Social networking has become one of the most popular communication tools to have evolved over the past decade, making it a powerful new information sharing resource in society. To date realising the potential of Social Networking Sites (SNSs) beyond their leisure uses has been severely restricted in a number of areas. This paper focuses on the application of SNSs in a learning environment and the impact this could have on academic practices. While undoubtedly, due to the very casual nature of social networking, there are serious concerns over how it could be integrated in a learning environment; the potential positive outcomes are many and varied. As a communication tool, its effectiveness is already manifesting in the millions who use these networks to communicate on a daily basis. So it is conceivable that educators should be able to create a learnscape - an environment for formal and informal learning - that adheres to educational guidelines, but also harnesses the social support system of these on-line communities. This paper examines the risks involved in the creation of this new learning ecology, and explores the challenges faced by both technology experts and teachers in delivering a truly innovative and effective new approach to education.

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Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

The image processing is the technique which is applied to process the digital information stored in the form of images. The edge detection is the technique of image processing which detect the points at which the image properties changed at steady rate. In this paper, the bee colony based edge detection technique is proposed which is the enhanced version of the existing edge detection technique based on ant colony optimization. The proposed technique is implemented in MATLAB and it is been analyzed that it performs well in terms of accuracy and execution time.

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