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Traffic surveillance is an important issue for Intelligent Transportation Systems (ITS) that helps detect incidents automatically, such as wrong-way drivers, still-standing vehicles and jams. Sometimes these systems require a fast and short-term deployment of video cameras. In these cases, ad-hoc networks could be a low-cost and feasible option, but they have poor performance for video delivery. Thus, we propose a smart live video adaptive streaming technique in order to transport video streams from the cameras to the external road traffic monitoring servers. To achieve this goal, these networks need a thorough study in order to analyze video quality under their inherent constraints, given by their specific topologies (once deployed), video streaming protocol, nodes’ performance and routing protocols. From our results both in simulated and real scenarios, we demonstrate the feasibility of our proposal in these specific scenarios, validated with several performance metrics in terms of Quality of Service and video quality.

Soundscape standard (ISO 12913) is mainly oriented to describe the psychoacoustic annoyance (PA) due to the perceived sound in different environments. The evaluation of this annoyance is commonly based on the Zwicker and Fastl model that defines several components related to this subjective annoyance, such as loudness, sharpness, roughness, and fluctuation strength. But due to their complexity, these components are difficult to be calculated on small board computers (SBCs) in real time in order to enable a wireless acoustic sensor network for PA monitoring. In this article, we describe the necessary procedures to implement the complete psychoacoustic model by Zwicker and Fastl in a precise and efficient way, keeping the standards and criteria defined for each model, adding hints to improve its performance. We evaluate their execution time on different platforms and languages, including SBCs, in particular on Raspberry Pi family. Thus, this article shows a feasible implementation of this monitoring approach on low-cost platforms as well as a visualization tool.

The proliferation of commercial low-cost Small Board Computers (SBC) devices have allowed the deployment of many Wireless Sensor Networks (WSN) focused on different applications, mainly based on monitoring issues. These networks are characterized by a set of these SBCs devices working in a collaborative way where each device is sensing, processing and later sending out the data to the sink. These devices are equipped with power supply, a processing unit and communications capabilities (in particular WiFi), making themselves very interesting to fit in many topologies. However, their performance in terms of communications basically depends on the environment and usually heuristic techniques are used with any guarantee if they will succeed or not. To model the different scenarios there are theoretically several approaches, such as ray tracing technique, two-ray, as well as semi-empirical models, but it should be interesting to analyze them also from a experimental point of view. In this paper we carry out a thorough performance analysis, in particular using Raspberry Pi3 (RPi3) devices, in order to understand their behavior in terms of Quality of Services (throughput, delay, losses and jitter (delay variation) for different types of communications: indoor and outdoor environments, with and without multipath reflections in order to establish precisely the amount of signal required depending on the required througput. In the outdoor environments, we analyze the QoS over the links between two nodes in a deployment.We conclude that using this type of devices we can achieve a throughput of more than 45 Mbps in indoor within distances shorter than 50 m and 40 Mbps in outdoor till 100 m and then it starts decaying till 300 m in normal conditions.

The Internet of Things (IoT) is a network widely used with the purpose of connecting almost everything, everywhere to the Internet. To cope with this goal, low cost nodes are being used, otherwise, it would be very expensive to expand so fast. These networks are set up with small distributed devices (nodes) that have a power supply, processing unit, memory, sensors, and wireless communications. In the market, we can find different alternatives for these devices, such as small board computers (SBCs), e.g., Raspberry Pi (RPi)), with different features. Usually these devices run a coarse version of a Linux operating system. Nevertheless, there are many scenarios that require enhanced computational power that these nodes alone are unable to provide. In this context, we need to introduce a kind of collaboration among the devices to overcome their constraints. We based our solution in a combination of clustering techniques (building a mesh network using their wireless capabilities), at the same time we try to orchestrate the resources in order to improve their processing capabilities in an elastic computing fashion. This paradigm is called fog computing on IoT. We propose in this paper the use of cloud computing technologies, such as Linux containers, based on Docker, and a container orchestration platform (COP) to run on the top of a cluster of these nodes, but adapted to the fog computing paradigm. Notice that these technologies are open source and developed for Linux operating system. As an example, in our results we show an IoT application for soundscape monitoring as a proof of concept that it will allow us to compare different alternatives in its design and implementation, in particular, with regard to the COP selection, between Docker Swarm and Kubernetes. We conclude that using and combining these techniques, we can improve the overall computation capabilities of these IoT nodes within a fog computing paradigm.

Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. This paper proposes the use of a deep convolutional neural network (CNN) trained on a large urban sound dataset capable of efficiently predicting psycho-acoustic annoyance from raw audio signals continuously. We evaluate the proposed regression model and compare the resulting computation times with the ones obtained by the conventional direct calculation approach. The results confirm that the proposed model based on CNN achieves high precision in predicting psycho-acoustic annoyance, predicting annoyance values with an average quadratic error of around 3%. It also achieves a very significant reduction in processing time, which is up to 300 times faster than direct calculation, making CNN designed a clear exponent to work in IoT devices.

In the last decade there has been an increasing interest and demand on the Internet of Things (IoT) and its applications. But, when a high level of computing and/or real time processing is required for these applications, different problems arise due to their requirements. In this context, low cost autonomous and distributed Small Board Computers (SBC) devices, with processing, storage capabilities and wireless communications can assist these IoT networks. Usually, these SBC devices run an operating system based on Linux. In this scenario, container-based technologies and fog computing are an interesting approach and both have led to a new paradigm in how devices cooperate, improving overall capacity in a cluster of these SBC devices. The use of containers is considered a lightweight virtualization, allowing an application to be broken into small tasks as services, enabling load balancing, flexibility and scalability. Nevertheless when the number of devices and containers increases in the cluster, it is required an orchestration layer. There are not many solutions and available alternatives using these technologies applied on these networks, and less an assessment of their performances. This paper focuses on these technologies when we use fog computing with low cost SBC devices in a context of IoT. We use Linux containers and different available orchestration platforms (in particular Docker Swarm and Kubernetes), to run on the top of the cluster of commercial SBC devices. Thus, we carry out a thorough functional and performance comparison with different real topologies (wired and wireless) and using both homogeneous and heterogeneous clusters of SBC devices, showing their results. We conclude that with the collected experimental results, Docker Swarm orchestration platform outperforms its counterparts in the scenarios shown.