In-network data aggregation is an effective technique to reduce communication cost in wireless sensornetworks. Recent works have focused on two issues individually: dynamic aggregation to handle irregular traffic of ...
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ISBN:
(纸本)9780769548432;9781467330848
In-network data aggregation is an effective technique to reduce communication cost in wireless sensornetworks. Recent works have focused on two issues individually: dynamic aggregation to handle irregular traffic of events and robust aggregation to tolerate packet losses. However, how to achieve both the objectives simultaneously is still not touched. In this paper, we propose a cross-layer approach to robust and dynamic data aggregation by making use of direct support from MAC layer. A new MAC protocol, DA-MAC is delicately designed to serve such purpose. Based on the channel contention information obtained from DA-MAC, a node can dynamically determine where and when to do aggregation. To cope with packet losses, a virtual overlay, Rings is adopted to forward one packet to multiple nodes. We have implemented our design in TinyOS based sensornetworks. Performance evaluations though simulations and experiments show that, compared with existing algorithms, our proposed solution is more efficient in terms of both time and energy cost.
The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objective...
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The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms.
作者：
Liang YinJun ZhangDepartment of Computer Science
Key Laboratory of Machine Intelligence and Sensor Network Ministry of Education Key Laboratory of Software Technology Education Department of Guangdong Sun Yat-sen University China
Mutation is a fundamental operation in genetic algorithm (GA) for it has a significant impact on global search ability and convergence rate. Traditional mutation operation of GA changes chromosomes randomly (or blindl...
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Mutation is a fundamental operation in genetic algorithm (GA) for it has a significant impact on global search ability and convergence rate. Traditional mutation operation of GA changes chromosomes randomly (or blindly), which would waste a lot of computational cost in searching less promising regions or those have been searched frequently. To address these drawbacks, this paper proposes a novel guide mutation. The proposed guide mutation makes use of history search experience to estimate the average fitness and search degree of sub-regions in the search space. New chromosomes generated by the guide mutation are more likely to be in regions with higher average fitness and less search degree. In this way, the search efficiency can be improved and the algorithm can have a stronger ability of jumping out of local optima. The proposed guide mutation is incorporated into a simple GA, forming a guided mutation GA (GMGA). The GMGA is validated by testing 23 benchmark functions and the experimental results reveal that the proposed guide mutation is very effective in improving the performance of GA.
In-network data aggregation is an effective technique to reduce communication cost in wireless sensornetworks. Recent works have focused on two issues individually: dynamic aggregation to handle irregular traffic of ...
详细信息
In-network data aggregation is an effective technique to reduce communication cost in wireless sensornetworks. Recent works have focused on two issues individually: dynamic aggregation to handle irregular traffic of events and robust aggregation to tolerate packet losses. However, how to achieve both the objectives simultaneously is still not touched. In this paper, we propose a cross-layer approach to robust and dynamic data aggregation by making use of direct support from MAC layer. A new MAC protocol, DA-MAC is delicately designed to serve such purpose. Based on the channel contention information obtained from DA-MAC, a node can dynamically determine where and when to do aggregation. To cope with packet losses, a virtual overlay, Rings is adopted to forward one packet to multiple nodes. We have implemented our design in TinyOS based sensornetworks. Performance evaluations though simulations and experiments show that, compared with existing algorithms, our proposed solution is more efficient in terms of both time and energy cost.
作者：
Wei-Neng ChenJun ZhangDepartment of Computer Science
Key Laboratory of Machine Intelligence and Sensor Network Ministry of Education Key Laboratory of Software Technology Education Department of Guangdong Province Sun Yat-sen University China
Cloud computing has emerged as a powerful computing paradigm that enables users to access computing services anywhere on demand. It provides a flexible way to implement computation-intensive workflow applications on a...
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Cloud computing has emerged as a powerful computing paradigm that enables users to access computing services anywhere on demand. It provides a flexible way to implement computation-intensive workflow applications on a pay-per-use basis. Since users are more concerned on the satisfaction of Quality of Service (QoS) in cloud systems, the cloud workflow scheduling problem that addresses different QoS requirements of users has become an important and challenging problem for workflow management in cloud computing. In this paper, we tackle a cloud workflow scheduling problem which enables users to define various QoS constraints like the deadline constraint, the budget constraint, and the reliability constraint. It also enables users to specify one preferred QoS parameter as the optimization objective. A set-based PSO (S-PSO) approach is proposed for this scheduling problem. As the allocation of service instances can be regarded as the selection problem from a set of service instances, it is found the set-based representation scheme in S-PSO is natural for the considered problem. In addition, the S-PSO provides an effective way to take advantage of problem-based heuristics to further accelerate search. We define penalty-based fitness functions to address the multiple QoS constraints and integrate the S-PSO with seven heuristics. A discrete version of the comprehensive learning PSO (CLPSO) algorithm based on the S-PSO method is implemented. Experimental results show that the proposed approach is very competitive especially on the instances with tight QoS constraints.
Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swar...
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Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.
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