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...

Full description

Bibliographic Details
Main Authors: Ming Liu, Yang Xu, Haixiao Hu, Abdul-Wahid Mohammed
Format: Article
Language:English
Published: MDPI AG 2016-12-01
Series:Sensors
Subjects:
M2M
Online Access:http://www.mdpi.com/1424-8220/16/12/2200
id doaj-8ea7aee589f1481f9a81e118af3d844b
record_format Article
spelling 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