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. In dense deployments, the dat...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. In dense deployments, the data collected at the closest locations between the MTC devices are spatially correlated. In this paper, we propose a k-means grouping technique to combine all MTC devices based on spatially correlated. The MTC devices collect the data on the event-based area and then transmit to the centralized aggregator for processing and computing. 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. 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. 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. 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.展开更多
Speed-flow relationship is the fundamental for the traffic simulation and traffic volume forecast. Traditional quadratic polynomial model can’t reflect the saturate flow at signal intersections. In order to determine...Speed-flow relationship is the fundamental for the traffic simulation and traffic volume forecast. Traditional quadratic polynomial model can’t reflect the saturate flow at signal intersections. In order to determine the speed-flow relationship at signal intersections, the speed and time-headway of vehicles at two signal intersections were investigated and the accuracy of software used to get the speed was tested. After vehicle starting-up from queuing, the time-headway reduces gradually with the increase of speed. The relationship of power exponential function between speed and time-headway is formulated. Traffic volume can be calculated by the vehicle time-headway. Then the speed-flow relationship was developed and an S-shaped curve model was built in this paper. In the S-shaped curve model, traffic flow approaches to the saturate when the speed doesn’t increase. Thus, S-shaped curve model is better to describe the speed-flow relationship at signal intersection. The results can provide a reference to determine the parameters in traffic simulation and for the study of level of service of intersections.展开更多
文摘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. In dense deployments, the data collected at the closest locations between the MTC devices are spatially correlated. In this paper, we propose a k-means grouping technique to combine all MTC devices based on spatially correlated. The MTC devices collect the data on the event-based area and then transmit to the centralized aggregator for processing and computing. 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. 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. 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. 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.
文摘Speed-flow relationship is the fundamental for the traffic simulation and traffic volume forecast. Traditional quadratic polynomial model can’t reflect the saturate flow at signal intersections. In order to determine the speed-flow relationship at signal intersections, the speed and time-headway of vehicles at two signal intersections were investigated and the accuracy of software used to get the speed was tested. After vehicle starting-up from queuing, the time-headway reduces gradually with the increase of speed. The relationship of power exponential function between speed and time-headway is formulated. Traffic volume can be calculated by the vehicle time-headway. Then the speed-flow relationship was developed and an S-shaped curve model was built in this paper. In the S-shaped curve model, traffic flow approaches to the saturate when the speed doesn’t increase. Thus, S-shaped curve model is better to describe the speed-flow relationship at signal intersection. The results can provide a reference to determine the parameters in traffic simulation and for the study of level of service of intersections.
