CityGuard: A Watchdog for Safety-Aware Conflict Detection in Smart Cities

Nowadays, increasing number of smart services are being developed and deployed in cities around the world. IoT platforms have emerged to integrate smart city services and city resources, and thus improve city performance in the domains of transportation, emergency, environment, public safety, etc. Despite the increasing intelligence of smart services and the sophistication of platforms, the safety issues in smart cities are not addressed adequately, especially the safety issues arising from the integration of smart services. Therefore, CityGuard, a safety-aware watchdog architecture is developed. To the best of our knowledge, it is the first architecture that detects and resolves conflicts among actions of different services considering both safety and performance requirements. Prior to developing CityGuard, safety and performance requirements and a spectrum of conflicts are specified. Sophisticated models are used to analyze secondary effects, and detect device and environmental conflicts. A simulation based on New York City is used for the evaluation. The results show that CityGuard (i) identifies unsafe actions and thus helps to prevent the city from safety hazards, (ii) detects and resolves two major types of conflicts, i.e., device and environmental conflicts, and (iii) improves the overall city performance.

[1]  Pierfrancesco Bellini,et al.  Km4City ontology building vs data harvesting and cleaning for smart-city services , 2014, J. Vis. Lang. Comput..

[2]  George J. Pappas,et al.  Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach , 2015, IEEE Transactions on Automation Science and Engineering.

[3]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[4]  John A. Stankovic,et al.  Detection of Runtime Conflicts among Services in Smart Cities , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).

[5]  Sirajum Munir,et al.  DepSys: Dependency aware integration of cyber-physical systems for smart homes , 2014, 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[6]  Daniel Krajzewicz,et al.  SUMO - Simulation of Urban MObility An Overview , 2011 .

[7]  Antonio Bicchi,et al.  Decentralized Cooperative Policy for Conflict Resolution in Multivehicle Systems , 2007, IEEE Transactions on Robotics.

[8]  Sirajum Munir,et al.  EyePhy: Detecting Dependencies in Cyber-Physical System Apps due to Human-in-the-Loop , 2015, EAI Endorsed Trans. e Learn..

[9]  Zhao Li,et al.  SIFT: building an internet of safe things , 2015, IPSN.

[10]  Robert L. Hester,et al.  HumMod: A Modeling Environment for the Simulation of Integrative Human Physiology , 2011, Front. Physio..

[11]  John A. Stankovic,et al.  Preclude: Conflict detection in textual health advice , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[12]  Paulo Carreira,et al.  Conflict detection and resolution in home and building automation systems: a literature review , 2013, Journal of Ambient Intelligence and Humanized Computing.

[13]  Maxim Raya,et al.  TraCI: an interface for coupling road traffic and network simulators , 2008, CNS '08.

[14]  Peter Palensky,et al.  Common approach to functional safety and system security in building automation and control systems , 2007, 2007 IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007).