<p>Today Internet of things (IoT) interconnects any object possessing sensing and computing capabilities to the internet. In this era, increasing number of electronic devices and applications in Internet of Things (IoT) requires secured communication with low power consumption capabilities. As security is a major challenge in internet of things, it is important to design a key management solution that considers resource constrained nodes and hence key management in public key cryptography is a crucial issue. In this paper, a novel key exchange algorithm was developed and implemented on a low powered “Raspberry pi machine” to realize the overall impact it creates on the device. The performance of the proposed algorithm had shown a great improvement over the popular Diffie Hellman key exchange algorithm and a two-level security for data exchange between the parties is implemented.</p>

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The usage of transportation systems is inevitable; any assistance module which can catalyze the flow involved in transportation systems, parallelly improving the reliability of processes involved is a boon for day-today human lives. This paper introduces a novel, cost-effective, and highly responsive Post-active Driving Assistance System, which is "Adaptive-Mask-Modelling Driving Assistance System" with intuitive wide field-of-view modeling architecture. The proposed system is a vision-based approach, which processes a panoramic-front view (stitched from temporal synchronous left, right stereo camera feed) & simple monocular-rear view to generate robust & reliable proximity triggers along with co-relative navigation suggestions. The proposed system generates robust objects, adaptive field-of-view masks using FRCNN+Resnet-101_FPN, DSED neural-networks, and are later processed and mutually analyzed at respective stages to trigger proximity alerts and frame reliable navigation suggestions. The proposed DSED network is an Encoder-Decoder-Convolutional-Neural-Network to estimate lane-offset parameters which are responsible for adaptive modeling of field-of-view range(157 0-210 0) during live inference. Proposed stages, deep-neural-networks, and implemented algorithms, modules are state-of-the-art and achieved outstanding performance with minimal loss(L{p, t}, L δ , L T otal) values during benchmarking analysis on our custom-built, KITTI, MS-COCO, Pascal-VOC, Make-3D datasets. The proposed assistance-system is tested on our custom-built, multiple public datasets to generalize its reliability and robustness under multiple wild conditions, input traffic scenarios & locations. INDEX TERMS Adaptive field of view modeling, Automotive applications, Driving assistance systems, Lane detection and analysis, Object detection and tracking, spatial auto-correlation