Abstract-In this work, we evaluate the performance of 802.11p-based vehicular communications in the presence of RF jamming attacks. Specifically, we characterize the transmission success rate of a car-to-car link subject to constant, periodic, and reactive RF jamming. First, we conduct extensive measurements in an anechoic chamber, where we study the benefits of built-in techniques for interference mitigation. In addition, we identify that the periodic transmission of preamblelike jamming signals can hinder successful communication despite being up to five orders of magnitude weaker than the signal of interest. We further provide the rationale behind this remarkably high jammer effectiveness. Additionally, we quantify the impact of reaction delay and interference signal length on the effectiveness of the reactive jammer. Next, by means of outdoor measurements, we evaluate the suitability of the indoor measurements for being used as a model to characterize the performance of car-to-car communications in the presence of RF jamming. Finally, we conduct outdoor measurements emulating a vehicular platoon and study the threats that RF jamming poses to this VANET application. We observe that constant, periodic, but also reactive jammer can hinder communication over large propagation areas, which would threaten road safety.
Machine-to-Machine (M2M) communications enable networked devices and services to exchange information and perform actions seamlessly without the need for human intervention. They are viewed as a key enabler of the Internet of Things (IoT) and ubiquitous applications, like mobile healthcare, telemetry, or intelligent transport systems. We survey existing work on mobile M2M communications, we identify open challenges that have a direct impact on performance and resource usage efficiency, especially the impact on energy efficiency, and we review techniques to improve communications. We review the ETSI standard and application protocols, and draw considerations on the impact of their use in constrained mobile devices. Nowadays, smartphones are equipped with a wide range of embedded sensors, with varied local and wide area connectivity capabilities, and thus they offer a unique opportunity to serve as mobile gateways for other more constrained devices with local connectivity. At the same time, they can gather context data about users and environment from the embedded sensors. These capabilities may be crucial for mobile M2M applications. Finally, in this paper, we consider a scenario where smartphones are used as gateways that collect and aggregate data from sensors in a cellular network. We conclude that, in order for their use to the feasible in terms of a normal depletion time of a smartphone's battery, it is a good advice to maximize the collection of data necessary to be transmitted from nearby sensors, and maximize the intervals between transmissions. More research is required to devise energy efficient transmission methods that enable the use of smartphones as mobile gateways.
Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modeling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on this Business Analytics usage, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analyzed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modeling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.
Due to multiple reasons, emergency wards can become overloaded with patients, some of which can be in critical health conditions. To improve the emergency service and avoid deaths and serious adverse events that could be potentially prevented, it is mandatory to do a continuous monitoring of patients physiological parameters. This is a good fit for Internet of Things (IoT) technology, but the scenario imposes hard constraints on autonomy, connectivity, interoperability, and delay. In this paper, we propose a full Internet-based architecture using open protocols from the wearable sensors up to the monitoring system. Particularly, we use low-cost and low-power WiFi-enabled wearable physiological sensors that connect directly to the Internet infrastructure and run open communication protocols, namely, oneM2M. At the upper end, our architecture relies on openEHR for data semantics, storage, and monitoring. Overall, we show the feasibility of our open IoT architecture exhibiting 20–50 ms end-to-end latency and 30–50 h sensor autonomy at a fraction of the cost of current non-interoperable vertical solutions.
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