Sensor networking is a promising technology that facilitates the monitoring of the physical world using tiny, inexpensive wireless devices that are spatially distributed across a wide region. These networks are highly constrained in power, computational capacities and memory. Incorporation of techniques based on the concept of Compressed Sensing (CS) which aims to encode sparse signals using a much lower sampling rate than the traditional Nyquist approach has revolutionized the wireless network scenarios. An exhaustive survey on the impact and applications of CS in WSN and research challenges has been presented in this paper.

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Cloud computing is a model where traditional resources such as CPU cycles, storage, security etc. are delivered through web based. It is a technology which has ability to change large part of software development cycle, 3D rendering or any other computationally expensive tasks execution. Much amount of time is wasted on compiling and rendering such computationally expensive tasks due to low power machines, which directly proportional to efficiency of user who is working on that project. Extreme computational tasks such as weather forecast, DNA analyses, encryption breaking takes so much time in consumer grade computing devices that they are realistically not possible to perform. We have proposed a novel approach to perform payload distribution, for the users who wanted to run their computationally expensive tasks efficiently. We have used virtualization technique on data center resources to perform scheduling. Up to 32% cost has been reduced in an environment of 30 users when our technology used instead of traditional standalone desktop environment. This is achieved by replacing 30 standalone computers with a powerful server and thin clients like Raspberry pi as clients. Time wasted in computational task such as rendering and compiling is greatly reduced. We have not only improved the efficiency, but also make sure both cloud producer and consumer are favorable. With simulations and outcomes, we validate that our methodology for payload distribution performs well.

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