Designing energy efficient communication protocols for wireless sensor networks (WSNs) to conserve the sensors' energy is one of the prime concerns. Clustering in WSNs significantly reduces the energy consumption in which the nodes are organized in clusters, each having a cluster head (CH). The CHs collect data from their cluster members and transmit it to the base station via a single or multihop communication. The main issue in such mechanism is how to associate the nodes to CHs and how to route the data of CHs so that the overall load on CHs are balanced. Since the sensor nodes operate autonomously, the methods designed for WSNs should be of distributed nature, i.e., each node should run it using its local information only. Considering these issues, we propose a distributed multiobjective‐based clustering method to assign a sensor node to appropriate CH so that the load is balanced. We also propose an energy‐efficient routing algorithm to balance the relay load among the CHs. In case any CH dies, we propose a recovery strategy for its cluster members. All our proposed methods are completely distributed in nature. Simulation results demonstrate the efficiency of the proposed algorithm in terms of energy consumption and hence prolonging the network lifetime. We compare the performance of the proposed algorithm with some existing algorithms in terms of number of alive nodes, network lifetime, energy efficiency, and energy population. 相似文献
With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.
研究阐述低碳园林在国内外的研究方向和研究热点,探讨低碳园林的研究发展过程及知识框架,为低碳园林领域的进一步研究与应用设计方法提供建议与参考.通过检索中国知网CNKI数据库和Web of Science核心合集数据库中2011—?2021年的低碳园林领域相关文献.中文文献检索词为"低碳园林OR低碳景观",外文文献检索词为... 相似文献