Spatial air index with neighbor information for processing k-nearest neighbor searches in IoT mobile computing

Shen, Jui-Lung; Yu, Cheng-Jung; Lu, Ching-Ta; Lin, Wen-Yen; Yen, Neil Y.; Huang, Tien‐Chi; Chu, Hong-Ray

doi:10.1007/s11227-019-02753-5

Cited by 2 publications

(1 citation statement)

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“…Moving the decision maker from the device to the cloud site increases the cooperation between AGVs, but making decisions from the cloud would delay decisions. Therefore, we used a cloud service to realize centralized management, the Internet of Things [3][4][5] to monitor AGVs' status, and edge computation [6] to minimize the communication cost for decision making. Thus, the proposed approach should be the best of both worlds by minimizing decision making delay and increasing the centralization of management.…”

Section: Introductionmentioning

confidence: 99%

On the Construction of an Edge-Based Remote Sensing Framework: The Applications on Automated Guided Vehicles and Drones

2022

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To monitor the status and mission progress of automated guided vehicles (AGVs), most platforms typically obtained real-time data through a data acquisition system that is deployed on the end vehicles. The data acquired from an end vehicle are generally sparse but are required frequently, and an examination process using cloud storage cannot commence until the device’s raw data are received. To reduce communication costs, the proposed edge-based monitoring system (EMS) applies edge computation to move the data examination from the cloud to an end site. The data buffered in the end device could be pre-processed by some detectors. For example, checking the energy is adequate for returning to the base. Thus, buffering data on the end device helps to minimize the time required by the decision maker for abnormal events, e.g., shutdowns caused by exhausted energy. In addition to adopting the common methods of storing, processing, and analyzing data at the data center, the EMS moves some time-sensitive services to the end vehicle. Moreover, after obtaining real-time motion data, the edge computing architecture immediately targets abnormal actions and sends reaction commands to shorten the decision making delay caused by the communication cost between the end vehicles and cloud storage sites, thereby avoiding collisions or accidents. The EMS has been implemented to monitor AGV and unmanned aerial vehicles. The EMS primarily monitored the power and motion of the vehicles. It also combined task-oriented motion commands for monitoring unexpected vehicle motions during tasks. If an abnormal event occurred, immediate warnings were provided through a notification interface and were immediately processed by the EMS to ensure safety during task execution. After checking data consistency between the EMS and the real device, the EMS reveals the corrected status of the device with very little delay. Therefore, the EMS could help with minimizing the time taken to make decisions. Moreover, the EMS has been modified to be deployed on drones to confirm its cross-platform applicability. In the simulations of drones, the EMS also got similar results to the simulations of AGVs. Therefore, the EMS could reduce the time in examining abnormal events and has cross-platform functionality.

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Section: Introductionmentioning

confidence: 99%