针对当前资产定位系统定位精度、建设成本和部署灵活性难以有效平衡的问题,基于BLE Mesh采用多维标度分析(MultiDimensional Scaling-Map,MDS-MAP)定位算法设计了一种资产定位系统。系统首先对原始接收信号强度(Received Signal Strengt...针对当前资产定位系统定位精度、建设成本和部署灵活性难以有效平衡的问题,基于BLE Mesh采用多维标度分析(MultiDimensional Scaling-Map,MDS-MAP)定位算法设计了一种资产定位系统。系统首先对原始接收信号强度(Received Signal Strength Indicator,RSSI)进行高斯-卡尔曼融合滤波,提高了RSSI值的准确性;然后利用生存时间(Time To Live,TTL)对中继节点进行约束,提高了数据传输的有效性;最后利用半径弥补法与Bellman-Ford融合迭代方案对生成的距离矩阵进行修正,减小了测距误差。实验结果表明,所设计的系统可有效完成蓝牙标签信息更新以及位置展示,平均定位精度达到了0.94 m。本系统具有成本低、工程实施方便的优点,有一定的应用价值和发展前景。展开更多
Indoor organization user activity’s (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoor fixed sensor networks. ...Indoor organization user activity’s (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoor fixed sensor networks. In this paper, indoor UA based direction detection monitoring system is achieved by the combination of both the orientation sensor and Bluetooth Low Energy (BLE) in user’s smartphones belonging to the Internet of Things (IoT). The orientation sensor senses the actual orientation of the user and BLE transmits the sensed BLE signals to monitoring system using star topology in IoT. In monitoring system, classification algorithm is used to identify the directions of the smartphone users. The emergency situation of the user is also predicted based on signal variation instantly in real time. The user activity’s signals are captured using LabVIEW toolkit then applied to various classification algorithms such asRF—91.42%, Ibk—90.55%, j48— 85.61%, K*—73.54% are the results obtained. An average of 85% was obtained in all the classifi- cation algorithims indicating the consistency and accuracy in detecting the directions of the users. RF was found to be the best among all the classification algorithms. IoT enabled devices have high demand in near coming future, moreover smartphones users increase day by day, hence implementing and maintaining the above said system would be much easier and cheaper compared to other conventional networks.展开更多
文摘针对当前资产定位系统定位精度、建设成本和部署灵活性难以有效平衡的问题,基于BLE Mesh采用多维标度分析(MultiDimensional Scaling-Map,MDS-MAP)定位算法设计了一种资产定位系统。系统首先对原始接收信号强度(Received Signal Strength Indicator,RSSI)进行高斯-卡尔曼融合滤波,提高了RSSI值的准确性;然后利用生存时间(Time To Live,TTL)对中继节点进行约束,提高了数据传输的有效性;最后利用半径弥补法与Bellman-Ford融合迭代方案对生成的距离矩阵进行修正,减小了测距误差。实验结果表明,所设计的系统可有效完成蓝牙标签信息更新以及位置展示,平均定位精度达到了0.94 m。本系统具有成本低、工程实施方便的优点,有一定的应用价值和发展前景。
文摘Indoor organization user activity’s (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoor fixed sensor networks. In this paper, indoor UA based direction detection monitoring system is achieved by the combination of both the orientation sensor and Bluetooth Low Energy (BLE) in user’s smartphones belonging to the Internet of Things (IoT). The orientation sensor senses the actual orientation of the user and BLE transmits the sensed BLE signals to monitoring system using star topology in IoT. In monitoring system, classification algorithm is used to identify the directions of the smartphone users. The emergency situation of the user is also predicted based on signal variation instantly in real time. The user activity’s signals are captured using LabVIEW toolkit then applied to various classification algorithms such asRF—91.42%, Ibk—90.55%, j48— 85.61%, K*—73.54% are the results obtained. An average of 85% was obtained in all the classifi- cation algorithims indicating the consistency and accuracy in detecting the directions of the users. RF was found to be the best among all the classification algorithms. IoT enabled devices have high demand in near coming future, moreover smartphones users increase day by day, hence implementing and maintaining the above said system would be much easier and cheaper compared to other conventional networks.
