Algorithmic issues in modeling motion
ACM Computing Surveys (CSUR)
ECOSystem: managing energy as a first class operating system resource
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
A scalable simulator for forest dynamics
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Currentcy: a unifying abstraction for expressing energy management policies
ATEC '03 Proceedings of the annual conference on USENIX Annual Technical Conference
A scalable algorithm for dispersing population
Journal of Intelligent Information Systems
From Data Reverence to Data Relevance: Model-Mediated Wireless Sensing of the Physical Environment
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Multi-armed Bandits with Metric Switching Costs
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
Hi-index | 0.00 |
Next-generation wireless sensor networks may revolutionize understanding of environmental change by assimilating heterogeneous data, assessing the relative value and costs of data collection, and scheduling activities accordingly. Thus, they are dynamic, data-driven distributed systems that integrate sensing with modeling and prediction in an adaptive framework. Integration of a range of technologies will allow estimation of the value of future data in terms of its contribution to understanding and cost. This balance is especially important for environmental data, where sampling intervals will range from meters and seconds to landscapes and years. In this paper, we first describe a general framework for dynamic data-driven wireless network control that combines modeling of the sensor network and its embedding environment, both in and out of the network. We then describe a range of challenges that must be addressed, and an integrated suite of solutions for the design of dynamic sensor networks.