In this paper, we introduce a new combined priority and admission control mechanism applying in the VCN （Vehicular Communication Network） which is designed with an integration of the Vehicular Ad-hoc Networks （VAN...In this paper, we introduce a new combined priority and admission control mechanism applying in the VCN （Vehicular Communication Network） which is designed with an integration of the Vehicular Ad-hoc Networks （VANETs） based on standard IEEE 802.11 p and IEEE 802.11 s WMNs （Wireless Mesh Networks）. Simulation experiments are intensively investigated to evaluate the novel combined priority and admission control mechanism to assure quality of the I2V （Infrastructure to Vehicle） emergency services occurred during the time video flows are being delivered between content servers and cars. The simulation results show effectiveness of proposed priority and admission control schemes in term of the minimized end-to-end delay as well as the increase of throughput and PDR （Packet Delivery Ratio） of the emergency data flow.展开更多
This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fin...This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fingerprints,which are composed of radio fingerprints at multiple points of time,that is,at multiple positions,and displacements between them estimated by dead reckoning.To avoid errors accumulated from dead reckoning,the method uses short-range dead reckoning.The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11×5 m with furniture inside.The Received Signal Strength Indicator(RSSI)values of the beacons were collected at 30 measuring points,which were points at the intersections on a 1×1 m grid with no obstacles.A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them.Random Forests(RF)was used to build regression models to estimate positions from location fingerprints.The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons.This error is lower than that received with a single-point baseline model,where a feature vector is composed of only RSSI values at one location.The results suggest that the proposed method is effective for indoor positioning.展开更多
文摘In this paper, we introduce a new combined priority and admission control mechanism applying in the VCN （Vehicular Communication Network） which is designed with an integration of the Vehicular Ad-hoc Networks （VANETs） based on standard IEEE 802.11 p and IEEE 802.11 s WMNs （Wireless Mesh Networks）. Simulation experiments are intensively investigated to evaluate the novel combined priority and admission control mechanism to assure quality of the I2V （Infrastructure to Vehicle） emergency services occurred during the time video flows are being delivered between content servers and cars. The simulation results show effectiveness of proposed priority and admission control schemes in term of the minimized end-to-end delay as well as the increase of throughput and PDR （Packet Delivery Ratio） of the emergency data flow.
文摘This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fingerprints,which are composed of radio fingerprints at multiple points of time,that is,at multiple positions,and displacements between them estimated by dead reckoning.To avoid errors accumulated from dead reckoning,the method uses short-range dead reckoning.The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11×5 m with furniture inside.The Received Signal Strength Indicator(RSSI)values of the beacons were collected at 30 measuring points,which were points at the intersections on a 1×1 m grid with no obstacles.A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them.Random Forests(RF)was used to build regression models to estimate positions from location fingerprints.The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons.This error is lower than that received with a single-point baseline model,where a feature vector is composed of only RSSI values at one location.The results suggest that the proposed method is effective for indoor positioning.
