Market trends of Machine-Type Communication (MTC) devices deployments are exponential increases. Then, the simulation results revealed that the proposed k-means grouping technique reduce the energy consumption at centralized aggregator while satisfying the required completion time. Based on this framework, we compare the energy consumption when all data processed locally at centralized aggregator or offloaded at mobile edge computing server with optimal solution obtained by the brute force method. As a sensing capability adopted on MTC devices, we use a power exponential function model to compute a correlation coefficient existing between the MTC devices. With the limitation of computational resources at the centralized aggregator, some grouped MTC devices data offloaded to the nearby base station collocated with the mobile edge-computing server. The MTC devices collect the data on the event-based area and then transmit to the centralized aggregator for processing and computing. In this paper, we propose a k-means grouping technique to combine all MTC devices based on spatially correlated.
In dense deployments, the data collected at the closest locations between the MTC devices are spatially correlated. Machine-type communication (MTC) devices provide a broad range of data collection especially on the massive data generated environments such as urban, industrials and event-enabled areas.
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