Mobile learning provides learners with the capability to assimilate courses anywhere in any time. However, most of the existing Mobile learning systems only allows learners to passively receive knowledge without considering the temporal and spatial information of the learners. The paper proposed a context-aware mobile augmented reality learning platform, called Historical Time Tunnel(HTT), as scaffolding platform for outdoor field learning. The location-based nature of Augmented Reality(AR) technology, AR-based Mobile learning systems could enable learners to acquire the historical interactive knowledge that are related to the specific geographical location. Through such direct interaction with location-based information provided by AR-based M-learning technology, the knowledge acquisition efficiency can be significantly boosted. The designed HTT system not only focuses on student learning management module but also provides teaching material management module for teachers. The conducted experiments on the elementary school student successfully prove that HTT system is an effective scaffolding tool for the novice teachers as well as a helpful assistant to the experienced teachers.

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This paper investigates how to efficiently charge sensor nodes in a wireless rechargeable sensor network (WRSN) with radio frequency (RF) chargers to make the network sustainable. An RF charger is assumed to be equipped with a uniform circular array (UCA) of 12 antennas with the radius λ, where λ is the RF wavelength. The UCA can steer most RF energy in a target direction to charge a specific WRSN node by the beamforming technology. Two evolutionary algorithms (EAs) using the evolution strategy (ES), namely the Evolutionary Beamforming Optimization (EBO) algorithm and the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, are proposed to nearly optimize the power ratio of the UCA beamforming peak side lobe (PSL) and the main lobe (ML) aimed at the given target direction. The proposed algorithms are simulated for performance evaluation and are compared with a related algorithm, called Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore), to show their superiority.