摘要翻译：
研究了一个在大范围内随机部署并由移动机器人(MR)辅助的簇状无线传感器网络(WSN)中的能量平衡问题。传感器节点的任务是监视感兴趣区域(ROI)，并将它们的测试统计信息报告给簇首(CHs)，簇首(CHs)随后通过无线衰落信道报告给融合中心(FC)。为了最大限度地延长无线传感器网络的寿命，部署MR作为CHs子集和FC之间的自适应中继。为了实现这一点，我们开发了一种由MR执行的多链路移动性分集算法(MDA)，该算法将允许同时补偿在已建立的无线链路（即，MR-to-FC以及各种CH-to-MR通信链路）处的小规模衰落。仿真结果表明，提出的MR辅助技术能够显著降低无线传感器网络所需的发射功率，从而延长无线传感器网络的工作寿命。我们还展示了如何通过使用由配备单个天线的MR执行的所提出的多链路MDA来减轻各种无线链路的小规模衰落的影响。
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英文标题：
《Energy balancing for robotic aided clustered wireless sensor networks
  using mobility diversity algorithms》
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作者：
Daniel Bonilla Licea, Edmond Nurellari, Mounir Ghogho
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Distributed, Parallel, and Cluster Computing        分布式、并行和集群计算
分类描述：Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
包括容错、分布式算法、稳定性、并行计算和集群计算。大致包括ACM学科类C.1.2、C.1.4、C.2.4、D.1.3、D.4.5、D.4.7、E.1中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  We consider the problem of energy balancing in a clustered wireless sensor network (WSN) deployed randomly in a large field and aided by a mobile robot (MR). The sensor nodes (SNs) are tasked to monitor a region of interest (ROI) and report their test statistics to the cluster heads (CHs), which subsequently report to the fusion center (FC) over a wireless fading channel. To maximize the lifetime of the WSN, the MR is deployed to act as an adaptive relay between a subset of the CHs and the FC. To achieve this we develop a multiple-link mobility diversity algorithm (MDA) executed by the MR that will allow to compensate simultaneously for the small-scale fading at the established wireless links (i.e., the MR-to-FC as well as various CH-to-MR communication links). Simulation results show that the proposed MR aided technique is able to significantly reduce the transmission power required and thus extend the operational lifetime of the WSN. We also show how the effect of small-scale fading at various wireless links is mitigated by using the proposed multiple-link MDA executed by a MR equipped with a single antenna.
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PDF链接：
https://arxiv.org/pdf/1805.08025