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Advancement in technology causes the rise in smart systems. City authorities want to make their cities smarter by making an intelligent decision at real time without the involvement of humans. Monitoring and controlling city traffic is one of the major challenges faced by the authorities. These days, city traffic is monitored by static network cameras deployed on few places of highways. Most of the vehicles are also equipped with cameras to store the videos as a black box. However, monitoring and controlling city traffic by using these thousands of cameras produce an overwhelming amount of high-speed videos, which is challenging to process at real time. Therefore, in this paper, we proposed a system to control city traffic by identifying illegal traffic behaviour, such as illegal U-turn, through continuous monitoring of city traffic. The continuous city traffic is monitored by the network static cameras placed on the road as well as by all the vehicles' cameras. An architecture is proposed to handle high-speed vast volume of real-time videos efficiently. For that, the two-level parallelism is achieved with the combination of Hadoop and graphics processing unit (GPU) while processing each frame using parallel environment of Hadoop and each block of a frame using GPU. MapReduce Hadoop programming paradigm is not suitable for real-time processing. Thus, we proposed a parameter calculation algorithm that is equivalent to MapReduce mechanism for image processing while dividing the images/frames into fixed-size blocks. We analyzed the city road traffic, which is collected by static cameras placed on various roads and also by vehicles' cameras while running on the road. Later, the illegal traffic behaviour are recognized, e.g. illegal U-turn, drunken drive, zig-zag drive, over-speed, etc. Finally, the efficiency of the designed system and algorithms are tested by considering the overall running time and system's throughput with respect to video duration as well as the number of frames. The findings indicate that the proposed architecture with GPU-based algorithm over the Hadoop system perform extraordinary. [ABSTRACT FROM AUTHOR]

Palladium (Pd) is an emerging eco-toxic pollutant from vehicle catalytic converters, emitted worldwide for more than two decades. Nowadays, the spatial extent of Pd fallout is growing along roads, but its subsequent fate in neighboring terrestrial ecosystems has not been extensively addressed yet. Two sites representative of contrasted natural environments (field, forest) but located under similar ambient conditions were selected to isolate and analyze the specific impact of vehicular Pd, along highway A71, France. Pd impregnation was assessed along 200-m-long transects perpendicular to the highway. Contents were measured in soils, earthworms, plant communities of the right of way (ROW), and the neighboring field (crop weeds), as well as in a moss, and bramble and ivy leaves in the forest. The direct impact of Pd fallouts appears to be confined in the grassy verge of the highway: ROW soils ([Pd] = 52-65 ng g), earthworms ([Pd] = 18-38 ng g), and plant community ([Pd] = 10-23 ng g). Pd footprint is pointed out by the accumulation index calculated for earthworms and plant communities even though transfer coefficients indicate the absence of bioaccumulation (TCs < 1). An indirect longer range transfer of Pd is identified, induced by hydric transport of organic matter. [ABSTRACT FROM AUTHOR]

A high-quality ecosystem plan may signify a milestone in the development of smart living. The Internet of Things (IoT) is the key to establishing a high quality ecosystem. This study developed an IP Multimedia Subsystem IoT architecture for ecosystem applications. The proposed traffic grouping mechanism will group dynamically attach request signaling by agent-based operations to reduce network congestion possibility and to enhance system Service Level Agreement. The Electronic Product Code Information Services platform is implemented and evaluated in a field trial. Performance analyses of the proposed architecture confirm that it can achieve a high-quality ecosystem in terms of CPU utilization and platform performance optimization. [ABSTRACT FROM AUTHOR]