An Introduction to MultiAgent Systems.

Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

A Survey of Indoor Localization Systems and Technologies

Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors, and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between nodes. In this paper, we give an overview of cooperative localization approaches and apply them to ultrawide bandwidth (UWB) wireless networks. UWB transmission technology is particularly attractive for short- to medium-range localization, especially in GPS-denied environments: wide transmission bandwidths enable robust communication in dense multipath scenarios, and the ability to resolve subnanosecond delays results in centimeter-level distance resolution. We will describe several cooperative localization algorithms and quantify their performance, based on realistic UWB ranging models developed through an extensive measurement campaign using FCC-compliant UWB radios. We will also present a powerful localization algorithm by mapping a graphical model for statistical inference onto the network topology, which results in a net-factor graph, and by developing a suitable net-message passing schedule. The resulting algorithm (SPAWN) is fully distributed, can cope with a wide variety of scenarios, and requires little communication overhead to achieve accurate and robust localization.

Cooperative Localization in Wireless Networks

Laboratory-based courses play a critical role in scientific education. Automation is changing the nature of these laboratories, and there is a long-running debate about the value of hands-on versus simulated laboratories. In addition, the introduction of remote laboratories adds a third category to the debate. Through a review of the literature related to these labs in education, the authors draw several conclusions about the state of current research. The debate over different technologies is confounded by the use of different educational objectives as criteria for judging the laboratories: Hands-on advocates emphasize design skills, while remote lab advocates focus on conceptual understanding. We observe that the boundaries among the three labs are blurred in the sense that most laboratories are mediated by computers, and that the psychology of presence may be as important as technology. We also discuss areas for future research.

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Hands-on, simulated, and remote laboratories: A comparative literature review

We provide a comprehensive overview of mathematical models and analytical techniques for millimeter wave (mmWave) cellular systems. The two fundamental physical differences from conventional sub-6-GHz cellular systems are: 1) vulnerability to blocking and 2) the need for significant directionality at the transmitter and/or receiver, which is achieved through the use of large antenna arrays of small individual elements. We overview and compare models for both of these factors, and present a baseline analytical approach based on stochastic geometry that allows the computation of the statistical distributions of the downlink signal-to-interference-plus-noise ratio (SINR) and also the per link data rate, which depends on the SINR as well as the average load. There are many implications of the models and analysis: 1) mmWave systems are significantly more noise-limited than at sub-6 GHz for most parameter configurations; 2) initial access is much more difficult in mmWave; 3) self-backhauling is more viable than in sub-6-GHz systems, which makes ultra-dense deployments more viable, but this leads to increasingly interference-limited behavior; and 4) in sharp contrast to sub-6-GHz systems cellular operators can mutually benefit by sharing their spectrum licenses despite the uncontrolled interference that results from doing so. We conclude by outlining several important extensions of the baseline model, many of which are promising avenues for future research.

Modeling and Analyzing Millimeter Wave Cellular Systems

In this article, we evaluate the first experience of computation offloading from drones to real fifth‐generation (5G) operator systems, including commercial and private carrier‐grade 5G networks. A follow‐me drone service was implemented as a representative testbed of remote video analytics. In this application, an image of a person from a drone camera is processed at the edge, and image tracking displacements are translated into positioning commands that are sent back to the drone, so that the drone keeps the camera focused on the person at all times. The application is characterised to identify the processing and communication contributions to service delay. Then, we evaluate the latency of the application in a real non standalone 5G operator network, a standalone carrier‐grade 5G private network, and, to compare these results with previous research, a Wi‐Fi wireless local area network. We considered both multi‐access edge computing (MEC) and cloud offloading scenarios. Onboard computing was also evaluated to assess the trade‐offs with task offloading. The results determine the network configurations that are feasible for the follow‐me application use case depending on the mobility of the end user, and to what extent MEC is advantageous over a state‐of‐the‐art cloud service.

Is the edge really necessary for drone computing offloading? An experimental assessment in carrier‐grade 5G operator networks

Concerns about the effect of greenhouse gases have motivated the development of certification protocols to quantify the industrial carbon footprint (cf). These protocols are manual, work-intensive, and expensive. All of the above have led to a shift towards automatic data-driven approaches to estimate the cf, including Machine Learning (ml) solutions. Unfortunately, as in other sectors of interest, the decision-making processes involved in these solutions lack transparency from the end user’s point of view, who must blindly trust their outcomes compared to intelligible traditional manual approaches. In this research, manual and automatic methodologies for cf estimation were reviewed, taking into account their transparency limitations. This analysis led to the proposal of a new explainable ml solution for automatic cf calculations through bank transaction classification. Consideration should be given to the fact that no previous research has considered the explainability of bank transaction classification for this purpose. For classification, different ml models have been employed based on their promising performance in similar problems in the literature, such as Support Vector Machine, Random Forest, and Recursive Neural Networks. The results obtained were in the 90 % range for accuracy, precision, and recall evaluation metrics. From their decision paths, the proposed solution estimates the co2 emissions associated with bank transactions. The explainability methodology is based on an agnostic evaluation of the influence of the input terms extracted from the descriptions of transactions using locally interpretable models. The explainability terms were automatically validated using a similarity metric over the descriptions of the target categories. Conclusively, the explanation performance is satisfactory in terms of the proximity of the explanations to the associated activity sector descriptions, endorsing the trustworthiness of the process for a human operator and end users.

Francisco J. González-Castaño

Papers

Is the edge really necessary for drone computing offloading? An experimental assessment in carrier‐grade 5G operator networks

Explainable Automatic Industrial Carbon Footprint Estimation From Bank Transaction Classification Using Natural Language Processing