Traffic congestion is a major problem in many cities. It happens due to the demand-supply imbalance in the transportation network and poor management. Traffic flow slows down when the number of vehicles that travels on the road increases or the roadway capacity decreases due to various reasons. In order to solve this problem, different solutions are proposed to provide reliable, real-time transport management services in an Intelligent Transportation System (ITS). In this paper, we propose a novel real-time video analytics using low-cost IoT devices and LoRaWAN networks to realize new services and applications that include traffic management through IoT edge computing. The use of LoRaWAN for such application is our main contribution. We retrain YOLO v3 object detection machine learning model (transfer learning) for vehicle detection and counting, to make it lightweight and fast enough to be able to run on a Raspberry Pi, a single-board computer with limited RAM. The edge node, with low-cost smart camera and connectivity through LoRaWAN networks counts the number of vehicles using real-time video analytic and report only traffic count to the server. This experimental work provides insight into the applicability of a low-cost IoT system to traffic management with a resource-constrained environment. Real-world video analysis of vehicle detection and counting show the effectiveness of the designed solution. The results demonstrate the effectiveness of the proposed approach.

In unstructured networks, gossiping protocols prescribe that a message, received by a node, is not forwarded with certainty to all its neighbors (as happens in flooding protocols), but only with some finite probability. Typically, in order to make the protocol adaptive, the packet forwarding probability is computed on the fly, as a function of the current node's degree, taken as an indicator of the node's capability of propagating information. This approach improves over flooding protocols, by reducing the traffic overhead, however it is plagued by a problem called early gossip termination, which occurs when the information propagation stops, before the message has reached all the intended target nodes. In the present work, we argue that early gossip termination takes place because the node degree is an inaccurate estimator of the information propagation capability of the node. Specifically: in those cases where this capability happens to be overestimated, the forwarding probability gets set to an insufficient value, which causes information propagation to fade out too early. We propose an approach relying on two interrelated prescriptions, which, being based on the local topological structure of the network, help in better quantifying the information propagation capability of a node. The first prescription consists in using, in place of the actual node degree, an effective node degree, defined on the basis of the nearest neighbors' degree information. The second prescription consists in taking into account the node clustering coefficient to identify special topological situations, which deserve specific treatment. We validated our approach by simulation, using ns2 in several operational scenarios: the results show that the approach can yield significant performance improvements.

This paper presents our latency hiding framework for access to big data in a constrained digital ecosystem with application to digital medical archives. Aiming to enhance ubiquitous access of big data such as patient-oriented access of medical archives, we apply complex/multi-context prefetching to reduce latency thereby improving response time. We propose a formal model for prefetch requests rate and network workload or stress bound that takes into account a diverse set of constraints a digital ecosystem could be in. In addition to that, components of our latency hiding framework such as a generic multi-context functional architecture, use case model, medical database model with emphasis on API (abstracted patient information) and a high-level system architecture have been designed. The development of a complex or multi-context prefetch algorithm that uses a patient's chief complaints, slackness sensitivity, popular content tag, user specified contexts and constraints is underway. A prototype system will also be developed to validate the proposed solutions. Moreover, input and output metrics will be developed to gauge the efficiency and effectiveness of the prefetch algorithm under development.

Video adaptation is an active research area aiming at delivering heterogeneous content to yet heterogeneous devices under different network conditions. This paper presents an architecture for generating variations (different versions) from methods such as video transcoding, media conversion and summarization. The work in this paper concentrates on video data and aims to show video variation supported with metadata as an approach to adaptation to enable ubiquitous access. By video products are defined and the variation factory is introduced It generates different versions of the source and an MPEG-7 metadata document. The information contained in the metadata document helps the system to identify the most appropriate version that meets the required quality of service (QoS). In addition to the implementation of the commonly used reduction methods, two novel methods, viz. object-based and segment-based variations are introduced. Our proposals are implemented and experimentally validated.

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This work introduces a framework for adapting MPEG-4 intra- and inter-Elementary Streams and for encoding the results in an MPEG-7 stream to be used for resource adaptation on the delivery path to the user.