Semantic Agent-Based Service Middleware and Simulation for Smart Cities
With the development of Machine-to-Machine (M2M) technology, a variety of embedded and mobile devices is integrated to interact via the platform of the Internet of Things, especially in the domain of smart cities. One of the primary challenges is that selecting the appropriate services or service co...
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doaj-8ea7aee589f1481f9a81e118af3d844b2020-11-24T21:11:59ZengMDPI AGSensors1424-82202016-12-011612220010.3390/s16122200s16122200Semantic Agent-Based Service Middleware and Simulation for Smart CitiesMing Liu0Yang Xu1Haixiao Hu2Abdul-Wahid Mohammed3School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Engineering, University for Development Studies, Tamale 00233, GhanaWith the development of Machine-to-Machine (M2M) technology, a variety of embedded and mobile devices is integrated to interact via the platform of the Internet of Things, especially in the domain of smart cities. One of the primary challenges is that selecting the appropriate services or service combination for upper layer applications is hard, which is due to the absence of a unified semantical service description pattern, as well as the service selection mechanism. In this paper, we define a semantic service representation model from four key properties: Capability (C), Deployment (D), Resource (R) and IOData (IO). Based on this model, an agent-based middleware is built to support semantic service enablement. In this middleware, we present an efficient semantic service discovery and matching approach for a service combination process, which calculates the semantic similarity between services, and a heuristic algorithm to search the service candidates for a specific service request. Based on this design, we propose a simulation of virtual urban fire fighting, and the experimental results manifest the feasibility and efficiency of our design.http://www.mdpi.com/1424-8220/16/12/2200smart cityagent-based middlewaresemantic serviceM2M |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ming Liu Yang Xu Haixiao Hu Abdul-Wahid Mohammed |
spellingShingle |
Ming Liu Yang Xu Haixiao Hu Abdul-Wahid Mohammed Semantic Agent-Based Service Middleware and Simulation for Smart Cities Sensors smart city agent-based middleware semantic service M2M |
author_facet |
Ming Liu Yang Xu Haixiao Hu Abdul-Wahid Mohammed |
author_sort |
Ming Liu |
title |
Semantic Agent-Based Service Middleware and Simulation for Smart Cities |
title_short |
Semantic Agent-Based Service Middleware and Simulation for Smart Cities |
title_full |
Semantic Agent-Based Service Middleware and Simulation for Smart Cities |
title_fullStr |
Semantic Agent-Based Service Middleware and Simulation for Smart Cities |
title_full_unstemmed |
Semantic Agent-Based Service Middleware and Simulation for Smart Cities |
title_sort |
semantic agent-based service middleware and simulation for smart cities |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2016-12-01 |
description |
With the development of Machine-to-Machine (M2M) technology, a variety of embedded and mobile devices is integrated to interact via the platform of the Internet of Things, especially in the domain of smart cities. One of the primary challenges is that selecting the appropriate services or service combination for upper layer applications is hard, which is due to the absence of a unified semantical service description pattern, as well as the service selection mechanism. In this paper, we define a semantic service representation model from four key properties: Capability (C), Deployment (D), Resource (R) and IOData (IO). Based on this model, an agent-based middleware is built to support semantic service enablement. In this middleware, we present an efficient semantic service discovery and matching approach for a service combination process, which calculates the semantic similarity between services, and a heuristic algorithm to search the service candidates for a specific service request. Based on this design, we propose a simulation of virtual urban fire fighting, and the experimental results manifest the feasibility and efficiency of our design. |
topic |
smart city agent-based middleware semantic service M2M |
url |
http://www.mdpi.com/1424-8220/16/12/2200 |
work_keys_str_mv |
AT mingliu semanticagentbasedservicemiddlewareandsimulationforsmartcities AT yangxu semanticagentbasedservicemiddlewareandsimulationforsmartcities AT haixiaohu semanticagentbasedservicemiddlewareandsimulationforsmartcities AT abdulwahidmohammed semanticagentbasedservicemiddlewareandsimulationforsmartcities |
_version_ |
1716751936415334400 |