In Wireless Sensor Network (WSN), cluster-based topology is believed to be an effective way for balancing energy consumption and prolonging the network lifespan. However, the clustering process itself can be an energy cost behavior, especially when it is executed periodically. Moreover, little attention has been paid to combine sleeping scheduling with topology formation. In order to solve the above problem, a novel distributed clustering algorithm called Adaptive Energy Efficient Clustering (AEEC) is proposed to maximize network lifetime in this study. Optimizations including the restricted global re-clustering, intra-cluster node sleeping scheduling and adaptive transmission range adjustment are introduced to fulfill the task of energy conservation, while connectivity and coverage is guaranteed. Simulation demonstrates that a great amount of energy is saved for sensed data transmission rather than control packet broadcast, and thus the network lifetime is extended significantly
Topology control is a fundamental technique in Wireless Sensor Network (WSN), which forms the underlying topology for routing and other protocols by power control and neighbor selection. In recent years, various topology control algorithms with very different design goals have been proposed, and all of them try to form one optimized topology for all types of communications. Such solution makes a serious mistake by neglecting the fact that different communications have very different requirements.
The most common communications in WSN are broadcast and data collection. For broadcast or message dissemination, the first priority is to spread the message throughout the network as quickly as possible. As for data collection, multi-factors should be taken into account, such as link length, hops to base station, node degree. With the very different goals, it is not possible to solve all those problems simultaneously with just one topology.
In this paper, we propose a approach, which contains a Fast Dissemination Tree (FDT) and a Balanced Data Collection Tree (BDCT) to fulfill the requirements of the above two communications, i.e. message dissemination and data collection. And analysis and simulation proves that our method has a better performance when compared to the existed ones.
Metrics can drive software processing, because they found the base of its quantizing management. There are two fundamental requirements in engineering: formal modeling and quantitative modeling. We must emphasize that metrics for software engineering is insufficient in quantification now. In order to improve software and software processing, the factors that affect schedule, cost and quality of software development should be measured. Metrics produces adjustable and iterative motions in software processing. Based on Jaynes' maximum entropy principle, this paper establishes a model to quantify the factors and introduces distance to compare the metric indicators. The authors propose that the metric estimation tree can be used and the nodes that stand for the software attributes in the tree can be marked with their corresponding evaluation values. Dynamic feedback in the software processing will be combined with AHP (analytic hierarchy process), and the project and process of development will be learned and analyzed entirely, concentratedly and dynamically.
Reasoning of metrics-driven software processingMetrics are the actions or processes that assign values to the selected attributes of entities. Software metrics are measurements of the elements affecting software in a software life cycle. By them we can quantificationally know and analyze schedule, production scale, qualities, and performance of project so that we can estimate a project and trace its efficiencies.The development of software engineering requires industrialization and standardization of software processing. It necessarily requires quantizing Second International Multisymposium on Computer and Computational Sciences 0-7695-3039-7/07 $25.00
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