Robust allocation of RF device capacity for distributed spectrum functions
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Real-time awareness of radio spectrum use across frequency, geography and time is crucial to effective communications and information gathering in congested airway environments, yet acquiring this awareness presents a challenging sensing and data integration problem. A recent proposal has argued that real-time generation of spectrum usage maps might be possible through the use of existing radios in the area of interest, by exploiting their sensing capacity when they are not otherwise being used. In this paper, we assume this approach and consider the task allocation problem that it presents. We focus specifically on the development of a network-level middleware for task management, that assigns resources to prospective mapping applications based on a distributed model of device availability, and allows mapping applications (and other related RF applications) to specify what is required without worrying about how it will be accomplished. A distributed, auction-based framework is specified for task assignment and coordination, and instantiated with a family of minimum set cover algorithms for addressing “coverage” tasks. An experimental analysis is performed to investigate and quantify two types of performance benefits: (1) the basic advantage gained by exploiting knowledge of device availability, and (2) the additional advantage gained by adding redundancy in subregions where the probability of availability of assigned devices is low. To assess the effectiveness of our minimum set cover algorithms, we compute optimal solutions to a static version of the real-time coverage problem and compare performance of the algorithms to these upper bound solutions.
KeywordsDistributed task allocation Contract net protocol Task allocation under uncertain resource availability Market-based procedures
This material is based upon work partially supported by DARPA under the RadioMap program, Contract No. FA8750-13-C-0014. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government. The authors would like to thank Jayanth Mogali for his help in formulating and implementing the upper bound MILP solution used to analyze the performance of our heuristic solution in Sect. 6.
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