensor Scheduling
IEEE Transactions on Signal Processing
Lower bounds on data collection time in sensory networks
IEEE Journal on Selected Areas in Communications
On Sample-Path Optimal Dynamic Scheduling for Sum-Queue Minimization in Forests
IEEE/ACM Transactions on Networking (TON)
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In this paper, we develop a mathematical framework for studying the problem of maximizing the "information" received at the sink in a data gathering wireless sensor network. We explicitly account for unreliable links, energy constraints, and in-network computation. The network model is that of a sensor network arranged in the form of a tree topology, where the root corresponds to the sink node, and the rest of the network detects an event and transmits data to the sink over one or more hops. This problem of sending data from multiple sources to a common sink is often referred to as the convergecasting problem. We develop an integer optimization based framework for this problem, which allows for tackling link unreliability using general error-recovery schemes. Even though this framework has a non-linear objective function, and cannot be relaxed to a convex programming problem, we develop a low complexity, distributed solution. The solution involves finding a Maximum Weight Increasing Independent Set (MWIIS) in rectangle graphs over each hop of the network, and can be obtained in polynomial time. Further, we apply these techniques to a target tracking problem where we optimally select sensors to track a given target such that the information obtained is maximized subject to constraints on the per-node sensing and communication energy. We validate our algorithms through numerical evaluations, and illustrate the advantages of explicitly considering link unreliability in the optimization framework.