Wireless Networking for Vehicle to Infrastructure Communication and Automatic Incident Detection

Vehicular wireless communication has recently generated wide interest in the area of wireless network research. Automatic Incident Detection (AID), which is the recent focus of research direction in Intelligent Transportation System (ITS), aims to increase road safety. These advances in technology enable traffic systems to use data collected from vehicles on the road to detect incidents. We develop an automatic incident detection method that has a significant active road safety application for alerting drivers about incidents and congestion. Our method for detecting traffic incidents in a highway scenario is based on the use of distance and time for changing lanes along with the vehicle speed change over time. Numerical results obtained from simulating our automatic incident detection technique suggest that our incident detection rate is higher than that of other techniques such as integrated technique. probabilistic technique and California Algorithm. We also propose a technique to maximize the number of vehicles aware of Road Side Units (RSUs) in order to enhance the accuracy of our AID technique. In our proposed Method. IEEE 802.11 standard is used at RSUs with multiple antennas to assign each lane a specific channel. To validate our proposed approach. we present both analytical and simulation scenarios. The empirical values which are obtained from both analytical and simulation results have been compared to show their consistency. Results indicate that the IEEE 802.11 standard with its beaconing mechanism can be successfully used for Vehicle to Infrastructure (V2I) communications

On the use of sniffers for spectrum occupancy measurements of Bluetooth low energy primary channels

The methods usually employed to measure channel occupancy show limitations in the context of Bluetooth Low Energy (BLE) advertisements. We propose and analyze the use of BLE sniffers as light and portable low-cost spectrum occupancy meters to be used in scenarios where real time signal analyzers are not adequate. For the measurement technique to be successful, several low-level effects must be considered. The paper argues about on-air time, receiving blind times due to processing and intra system interference, buffer saturation and frequency anchoring. Hence, a compensation procedure based on collision rate estimation is proposed. Results with the refined method show that occupancies of 40% can be measured with an overestimation error whose percentile 95% is 5 percentage points. This is reduced to 1.9 points when the occupancy is 15%. The sniffers perform in real time and are shown to correctly track short term load variations. The strategy has been successfully used to characterize occupancy in highly variable and loaded scenarios such as subway platforms and a shopping mall. Values up to 25% have been observed, which implies a relevant packet error rate. Hence, the tool can be used to make agile audits and configure the parameters that control communication redundancy in new or existing networks.The work by UPC has been funded by MCIN/ AEI /10.13039/501100011033 and by ERDF A way of making Europe, with the grant RTI2018-099880-B-C32 and PID2021-125799OA-I00. The work by I3A-UZ has been funded by MCIN/ AEI /10.13039/501100011033 and by ERDF A way of making Europe, with the grants RTI2018-095684-B-I00 and RTI2018-099063-B-I00, and by the Government of Aragon (Reference Group T31 20R).Peer ReviewedPostprint (published version

On the use of sniffers for spectrum occupancy measurements of Bluetooth low energy primary channels

The methods usually employed to measure channel occupancy show limitations in the context of Bluetooth Low Energy (BLE) advertisements. We propose and analyze the use of BLE sniffers as light and portable low-cost spectrum occupancy meters to be used in scenarios where real time signal analyzers are not adequate. For the measurement technique to be successful, several low-level effects must be considered. The paper argues about on-air time, receiving blind times due to processing and intra system interference, buffer saturation and frequency anchoring. Hence, a compensation procedure based on collision rate estimation is proposed. Results with the refined method show that occupancies of 40% can be measured with an overestimation error whose percentile 95% is 5 percentage points. This is reduced to 1.9 points when the occupancy is 15%. The sniffers perform in real time and are shown to correctly track short term load variations. The strategy has been successfully used to characterize occupancy in highly variable and loaded scenarios such as subway platforms and a shopping mall. Values up to 25% have been observed, which implies a relevant packet error rate. Hence, the tool can be used to make agile audits and configure the parameters that control communication redundancy in new or existing networks

Achieving multi-user capabilities through an indoor positioning system based on BLE beacons

The multiple user challenge is one of the issues that need to be addressed in order to facilitate the adoption of intelligent environments in everyday activities. The development of multi-user capabilities in smart homes is closely related to the creation of effective indoor positioning systems. This research work reports on the development and evaluation of an indoor positioning system that allows multi-user management in a smart home environment. The design of the BLE based system is presented, as well as its implementation and evaluation in the Smart Spaces Lab at Middlesex University. The validation of the system is shown as a case study in which it is used to develop multi-user capabilities in two context-aware systems of the laboratory. Video demonstrations are provided to illustrate the multi-user capabilities that were developed in the validation

Multi-User Low Intrusive Occupancy Detection

Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach

Building Decentralized Fog Computing-Based Smart Parking Systems: From Deterministic Propagation Modeling to Practical Deployment

[Abstract] The traditional process of finding a vacant parking slot is often inefficient: it increases driving time, traffic congestion, fuel consumption and exhaust emissions. To address such problems, smart parking systems have been proposed to help drivers to find available parking slots faster using latest sensing and communications technologies. However, the deployment of the communications infrastructure of a smart parking is not straightforward due to multiple factors that may affect wireless propagation. Moreover, a smart parking system needs to provide not only accurate information on available spots, but also fast responses while guaranteeing the system availability even in the case of lacking connectivity. This article describes the development of a decentralized low-latency smart parking system: from its conception, design and theoretical simulation, to its empirical validation. Thus, this work first characterizes a real-world scenario and proposes a fog computing and Internet of Things (IoT) based communications architecture to provide smart parking services. Next, a thorough analysis on the wireless channel properties is carried out by means of an in-house developed deterministic 3D-Ray Launching (3D-RL) tool. The obtained results are validated through a real-world measurement campaign and then the communications architecture is implemented by using ZigBee sensor nodes. The implemented architecture also makes use of Bluetooth Low Energy beacons, an Android app, a decentralized database and fog computing gateways, whose performance is evaluated in terms of response latency and processing rate. Results show that the proposed system is able to deliver information to the drivers fast, with no need for relying on remote servers. As a consequence, the presented development methodology and communications evaluation tool can be useful for future smart parking developers, which can determine the optimal locations of the wireless transceivers during the simulation stage and then deploy a system that can provide fast responses and decentralized services.Xunta de Galicia; ED431G2019/01Agencia Estatal de Investigación of Spain; TEC2016-75067-C4-1-RAgencia Estatal de Investigación of Spain; RED2018-102668-TAgencia Estatal de Investigación of Spain; PID2019-104958RB-C42Ministerio de Ciencia, Innovación y Universidades; RTI2018-095499-B-C3

Low-Power Wireless for the Internet of Things: Standards and Applications: Internet of Things, IEEE 802.15.4, Bluetooth, Physical layer, Medium Access Control,coexistence, mesh networking, cyber-physical systems, WSN, M2M

International audienceThe proliferation of embedded systems, wireless technologies, and Internet protocols have enabled the Internet of Things (IoT) to bridge the gap between the virtual and physical world through enabling the monitoring and actuation of the physical world controlled by data processing systems. Wireless technologies, despite their offered convenience, flexibility, low cost, and mobility pose unique challenges such as fading, interference, energy, and security, which must be carefully addressed when using resource-constrained IoT devices. To this end, the efforts of the research community have led to the standardization of several wireless technologies for various types of application domains depending on factors such as reliability, latency, scalability, and energy efficiency. In this paper, we first overview these standard wireless technologies, and we specifically study the MAC and physical layer technologies proposed to address the requirements and challenges of wireless communications. Furthermore, we explain the use of these standards in various application domains, such as smart homes, smart healthcare, industrial automation, and smart cities, and discuss their suitability in satisfying the requirements of these applications. In addition to proposing guidelines to weigh the pros and cons of each standard for an application at hand, we also examine what new strategies can be exploited to overcome existing challenges and support emerging IoT applications

Detecting User’s Behavior Shift with Sensorized Shoes and Stigmergic Perceptrons

As populations become increasingly aged, health monitoring has gained increasing importance. Recent advances in engineering of sensing, processing and artificial learning, make the development of non-invasive systems able to observe changes over time possible. In this context, the Ki-Foot project aims at developing a sensorized shoe and a machine learning architecture based on computational stigmergy to detect small variations in subjects gait and to learn and detect users behaviour shift. This paper outlines the challenges in the field and summarizes the proposed approach. The machine learning architecture has been developed and publicly released after early experimentation, in order to foster its application on real environments