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Adaptive UAV-Trajectory Optimization Under Quality of Service Constraints: A Model-Free Solution

IEEE ACCESS 2020年 8卷 112253-112265页

作者： Cui, Jingjing Ding, Zhiguo Deng, Yansha Nallanathan, Arumugam Hanzo, Lajos Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England Kings Coll London Dept Informat London WC2R 2LS England Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

Unmanned aerial vehicles (UAVs) with the potential of providing reliable high-rate connectivity, are becoming a promising component of future wireless networks. A UAV collects data from a set of randomly distributed sensors, where both the locations of these sensors and their data volume to be transmitted are unknown to the UAV. In order to assist the UAV in finding the optimal motion trajectory in the face of the uncertainty without the above knowledge whilst aiming for maximizing the cumulative collected data, we formulate a reinforcement learning problem by modelling the motion-trajectory as a Markov decision process with the UAV acting as the learning agent. Then, we propose a pair of novel trajectory optimization algorithms based on stochastic modelling and reinforcement learning, which allows the UAV to optimize its flight trajectory without the need for system identification. More specifically, by dividing the considered region into small tiles, we conceive state-action-reward-state-action (Sarsa) and Q-learning based UAV-trajectory optimization algorithms (i.e., SUTOA and QUTOA) aiming to maximize the cumulative data collected during the finite flight-time. Our simulation results demonstrate that both of the proposed approaches are capable of finding an optimal trajectory under the fight-time constraint. The preference for QUTOA vs. SUTOA depends on the relative position of the start and the end points of the UAVs.

关键词： sensors Trajectory optimization Unmanned aerial vehicles Reinforcement learning Wireless networks Reinforcement learning sensor data collection trajectory optimization UAV communications

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Optimization strategies for the selection of mobile edges in hybrid crowdsensing architectures

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COMPUTER COMMUNICATIONS 2020年 157卷 132-142页

作者： Belli, Dimitri Chessa, Stefano Corradi, Antonio Foschini, Luca Girolami, Michele Univ Pisa Dept Comp Sci Pisa Italy Univ Bologna Dipartimento Informat Sci & Ingn Bologna Italy Natl Council Res Ist Sci & Tecnol Informaz Pisa Italy

Communication infrastructures are rapidly evolving to support 5G enabling lower latency, high reliability, and scalability of the network and of the service provisioning. An important element of the 5G vision is Multi-access Edge Computing (MEC), that leverages the availability of powerful and low-cost middle boxes, i.e., MEC nodes, statically deployed at suitable edges of the network to extend the centralized cloud backbone. M the same time, after almost a decade of research, Mobile CrowdSensing (MCS) has established the technology able to collect sensing data on the environment by using personal devices, usually smartphones, as powerful sensing-and-communication platforms. Even though, mutual benefits due to the integration of MEC and Mobile CrowdSensing (MCS) are still largely unexplored. In this paper, we address and analyze the potential of the synergic use of MCS and MEC by thoroughly assessing various strategies for the selection of both traditional Fixed MEC (FMEC) edges as well as human-enabled Mobile MEC ((MEC)-E-2) edges to support the collection of mobile CrowdSensing data. Collected results quantitatively show the effectiveness of the proposed optimization strategies in elastically scaling the load at edge nodes according to runtime provisioning needs.

关键词： Mobile CrowdSensing Multi-access edge computing Clustering sensor data collection

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A novelty detection approach to diagnosing hull and propeller fouling

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OCEAN ENGINEERING 2019年 176卷 65-73页

作者： Coraddu, Andrea Lim, Serena Oneto, Luca Pazouki, Kayvan Norman, Rose Murphy, John Strathclyde Univ Naval Architecture Ocean & Marine Engn Glasgow G1 1XW Lanark Scotland Univ Genoa DIBRIS Via Opera Pia 13 I-16145 Genoa Italy Newcastle Univ Sch Engn Marine Offshore & Subsea Technol Grp Newcastle Upon Tyne NE1 7RU Tyne & Wear England

Hull and propeller performance have a primary role in overall vessel efficiency. Vessel fouling is a common phenomenon where undesirable substances attach or grow on the ship hull. A clear understanding of the extent of the degradation of the hull will allow better management of assets and prediction of the best time for dry docking and hull maintenance work. In this paper, the authors investigate the problems of predicting the hull condition in real operations based on data measured by the on-board systems. The proposed solution uses an unsupervised Machine Learning (ML) modelling technique to eliminate the need for collecting labeled data related to the hull and propeller fouling condition. Two anomaly detection methods based on Support Vector Machines and k-nearest neighbour have been applied to predict the hull condition using the available parameters measured on-board. data from the Research Vessel The Princess Royal has been exploited to show the effectiveness of the proposed methods and to benchmark them in a realistic maritime application.

关键词： sensor data collection Ship efficiency Hull and propeller performance data analytics Supervised learning Hull fouling detection

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Selection of Mobile Edges for a Hybrid CrowdSensing Architecture

Selection of Mobile Edges for a Hybrid CrowdSensing Architec...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Belli, Dimitri Chessa, Stefano Corradi, Antonio Di Paolo, Giampiero Foschini, Luca Girolami, Michele Univ Pisa Dept Comp Sci Pisa Italy Univ Pisa Ist Sci & Tecnol Informaz CNR ISTI Pisa Italy Univ Bologna Dipartimento Informaf Sci & Ingn Bologna Italy Natl Council Res Ist Sci & Tecnol Informaz CNR ISTI Pisa Italy

ISBN: (纸本)9781728129990

Mobile crowdsensing aims at the collection of sensor data on the environment by leveraging personal devices, usually smartphones. Its popularity is due to the ability of reaching capillary even the most remote areas (provided humans live there), with no infrastructure costs. This is possible because it leverages on existing 4G/5G communication infrastructures that are now rapidly evolving towards edge computing models. In this work we address the synergy between mobile crowdsensing and multi-access edge computing by analysing and assessing strategies for the selection of fixed and mobile edges to support the collection of mobile crowdsensing data.

关键词： mobile crowdsensing nuilti-access edge computing clustering sensor data collection

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A general framework for sensor-based human activity recognition

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COMPUTERS IN BIOLOGY AND MEDICINE 2018年 95卷 248-260页

作者： Koeping, Lukas Shirahama, Kimiaki Grzegorzek, Marcin Univ Siegen Pattern Recognit Grp Siegen Germany Univ Econ Katowice Fac Informat & Commun Katowice Poland

Today's wearable devices like smartphones, smartwatches and intelligent glasses collect a large amount of data from their built-in sensors like accelerometers and gyroscopes. These data can be used to identify a person's current activity and in turn can be utilised for applications in the field of personal fitness assistants or elderly care. However, developing such systems is subject to certain restrictions: (i) since more and more new sensors will be available in the future, activity recognition systems should be able to integrate these new sensors with a small amount of manual effort and (ii) such systems should avoid high acquisition costs for computational power. We propose a general framework that achieves an effective data integration based on the following two characteristics: Firstly, a smartphone is used to gather and temporally store data from different sensors and transfer these data to a central server. Thus, various sensors can be integrated into the system as long as they have programming interfaces to communicate with the smartphone. The second characteristic is a codebook-based feature learning approach that can encode data from each sensor into an effective feature vector only by tuning a few intuitive parameters. In the experiments, the framework is realised as a real-time activity, recognition system that integrates eight sensors from a smartphone, smartwatch and smartglasses, and its effectiveness is validated from different perspectives such as accuracies, sensor combinations and sampling rates.

关键词： sensor-based activity recognition Feature learning sensor data collection

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Networked Wireless sensor data collection: Issues, Challenges, and Approaches

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2011年第4期13卷 673-687页

作者： Wang, Feng Liu, Jiangchuan Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

Wireless sensor networks (WSNs) have been applied to many applications since emerging. Among them, one of the most important applications is sensor data collections, where sensed data are collected at all or some of the sensor nodes and forwarded to a central base station for further processing. In this paper, we present a survey on recent advances in this research area. We first highlight the special features of sensor data collection in WSNs, by comparing with both wired sensor data collection network and other WSN applications. With these features in mind, we then discuss the issues and prior solutions on the utilizations of WSNs for sensor data collection. Based on different focuses of previous research works, we describe the basic taxonomy and propose to break down the networked wireless sensor data collection into three major stages, namely, the deployment stage, the control message dissemination stage and the data delivery stage. In each stage, we then discuss the issues and challenges, followed by a review and comparison of the previously proposed approaches and solutions, striving to identify the research and development trend behind them. In addition, we further discuss the correlations among the three stages and outline possible directions for the future research of the networked wireless sensor data collection.

关键词： Wireless sensor network sensor data collection deployment data gathering message dissemination

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Hybrid Sensing and Behavior-Aware in Pedestrian Hazard Detection

Hybrid Sensing and Behavior-Aware in Pedestrian Hazard Detec...

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12th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE) / 9th International Conference on Computer Science and its Applications (CSA)

作者： Kim, Svetlana Yoon, YongIk Sookmyung Womens Univ Dept IT Engn Chungpa Dong 2 Ga Seoul 140742 South Korea

ISBN: (纸本)9789811076053;9789811076046

The advances in multiple types of sensing technology, wireless communication, and context-aware services increase interest in the design and development of pedestrian behavior for hazard detection. This paper focuses on research of the hybrid sensing fusion approach that identifies behavior activities and provides behavior-aware alerts for safety to pedestrians. Hybrid sensing techniques use to integrate data gathered from several sensors and increase the reliability of the algorithm for identifying various activities. The main purpose of this paper is to present the overview of hybrid sensing and behavior-aware to apply for the pedestrian hazard detection.

关键词： Hybrid sensing sensor data collection sensor fusion Behavior aware

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A Fully Distributed Approach for Economic Dispatch Problem of Smart Grid

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ENERGIES 2018年第8期11卷 1993页

作者： Li, Bo Wang, Yudong Li, Jian Cao, Shengxian Northeast Elect Power Univ Sch Automat Engn Jilin 132012 Jilin Peoples R China

The cooperative, reliable and responsive characteristics make smart grid more popular than traditional power grid. However, with the extensive employment of smart grid concepts, the traditional centralized control methods expose a lot of shortcomings, such as communication congestion, computing complexity in central management systems, and so on. The distributed control method with flexible characteristics can meet the timeliness and effectiveness of information management in smart grid and ensure the information collection timely and the power dispatch economically. This article presents a decentralized approach based on multi agent system (MAS) for solving data collection and economic dispatch problem of smart grid. First, considering the generators and loads are distributed on many nodes in the space, a flooding-based consensus algorithm is proposed to achieve generator and load information for each agent. Then, a suitable distributed algorithm called lambda-consensus is used for solving the economic dispatch problem, eventually, all generators can automatically minimize the total cost in a collective sense. Simulation results in standard test cases are presented to demonstrate the effectiveness of the proposed control strategy.

关键词： optimal resource management consensus algorithm distributed control sensor data collection economic dispatch problem

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Information collection and Presentation Enriched by Remote sensor data 13

Information Collection and Presentation Enriched by Remote S...

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13th IEEE International Symposium on Intelligent Systems and Informatics (SISY)

作者： Gatial, Emil Balogh, Zoltan Hluchy, Ladislav Slovak Acad Sci Inst Informat Bratislava 84507 Slovakia

ISBN: (纸本)9781467393881

This paper discusses a process of data collection from remote sensors and presentation on mobile devices. The presented research and work in this paper is done in the scope of REDIRNET project. The main motivation in the project is to support the rescuers facing operational problems in accessing and analyzing information from multiple sources, and providing advanced features for command and control of the emergencies. Timely exchange of information between competent authorities, a common understanding of data and a swift transmission of knowledge can save the lives and protect properties. Useful information are often heterogeneous and distributed between many organizations in the diverse information sources with the different access rights and with the different qualities. This work focuses on delivering the data from remote sensor nodes with intermittent connected networks and dispatching the results to the end users connected by mobile devices. A specific of this work is the means of sensor data collection, where the mobile devices are used as the data producers. The specified information and the data provided by mobile device sensors are collected and aggregated into a centralized system. In addition the sensor data are collected using remote sensors passing through an ad-hoc gateway that may be presented in a convenient way on the mobile devices.

关键词： data acquisition electronic data interchange emergency management mobile computing REDIRNET project access rights data delivery data producers emergencies command and control information collection information exchange information presentation information sources intermittent connected networks mobile device sensors mobile devices operational problems remote sensor data remote sensor nodes rescuers sensor data collection Androids Cameras data collection Humanoid robots Mobile handsets Remote sensing Servers data collection Methods sensor data information gathering Mobile devices access privileges Androids Androids Information source information exchange Remote sensing equipment Mobile phones Electronic data interchange Humanoid robots crisis management Servers

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Performance Analysis of Tree Cluster Based data Gathering for WSNs

Performance Analysis of Tree Cluster Based Data Gathering fo...

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International Conference on Intelligent Computing and Control (I2C2)

作者： Kakde, Kavita R. Kadam, Mahesh Terna Engn Coll Dept Elect & Telecommun Engn Navi Mumbai 400706 India

ISBN: (纸本)9781538603741

Wireless sensor network (WSNs) contain there are several devices of sensor which is transferring data to base station for the purpose of processing this is known as direct delivery. This cause to the heavy traffic into the network because this is reduces lifetime of network. Into the many industries WSN has been used widely, because it was taking the hug quantity of heterogeneous sensory data. Whatever, into the whole or general deployment area the hotspot issue was not ignored by many WSN data gathering techniques. network connectivity affected by the hotspot issues & reduces the network lifetime. WSN suffers from many hurdles such as small memory, low computational capability, and limited energy resources then, the method of data gathering described to enhance the lifetime of network. Therefore to improve performance large numbers of protocols are introduced. From the previous scholars this kind of clusters has been utilized, tree & cluster based for utilizing energy efficient routing protocols. This article proposes the enhanced version which is known as tree-cluster data gathering method & it is utilizing both tree & cluster based protocols. The TCDGT is significantly stable the load of entire network, prolong the network lifetime, decrease energy consumption & alleviate the hotspot problem has been shown by comparison & simulation along with another method.

关键词： WSNs sensor data collection Energy Efficient data gathering Clustering Cluster Head Selection

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