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coverage control of unicycle multi-agent network in dynamic environment

MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2021年第0期

作者： Sun, Qihai Liu, Zhi-Wei Chi, Ming Dou, Yangming He, Dingxin Qin, Yijie Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

This paper studies the coverage control problem of an unicycle multi-agent network with external disturbance in a dynamic environment which is described by a time-varying density function. It is a challenging task to design a coverage control to simultaneously handle the external disturbance, underactuated model and time-varying density function. Based on Voronoi partition, we proposed a coverage control approach which can maximize the metric function via robust tracking the Voronoi centroid with external disturbance. It is worth to mentioning that the angle of motion of the unicycle agent can align with the Voronoi centroid in finite time. Finally, the effectiveness of the proposed control is shown by simulation results.

关键词： coverage optimization multi-agent system time-varying density function unicycle model Voronoi partition

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Adaptive Power Control Protocol With Hardware Implementation for Wireless Sensor and RFID Reader Networks

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IEEE SYSTEMS JOURNAL 2007年第2期1卷 145-159页

作者： Cha, Kainan Jagannathan, S. Pommerenke, David Garmin Int Kansas City KS 66062 USA Univ Missouri Dept Elect & Comp Engn Rolla MO 65409 USA

The development and deployment of radio frequency identification (RFID) systems render a novel distributed sensor network which enhances visibility into manufacturing processes. In RFID systems, the detection range and read rates will suffer from interference among high-power reading devices. This problem grows severely and degrades system performance in dense RFID networks. Consequently, medium access protocols (MAC) protocols are needed for such networks to assess and provide access to the channel so that tags can be read accurately. In this paper, we investigate a suite of feasible power control schemes to ensure overall coverage area of the system while maintaining a desired read rate. The power control scheme and MAC protocol dynamically adjust the RFID reader power output in response to the interference level seen during tag reading and acceptable signal-to-noise ratio (SNR). We present novel distributed adaptive power control (DAPC) as a possible solution. A suitable back off scheme is also added with DAPC to improve coverage. A generic UHF wireless testbed is built using UMR/SLU GEN4-SSN for implementing the protocol. Both the methodology and hardware implementation of the schemes are presented, compared, and discussed. The results of hardware implementation illustrate that the protocol performs satisfactorily as expected.

关键词： coverage optimization distributed power control frequency interference radio frequency identification (RFID) reader collision sensor networks

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optimization of the MANET Topology in Urban Area Using Redundant Relay Points

Optimization of the MANET Topology in Urban Area Using Redun...

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International Conference on Military Communications and Information Systems (ICMCIS)

作者： Malon, Krzygitof Skokowski, Pawel Lopatka, Jerzy Mil Univ Technol Inst Telecommun Warsaw Poland

ISBN: (纸本)9781538645598

Mobile ad-hoc networks become a standard architecture for wireless tactic networks. Due to possibility of signal relay, achieved ranges are larger, required output power is smaller and overall network throughput is also improved. However in urban areas, achieved ranges of communication may be very low because of terrain shape, buildings and other obstacles. It can cause 'black holes' - areas with limited communications or even lack of possibility to exchange information. The elaborated solution enables determination of such 'black holes' and proposes an optimum localization of redundant relay points, to increase overall performance of the network.

关键词： mobile ad-hoc networks redundant points coverage optimization Radio Environment Map

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Signal Distribution optimization for Cabin Visible Light Communications by Using Weighted Search Bat Algorithm 3

Signal Distribution Optimization for Cabin Visible Light Com...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Yang, Ming Wang, Aimin Sun, Geng Liang, Shuang Zhang, Jin Wang, Fan Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Xinhua News Agcy Jilin Branch Changchun 130012 Jilin Peoples R China

ISBN: (纸本)9781509063529

With increasing demand for travelling, high-quality network service is important to people in vehicle cabins. Visible light communication (VLC) system is more appropriate than wireless local area network considering the security, communication speed, and narrow shape of the cabin. However, VLC exhibits technical limitations, such as uneven distribution of optical signals. In this regard, we propose a novel weight search bat algorithm (WSBA) to calculate a set of optimal power adjustment factors to reduce fluctuation in signal distributions. Simulation results show that the fairness of signal distribution in the cabin optimized by WSBA is better than that of the non-optimized signal distribution. Moreover, the coverage rate of WSBA is higher than that of genetic algorithm and particle swarm optimization.

关键词： cabin communication visible light communication coverage optimization bat algorithm

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An Empirical Study on Using CNNs for Fast Radio Signal Prediction

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SN Computer Science 2022年第2期3卷 131页

作者： Ozyegen, Ozan Mohammadjafari, Sanaz Cevik, Mucahit El mokhtari, Karim Ethier, Jonathan Basar, Ayse Mechanical Industrial Engineering Department Ryerson University Toronto M5B 2K3 ON Canada Communications Research Centre Canada Ottawa Canada

Accurate radio frequency power prediction in a geographic region is a computationally expensive part of finding the optimal transmitter location using a ray tracing software. We empirically analyze the viability of deep learning models to speed up this process. Specifically, deep learning methods including CNNs and UNET are typically used for segmentation, and can also be employed in power prediction tasks. We consider a dataset that consists of radio frequency power values for five different regions with four different frame dimensions. We compare deep learning-based prediction models including RadioUNET and four different variations of the UNET model for the power prediction task. More complex UNET variations improve the model on higher resolution frames such as 256×256. However, using the same models on lower resolutions results in overfitting and simpler models perform better. Our detailed numerical analysis shows that the deep learning models are effective in power prediction and they are able to generalize well to the new regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.

关键词： CNN coverage optimization Deep learning Ray tracing UNET

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Enhancing coverage in Wireless Sensor Networks Using Machine Learning Techniques 4

Enhancing Coverage in Wireless Sensor Networks Using Machine...

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4th IEEE Interdisciplinary Conference on Electrics and Computer (INTCEC)

作者： Priyadarshi, Rahul Vikram, Raj Huang, ZeKun Yang, Tiansheng Rathore, Rajkumar Singh Siksha O Anusandhan Deemed Univ Fac Engn & Technol ITER Bhubaneswar 752054 India CV Raman Global Univ Dept Comp Sci & Engn Bhubaneswar 752054 India Shanxi Univ 92 Wucheng Rd Taiyuan Shanxi Peoples R China Univ South Wales Llantwit Rd Pontypridd CF37 1DL M Glam Wales Cardiff Metropolitan Univ Cardiff Sch Technol Llandaff CampusWestern Ave Cardiff CF5 2QS Wales

ISBN: (纸本)9798350349467;9798350349450

Wireless Sensor Networks (WSNs) are essential for tracking environmental and physical variables in a range of applications. One of the biggest challenges in WSN deployment is still achieving excellent coverage while preserving energy. In this work, we provide a unique machine learning-based method to improve WSN coverage efficiency. Our technique minimizes energy consumption and delivers better coverage by dynamically modifying sensor node placement tactics depending on current environmental data. The results of the experiments confirm the efficacy of the suggested methodology and underscore its practicability for implementation in many applications that need extensive coverage in ever-changing settings.

关键词： Wireless Sensor Networks coverage optimization Machine Learning Dynamic Deployment Energy Efficiency

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A Model for Self-deployment of Autonomous Mobile Sensor Network in an Unknown Indoor Environment 9th

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9th EAI International Conference on Ad Hoc Networks (AdHocNets)

作者： Eledlebi, Khouloud Ruta, Dymitr Saffre, Fabrice Al-Hammadi, Yousof Isakovic, A. F. Khalifa Univ Sci & Technol Emirates ICT Innovat Ctr EBTIC PO 127788 Abu Dhabi U Arab Emirates

ISBN: (纸本)9783319744391;9783319744384

Wireless sensor networks (WSN) emerge at the center of the fast expanding Internet of Things (IoT) revolution, and hence increased research efforts are being directed towards its efficient deployment, optimization and adaptive operation. Rapid deployment of WSN in an unknown open environment is a critical challenge that involves finding optimal locations for the network nodes to deliver optimally balanced sensing and communication services at the maximum possible coverage subject to complex mutual constraints. We address this challenge with a variant of the Voronoi-based algorithm that leverages the converged movement towards Voronoi cells' centers with the intelligent nodes' provisioning algorithm to deliver fully automated and autonomous WSN that rapidly self-deploys itself to any finite indoor environment without using any prior knowledge of the size and structure of the target space. Sequential provisioning supports realistic implementation that accounts for collision avoidance and mitigates the risk of wasteful over-deployment. The preliminary comparative simulation results carried out in a simplified environment indicate very fast convergence to the well balanced WSN at the fairly small deployment cost and thereby validate our model as a very promising compared to the previous approaches to WSN deployment.

关键词： Mobile sensor nodes Self-deployment network Voronoi-based algorithm coverage optimization

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Optimal configuration of PTZ camera networks based on visual quality assessment and coverage maximization

Optimal configuration of PTZ camera networks based on visual...

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7th International Conference on Distributed Smart Cameras (ICDSC)

作者： Konda, Krishna Reddy Conci, Nicola Univ Trent Dept Engn & Comp Sci DISI Trento Italy

ISBN: (纸本)9781479921645

In this paper we present a novel method for video cameras positioning and reconfiguration, to maximize visual coverage in complex indoor environments. Based on a suitable modeling of the camera field-of-view and of the environmental setup, the optimization procedure determines the most appropriate configuration of cameras to satisfy a coverage objective, taking into account a number of parameters on the quality of view at the camera position. This includes the global ground area coverage, the expected geometric distortion, and the entropy of the image. The proposed solution has been validated in different environmental setups, including synthetic settings, taking into account the presence of obstacles and constraints.

关键词： Distributed camera networks particle swarm optimization coverage optimization

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Online Evolution of Femtocell coverage Algorithms Using Genetic Programming

Online Evolution of Femtocell Coverage Algorithms Using Gene...

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IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Ho, Lester Claussen, Holger Cherubini, Davide Alcatel Lucent Bell Labs Dublin 15 Ireland

ISBN: (纸本)9781467362351

The wide adoption of smartphones has resulted in an exponential increase in the demand for wireless data. To address this problem, operators have started deploying large numbers of small cells. In order to operate such small cell network cost-effectively they need to be able to intelligently optimize their configuration, which can be achieved by applying machine learning techniques such as genetic programming. The use of genetic programming has previously been used to derive joint coverage algorithms for a group of enterprise femtocells. However, the evolution of the algorithms was performed in an offline manner, on a pre-defined simulation model of the deployment scenario. In this paper, an approach to perform the evolution in an online manner using an automated model building process is presented. The model building process uses network traces as inputs to create a hierarchical Markov model that is shown to be able to capture the behavior of the femtocell network well. It is shown that the resulting environment model can effectively drive the on-line evolution of coverage optimization algorithms.

关键词： Femtocell coverage optimization online genetic programming model building

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Deep Learning based Mobile Network Management for 5G and Beyond 15

Deep Learning based Mobile Network Management for 5G and Bey...

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15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)

作者： Maksymyuk, Taras Gazda, Juraj Ruzicka, Marek Slapak, Eugen Bugar, Gabriel Han, Longzhe Lviv Polytech Natl Univ Dept Telecommun Lvov Ukraine Tech Univ Kosice Dept Comp & Informat Kosice Slovakia Tech Univ Kosice Dept Elect & Multimedia Commun Kosice Slovakia Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781728155661

Over the last decade the deployment paradigm of the mobile networks has shifted from complex hierarchical structures towards decoupling of the control and transmission planes. Such transformation enables software defined network configurations that allow improving the scalability and flexibility of the network. However, the process of network management is not trivial, considering the complexity of modern 5G infrastructure, and requires new advanced solutions. In this paper, we propose a new architecture with additional plane, which is responsible for intelligent knowledge generation and decision making. Proposed approach allows to train deep neural networks by using generative adversarial learning for the scenario with limited amount of real data. We study the performance of the proposed approach for the particular use case of small cells coverage optimization considering given spatial and temporal variation of the traffic demand. Simulation results show that proposed approach achieves quasi-optimal small cells coverage in terms of overall network throughput even in conditions when there is a lack of real world statistical data.

关键词： 5G AI GAN coverage optimization

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