Natural and manmade disasters such as earthquakes, floods, unprecedented rainfall, etc. pose several threats to our society. The citizens upload disaster information in the form of multimedia content such as pictures, audio, and videos. Efficient information and communication framework are critical for disaster management. Mobile Adhoc Networks (MANET) have been used effectively for disaster management. However, Disaster management prerequisites following Quality of Service (QoS) requirements such as bandwidth, high delivery ratio, low overhead, and minimal latency; however, the existing data transmission scheme induces high latency and overhead among intermediate devices; In order to meet the QoS requirement of disaster management applications in this paper, High Delivery Efficiency and Low Latency Multimedia Content Transmission (HDELL-MCT) scheme for MANETs is presented. Then, an improved buffer management scheme is presented for meeting disaster management performance and latency prerequisites. The experiment is conducted using ONE Simulator, the outcome shows the HDELL-MCT scheme achieves very good performance considering different QoS metrics such as improving delivery ratio by 38.02%, reducing latency by 7.53% and minimizing hop communication overhead by 65.1% in comparison with existing multimedia content transmission model.

Despite the technical changes and enormous day by day upgradiation in the field of mobile computing the smart devices as well as IOT devices had experienced tremendous technical glitch, which narrow’s the life span and survivability of small scale processing devices. Today, end users are becoming more demanding and are expecting to run computational intensive tasks on their Smart phone devices and IOT devices. Therefore, virtual cloud computing (VCC) integrates local device computing and Cloud Computing (CC) in order to extend computational capabilities of smart phone devices and IOT devices using cloud offloading techniques. Computation Offloading tackles limitations of Smart phone devices and IOT devices such as limited battery duration, limited computational capabilities, and limited storage capacity by offloading the execution and workload to cloud which has better systems with better computation and storage capabilities. This paper aims to present the techniques to offload computational intensive tasks to cloud framework and analyses them along with traditional local execution techniques and their issues. Furthermore, it explores other important parameters based on which the applications are implemented such as offloading technique and partitioning of tasks.

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Communication is the fundamental channel to share thoughts. As of late the hard of hearing, stupid and visually impaired unfortunate casualties expanded. Since almost totally senseless can't speak with ordinary individual. New situation is the place the idiotic, hard of hearing speak with visually impaired individuals. Signal acknowledgment is the scientific explanation of a human movement by a registering gadget. Gesture based communication give best correspondence stage to the consultation hindered and moronic individual to speak with ordinary individual. The Deaf and dumb use hand motions to convey though dazzle individuals can hear just the voice and correspondence through voice. So change of hand signals to voice yield is the arrangement. So as to draw a stage nearer to these objective applications we use KNN calculation with Deep learning and Tensor flow method. The thoughts comprised of structuring and actualize a framework utilizing man-made brainpower, picture preparing and information mining ideas to accept contribution as hand signals and produce unmistakable yields as content and voice with 95% accuracy and above through proposed work.

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