Manhole Depth Analysis and Logistics Planning using TA-Enabled IoT Cloud

Smart cities have marked an incessant push to modernize technologies for easing citizens. Smarter manholes and planning the associated logistics have become a crucial component for assisting city authorities while coining decisions. This article proposes an automatic manhole depth analysis system using sensors mounted on manhole units. The sensor data collected in cloud services are utilized to schedule logistics using the proposed Threshold Acceptance (TA) algorithm. The algorithm attempts to minimize the distance between manhole operation sites and to improve the cost efficiencies associated with the operations. The proposed method was experimented at the IoT Cloud Research laboratory; the proposed TA-enabled logistics schedule produced a faster logistics plan to rectify manhole issues of streets based on the IoT-enabled monitoring system. The experiments manifested the cost efficiency improvement of over 38 percent and distance efficiency of over 78 percent when compared to the traditional approaches of handling manhole issues.