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The WSSs (wireless sensor systems) concept is applied to implement an uninterrupted solar energy surveillance system in this work. The completed system is comprised of three major sub-systems that include a charging sub-system, a control sub-system and a display sub-system. Based on several transmission standards, including Bluetooth, Wifi and Zigbee capability combined with wireless transmission techniques, the proposed surveillance system is designed for monitoring a solar energy system. The performance of the simulated WSSs is evaluated using statistical report results. The proposed surveillance system can be fully extended to several different kinds of applications, such as, health care and environmental inspection. The experimental measurement results significantly show that channel fading over the propagation channel dominates the developed system performance.

An algorithm for enhancing the throughput of CAS (Cooperation-Aware Scheme) over a WSN (wireless sensor network) is presented in this article. The WSNs are classified as a task-oriented framework due to the fact that the power supply, memory and computing power of wireless sensor nodes are greatly hampered in WSNs. This study focused on exploring problems caused by traffic congestion on the WSNs with a large amount of flow, such as packet loss, bandwidth reduction, and waste of energy on the sensor nodes. On the other hand, the proposed CAS is a cooperative strong node mechanism in which a threshold is preset in order to determine whether the node traffic is over or not. The privilege of corresponding sensor nodes is upgraded when the load exceeds the threshold. Therefore, the sensor node can command its child nodes to change the transmission path for distributing the traffic effectively. Moreover, once the traffic is over the overall network flow threshold, it is necessary to add the other new sensor nodes into the network for relieving the traffic.

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In this article the MGF (moment generating function) obtained to determine the ergodic (average) channel capacity, and which is by passing the difficulty in calculating the pdf (probability density function) of SNR (signal-to-noise ratio) traditionally. Some numerical results are offered for validating the accuracy the theoretical deriver formulas. Furthermore, many plots work out from combination with different number of transmitter and receiver for comparison. It is valuable to note that the more the antenna numbers the larger channel capacity is not acceptable when the correlation coefficient is taken into account the system evaluation of a MIMO system.