Randomness and uncertainty are the outstanding characteristics of renewable energy, and the extremely high proportion of distributed renewable energy connected to the grid is bound to cause a serious impact. To compre...
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Because of the small number of users, low concurrency and small amount of data in the early electronic commerce system, the application software only needs to deploy a single server to meet the throughput requirements...
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A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). While the sensing stage is typically performed locally through a dedicated Intern...
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ISBN:
(纸本)9781665495127
A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). While the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement - as in emergency response and disaster relief operations - edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication *** simulate - with Software-In-The-Loop realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.
Digital Twins and the Internet of Things (IoT) are two of the most prominent recent concepts and technologies. The IoT supports many applications that merge the physical with the cyber world. This highlights the need ...
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ISBN:
(纸本)9781665495127
Digital Twins and the Internet of Things (IoT) are two of the most prominent recent concepts and technologies. The IoT supports many applications that merge the physical with the cyber world. This highlights the need for improved security. Here, we argue that digital twins can help in securing and strengthening the IoT by using them for interacting with the actual IoT devices. While most digital twins implementations are centralized, we propose to integrate distributed Ledger Technologies (DLTs) and digital twins into the IoT to realize decentralized, secure, available, flexible, and auditable blockchain-based IoT services for IoT devices that follow the W3C Web of Things (WoT) standards. In this work, we present the design of SmartTwin, a blockchain-based digital twin framework for which we provide two different implementations using two different blockchains, we present the design trade-offs, and we discuss future research and development directions.
Large-scale classification is an important task of machine learning, especially in the smart city field, which is a big data environment. In recent years, single-threaded optimization algorithms can no longer meet the...
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One of the most important systems for smart cities is an edge computing system to facilitate decision-making by using real-time awareness and data analytics. Although existing works focused on the safety of pedestrian...
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ISBN:
(数字)9781665488792
ISBN:
(纸本)9781665488792
One of the most important systems for smart cities is an edge computing system to facilitate decision-making by using real-time awareness and data analytics. Although existing works focused on the safety of pedestrians, the safety of personalmobility vehicles is what smart cities need to address toward the future. On-boardsensors could be easily disabled because of external issues of hardware caused by dirt or snow, internal issues of hardware, and software issues such as malware infection. We propose an edge computing system that supports robust vehicle automation with a sensor network using multiple light-detection-and-ranging (LIDAR) sensors deployed like roadside cameras or indoor security cameras for surveillance for personal-mobility vehicles. We develop a prototype system of the proposed system using real LIDAR units and a mobility scooter and evaluate the system in terms of route errors to verify the feasibility of the proposed system with a scenario where the vehicle is enabled to continue self-driving even when its on-boardsensor is disabled.
This paper studies the belief rule base (BRB) expert system based distributed fault diagnosis problem for a group of heterogeneous multi-agent systems (MASs) subject to unpredictable disturbances. First, a novel distr...
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In order to further improve the efficiency and reliability of the local power grid in the process of fault recovery, the local power grid with high proportion of distributed generation is divided into islands based on...
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Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing and communication resources, the strea...
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ISBN:
(纸本)9798350363999;9798350364002
Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing and communication resources, the streaming algorithm at hand should maximize the average quality of experience (QoE) while guaranteeing a minimum rate for each user. In this paper, we propose a cross layer optimization approach that maximizes the available rate to each user and efficiently uses it to maximize users' QoE. Particularly, we consider a tile based 360 degree video streaming, and we optimize a QoE metric that balances the tradeoff between maximizing each user's QoE and ensuring fairness among users. We show that the problem can be decoupled into two interrelated subproblems: (i) a physical layer subproblem whose objective is to find the download rate for each user, and (ii) an application layer subproblem whose objective is to use that rate to find a quality decision per tile such that the user's QoE is maximized. We prove that the physical layer subproblem can be solved optimally with low complexity and an actor-critic deep reinforcement learning (DRL) is proposed to leverage the parallel training of multiple independent agents and solve the application layer subproblem. Extensive experiments reveal the robustness of our scheme and demonstrate its significant performance improvement compared to several baseline algorithms.
Local discharge detection is an important technology used to evaluate the state of electrical equipment. It can realize early fault diagnosis, accident prevention, life extension and operation efficiency improvement o...
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