Conflict detection for smart cities services: Poster abstract

Shuxin Zhong, Desheng Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The effort of trying to integrate individual services leads to the increasing potential conflicts across smart city services. This kind of conflicts might cause unsafe environment or degrade the overall performance. In this paper, we mainly focus on conflict detection by exploring the fundamental correlations among services and intend to monitor the city's safety and performance collectively. The key insight is that there is a multi-dimensional dependency in predicted conflicts. Therefore, we propose a novel multitask learning framework to detect various conflicts simultaneously. We evaluate this service conflict detection model with a large-scale real dataset across a few transportation and environment services, including the location and transaction information from the taxi, bus and subway systems. The results show the advantage of our system.

Original languageEnglish (US)
Title of host publicationSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems
EditorsMi Zhang
PublisherAssociation for Computing Machinery, Inc
Pages416-417
Number of pages2
ISBN (Electronic)9781450369503
DOIs
StatePublished - Nov 10 2019
Event17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019 - New York, United States
Duration: Nov 10 2019Nov 13 2019

Publication series

NameSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems

Conference

Conference17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019
Country/TerritoryUnited States
CityNew York
Period11/10/1911/13/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Conflict detection
  • Multi-task learning
  • Smart city services

Fingerprint

Dive into the research topics of 'Conflict detection for smart cities services: Poster abstract'. Together they form a unique fingerprint.

Cite this