Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

Industrial Internet of Things: Challenges, Opportunities, and Directions

Over the coming decades, high-definition situationally-aware networks have the potential to create revolutionary applications in the social, scientific, commercial, and military sectors Ultrawide bandwidth (UWB) technology is a viable candidate for enabling accurate localization capabilities through time-of-arrival (TOA)-based ranging techniques These techniques exploit the fine delay resolution property of UWB signals by estimating the TOA of the first signal path Exploiting the full capabilities of UWB TOA estimation can be challenging, especially when operating in harsh propagation environments, since the direct path may not exist or it may not be the strongest In this paper, we first give an overview of ranging techniques together with the primary sources of TOA error (including propagation effects, clock drift, and interference) We then describe fundamental TOA bounds (such as the Cramer-Rao bound and the tighter Ziv-Zakai bound) in both ideal and multipath environments These bounds serve as useful benchmarks in assessing the performance of TOA estimation techniques We also explore practical low-complexity TOA estimation techniques and analyze their performance in the presence of multipath and interference using IEEE 802154a channel models as well as experimental data measured in indoor residential environments

/pdf/ranging-with-ultrawide-bandwidth-signals-in-multipath-illizjdvoz.pdf

Ranging With Ultrawide Bandwidth Signals in Multipath Environments

https://hal.archives-ouvertes.fr/hal-01009386/document

Energy efficiency in wireless sensor networks: A top-down survey

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

/pdf/federated-learning-for-internet-of-things-a-comprehensive-3ttswrbjdg.pdf

Federated Learning for Internet of Things: A Comprehensive Survey

Federated learning is a set-up in which multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator. This setting also allows the training data decentralized to ensure the data privacy of each device. Federated learning adheres to two major ideas: local computing and model transmission, which reduces some systematic privacy risks and costs brought by traditional centralized machine learning methods. The original data of the client is stored locally and cannot be exchanged or migrated. With the application of federated learning, each device uses local data for local training, then uploads the model to the server for aggregation, and finally the server sends the model update to the participants to achieve the learning goal. To provide a comprehensive survey and facilitate the potential research of this area, we systematically introduce the existing works of federated learning from five aspects: data partitioning, privacy mechanism, machine learning model, communication architecture and systems heterogeneity. Then, we sort out the current challenges and future research directions of federated learning. Finally, we summarize the characteristics of existing federated learning, and analyze the current practical application of federated learning.

A survey on federated learning

The performance of iterative (turbo) equalization strongly relies on the quality of the channel state information (CSI). The use of soft information for channel estimation has been largely investigated in the literature to improve the reliability of the conventional training-based estimate. In this paper we propose a new soft method for the estimation of block-fading channels based on a multi-block (MB) processing. As turbo equalization usually requires a joint processing of several data-blocks, the idea is to take advantage of this latency to improve the performance of channel estimation. The MB estimator exploits the invariance of the subspace spanned by the channel vector over a frame of blocks and it estimates the corresponding basis from different blocks. In this paper the MB method is extended to incorporate the soft information provided by the turbo equalizer. The mean square error for the soft estimate is evaluated analytically and validated by simulations. The comparison with the conventional block-by-block estimate shows the benefits of the proposed approach on the turbo equalization convergence.

A subspace method for soft estimation of block fading channels in turbo equalization

Ultra-reliable low-latency Vehicle-to-Everything (V2X) communications are needed to meet the extreme requirements of enhanced driving applications. Millimeter-Wave (24.25-52.6 GHz) or sub-THz (>100 GHz) V2X communications are a viable solution, provided that the highly collimated beams are kept aligned during vehicles' maneuverings. In this work, we propose a sensor-assisted dynamic Beamwidth and Power Control (BPC) system to counteract the detrimental effect of vehicle dynamics, exploiting data collected by on-board inertial and positioning sensors, mutually exchanged among vehicles over a parallel low-rate link, e.g., 5G New Radio (NR) Frequency Range 1 (FR1). The proposed BPC solution works on top of a sensor-aided Beam Alignment and Tracking (BAT) system, overcoming the limitations of fixed-beamwidth systems and optimizing the performance in challenging Vehicle-to-Vehicle (V2V) scenarios, even if extensions to Vehicle-to-Infrastructure (V2I) use-cases are feasible. We validate the sensor-assisted dynamic BPC on real trajectories and sensors' data collected by a dedicated experimental campaign. The goal is to show the advantages of the proposed BPC strategy in a high data-rate Line-Of-Sight (LOS) V2V context, and to outline the requirements in terms of sensors' sampling time and accuracy, along with the end-to-end latency on the control channel.

Sensor-Aided Beamwidth and Power Control for Next Generation Vehicular Communications.

Deployment and Design of Multi-Antenna WiMax Systems in a Non-Stationary Interference Environment

The spacing between adjacent receiving antennas of an ULA located in a base station of a cellular communication system is optimized to both the channel and the interference space-time multipath. The spacing is lager than the canonical λ/2 for introducing a certain degree of angular equivocation aimed to see the interferers (all or a certain number depending on the degree of freedom of the directivity function) as they were grouped together along an unique direction. In an ideal case of fixed interferers with null angular power spread, a given cellular planning and known aperture of the array, the optimal spacing Δopt between adjacent antennas is directly calculable in closed mathematical form in function of the equal angular separation Δt between the DOAs of the interferers. When the restrictive hypotheses are neglected, the optimal spacing Δopt is calculable as the spacing that minimizes the spread between the NI wave numbers associated to the barycentric DOAs of the NI interfering cells. Moreover closed form solution can be dealt with on condition that NI interfering cells (with one broadside interfering cell) are considered; said angular separation between the interferers is assumed as being the average ΔtB among adjacent angular separations between barycentric DOAs weighted by the respective barycentric received power. Assuming a multipath channel with an arbitrary number of paths Np, the ith barycentric DOA is calculated by executing a weighted average extended to the Np x S directions of arrival of the Np paths by the S points of a grid indicative of the positions spanned by the ith interfering station inside its cell, weighting each DOA by the power received on that path. In a SIMO scenario with square cell planning according to the fixed or mobile WiMAX IEEE 802.16d802.16-2004/e, the value Aopt = 1.8λ is found as an optimum trade-off between beamforming and diversity (fig.5).

Method for optimizing the spacing between receiving antennas of an array usable for counteracting both interference and fading in cellular systems

Wireless Cloud Network for Augmented Communication and Sensing in 5G Massive Industrial IoT

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