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Abstract-Micro-climate monitoring usually requires deploying a large number of measurement tools. By adopting vehicular wireless sensor networks (VSNs), we can use fewer tools to achieve fine-grained monitoring. This work proposes a VSN architecture to realize micro-climate monitoring based on GSM short messages and availability of GPS receivers on vehicles. We demonstrate our prototype of a ZigBee-based car network to monitor the concentration of carbon dioxide (CO 2 ) gas in areas of interest. The reported data are sent to a server, which is integrated with Google Maps as our user interface. Since mobility of these vehicles is not controllable and sending short messages incurs charges, we also design an on-demand approach to adjust vehicles' reporting rates to balance between the micro-climate accuracy and the communication cost.

Summary Because static pricing models (such as flat‐rate or tiered‐rate models) cannot improve user utility for subscribers and ease network congestion for operators during peak time, Smart Data Pricing has become an important incentive for mobile data markets. Paris Metro Pricing (PMP), which is a static pricing mode inspired by the pricing model for the Paris metro system, uses differentiated prices to motivate users to choose different train classes. Before choosing a class, people will consider their expected quality of service versus the prices that they are willing to pay. Even though PMP cannot guarantee the actual quality of service during service time, a balance between users' utilities and operators' revenue is achieved. In this paper, we propose an adaptive PMP scheme, so‐called APMP, which determines the dynamic access prices of different classes for the next 24 h. The accessible prices should try to increase the revenue while operators can serve more subscribers. Our simulation results show that APMP can significantly improve total revenue and average revenue per user for the operator. Copyright © 2016 John Wiley & Sons, Ltd.

Recent study has shown that the energy gain of a solar panel will be significantly influenced by the incidence angle of the sun radiation. The main goal of this paper is to present a sensor-based sun-tracking system. First, we design a sensing tower for measuring the direction of sun radiation. Then we design a sun-tracking algorithm, which adopts an iterative process to identify the direction of sun radiation to the earth's surface. This information is then passed to the nearby solar panels to adjust their angles. We have also developed a prototype and conducted several simulations to verify our suntracking system. Simulation results show that our system has better performance on the solar energy generation than existing ones.

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