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Efficient placement of edge computing devices for vehicular applications in smart cities

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Premsankar, Gopika
dc.contributor.author Ghaddar, Bissan
dc.contributor.author Di Francesco, Mario
dc.contributor.author Verago, Rudi
dc.date.accessioned 2018-12-10T10:16:59Z
dc.date.available 2018-12-10T10:16:59Z
dc.date.issued 2018
dc.identifier.citation Premsankar , G , Ghaddar , B , Di Francesco , M & Verago , R 2018 , Efficient placement of edge computing devices for vehicular applications in smart cities . in IEEE/IFIP Network Operations and Management Symposium : Cognitive Management in a Cyber World, NOMS 2018 . IEEE/IFIP Network Operations and Management Symposium , IEEE , pp. 1-9 , IEEE/IFIP Network Operations and Management Symposium , Taipei , Taiwan, Republic of China , 23/05/2018 . https://doi.org/10.1109/NOMS.2018.8406256 en
dc.identifier.isbn 9781538634165
dc.identifier.issn 2374-9709
dc.identifier.other PURE UUID: 5051cf40-121e-4d6b-8ff3-d1d01953e7b1
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/efficient-placement-of-edge-computing-devices-for-vehicular-applications-in-smart-cities(5051cf40-121e-4d6b-8ff3-d1d01953e7b1).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/26392860/premsankar2018efficient.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/35039
dc.description.abstract Vehicular applications in smart cities, including assisted and autonomous driving, require complex data processing and low-latency communication. An effective approach to address these demands is to leverage the edge computing paradigm, wherein processing and storage resources are placed at access points of the vehicular network, i.e., at roadside units (RSUs). Deploying edge computing devices for vehicular applications in urban scenarios presents two major challenges. First, it is difficult to ensure continuous wireless connectivity between vehicles and RSUs, especially in dense urban areas with many buildings. Second, edge computing devices have limited processing resources compared to the cloud, thereby requiring careful network planning to meet the computational and latency requirements of vehicular applications. This article specifically addresses these challenges. In particular, it targets efficient deployment of edge computing devices in an urban scenario, subject to application- specific quality of service constraints. To this end, this article introduces a mixed integer linear programming formulation to minimize the deployment cost of edge devices by jointly satisfying a target level of network coverage and computational demand. The proposed approach is able to accurately model complex urban environments with many buildings and a large number of vehicles. Furthermore, this article presents a simple yet effective heuristic to deploy edge computing devices based on the knowledge of road traffic in the target deployment area. The devised methods are evaluated by extensive simulations with data from the city of Dublin. The obtained results show that the proposed solutions can effectively guarantee a target application- specific quality of service in realistic conditions. en
dc.format.extent 1-9
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher IEEE
dc.relation.ispartof IEEE/IFIP Network Operations and Management Symposium en
dc.relation.ispartofseries IEEE/IFIP Network Operations and Management Symposium en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Efficient placement of edge computing devices for vehicular applications in smart cities en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Professorship Di Francesco Mario
dc.contributor.department University of Waterloo
dc.contributor.department Department of Computer Science en
dc.subject.keyword edge computing
dc.subject.keyword roadside units
dc.subject.keyword deployment
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201812106054
dc.identifier.doi 10.1109/NOMS.2018.8406256
dc.type.version acceptedVersion


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