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SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
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MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
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MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Research challenges in environmental observation and forecasting systems
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
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MobiHoc '00 Proceedings of the 1st ACM international symposium on Mobile ad hoc networking & computing
Radio Propagation for Modern Wireless Systems
Radio Propagation for Modern Wireless Systems
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HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
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ACM SIGCOMM Computer Communication Review - Workshop on data communication in Latin America and the Caribbean
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Proceedings of the 3rd international symposium on Information processing in sensor networks
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DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
Computer
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IEEE Pervasive Computing
Adaptive sampling for sensor networks
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
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ACM Transactions on Sensor Networks (TOSN)
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
ATPC: adaptive transmission power control for wireless sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
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ACM Transactions on Sensor Networks (TOSN)
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IEEE Intelligent Systems
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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ACM Transactions on Sensor Networks (TOSN)
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This article reports on the development of a utility-based mechanism for managing sensing and communication in cooperative multisensor networks. The specific application on which we illustrate our mechanism is that of GlacsWeb. This is a deployed system that uses battery-powered sensors to collect environmental data related to glaciers which it transmits back to a base station so that it can be made available world-wide to researchers. In this context, we first develop a sensing protocol in which each sensor locally adjusts its sensing rate based on the value of the data it believes it will observe. The sensors employ a Bayesian linear model to decide their sampling rate and exploit the properties of the Kullback-Leibler divergence to place an appropriate value on the data. Then, we detail a communication protocol that finds optimal routes for relaying this data back to the base station based on the cost of communicating it (derived from the opportunity cost of using the battery power for relaying data). Finally, we empirically evaluate our protocol by examining the impact on efficiency of a static network topology, a dynamic network topology, the size of the network, the degree of dynamism of the environment, and the mobility of the nodes. In so doing, we demonstrate that the efficiency gains of our new protocol, over the currently implemented method over a 6 month period, are 78%, 133%, 100%, and 93%, respectively. Furthermore, we show that our system performs at 65%, 70%, 63%, and 70% of the theoretical optimal, respectively, despite being a distributed protocol that operates with incomplete knowledge of the environment.