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In the last few years, a mobile wireless sensor and its application in wireless sensor network (WSN) are commonly used. Localization of a mobile sensor node is considered a critical issue in some WSN applications. In this paper, an outdoor environments experiment was carried out to measure the distance between the mobile node and the coordinator node in a simple point-topoint ZigBee WSN. The distance was determined based on the measured Received Signal Strength Indicator (RSSI) of the mobile node by the coordinator node. In addition, a Log-Normal Shadowing Model (LNSM) was derived for outdoor condition. Moreover, the parameters of the propagation channel such as standard deviation and a path loss exponent were estimated. The RSSI was measured and analysed for outdoor environments for a distance range 1-100 m. The measurements were carried out by using 2.4 GHz ZigBee wireless protocol based on XBee series 2 modules.The results disclosed that the mean absolute error (MAE) of 3.44 and 6.72 m for a distance range 0-65 m and 0-100 m, respectively. These results point that the LNSM is only suited for short distance.

A wheelchair control system based on Gyroscope of wearable tool can serve the disabled, especially in helping them move freely. The recent evolution of new technology means that unassisted, free movement has become possible. For this purpose, human–machine interface hands-free command of an electric-powered wheelchair can be achieved. In this paper, an electroencephalogram instrument, namely the EMOTIV Insight, was implemented in a human–computer interface to acquire the user’s head motion signals. The system can be operated based on the user’s head motions to carry out motion orders and control the motor of the wheelchair. The proposed system consists of an EMOTIV Insight brain-based gyroscope to sense head tilt, a DC motor driver to control wheelchair speed and directions, an eclectic-powered wheelchair, microcontroller, and laptop. We implemented the system in practice and tested it on smooth and rough surfaces in indoor/outdoor settings. The experimental results were greatly encouraging: disabled users were able to drive the wheelchair without any limitations. We obtained a significant average response time of 2 seconds. In addition, the system had accuracy, sensitivity, and specificity of 99%, 99.16%, and 98.83%, respectively.

Wireless sensor networks (WSNs) and their applications have received considerable interest in the last few years. In WSNs, accurate path loss models should be considered to achieve a successful distribution of several nodes. In this work, two path loss models are proposed to evaluate the distance between two ZigBee WSNs. First, a path loss model based on conventional Log-Normal Shadowing Model (LNSM) is derived using the collected received signal strength indicator (RSSI) of the ZigBee in real time. Second, a new path loss model based on Particle Swarm Optimization (PSO) algorithm hybridized with Polynomial Equation (PE) is proposed. The PSO algorithm is used to select the optimum coefficients of PE. These coefficients can be utilized to optimize the distance estimation error based on the curve fitting. Therefore, the new path loss model called hybrid PE-PSO is innovated in this work. The hybrid PE-PSO model considerably improves the distance estimation accuracy compared with the LNSM. Results show that the hybrid PE-PSO achieves 85% improvement in distance error compared with the traditional LNSM. The mean absolute error of 0.77 m is obtained for distance estimation, which outperforms that by state of the arts.

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