Abstract
Sensing devices acting as interconnected data sources are becoming increasingly ubiquitous in concepts of Internet of Things (IoT)-enabled smart cities, but they typically lack physical protection and are susceptible to being compromised. To address this issue, a great-alternative-region (GAR)-based approach for deploying network monitors to locate compromised data sources is proposed. The GAR concept is introduced according to the network topology and connectivity characteristics, and the GARs with the most complete connectivity are identified as the candidate monitor locations, thereby transforming the problem of monitor deployment into a traditional $K$ -center problem. Based on the demonstrated relationship between the monitor locations and the locating accuracy, the optimization objective for reasonably deploying monitors is designed to minimize the maximum number of hops between the data sources and their nearest monitors, and the optimal deployment pattern is achieved using an improved genetic algorithm. Finally, simulation-based results are presented to illustrate the performance of this approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.