Radio Frequency (RF) sensing is attracting interest in research, standardization, and industry, especially for its potential in Internet of Things (IoT) applications. By leveraging the properties of the ElectroMagnetic (EM) waves used in wireless networks, RF sensing captures environmental information such as the presence and movement of people and objects, enabling passive localization and vision applications. This paper investigates the theoretical bounds on accuracy and resolution for RF sensing systems within dense networks. It employs an EM model to predict the effects of body blockage in various scenarios. To detect human movements, the paper proposes a deep graph neural network, trained on Received Signal Strength (RSS) samples generated from the EM model. These samples are structured as dense graphs, with nodes representing antennas and edges as radio links. Focusing on the problem of identifying the number of human subjects co-present in a monitored area over time, the paper analyzes the theoretical limits on the number of distinguishable subjects, exploring how these limits depend on factors such as the number of radio links, the size of the monitored area and the subjects physical dimensions. These bounds enable the prediction of the system performance during network pre-deployment stages. The paper also presents the results of an indoor case study, which demonstrate the effectiveness of the approach and confirm the model’s predictive potential in the network design stages.

Frequency-domain methods in signal reconstruction problems rely on the sparsity of many real-world objects in frequency space. Conversely, in this letter, we investigate the solution set of inverse problems that deal with the reconstruction of an infinite length, causal, finite energy discrete-time signal. Our solution is in time-domain, which is complementary to frequency-domain approaches. A theoretical investigation involving Hardy spaces enabled the derivation of a representer theorem, which expresses the solution as parsimonious superpositions of decaying shifted Heaviside functions. Furthermore, we show that the energy of this solution corresponds to the energy of the weights associated to it's representation. We numerically validate our theoretical findings by showing that our approach yields better image reconstruction quality when compared to the Least Absolute Shrinkage and Selection Operator (LASSO) method and Tikhonov regularization. For image denoising, the proposed approach is also shown to be robust against increased noise levels.

Federated learning (FL) can be used to distribute machine learning (ML) tasks across edge and Internet of Things (IoT) devices with limited resources. FL provides an alternative and much more practical solution to classical artificial intelligence (AI), which requires moving large data volumes to energy-hungry data centers. On the other hand, sustainability of FL processes should be accurately quantified as limiting energy consumption might require sacrificing accuracy. This article proposes a framework for real-time monitoring of energy and green house gas (GHG) emissions (carbon footprints) of FL systems. The framework is developed for both classical FL policies relying on the parameter server and emerging fully decentralized ones. The proposed approach considers, for the first time, the impact of ML model quantization and sparsification on the energy/carbon budget while also discussing novel gradient tracking (GT) FL strategies that are robust to data heterogeneity but require higher communication bandwidth. General guidelines for energy-efficient designs are discussed based on several case studies on real datasets. This article quantifies the energy footprint of continual FL processes that implement periodic adaptation on new data as foreseen by emerging IoT industry verticals. Results show that centralized FL is advantageous when strict carbon budgets are imposed or energy-inefficient (80%), provided the ML model compression is properly tuned.

Bayesian Federated Learning (FL) policies enable multiple nodes to collaboratively train a shared Machine Learning (ML) model while accounting for the uncertainty of its predictions. This is accomplished by estimating the global posterior distribution in the model parameter space. Currently, Bayesian FL strategies are impaired by large communication costs that need to be reduced to provide more sustainable training platforms. This letter investigates the impact of compression strategies in centralized Bayesian FL setups, where a Parameter Sever (PS) is tasked to supervise the learning process. The goal is to study how compression affects the ability of Bayesian FL systems to provide high-quality, yet well-calibrated ML models. The analysis is carried out in the healthcare domain, where the prediction reliability is particularly critical, focusing on a medical imaging task. Numerical results show that applying aggressive compression policies highly reduces the ability of Bayesian FL systems to provide accurate and reliable ML models. On the contrary, light compression stages maximize accuracy and calibration at the cost of larger communication overheads.

Ensuring operator safety in collaborative workspaces with robots presents significant challenges within the Industry 5.0 landscape. Advanced algorithms play a crucial role in processing data from robotic workcells, enhancing operator monitoring accuracy while upholding privacy and ownership standards in industrial networks. This paper introduces a novel privacy-preserving approach for operator monitoring, leveraging edge-based federated learning (FL) and passive localization techniques. Our proposed localization system also accounts for the diverse execution time-cycles of robots in workcells, enabling evaluation of operator localization accuracy across different levels of robot productivity in various configurations. We validate the effectiveness of our approach through extensive experimental activities in robotic workcells equipped with radar sensors. Our evaluation considers diverse and realistic scenarios where training data is collected over heterogeneous time periods, representing robotic cells with varying characteristics and involving different operators. The results affirm the efficacy of the FL approach, particularly when utilizing heterogeneous datasets sourced from industrial robotic cells.

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.

Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particu-larly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed archi-tecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication pro-tocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and to increase trustworthiness.

The paper considers the problem of human-scale RF sensing utilizing a network of resource-constrained MIMO radars with low range-azimuth resolution. The radars operate in the mmWave band and obtain time-varying 3D point cloud (PC) information that is sensitive to body movements. They also observe the same scene from different views and cooperate while sensing the environment using a sidelink communication channel. Conventional cooperation setups allow the radars to mutually exchange raw PC information to improve ego sensing. The paper proposes a federation mechanism where the radars exchange the parameters of a Bayesian posterior measure of the observed PCs, rather than raw data. The radars act as distributed parameter servers to reconstruct a global posterior (i.e., federated posterior) using Bayesian tools. The paper quantifies and compares the benefits of radar federation with respect to cooperation mechanisms. Both approaches are validated by experiments with a real-time demonstration platform. Federation makes minimal use of the sidelink communication channel (20 ÷ 25 times lower bandwidth use) and is less sensitive to unresolved targets. On the other hand, cooperation reduces the mean absolute target estimation error of about 20%.

In recent years, automotive systems have been integrating Federated Learning (FL) tools to provide enhanced driving functionalities, exploiting sensor data at connected vehicles to cooperatively learn assistance information for safety and maneuvering systems. Conventional FL policies require a central coordinator, namely a Parameter Server (PS), to orchestrate the learning process which limits the scalability and robustness of the training platform. Consensus-driven FL methods, on the other hand, enable fully decentralized learning implementations where vehicles mutually share the Machine Learning (ML) model parameters, possibly via Vehicle-to-Everything (V2X) networking, at the expense of larger communication resource consumption compared to vanilla FL approaches. This paper proposes a communication-efficient consensus-driven FL design tailored for the training of Deep Neural Networks (DNN) in vehicular networks. The vehicles taking part in the FL process independently select a pre-determined percentage of model parameters to be quantized and exchanged on each training round. The proposed technique is validated on a cooperative sensing use case where vehicles rely on Lidar point clouds to detect possible road objects/users in their surroundings via DNN. The validation considers latency, accuracy and communication efficiency trade-offs. Experimental results highlight the impact of parameter selection and quantization on the communication overhead in varying settings.

Recent advances in Federated Learning (FL) have paved the way towards the design of novel strategies for solving multiple learning tasks simultaneously, by leveraging cooperation among networked devices. Multi-Task Learning (MTL) exploits relevant commonalities across tasks to improve efficiency compared with traditional transfer learning approaches. By learning multiple tasks jointly, significant reduction in terms of energy footprints can be obtained. This article provides a first look into the energy costs of MTL processes driven by the Model-Agnostic Meta-Learning (MAML) paradigm and implemented in distributed wireless networks. The paper targets a clustered multi-task network setup where autonomous agents learn different but related tasks. The MTL process is carried out in two stages: the optimization of a meta-model that can be quickly adapted to learn new tasks, and a task-specific model adaptation stage where the learned meta-model is transferred to agents and tailored for a specific task. This work analyzes the main factors that influence the MTL energy balance by considering a multi-task Reinforcement Learning (RL) setup in a robotized environment. Results show that the MAML method can reduce the energy bill by at least 2 × compared with traditional approaches without inductive transfer. Moreover, it is shown that the optimal energy balance in wireless networks depends on uplink/downlink and sidelink communication efficiencies.

Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers. Novel paradigms, such as federated learning (FL), are suitable for decentralized model training across devices or silos that simultaneously act as both data producers and learners. Unlike centralized learning (CL) techniques, relying on big-data fusion and analytics located in energy hungry data centers, in FL scenarios devices collaboratively train their models without sharing their private data. This article breaks down and analyzes the main factors that influence the environmental footprint of FL policies compared with classical CL/Big-Data algorithms running in data centers. The proposed analytical framework takes into account both learning and communication energy costs, as well as the incurred greenhouse gas, or carbon equivalent, emissions. The framework is evaluated in an industrial setting assuming a real-world robotized workplace. Results show that FL allows remarkable end-to-end energy savings (30%÷40%) in low-rate/power IoT communications (with limited energy efficiency). On the other hand, FL is slower to converge when local data are unevenly distributed (often 2x slower than CL).

Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots. Collaborative robotics is an active research area where Internet of Things (IoT) and novel sensing technologies are expected to play a critical role. Considering that no single technology can currently solve the problem of continuous worker monitoring, the article targets the development of an IoT multisensor data fusion (MDF) platform. It is based on an edge-cloud architecture that supports the combination and transformation of multiple sensing technologies to enable the passive and anonymous detection of workers. Multidimensional data acquisition from different IoT sources, signal preprocessing, feature extraction, data distribution, and fusion, along with machine learning (ML) and computing methods are described. The proposed IoT platform also comprises a practical solution for data fusion and analytics. It is able to perform opportunistic and real-time perception of workers by fusing and analyzing radio signals obtained from several interconnected IoT components, namely, a multiantenna WiFi installation (2.4-5 GHz), a sub-THz imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors (8-13 μm). The performance of the proposed IoT platform is validated through real use case scenarios inside a pilot industrial plant in which protective human-robot distance must be guaranteed considering latency and detection uncertainties.

Federated learning (FL) is emerging as a new paradigm for training a machine learning model in cooperative networks. The model parameters are optimized collectively by large populations of interconnected devices, acting as cooperative learners that exchange local model updates with the server, rather than user data. The FL framework is however centralized, as it relies on the server for fusion of the model updates and as such it is limited by a single point of failure. In this paper we propose a distributed FL approach that performs a decentralized fusion of local model parameters by leveraging mutual cooperation between the devices and local (in-network) data operations via consensus-based methods. Communication with the server can be partially, or fully, replaced by in-network operations, scaling down the traffic load on the server as well as paving the way towards a fully serverless FL approach. This proposal also lays the groundwork for integration of FL methods within future (beyond 5G) wireless networks characterized by distributed and decentralized connectivity. The proposed algorithms are implemented and published as open source. They are also designed and verified by experimental data.

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems. Rather than sharing and disclosing the training data set with the server, the model parameters (e.g., neural networks' weights and biases) are optimized collectively by large populations of interconnected devices, acting as local learners. FL can be applied to power-constrained Internet of Things (IoT) devices with slow and sporadic connections. In addition, it does not need data to be exported to third parties, preserving privacy. Despite these benefits, a main limit of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters; this has the drawback of a single point of failure and scaling issues for increasing network size. This article proposes a fully distributed (or serverless) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network by iterating local computations and mutual interactions via consensus-based methods. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing, with intelligence distributed over the end devices. The proposed methodology is verified by the experimental data sets collected inside an Industrial IoT (IIoT) environment.

Passive radio sensing technique is a well established research topic where radio-frequency (RF) devices are used as real-time virtual probes that are able to detect the presence and the movement(s) of one or more (non instrumented) subjects. However, radio sensing methods usually employ frequencies in the unlicensed 2.4-5.0 GHz bands where multipath effects strongly limit their accuracy, thus reducing their wide acceptance. On the contrary, sub-terahertz (sub-THz) radiation, due to its very short wavelength and reduced multipath effects, is well suited for high-resolution body occupancy detection and vision applications. In this paper, for the first time, we adopt radio devices emitting in the 100 GHz band to process an image of the environment for body motion discrimination inside a workspace area. Movement detection is based on the real-time analysis of body-induced signatures that are estimated from sub-THz measurements and then processed by specific neural network-based classifiers. Experimental trials are employed to validate the proposed methods and compare their performances with application to industrial safety monitoring.

Thermal vision systems based on low-cost IR array sensors are becoming attractive in many smart living scenarios. This paper proposes a Bayesian framework for recognition and discrimination of body motions based on real-time analysis of thermal signatures. Unlike conventional frame-based methods, the proposed approach exploits a statistical model for the extraction of body-induced thermal signatures and a mobility model for tracking multi-body motions inside an indoor area. This approach prevents typical detection problems and can be also used in presence of interfering thermal sources such as heaters, radiators and other thermal devices. The Bayesian method is verified experimentally for ceiling mounted sensors and shows high accuracy and robustness even in cases where thermal signatures are closer to the ambient temperature.

The paper considers the adoption of pattern reconfigurable antenna devices as a new opportunity for human-scale passive radio sensing using ambient (or stray) WiFi signals designed for wireless communications. Pattern reconfigurable antennas allow to dynamically channelize the antenna radiation pattern to pre-defined directions of interest. These antennas are essential RF components both in forthcoming 5G devices and next generation WiFi radios. We discuss signal modeling, beam-steering technology integration and adaptation for passive motion detection. First case studies and performance analysis inside a smart workspace are also considered.

We propose a platform for the integration of passive radio sensing and vision technologies into a cloud-IoT framework that performs real-time channel quality information (CQI) time series processing and analytics. Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2-D/3-D view of the environment they propagate through. View reconstruction from the received radio signals, or CQI, is based on real-time data processing tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The proposed platform is designed to efficiently store and analyze CQI time series of different types and provides formal semantics for CQI data manipulation-ontology models (OMs). Post-processed data can be then accessible to third parties via JSON-REST calls. Finally, the proposed system supports the reconfiguration of CQI data collection based on the respective application. The performance of the proposed tools are evaluated through two experimental case studies that focus on assisted living applications in a smartspace environment and on driver behavior recognition for in-car control services. Both studies adopt and compare different CQI manipulation models and radio devices as supported by current and future (5G) standards.

Fall detection and localization of human operators inside a workspace are major issues in ensuring a safe working environment. Recent research has shown that the perturbations of the radio-frequency (RF) signals commonly adopted for wireless communications can also be used as sensing tools for device-free human motion detection. Device-free RF-based human sensing applications range from tag-less body localization to detection and monitoring of human well-being (e-Health). In this paper, we propose a real-time system for human body motion sensing with special focus on joint body localization and fall detection. The proposed system continuously monitors and processes the RF signals emitted by industry-compliant radio devices operating in the 2.4 GHz ISM band and supporting machine-to-machine communication functions. Human-induced diffraction and multipath phenomena that affect RF signal propagation are leveraged for body localization while for fall detection a hidden Markov model is applied to discern different postures of the operator and to detect safety-relevant events by tracking the received signal strength indicator footprints. Fall detection performances are corroborated by extensive experimental measurements in different settings. In addition, we propose also a sensor fusion tool that is able to integrate the device-free RF-based sensing system within an industrial image sensors framework. Preliminary results, conducted during field trial measurements, confirm the effectiveness of the proposed approach in terms of localization accuracy, and sensitivity/specificity to correctly detect a fall event from preimpact postures.

In this paper, a time-varying dynamic Bayesian network model is shown to describe human-induced RF fluctuations for the purpose of non-cooperative and device-free radiobased body motion recognition (radio vision). The technology relies on pre-existing wireless communication network infrastructures and processes channel quality information (CQI) for human-scale sensing. Body movements leave a characteristic footprint on the CQI sequences collected during consecutive radio transmissions over multiple co-located links. Body-induced RF footprints are proved to be effectively characterized by temporarily coupled hidden Markov chains: abrupt changes of body postures make CQIs observed over co-located links temporarily coupled while being uncoupled for slow body movements. Learning and classification/inference problems are discussed based on experimental measurements. Device-free radio vision performances are evaluated for arm gesture and fall detection applications.

Abstract High‐speed train (HST) is revitalizing the train as a preferred mid‐range transportation system. The provision of broadband Internet connectivity onboard trains is one of the key challenges in the competition among train operators. Unprecedented spectral efficiency and data rates (up to 100Mbps in high mobility) of the Universal Mobile Telecommunications System long‐term evolution (LTE) are expected to offer the solution for high‐speed Internet access onboard in HSTs. Massive wireless access and frequent handovers (HOs) of a large number of users might potentially cause service interruptions, and this, in turn, degrades intolerably the quality of experience (QoE) of users' onboard Internet access. In this paper, we propose a solution based on distributed antenna system that combines directional and omni‐directional antennas as train‐to‐ground radio‐access terminals (T‐RATs) and LTE femtocells in each carriage. Directional antennas are deployed at both ends of the HST to provide multi‐cell access by diversifying the HOs over multiple LTE cells. This mechanism virtually elongates the train size by connecting the front and rear carriages' T‐RATs to the faraway eNodeBs and augmenting the number of cells the HST can be simultaneously connected to. An ad hoc distributed load‐balancing mechanism that consists in offloading backlogged packets to the on‐service T‐RATs is mandatorily paired with multi‐cell access scheme to tie up with the request of seamless onboard Internet service at high QoE level. Copyright © 2015 John Wiley & Sons, Ltd.

Safe human-machine interactions promote high flexibility in collaborative workspaces. Fall detection and localization of the operator are major issues in ensuring a safe working environment. However, many proposed solutions are not applicable for deployment in industrial environments due to their performance limitations in practical contexts. In this paper, we propose an integrated framework for both localization and fall detection of operators inside a shared workspace that employs radio-frequency (RF) signal analysis in real-time. Multipath and non-line-of-sight (NLOS) scattering that affect RF signal propagation can be leveraged for human sensing in complex workspaces: the proposed system continuously monitors the fluctuations of the RF field across the space by a dense network of WiFi compliant radio devices operating at 2.4GHz. To increase the accuracy of the localization system, a sensor fusion algorithm using Extended Kalman Filter techniques is employed. The proposed method may be used for integrating measurements from both RF nodes and an additional image-based system. For fall detection, a Hidden Markov Model is applied to discern different postures of the operator and to detect a fall event by tracking the fluctuations of the wireless signal quality. Fall detector performances are validated through experimental measurements. The preliminary results confirm the effectiveness of the proposed approach for different body configurations and pre-impact postures to correctly detect a fall event. Finally, some results about sensor fusion for improved operator localization are presented.

In this letter, an analytically tractable model based on diffraction theory is proposed to describe the perturbations of the electromagnetic propagation of radio signals caused by the presence of a moving object in the two-dimensional (2-D) area near the transmitting/receiving devices. This novel model is instrumental to the evaluation of non-cooperative device-free localization (DFL) systems as it allows to relate the received signal strength measurements of multiple radio links to the object size, orientation and position. The proposed model is validated experimentally using radio devices and it is used to derive closed-form fundamental limits to DFL accuracy, providing an analytical tool for DFL system design and network 2-D pre-deployment assessment.

Cloud-enabled wireless industrial sensor networks is an emerging paradigm in machine-type communication. It supports “device-centric” architectures that efficiently exploit intelligence at field device side replacing Host-centric architectures to handle critical condition. In this paper we propose a flexible architecture for integrating a self-contained network-embedded cloud system (Wireless Cloud Network, WCN) with a wireless industrial network infrastructure implementing the Time Synchronized Channel Hopping (TSCH) as supported by commercial WirelessHART systems. The WCN acts as a small-cell embedded network consisting of devices that gather and process pervasive information about the state of the industrial plant. Devices member of the cloud support advanced communication services and enable early and localized detection of dangerous conditions. In addition, they act as data processing centers distributed at the edge of the TSCH network. A hardware and software architecture is developed and tailored for WirelessHART (IEC 62591) protocol while preliminary experimental measurements are also carried out to evaluate the feasibility and the effectiveness of the proposed system.

Adaptable workflows in human-robot cooperation (HRC) require a flexible sharing of the same workspace with major impact on human-centered robot motion planning. The standard EN ISO 10218 is fostering the implementation of hybrid production systems characterized by a close relationship among human operators and robots in cooperative tasks. A primary contribution in workers protection is given by real time monitoring of the entire workspace, including tracking of operators trajectories and tentative estimation of motion intentions. Operators localization has the purpose of enabling the Speed and Separation Monitoring (SSM) safety mode, as in draft ISO/TS 15066, and adapting the robot motion to approaching users. The present work discloses some preliminary results about methods of “sensor-less” localization of operators in industrial HRC scenarios, based on wireless sensor networks techniques. The proposed system is composed of a network of small, embedded RF transceivers pervasively distributed in fixed positions inside the robotic cell layout in order to localize the operators, who carry neither wireless active devices (device-free) nor specific tracking sensors (sensor-less sensing). Users positions over time are estimated from the perturbation of the radio field, considering the effect of the concurrently moving robots. Finally, the sensors-robots system is functionally integrated into a safety architecture.

The adoption of dense industrial wireless network technologies in industrial plants is mandatorily paired with the development of methods and tools for connectivity prediction. These can be used to certify the quality (or reliability) of network information flow in industrial scenarios characterized by harsh propagation environments. Connectivity prediction must account for possibly coexisting heterogeneous radio access technologies as part of the internet of things (IoT) paradigm and easily allow post layout validation steps. The goal of the paper is to provide a practical evaluation of relevant coexistence problems between IEEE 802.15.4 and IEEE 802.11 networks, adopted here as widely used industry standards. Two different scenarios are tested with different radio platforms. Experimental results highlight the tolerable interference levels and sensitivity thresholds under different channel overlapping scenarios.

In peer-to-peer wireless networks, knowledge of the channel quality information of multiple links is fundamental to calibrate cooperative communication/processing techniques and design efficient resource sharing strategies. This paper is focused on distributed estimation algorithms that enable the network to self-learn key environment-dependent parameters that rule the channel quality of all links in the network. Considering an indoor scenario with fixed wireless terminals and moving objects/people in the environment, we parameterize the channel quality of each link in terms of path-loss and Rician K-factor, modelling these macro-parameters according to a site-specific stochastic model. Contribution of the paper is twofold: a measurement campaign carried out with IEEE 802.15.4 devices to validated the stochastic model; distributed algorithms to estimate the environment-dependent parameters of the model. Various schemes of weighted average consensus are proposed to enable the convergence to the equivalent global (centralized) estimate. Performance analysis is carried out in terms of convergence speed, error at convergence and communication overhead using both experimental and simulated data.

Large-scale adoption of dense cloud-based wireless network technologies in industrial plants is mandatorily paired with the development of methods and tools for connectivity prediction and deployment validation. Layout design procedures must be able to certify the quality (or reliability) of network information flow in industrial scenarios characterized by harsh propagation environments. In addition, these procedures must account for possibly coexisting heterogeneous radio access technologies as part of the Internet of Things (IoT) paradigm, easily allow post-layout validation steps, and be integrated by industry-standard CAD-based planning systems. The goal of the paper is to set the fundamentals for comprehensive industry-standard methods and procedures supporting plant designer during wireless coverage prediction, virtual network deployment, and post-layout verification. The proposed methods carry out the prediction of radio signal coverage considering typical industrial environments characterized by highly dense building blockage. They also provide a design framework to properly deploy the wireless infrastructure in interference-limited radio access scenarios. In addition, the model can be effectively used to certify the quality of machine-type communication by considering also imperfect descriptions of the network layout. The design procedures are corroborated by experimental measurements in an oil refinery site [modeled by three-dimensional (3-D) CAD] using industry-standard ISA IEC 62734 devices operating at 2.4 GHz. A graph-theoretic approach to node deployment is discussed by focusing on practical case studies, and also by looking at fundamental connectivity properties for random deployments.

In past years, ultrawideband technology has attracted great attention from academia and industry for wireless personal area networks and wireless sensor networks. Maintenance of connectivity and exchange of data require an efficient way to manage the devices. Distributed beaconing defined by ECMA-368 is used to manage the network in fully distributed fashion. All the devices must acquire a unique beacon slot, with the beacon period accessed using a slotted Aloha scheme. In this paper, we study the efficiency of distributed beaconing in the presence of k newcomer devices forming a closed system. Efficiency is measured in terms of energy consumption and network setup delay. ECMA-368 defines two distinct phases: extension and contraction. Both phases are analyzed with particular emphasis on the extension phase by means of an absorbing Markov chain model. The main contributions of this paper are: 1) a systematic approach to model distributed beaconing by formulating two equivalent urn occupancy problems of the extension and contraction phases; 2) the use of exponential generating functions to obtain closed-form expressions of the transition probabilities of the absorbing Markov chain; and 3) comparison to computer simulations based on Opnet modeling and with the preexisting literature.

In this paper, we develop a partner selection protocol for enhancing the network lifetime in cooperative wireless networks. The case-study is the cooperative relayed transmission from fixed indoor nodes to a common outdoor access point. A stochastic bivariate model for the spatial distribution of the fading parameters that govern the link performance, namely the Rician K-factor and the path-loss, is proposed and validated by means of real channel measurements. The partner selection protocol is based on the real-time estimation of a function of these fading parameters, i.e., the coding gain. To reduce the complexity of the link quality assessment, a Bayesian approach is proposed that uses the site-specific bivariate model as a-priori information for the coding gain estimation. This link quality estimator allows network lifetime gains almost as if all K-factor values were known. Furthermore, it suits IEEE 802.15.4 compliant networks as it efficiently exploits the information acquired from the received signal strength indicator. Extensive numerical results highlight the trade-off between complexity, robustness to model mismatches and network lifetime performance. We show for instance that infrequent updates of the site-specific model through K-factor estimation over a subset of links are sufficient to at least double the network lifetime with respect to existing algorithms based on path loss information only.

Radio imaging allows to locate and track passive targets (i.e., not carrying electronic device) moving in an area monitored by a dense network of low-power and battery-operated wireless sensors. The technology is promising for a wide number of applications ranging from intrusion detection to emergency and rescue operations in critical areas. In this paper, a new approach is proposed where both the average and the variance of the fluctuations of the received signal strength (RSS) induced by the target movement over the links are jointly and optimally exploited for sensing the target location. A link-layer protocol is developed on top of an existing IEEE 802.15.4 compliant PHY/MAC layer to allow the wireless nodes to cooperatively exchange RSS measurements. A log-normal model is defined to relate these measurements to the target location. Grid-based Bayesian estimation is proposed for real-time mobile positioning. The proposed system is validated by an indoor experimental study that analyzes the problem of model calibration and compares the performance of different localization algorithms.

Cooperative transmission techniques for ad hoc and wireless sensor networks are known to increase the network lifetime. Indeed, the improved spatial diversity allows a more efficient energy usage. Under the Rayleigh fading assumption, the selection of cooperative partners is typically based on the knowledge of the average channel power. However, Rayleigh fading is not a suitable model in a large number of practical scenarios, in particular for indoor-to-outdoor applications. In these scenarios additional information of the fading distribution is needed for partner selection. The main focus of this work is to provide an analytical framework to evaluate the impact of the fading statistics on partner selection algorithms. A distributed multi-link channel model is derived from indoor-to-indoor and indoor-to-outdoor channel measurements in order to simulate practical scenarios where the proposed analytical framework is tested. Finally, we introduce a novel partner selection strategy that exploits the distributed knowledge of the effective coding gains provided by the wireless links fading statistics.

Acquisition systems for sub-surface diagnostic (e.g., earthquake monitoring) require large number of sensors (geo-phones or accelerometers) to be deployed outdoor over large areas (tens of sqkm) to measure backscattered wave fields that are collected into a storage/processing unit (sink node). Aggregated data sets are analyzed to obtain an image of the sub-surface, monitor seismic activity, and declare possible alarm conditions. Cable based connectivity is the bottleneck of current systems, in terms of power consumption and degradation in accuracy. Replacing cables with wireless is now becoming attractive to improve the monitoring quality and reduce the probability of false negatives. Strict sampling synchronization constraint over large geographic areas, high precision sensor localization, high data-rate, and low delay are all topics that call for a scalable network system: Multi-Band Ultra Wide-Band radio transmissions (MB-UWB) play a key role as the only viable technology. This paper introduces the system and UWB network architecture based on ECMA-368 standard, moreover it provides a novel analytical tool to evaluate the energy consumption and delay during network set-up.

Wireless Sensor Networks (WSN) is the most promising technological paradigm to support the next generation highly efficient emergency management systems. Optimal design of WSN involves all the layers of the protocol stack: from the physical (PHY), the medium access layer (MAC) to the application layer. The design problem is conveniently cast in this paper for linear sensor network topologies where the terminals are equidistantly placed on the line between the source and the destination and are monitoring a correlated field. This simple topology can be adopted to provide insights to the performance of multihop networks used in several applications as monitoring systems, acoustic sensor arrays, seismic systems etc...

Dense monitoring of time-varying 2D field by Wireless Sensor Networks (WSN) needs to define new strategies to aggregate and encode data according to 2D sensor deployments. In this paper optimal design of communication protocol for WSN is conveniently cast for a set of linear sensor networks (sensor arrays) synchronously monitoring a correlated 2D time-varying field (e.g., for tracking time variations). Sensors aggregate data hop-by-hop by compressing the new samples gathered from the sensor board and forwarding towards the next sensor in the route. Compression is based on a linear predictive encoding to exploit the correlation properties of the field. Cross-layer design of source, channel coding and medium access control are jointly analyzed for optimal resource allocation and interference management to minimize energy consumption. Seismic monitoring application is analyzed to corroborate the design approach.

The fluctuations of the price of crude oil is pushing the oil companies to increase the investments in seismic exploration of new oil and gas reservoir. Seismic exploration requires a large number (500 divide 2000 nodes/sqkm) of sensors (geophones or accelerometers) to be deployed in outdoor over large areas (ges 20 sqkm) to measure backscattered wave fields. A storage/processing unit (sink node) collects the measurements from all the geophones to obtain an image of the sub-surface in real-time. Current connectivity is cable based and requires hundreds of kilometers of cabling causing delays, high logistic costs and low imaging quality. This paper serves as a tutorial to introduce the basic principles of seismic acquisition systems from a wireless communication perspective and provides a number of requirements/specifications for the physical, MAC and network layer to develop wireless sensors networks tailored for oil (and gas) exploration. The wireless geophone network (WGN) system will replace the actual cabled systems used in on-shore seismic acquisition. Oil companies are currently pushing for wireless solutions. Early results suggested that a fully WiFi network does not satisfy all the requirements. This motivates the use of a mixture of technologies. In the proposed system wireless UWB devices/sensors are simultaneously sensing, self-localizing and synchronizing while delivering data to gateway devices in mesh mode. Gateways forward the aggregated traffic to storage unit over long range.

In this paper we evaluate outage performances of multirelay amplify and forward (AF) transmission over fading channels. We focus on the uplink of a two-hop based network where N (with N ges 1) single antenna relay stations (RSs) serve as non-regenerative repeaters for the message transmitted by a single antenna source node (or mobile station MS). Performances are derived at high SNR for arbitrary fading distributions over each cooperative link. In Rayleigh fading the high price of cooperation in terms of signalling (e.g. during MS-to-RSs transmissions and control messages broadcasts) causes a significant loss in spectral efficiency. Instead, provided that the RSs can be strategically positioned to benefit from a channel with marginal diffusive fading component compared to Rayleigh, this paper shows that this loss in efficiency can be significantly alleviated depending on the particular propagation environment. Closed form conditions on the fading statistics for the relayed links are derived to guarantee that collaborative transmission performs as if the MS node would benefit from the same diversity and bandwidth efficiency provided by multi-antenna transmission. The proposed conditions are computed for arbitrary distributed fading for the relays-to-BS link and are specialized for Rice faded RSs-to-BS links.

In common wireless systems pilot placing can be interpreted as a way to sample the channel with some degree of accuracy. In this letter we investigate the necessary conditions on the pilots spacing for time-varying fading to guarantee a specified average bit error rate. These constraints are evaluated in closed form for large SNR and small fading dynamics and specialized for varying fading correlation and coded/uncoded binary transmissions.

In fast-varying faded channels the transmission can be organized into frames where the channel estimation is mainly training-based. For fast-varying fading channels the training length and training interval can be optimized jointly to maximize the throughput. The optimal balance of training and payload depends on the combination of Doppler frequency and frame length. Here we show that, depending on the degree of mobility for large enough signal to noise ratio, there is a definite advantage in fragmenting the frame with dispersed segments of training symbols of smaller length rather than having a highly reliable channel estimate by concentrating all the training symbols at the beginning of the frame.

The concept of cognitive radio (or secondary spectrum access) is currently under investigation as a promising paradigm to achieve efficient use of the frequency resource by allowing the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth. According to the property-rights model of cognitive radio, the primary terminals own a given bandwidth and may decide to lease it for a fraction of time to secondary nodes in exchange for appropriate remuneration. In this paper, we propose and analyze an implementation of this framework, whereby a primary link has the possibility to lease the owned spectrum to an ad hoc network of secondary nodes in exchange for cooperation in the form of distributed space-time coding. On one hand, the primary link attempts to maximize its quality of service in terms of either rate or probability of outage, accounting for the possible contribution from cooperation. On the other hand, nodes in the secondary ad hoc network compete among themselves for transmission within the leased time-slot following a distributed power control mechanism. The investigated model is conveniently cast in the framework of Stackelberg games. We consider both a baseline scenario with full channel state information and information-theoretic transmission strategies, and a more practical model with long-term channel state information and randomized distributed space-time coding. Analysis and numerical results show that spectrum leasing based on trading secondary spectrum access for cooperation is a promising framework for cognitive radio.

Cooperative transmission protocols over fading channels are based on a number of relaying nodes to form virtual multi-antenna transmissions. Diversity provided by these techniques has been widely analyzed for the Rayleigh fading case. However, short range or fixed wireless communications often experience propagation environments where the fading envelope distribution is meaningfully different from Rayleigh. The main focus in this paper is to investigate the impact of fading distribution on performances of collaborative communication. Cooperative protocols are compared to co-located multi-antenna systems by introducing the concept of cooperative fading region. This is the collection of fading distributions for which relayed transmission can be regarded as a competitive option (in terms of performances) compared to multi-antenna direct (noncooperative) transmission. The analysis is dealt with by adopting the information theoretic outage probability as the performance metric. Cooperative link performances at high SNR are conveniently expressed here in terms of diversity and coding gain as outage parameters that are provided by the fading statistics of the channels involved in collaborative transmission. Advantages of cooperative transmission compared to multi-antenna are related to the propagation environment so that the analysis can be used in network design.

In this paper we evaluate outage performance of a cooperative transmission protocol over fading channels that requires a number of relaying nodes to employ a distributed space-time coding scheme. Diversity provided by this technique has been widely analyzed for the Rayleigh fading case. However, ad-hoc and sensors networks often experience propagation environments where the line-of-sight component is either non zero or, in some cases, dominates compared to the random non line-of- sight components. By considering the Nakagami-m as a generic framework for describing the statistic of the fading impairments, this paper evaluates a set of fading inequalities that define settings where the benefits of collaborative transmission from multiple relays when varying fading parameters. These cooperative fading regions define the propagation settings that make cooperation preferable to multi-antenna non-cooperative transmission.

This paper is focused on the optimization of transmitted power in a cooperative decoded relaying scheme for nodes belonging to the single primary route towards. a destination. The proposed transmission protocol, referred to as Multihop Cooperative Transmission Chain (MCTC), is based on the linear combination of copies of the same message by multiple previous terminals along the route in order to maximize the multihop diversity. Power allocations among transmitting nodes in the route can be obtained according to the average (not instantaneous) node-to-node path attenuation using a recursive power assignment. The latter can be employed locally on each node with limited signalling exchange (for fixed or nomadic terminals) among nodes. In this paper the power assignments for the MCTC strategy employing conventional linear combining schemes at receivers (i.e., selection combining, maximal ratio combining and equal gain combining) have been derived analytically when the power optimization is constrained to guarantee the end-to-end outage probability. In particular, we show that the power assignment that minimize the maximum spread of received power (min-max strategy) can efficiently exploit the multihop diversity. In addition, for ad hoc networks where the energy of each node is an issue, the MCTC protocol with the min-max power assignment increases considerably the network lifetime when compared to non-cooperative multihop schemes

In this paper we consider a cooperative wireless network where each terminal communicates to a destination node with the aid of multiple relaying nodes. The focus is on cooperative transmission protocols that are based on the simultaneous transmission by a number of cooperating nodes. Outage performances are analyzed by assuming a distributed randomized orthogonal space-time coding scheme (DR-OSTC) to be employed by the relaying terminals during the transmission session. The DR-OSTC scheme requires that each cooperating node chooses randomly and independently to serve as one of the space-time virtual antennas. By avoiding any pre-defined terminal-to-codeword mapping, the random selection of the space-time codewords substantially reduces the needed control overhead with respect to other distributed space-time coding strategies simplifying the node coordination task. According to this scheme, in this paper it is tackled the problem of designing both the minimum number of cooperating nodes M and the spatial dimension L of the space-time code matrix so as to meet a specific outage probability requirement at the destination. Outage performances are also analyzed by developing simple but effective design rules tailored for two cooperative transmission protocols in realistic propagation environments.

In this paper we propose a spatial multiplexing technique for the downlink of a multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) system. For outdoor environments with a limited angular spread at the base station, the proposed technique is able to separate the users' streams through a joint spatial processing at both the transmitter and the receiver requiring only a limited feedback from each user. Adaptive transmission is adopted on each stream to set a fixed probability of error. Numerical simulations show that the proposed technique is able to provide significant throughput gains compared to fixed-beams based approaches proposed in the literature.