Abstract.The Multiple-Choice Multi-Dimension Knapsack Problem (MMKP) is a variant of the 0-1 Knapsack Problem, an NP-Hard problem. Hence algorithms for finding the exact solution of MMKP are not suitable for application in real time decision-making applications, like quality adaptation and admission control of an interactive multimedia system. This paper presents two new heuristic algorithms, M-HEU and I-HEU for solving MMKP. Experimental results suggest that M-HEU finds 96% optimal solutions on average with much reduced computational complexity and performs favorably relative to other heuristic algorithms for MMKP. The scalability property of I-HEU makes this heuristic a strong candidate for use in real time applications.

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High resolution data collection using low-cost wireless sensor networks has recently become feasible due to advances in electronics and wireless networking technologies. The Geographic Grid Routing (GGR) protocol described in this paper aims to provide robust task dissemination and data collection from large sensor network such that the useful lifetime of the network is prolonged. The work builds on a previously developed routing protocol called TWO-Tier Data Dissemination (TTDD). Our work is difTerentiated by the use of multiple paths, a more efficient and useful data collection model, and more authentic environmental assumptions, including the presence of asymmetric links. Extensive and realistic experiments were used to evaluate the performance of GGR. The results show GGR to be a highly efficient, scalable, versatile and robust solution.