Optimizing on-demand data broadcast scheduling in pervasive environments

Data dissemination in pervasive environments is often accomplished by on-demand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in on-demand broadcast scheduling has focused on the timely servicing of requests so as to minimize the number of missed deadlines. However, there exists many pervasive environments where the utility of the data is an equally important criterion as its timeliness. Missing the deadline reduces the utility of the data but does not make it zero. In this work, we address the problem of scheduling on-demand data broadcasts with soft deadlines. We investigate search based optimization techniques to develop broadcast schedulers that make explicit attempts to maximize the utility of data requests as well as service as many requests as possible within the acceptable time limit. Our analysis shows that heuristic driven methods for such problems can be improved by hybridizing them with local search algorithms. We further investigate the option of employing a dynamic optimization technique to facilitate utility gain, thereby surpassing the requirement of a heuristic in the process. An evolution strategy based stochastic hill climber is investigated in this context.

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