One of the key requirements for future 5th Generation (5G) Industrial Internet of Things (IIoT) networks will be to deliver low latency to support different production processes. To this end, 5G New Radio (NR) provides Configured Grant (CG) scheduling for periodic traffic, additionally to conventional Grant-Based Scheduling (GBS). However, in view of the complexities introduced by spatio-temporal traffic correlations in IIoT, a fixed scheduler configuration may be suboptimal: GBS introduces excessive signaling overhead, while CG leads to inefficient resource utilization and latency degradation when traffic is not perfectly periodic. To solve these critical issues, we propose Hybrid Centralized Uplink Scheduler (HCUS), a new scheduling framework that dynamically learns the type of traffic generated by User Equipments (UEs), and adapts resource allocation accordingly. HCUS operates per-UE, and dynamically switches between GBS and Configured Grant (CG), optimizing resource allocation while preserving low End-to-End (E2E) latency. We consider both mixed periodic and aperiodic uplink traffic to model different network load conditions and IIoT applications. Extensive simulations show that HCUS achieves up to four times lower latency than GBS and CG while maintaining high reliability, even considering traffic correlations or periodicity changes, making it a robust and scalable solution for next-generation IIoT scenarios.

Industry 5.0 envisions the seamless integration of automation, human-machine collaboration, and intelligent systems to enable highly flexible and adaptive manufacturing. However, this puts stringent requirements on the communication systems extending beyond the capabilities of current 5G technologies. This gap motivates the adoption of the digital twin (DT) paradigm, where a digital replica of physical assets and networks enables continuous monitoring, simulation, and optimization of industrial processes. This paper investigates the optimization of network access protocols, which are critical to maintaining uninterrupted industrial production. Specifically, we develop a reinforcement learning (RL) algorithm embedded within the DT framework to ensure reliable communication and production flow continuity. In a congested factory environment, an autonomous guided vehicle (AGV) must transmit data to a base station (BS) using an ALOHA-like protocol at terahertz (THz) frequencies. The DT-enabled RL model dynamically learns traffic patterns and adaptively selects backoff (BO) times for network access, maximizing reliability without requiring prior knowledge of system topology. Extensive evaluations across diverse operating scenarios–ranging from static to mobile settings, varying traffic loads, and different data and action space sizes–confirm the robustness, scalability, and adaptability of the proposed solution in highly dynamic industrial environments against conventional and advanced baselines.

One of the most prominent requirements of future Industrial Internet of Things (IIoT) networks will be to provide Ultra-Reliable Low-Latency Communication (URLLC) in support of critical physical processes underlying the production chains. However, standard protocols for allocating wireless resources may not be able to optimize the latency-reliability trade-off, especially for uplink communication. For example, centralized (e.g., grant-based) scheduling can ensure almost zero collisions, but introduces delays in the way resources are requested by the User Equipments (UEs) and then granted by the Next Generation Node B (gNB). On the other hand, distributed scheduling (e.g., based on random access), in which UEs autonomously choose the physical resources to transmit uplink data, may lead to potentially many collisions especially when the density of UEs (and so the traffic) increases. Along these lines, in this work we propose DIStributed combinatorial NEural linear Thompson Sampling (DISNETS), a novel scheduling framework that combines the best of the two worlds. By leveraging a feedback signal sent from the gNB and reinforcement learning, the UEs are trained to autonomously optimize their uplink transmissions by selecting the available physical resources so as to minimize the number of collisions, disaggregated from the network and without additional message exchange to/from the gNB. DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm, which has been further extended to admit multiple actions in parallel. We apply DISNETS to the context of IIoT, and demonstrate by extensive simulations the superior performance of the proposed approach in addressing URLLC compared to other baselines.

The traditional black-box and monolithic approach to Radio Access Networks (RANs) has heavily limited flexibility and innovation. The Open RAN paradigm, and the architecture proposed by the O-RAN ALLIANCE, aim to address these limitations via openness, virtualization and network intelligence. In this work, first we propose a novel, programmable scheduler design for Open RAN Distributed Units (DUs) that can guarantee minimum throughput levels to User Equipments (UEs) via configurable weights. Then, we propose an O-RAN xApp that reconfigures the scheduler’s weights dynamically based on the joint Complementary Cumulative Distribution Function (CCDF) of reported throughput values. We demonstrate the effectiveness of our approach by considering the problem of asset tracking in 5G-powered Industrial Internet of Things (IIoT) where uplink video transmissions from a set of cameras are used to detect and track assets via computer vision algorithms. We implement our programmable scheduler on the OpenAirInterface (OAI) 5G protocol stack, and test the effectiveness of our xApp control by deploying it on the O-RAN Software Community (OSC) near-RT RAN Intelligent Controller (RIC) and controlling a 5G RAN instantiated on the Colosseum Open RAN digital twin. Our experimental results demonstrate that our approach enhances the success percentage of meeting throughput requirements by 33% compared to a reference scheduler. Moreover, in the asset tracking use case, we show that the xApp improves the detection accuracy, i.e., the F1 score, by up to 37.04%.

Efficient multiple access remains a key challenge for emerging Internet of Things (IoT) networks comprising a large set of devices with sporadic activation, thus motivating significant research in the last few years. In this paper, we consider a network wherein IoT sensors capable of energy harvesting (EH) send updates to a central server to monitor the status of the environment or machinery in which they are located. We develop energy-aware ALOHA-like multiple access schemes for such a scenario using the Age of Information (AoI) metric to quantify the freshness of an information packet. The goal is to minimize the average AoI across the entire system while adhering to energy constraints imposed by the EH process. Simulation results show that applying the designed multiple access scheme improves performance from 24 % up to 90 % compared to previously proposed age-dependent protocols by ensuring low average AoI and achieving scalability while simultaneously complying with the energy constraints considered.

Digital Twin (DT) technology has emerged as a transformative paradigm across various domains, offering powerful capabilities to monitor and optimize processes prior to real-world deployment. DTs are well-suited for next-generation deployment, as well as industrial applications, in which the dynamicity and complexity of processes pose significant challenges. This study proposes a novel DT-based framework that integrates network infrastructure with shop-floor industrial operations, where robotic arms execute pick-and-place tasks and communicate with a base station (BS) using a contention-based medium access control (MAC) protocol (i.e., ALOHA and carrier sense multiple access (CSMA)) at THz frequencies. The framework aims to optimize closed-loop production systems by enabling continuous and bidirectional data exchange between the robotic arms and the BS. A centralized reinforcement learning (RL) model enables joint optimization of backoff (BO) selection and task allocation, enhancing production efficiency while preserving workflow continuity. The results prove that the proposed framework significantly outperforms state-of-the-art (SoTA) solutions for network and industrial operation performance, achieving a 55.2% reduction in average latency, a 9.8% improvement in success probability, and a 27.96% increase in processed product rate. These findings highlight the robustness and effectiveness of the proposed framework across varying operational scenarios and MAC protocols.

Industry 5.0 marks a transition from the digitalization focus of Industry 4.0 to a paradigm emphasizing resilience, sustainability, and human-centric processes. In such dynamic networks, reinforcement learning (RL) algorithms can play a crucial role in enhancing performance. The paper proposes a novel approach using a centralized RL algorithm to optimize the medium access control for a moving autonomous guided vehicle (AGV) on an industrial shop floor. This ensures uninterrupted production flow by dynamically managing the network traffic. The use case scenario considers a mobile AGV transmitting data to a base station (BS) within a harsh industrial environment. It uses the RL algorithm to dynamically select an optimal backoff (BO) time for an ALOHA-like channel access protocol. This enables accurate data transmission without prior knowledge of the industrial environment. The results show an improvement of up to 34.6% in success probability compared to traditional BO design approaches. The RL model achieves outstanding performance, guaranteeing a minimum success probability of 99.46%.

Beamforming is a crucial component in modern large-scale multiple-input single-output (MISO) wireless networks. However, its practical deployment is often constrained by the incomplete channel state information (CSI) and the overhead associated with centralized optimization methods, such as the weighted minimum mean-square error (WMMSE) algorithm. In this work, we propose the problem of distributed beamforming with varying degrees of CSI incompleteness and develop a graph neural network (GNN)-based approach, which enables transmitters to collaboratively optimize their beamforming strategies using only local and partial CSI. Our approach combines imitation learning with unsupervised learning for model training, where the former provides a warm start and the latter allows for superior performance beyond the "expert" strategy, while leveraging the GNN for a distributed implementation. We evaluate the proposed approach using ray tracing-based simulations in both indoor and outdoor scenarios, demonstrating that it outperforms WMMSE, particularly in environments with noisy or incomplete CSI. These results highlight the potential of our GNN-based solution for scalable, robust, and distributed beamforming strategies in future wireless networks.

The emergence of Terahertz (THz) frequency wireless networks holds great potential for enabling various high-demand services, including Industrial Internet of Things (IIoT) applications. These applications benefit significantly from the ultra-high data rates, low latency, and high spatial resolution offered by THz frequencies. However, a primary well-known challenge of THz networks is their limited coverage range due to high path loss and vulnerability to obstructions. This paper addresses this limitation by proposing two novel multi-hop protocols, Table-Less (TL) and Table-Based (TB), respectively, both avoiding centralized control and/or control plane transmissions. Indeed, both solutions are distributed, simple, and rapidly adaptable to network changes. Simulation results demonstrate the effectiveness of our approaches, as well as revealing interesting trade-offs between TL and TB routing protocols, both in a real IIoT THz network and under static and dynamic conditions.

The ever-increasing demand for high-quality and heterogeneous wireless communication services has driven extensive research on dynamic optimization strategies in wireless networks. Among several possible approaches, multi-agent deep reinforcement learning (MADRL) has emerged as a promising method to address a wide range of complex optimization problems like power control. However, the seamless application of MADRL to network optimization problems faces several challenges related to convergence. In this paper, we propose the use of graphs as communication-inducing structures among distributed agents as an effective means to mitigate these challenges. Specifically, we harness graph neural networks (GNNs) as neural architectures for policy parameterization to introduce a relational inductive bias in the collective decision-making process. Most importantly, we focus on modeling the dynamic interactions among sets of neighboring agents through the introduction of innovative methods for defining a graph-induced framework for integrated communication and learning. Finally, the superior generalization capabilities of the proposed methodology to larger networks and to networks with different user categories is verified through simulations.

The rapid expansion of connected and autonomous vehicles (CAVs) and the shift towards millimiter-wave (mmWave) frequencies offer unprecedented opportunities to enhance road safety and traffic efficiency. Sidelink communication, enabling direct Vehicle-to-Vehicle (V2V) communications, play a pivotal role in this transformation. As communication technologies transit to higher frequencies, the associated increase in bandwidth comes at the cost of a severe path and penetration loss. In response to these challenges, we investigate a network configuration that deploys beamforming-capable Unmanned Aerial Vehicles (UAVs) as relay nodes. In this work, we present a comprehensive analytical framework with a groundbreaking performance metric, i.e. average access probability, that quantifies user satisfaction, considering factors across different protocol stack layers. Additionally, we introduce two Radio Resources Assignment (RRA) methods tailored for UAVs. These methods consider parameters such as resource availability, vehicle distribution, and latency requirements. Through our analytical approach, we optimize the average access probability by controlling UAV altitude based on traffic density. Our numerical findings validate the proposed model and strategy, which ensures that Quality of Service (QoS) standards are met in the domain of Vehicle-to-Anything (V2X) sidelink communications.

Building upon the foundations laid in our previous work [1], which introduced a LoRa-based wireless train network, this paper extends its scope to explore a LoRaWAN Network Architecture. We present a comprehensive mathematical model evaluating the probability of success for multi-hop localization packet transmissions encompassing the dynamic interactions between trains, catenary masts, and stations. The proposed system serves as a redundant solution in the event of GPS/GSM system failures, which are typically relied upon for localization in railway signaling systems of this nature. Additionally, the paper delves into a thorough analysis of the interference impact, assessing Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), and Frequency Division Multiple Access (FDMA) techniques. Our research extends beyond theoretical propositions to practical validation through real-world measurements conducted on the Bologna-Vignola railways, providing insights into system robustness under varying environment conditions, to ensure continuous operation in railway signaling. This research enhances understanding and offers practical insights for implementing reliable secondary line signalling systems, making a valuable contribution to the evolution of railway communication technologies, particularly in real-world applicability.

This paper analyses an Industrial IoT scenario with sensing capabilities where a set of base stations (BS) is deployed to provide connectivity to devices located over industry assets. The objective of this study is to propose a machine learning (ML)-based channel propagation model which can be applied to sub-6 GHz, mmWaves, and THz frequencies for the purpose of sensing the surrounding environment. The dataset, generated with the 3DScat ray tracing tool and made publicly available, is initially processed by a ML classifier to identify line-of-sight (LoS) and non-LoS (NLoS) links, achieving an accuracy of over 99.1%. Subsequently, the best fit line for both types is derived through regression and it is employed to construct the channel model by extrapolating channel parameters such as the path loss exponent and the standard deviation of shadowing. Finally, we investigate the impact of the industrial layout on channel propagation and network performance, to determine the optimal BS height.

This paper considers an Industrial Internet of Things (IIoT) scenario, where wireless devices embedded with sensors are deployed over an industrial machine, and transmit the measured data to a final Gateway (GW) using Terahertz (THz) frequencies. To mitigate the path loss of such high frequencies, the GW is equipped with multiple radiating elements, thereby generating highly directive beams, while sensors have just one single radiating element for miniaturization purposes. In this scenario, we study the applicability of a slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) from a mathematical perspective. The analytical model is validated via comparison with simulations, and the impact of different simplifying assumptions in shown. We also demonstrate the effectiveness of the CSMA/CA when compared to ALOHA, and we prove that propagation delays cannot be neglected at THz frequencies.

In the context of mobile and Internet of Things (IoT) networks, data naturally originates at the edge, making crowdsourcing a convenient and inherent approach to data collection. However, crowdsourcing presents challenges related to privacy, sampling bias, statistical sufficiency, and the need for time-consuming post-processing. To this end, generating synthetic data using deep learning techniques emerges as a promising solution to overcome such limitations. In this study, we propose an innovative framework that transcends applications and data types, enabling the conditional generation of crowdsourced datasets with location information in mobile and IoT networks. A crucial aspect of our methodology lies in the ability to assess uncertainty in newly generated samples and produce calibrated predictions through approximate Bayesian methods. Without loss of generality, we ascertain the validity of our method on the task of minimization of drive test (MDT) data generation, presenting for the first time a comparison of synthetically generated data with an original large-scale MDT set collected from a mobile network operator's network infrastructure. By offering a versatile solution to data generation, our framework contributes to overcoming challenges associated with crowdsourced data, opening up possibilities for advanced analytics and experimentation in mobile and IoT networks.

This paper explores the issue of enabling Ultra-Reliable Low-Latency Communications (URLLC) in view of the spatio-temporal correlations that characterize real 5th generation (5G) Industrial Internet of Things (IIoT) networks. In this context, we consider a common Standalone Non-Public Network (SNPN) architecture as promoted by the 5G Alliance for Connected Industries and Automation (5G-ACIA), and propose a new variant of the 5G NR semi-persistent scheduler (SPS) to deal with uplink traffic correlations. A benchmark solution with a “smart” scheduler (SSPS) is compared with a more realistic adaptive approach (ASPS) that requires the scheduler to estimate some unknown network parameters. We demonstrate via simulations that the 1-ms latency requirement for URLLC is fulfilled in both solutions, at the expense of some complexity introduced in the management of the traffic. Finally, we provide numerical guidelines to dimension IIoT networks as a function of the use case, the number of machines in the factory, and considering both periodic and aperiodic traffic.

Remaining Useful Life (RUL) prediction is a critical task that aims to estimate the amount of time until a system fails, where the latter is formed by three main components, that is, the application, communication network, and RUL logic. In this paper, we provide an end-to-end analysis of an entire RUL-based chain. Specifically, we consider a factory floor where Automated Guided Vehicles (AGVs) transport dangerous liquids whose fall may cause injuries to workers. Regarding the communication infrastructure, the AGVs are equipped with 5G User Equipments (UEs) that collect real-time data of their movements and send them to an application server. The RUL logic consists of a Deep Learning (DL)-based pipeline that assesses if there will be liquid falls by analyzing the collected data, and, eventually, sending commands to the AGVs to avoid such a danger. According to this scenario, we performed End-to-End 5G NR-compliant network simulations to study the Round-Trip Time (RTT) as a function of the overall system bandwidth, subcarrier spacing, and modulation order. Then, via real-world experiments, we collect data to train, test and compare 7 DL models and 1 baseline threshold-based algorithm in terms of cost and average advance. Finally, we assess whether or not the RTT provided by four different 5G NR network architectures is compatible with the average advance provided by the best-performing one-Dimensional Convolutional Neural Network (1D-CNN). Numerical results show under which conditions the DL-based approach for RUL estimation matches with the RTT performance provided by different 5G NR network architectures.

This paper addresses the problem of enabling intra-machine communication in an Industrial Internet of Things (IIoT) scenario using Terahertz (THz) frequencies. To mitigate the path loss of such high frequencies, a tree-based topology is built: each automation machine of the factory is controlled by a Router that collects data measured by wireless sensor devices located on the machine and forwards them to a final Gateway (GW) for elaboration purposes. Routers are equipped with multiple antenna elements, while sensor devices have only a single antenna element. In this scenario, we study the applicability of a contention-based access protocol, based on Carrier Sense Multiple Access, and we demonstrate its effectiveness when compared to Aloha. The performance of an uplink communication is evaluated in terms of Packet Success Probability and Network Throughput. Results demonstrate the impact of different parameters, like the sensing range of devices, the number of radiating elements at the Routers, the size and number of machines, and demonstrate that propagation delays cannot be neglected at THz frequencies.

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in Machine Learning (ML), such as Bayesian Learning (BL) and Machine Learning on Graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications.

The fourth industrial revolution is paving the way for Industrial Internet of Things applications where a large number of wireless nodes, equipped with sensors and actuators, monitor the production cycle of industrial goods. This article proposes and analyzes LoRa for industrial networks (LoRaIN), a network architecture and medium access control-layer protocol thought for on-demand monitoring of industrial machines. Our proprietary system is an energy-efficient, reliable, and scalable solution, where the protocol is built on top of LoRa at 2.4 GHz. Indeed, the low-power characteristics of LoRa allow to reduce energy consumption, while Wireless Power Transfer is used to recharge batteries, avoiding periodic battery replacement. High reliability is obtained through the joint use of Frequency and Time Division Multiple Access. A dynamic LoRaIN scheduler manages the communication and recharging phases depending on the tasks assigned to the nodes, as well as the number of monitoring devices. Performance is measured in terms of network throughput, energy consumption and latency. Results demonstrate that the proposed solution is suitable for monitoring applications of industry machines.

In this paper, we study the potential advantages of integrating Unmanned Aerial Vehicles (UAVs) equipped with Base Stations, with a terrestrial mobile network infrastructure. In particular, our main focus here lays on handover and its role in B5G (Beyond 5G) networks. We consider a scenario where multiple UAVs and terrestrial bases jointly provide service to mobile ground users. By combining the UAV dynamic trajectory and radio access procedures, we investigate the impact and network performance of ground-to-air handovers. With respect to previous papers, we discuss how to optimise the UAV segment of the network. Final results show that the UAV needs to adjust its speed, height and antenna radiation angle to improve network performance.

Connectivity and automation are increasingly getting importance in the automotive industry, which is observing a radical change from vehicles driven by humans to fully automated and remotely controlled ones. The test and validation of all the related devices and applications is thus becoming a crucial aspect; this is raising the interest on hardware-in-the-loop (HiL) platforms which reduce the need for complicated field trials, thus limiting the costs and delay added to the process. With reference to the test and validation of vehicle-to-everything (V2X) communications aspects, and assuming either sidelink LTE/5G-V2X or IEEE 802.11p/bd technologies, in this work we focus on the real-time HiL simulation of the information exchanged by one vehicle under test and the surrounding, simulated, objects. Such exchange must be reproduced in a time-efficient manner, with elaborations done fast enough to allow testing the applications in real-time. More precisely, we discuss the simulation of non-ideal positioning and channel propagation taking into account current impairments. We also provide details on optimization solutions that allowed us to trade-off minor loss in accuracy with a significant reduction of the computation time burden, reaching up to more than one order of magnitude speed increase in our experiments.

In this paper we analyse and compare the performance of Medium Access Control (MAC) protocols for networks of tags deployed over an industrial machine using THz communications. The challenging requirements of Industrial Internet of Things (IIoT) applications make the THz frequencies very attractive. However, one major drawback from the protocol viewpoint is that, even at short distances, propagation delays at THz frequencies can be of the same order of magnitude as the transmission time of the (short) packets sent by tags. This requires proper investigation of MAC protocols accounting for propagation delays. The paper compares contention-free and random MAC options considering two scenarios, where tags are still or they move in the automation machine.

In this paper, we consider a NarrowBand-Internet of Things (NB-IoT) network where an Unmanned Aerial Vehicle (UAV) is employed to gather data from IoT devices deployed in a given area. It is well known that UAVs may fly over the terrestrial plane, where and when needed, acting as Unmanned Aerial Base Stations (UABs). In order to serve as many ground IoT devices as possible, a proper trajectory design is fundamental. As we show in the paper, the optimization of the UAV speed and the radio parameters are also essential. Specifically, this paper studies a cluster-based scenario, where IoT devices are deployed according to a Thomas process, and applies a Traveling Salesman Problem approach to design the UAB trajectory. Notably, our model considers the protocol constraints on the number of resource units available on the UAB's NPUSCH, and the data rate that it can provide to IoT devices. Our results reveal the impact of different design parameters, such as UAB speed and NPRACH periodicity on the network throughput and the number of requests served.

A mobile wireless delay-tolerant network (DTN) model is proposed and analyzed, in which infinitely many nodes are initially placed on R2according to a uniform Poisson point process (PPP) and subsequently travel, independently of each other, along trajectories comprised of line segments, changing travel direction at time instances that form a Poisson process, each time selecting a new travel direction from an arbitrary distribution; all nodes maintain constant speed. A single information packet is traveling towards a given direction using both wireless transmissions and sojourns on node buffers, according to a member of a class of utility-based routing rules. For this model, we compute the long-term averages of the speed with which the packet travels towards its destination and the rate with which the wireless transmission cost accumulates. Because of the complexity of the problem, we employ two intuitive, simplifying approximations; simulations show that the approximation error is typically small. Our results provide intuition on the fundamental trade-off that exists in mobile wireless DTNs between the packet speed and the packet delivery cost. The framework developed here is both general and versatile, and can be used as a starting point for further investigation.

We study a mobile wireless network in which nodes travel on an infinite plane along trajectories comprised of linear segments; a single packet is traveling towards a destination located at an infinite distance, employing both wireless transmissions and sojourns on node buffers. In this setting, we calculate analytically the long-term averages of (i) the speed with which the packet travels towards its destination and (ii) the rate with which the wireless transmission cost accumulates with distance traveled. Due to the problem's complexity, we use two approximations; simulations show that the resulting errors are modest.

An infinite number of nodes move in ℝ2according to a random waypoint model; a single packet is traveling towards a destination (located at an infinite distance away) using combinations of wireless transmissions and physical transport on the buffers of nodes. In earlier work [1] we defined two performance metrics, namely, the long-term average speed with which the packet travels towards its destination, and the rate with which transmission cost accumulates with distance covered. Explicit expressions were derived for these metrics, under specific ergodicity assumptions. In this paper we give a precise description of the induced Markov process, we show that it is indeed (uniformly) geometrically ergodic, and that the law of large numbers holds for the random variables of interest. In particular, we show that the two performance metrics are well-defined and asymptotically constant with probability one.

The use of Unmanned Aerial Vehicles (UAVs) for remote sensing and surveillance applications has become increasingly popular in the last decades. This paper investigates the communication between a UAV and a final control center (CC), using static relays located on the ground, to overcome the intermittent connectivity between the two end points, due to the UAV flight. Backpressure and FlashLinQ routing and scheduling algorithms are jointly applied to this scenario. Backpressure has been shown to be able stabilize any input traffic within the network capacity region without requiring knowledge of traffic arrival rates and channel state probabilities. FlashLinQ is used in the scheduling phase to derive a maximal feasible subset of links which can coexist on a given slot without causing harmful interference to each other. Moreover, to overcome the limit on long end-to-end delays of backpressure, we propose a modified algorithm, where relays are selected depending on their proximity to the CC and on the UAV trajectory. Through extensive simulations, we demonstrate that, compared to the benchmark solution based on backpressure, the proposed algorithm is able to reduce delay significantly without any loss in throughput gain.

We consider a mobile radio network where Unmanned Aerial Vehicles (UAVs), also known as drones, are used as a viable alternative to traditional static Terrestrial Base Stations (TBSs). UAVs can fly over the terrestrial plane, where and when TBSs cannot provide the requested coverage and capacity, that is acting as Unmanned Aerial Base Stations (UABs). To this aim, it is important to optimize both the path of the drone and the number of radio resources to be assigned to the users along the path. This paper formulates the problem as an Integer Linear Programming (ILP) model with the aim of maximizing the number of served users, weighted according to their priorities, while considering constraints on UAB battery, speed, data rate and radio resources that the UAB can provide to the users. A Rolling Horizon heuristic algorithm is proposed to heuristically solve the problem: it is based on dividing the time horizon in time windows, and iteratively solving the ILP model on the single time windows. Even though the Rolling Horizon heuristic algorithm provides sub-optimal solutions, the computing time is acceptable, thus allowing the algorithm to be used in practice.

The Internet of Things (IoT) addresses a huge set of possible application domains, requiring both short- and longrange communication technologies. When long distances are present, a number of proprietary and standard solutions for low power wide area networks are already available. Among them, LoRaWAN is a candidate technology supported by many network operators. This paper discusses the possible role of LoRaWAN for the IoT. First, the LoRaWAN technology is assessed experimentally through a set of laboratory and on-field tests, to characterize it from the link-level viewpoint; then, a system-level analysis is performed through simulation, to evaluate the capacity of an LoRaWAN gateway and a multigateway network to serve a large area. Finally, the results are discussed considering a broad set of use cases.

Using an Unmanned Aerial Base Station (UABS) i.e., a base station carried by a UAV (Unmanned Aerial Vehicle) or drone, is a promising approach to offer coverage and capacity to those users that are not being served by the base stations of the terrestrial network. In this paper, we propose an approach to the design of the drone's trajectory to account for the quickly varying user traffic and pattern. This approach is based on the identification of clusters made of nearby users to be served. The decision on which cluster to visit next by the UABS depends on a cost-function considering the distance to the next cluster, the user density and spread in the cluster, and the direction compared to the previously visited cluster. Furthermore, we propose a radio resource assignment algorithm to minimize the interference from the UABS to the terrestrial network when both are operating in the same frequency band. The potential improvements in terms of network capacity (sum throughput) and user satisfaction are estimated in this study.

This paper proposes a novel approach to assess the performance in terms of end-to-end throughput of an ad hoc multi-hop wireless network, where each link is affected by interference coming from other multi-hop paths nearby. The approach captures the mutual impact of each path on all others. It can be applied to both, contention-based and scheduled, medium access control (MAC) protocols. Sources have data to send to destination nodes through n relays. Nodes are assumed to be uniformly and randomly distributed in the 2-D infinite plane. The model shows the impact on the end-to-end throughput of n; it also captures the influence of node density, traffic generated, number of retransmissions, and other MAC parameters. Finally, the model provides the throughput-delay tradeoff. Unlike most previous approaches, the mathematical tool proposed appears to be scalable, allowing easy extension to any number of hops. Comparison with simulation results is provided to prove that the impact of the approximations introduced in the analysis is almost negligible.

Resource allocation is a challenging issue in multiuser wireless systems. Since users are not all in the same conditions and do not achieve the same performance given the same amount of resources, resource allocation must typically deal with the following two conflicting objectives: on the one hand, the overall performance should be maximized, tending to prioritize few users in better conditions, and on the other hand, fairness among users should be maximized, consequently reducing the overall performance. Identifying the compromise that is somehow optimum is all but simple and this aspect is normally worsened by the absence of a curve describing how the maximum performance varies changing the accepted level of fairness. To cover this gap, in this paper, we propose a unified and general mathematical formulation of the optimal performance versus fairness tradeoff in multiuser wireless communication systems. Differently from the existing literature, our approach is based on the generalized Lagrange method and adopts general definitions for both performance and fairness. Besides the exact solving equations describing how the maximum performance varies with fairness, we also derive a simpler lower bound with reduced computational cost. Example results are provided for two case studies, respectively, concerning linear and logarithmic dependence of performance on resources.

We study a wireless network comprising an infinite number of nodes moving in ℝ2according to a Random Waypoint (RWP) mobility model. Each node is equipped with a radio transceiver with transmission range R; a transmission across a distance d incurs a quadratic cost d2. We assume that a packet is generated at one node and must be delivered to a destination located at an infinite distance in the direction of the positive x-axis through a combination of wireless transmissions and physical transports on the buffers of nodes. A routing rule specifies when a wireless transmission should take place. Given this setting, we develop an analytic framework, using tools from stochastic geometry and Markov chains theory, to evaluate, with certain approximations, the tradeoff between the speed with which the packet travels toward the destination and the transmission cost incurred per unit of distance. Simulation results show a good match with the analytical results.

Several approaches have been considered by research community as possible enablers for the Internet of Things (IoT) implementation. This paper presents the results obtained by testing and comparing three different solutions. In particular, we compare a centralized solution based on software defined network (SDN), called software defined wireless networking (SDWN), with two standard and distributed solutions, that are ZigBee and IPv6 over low-power wireless personal area networks (6LoWPAN). SDWN uses a centralized network layer protocol, where routing policies are defined by an external controller that can be positioned anywhere in the network. The other two solutions are actually the most common protocol stacks for wireless sensor networks, and they both use a distributed routing protocol. The comparison is achieved by experimentations performed on the European Laboratory of Wireless Communications for the Future Internet (EuWIn) platform developed within the network of excellence, NEWCOM#. Results show that SDWN is the best solution in static or quasi-static environments, while the performance degrades in highly dynamic conditions. However, ZigBee has a good reactivity to environmental changes. This paper reports the evaluation of several performance metrics, including packet loss rate, round-trip-time, and overhead generated in the network, under different conditions and considering different kinds of traffic.

This paper proposes a novel methodology for the spatial downscaling ofreal-world deploymentsof wireless networks, running protocols, and/or applications for the Internet of Things (IoT). These networks are often deployed in environments not easily accessible and highly unpredictable, where doing experiments is very expensive and time consuming. The latter calls for the need to develop downscaled testbeds, deployed in controlled environments, where tests can be conducted under predictable conditions. This paper presents a methodology to realize the downscaling of a real deployment on an experimental platform, calledcontrollable testbedthat has a much larger number of nodes with respect to the real one. The downscaling procedure proposed is based on the identification of the most appropriate subset of nodes of the controllable testbed, to be used to reproduce the channel gains between each node pair in the real world. The latter, in fact, results in obtaining the same network topologies, bringing to the same performance on average, when the same protocol stack and software are run. After the description of the procedure, an example of its implementation is provided. Comparison of results, in terms of packet loss rate (PLR), network throughput, and topologies achieved on the downscaled testbed and on the real-world deployment, is given; results show a very good fit and demonstrate the efficacy of the proposed methodology.

In this paper we address multi-hop Carrier Sense Multiple Access (CSMA)-based networks and we propose a novel approach based on a semi-Markov chain analysis, decoupling node and network levels. We model node states through a Semi-Markov Process and, on the other hand, we define a finite state transition diagram to describe the network states. The latter is used to derive the parameters included in the Semi-Markov Chain that depend on the network status, as for example, the probability of finding the channel busy. In particular, we consider a multi-hop wireless network, where nodes are deployed over a straight line. Each node sends its data to the next node in the line, to reach the destination located at the end. Nodes use a CSMA-based protocol, where priority in the access to the channel is given to nodes closer to the destination. We mathematically derive the network performance, in terms of throughput and energy consumption. The model is validated through comparison with simulations.

This paper presents a channel characterisation for Body-to-Body communications. Starting from indoor real-time measurements at 2.45 GHz, the radio channel was modeled, both in Line-of-Sight (LOS) and Non Line-of-Sight (NLOS) conditions, through a mean channel gain component plus a fading contribution, the latter arising from the body movements. The body shadowing effect was also evaluated considering the orientation of one human body with respect to another one. Two different antennas were employed in the measurements, to assess their impact on the radio channel characteristics.

The sleeping strategy has become popular to reduce power consumption of base stations (BSs) by shutting down underutilized BSs in the management of green cellular networks. In this paper, we propose a novel solution for an energy efficient use of cellular networks, based on traffic load balancing. By modeling the power consumption for BSs connected to uniformly distributed users, the relationship between the optimal number of active (or shut down) BSs and the traffic load is then derived through the power ratio, which is the ratio between dynamic and fixed power part of BS power consumption. Both analytical and simulation results demonstrate that, in order to achieve significant energy savings, less BSs should be turned on at low traffic load while more BSs turned on at high traffic load.

In this paper we consider a multi-hop ad hoc network where nodes are distributed according to a Poisson point process (PPP) over a line. A slotted Carrier Sense Multiple Access protocol is used to transmit data from a source to a destination node, through multiple relays. Due to the hidden terminal node problem interferences are present and reduce the network capacity. Performance is evaluated in terms of mean ergodic capacity of the generic link. Results show that there exists an optimum value of nodes' density maximizing the performance and this value changes when varying the source-destination distance.

In this paper, we present a mathematical framework to study decentralized detection in IEEE 802.15.4-compliant clustered wireless sensor networks (WSNs). Sensors are organized in clusters, with fusion centers (FCs) acting as cluster heads, and are supposed to observe the same common binary phenomenon. The FCs periodically send queries to sensors and wait for replies. Once data are received, the FCs perform majority fusion. Decisions are sent to an access point (AP), where a final data fusion is carried out and an estimate of the phenomenon is obtained. A data aggregation strategy at sensors is also investigated. The goal of this paper is to jointly investigate decentralized detection, medium access control (MAC), and data aggregation issues. Our results show that an optimum WSN clustering configuration exists and it depends on the data aggregation strategy used and the MAC parameters.

We consider a Wireless Body Area Network (WBAN) where wearable sensor devices are distributed on a body and have to send the measured data to a coordinator. The network uses the Carrier Sense Multiple Access with Collision Avoidance algorithm defined by the IEEE 802.15.4 standard as Medium Access Control protocol, whereas different modulation schemes are assumed to be available at the physical layer. We propose to use the Link Adaptation (LA) in this WBAN scenario, with the aim of reducing packet losses. Nodes select the modulation scheme according to the experienced channel quality: when the Signal-to-Noise Ratio is large, regardless of the actual interference, nodes increase the bit rate, in order to decrease the time the channel is occupied and, therefore, the collision probability. Performance is evaluated in terms of packet error rate and results achieved with and without LA are compared. Results show that the proposed strategy improves performance.

Femtocells are envisioned to be deployed in indoor environments in order to improve both radio coverage and spectrum efficiency. This paper focuses on the self-organization of indoor femtocells, which includes mechanisms of cooperation. In this context, we propose a solution to automatically form cooperating groups among severely interfered femtocells in order to avoid interference. A hybrid access policy is proposed and compared to the closed and open policies. Results show that, as stated in the Braess Paradox, pervasive cooperation may be detrimental, when available resources are highly contended. Conversely, in particular cases, a marginal selfish behavior of femtocells can be preferable.

Overlaying low-power, low-cost, femtocells, over existing wireless networks has recently emerged as a means to significantly improve the coverage and performance of next-generation wireless networks. While most existing literature focuses on spectrum sharing and interference management among non-cooperative femtocells, in this paper, we propose a novel cooperative model that enables the femtocells to improve their performance by sharing spectral resources, minimizing the number of collisions, and maximizing the spatial reuse. We model the femtocell spectrum sharing problem as a coalitional game in partition form and we propose a distributed algorithm for coalition formation. Using the proposed algorithm, the femtocells can take autonomous decisions to cooperate and self-organize into a network partition composed of disjoint femtocell coalitions and that constitutes a stable partition which lies in the recursive core of the considered game. Whenever a coalition forms, the femtocells inside this coalition can cooperatively pool the occupied spectral resources. Additionally, the members of any given coalition jointly schedule their transmissions in order to avoid collisions, in a distributed way. Simulation results show that the proposed coalition formation algorithm yields a performance advantage, in terms of the average payoff (rate) per femtocell reaching up to 380% relative to the non-cooperative case.

In this paper, the impact of edge effects on the performance of MAC protocols is evaluated. We consider a network where packets are distributed in finite space according to a 2-D PPP, while packets arrive in time according to a 1-D PPP. Each transmitter communicates with its own receiver a fixed distance away using either the ALOHA or CSMA protocol. The metric used for analysis is outage probability, which is the probability that the measured SINR falls below a predefined threshold for the duration of a packet. Approximate analytical expressions are derived for the outage probability of the different MAC protocols assuming a fading channel. Compared to unbounded regions, ALOHA and CSMA are shown to yield lower outage probabilities in finite regions, since the boundedness can be regarded as spatial filtering. Furthermore, the benefit seems to be greater for small outage probabilities, i.e., in the region of practical interest.

This paper considers the impact of backoffs and retransmissions on the performance of the ALOHA and CSMA protocols in wireless mobile ad hoc networks. In our network model, packets belonging to specific transmitters arrive randomly in space and time according to a 3-D PPP, and are then transmitted to their intended destinations using a fully-distributed MAC protocol. A packet transmission is considered successful if it is initiated and the received SINR is above a predefined threshold for the duration of the packet. Approximate expressions are derived for the outage probability of ALOHA (slotted and unslotted) and CSMA (with transmitter sensing and with receiver sensing), as a function of the transmission density and the number of backoff and retransmission attempts. Simulations verify our analytical expressions, and it is shown that the outage probability is a decreasing function of the maximum allowable number of backoffs and retransmissions. By allowing for high number of backoffs, CSMA with receiver sensing can even outperform slotted ALOHA. For high number of retransmissions, however, ALOHA may outperform CSMA. Finally, the average transmission delay of packets is also evaluated through simulations.

In this demo we present a solution for detailed coverage assessment and failures recovery in an IEEE 802.11 Wi-Fi network deployed in highly dynamical environment (e.g., where signal propagation is impacted by frequent topological changes). Monitoring of the electromagnetic energy received by an Access Point (AP) is performed by multiple battery-powered ZigBee-compliant devices dislocated in the environment. The key is a novel use of the Energy Detection Scan functionality implemented in the IEEE 802.15.4 standard. ZigBee devices wirelessly send their readings to a coordinator device which is in turn connected to a workstation. The latter serves as a i) configuration spot of the ZigBee network, ii) concentrator and data processor/visualizer, and iii) actuator. Such coordinating workstation can indeed detect and switch off an AP that is deemed ineffective (due to, e.g., damages or unfavorable propagation conditions), and start up one or more pre-deployed backup APs, so that coverage is preserved and clients can seamlessly keep on performing their activities. Hence, our scheme optimizes the performance of a Wi-Fi network by means of a low-cost, independent and self-maintaining mesh network, while strongly limits human intervention.

In this paper we present a mathematical model to study a multi-sink Wireless Sensor Network (WSN). Both sensors and sinks are assumed to be Poisson distributed in a given finite domain. Sinks send periodic queries, and each sensor transmits its sample to a sink, selected among those that are audible, thus creating a clustered network. Our aim is to describe how the Area Throughput, defined as the amount of samples per unit of time successfully transmitted to the sinks from the given area, depends on the density of sensors and the query interval. We jointly account for radio channel, Physical (PHY), Medium Access Control (MAC) and Network (NET) aspects (i.e., different network topologies, packet collisions, power losses and radio channel behaviour), and we compare the performance of two different simple data aggregation strategies. Performance is evaluated by varying the traffic offered to the network (i.e., the density of sensors deployed), the packet size, and, by considering IEEE 802.15.4 as a reference case, the number of Guaranteed Time Slots allocated, and the Superframe Order. The mathematical model shows how the Area Throughput can be optimized.

Channel fluctuations affecting links of ad-hoc and sensor networks show an evident spatial correlation, besides the random behavior. Nonetheless, the vast majority of models used in the literature assign edges between pairs of vertices of a graph according to either the deterministic disk model or some random connection model assuming i.i.d. fluctuations. We believe none of the approaches reflects the reality. In this paper we introduce a Correlated Random Connection Model (CRCM) which accounts for angular correlation, by means of a tunable parameter, in the fluctuations that affect two links sharing one of the endpoints. Assuming a constant average number of neighbors, we study the percolating properties of correlated footprints on random graphs by computing the relative size of the two largest components of the graph and the probability of the event of (almost) connectivity. We also compare it to the case of some non- probabilistic shapes of both theoretical and practical flavor. Our results show that the presence of correlation may be beneficial or detrimental, depending of whether one considers undirected or directed graphs, i.e., ultimately, on the application.

This paper introduces a new method for distributed interference mitigation in full spectral-reuse OFDMA cellular networks. This considers the use of pre-defined frequency-domain power profiles helping make the interference more predictable across the subcarriers. We propose a method for computing the power profiles so as to maximize the capacity of the system in case of maximum throughput scheduling, and a simple linear model implemented also in presence of a fairness-oriented scheduler. We prove that our idea of power planning gives substantial improvements in terms of outage capacity in case of fairness-oriented scheduling. The advantage of our method over previously proposed approaches for interference mitigation based on power control is that our algorithm is fully distributed and does not require any exchange of signaling between the different cells.

In this paper we present a mathematical approach to evaluate the area throughput and the energy consumption of a multi-sink Wireless Sensor Network (WSN). The WSN is organised into clusters, with one sink per cluster collecting data from sensors. A small variation of the Thomas point process is used to model sensors and sinks positions in the target area. We denote as area throughput the amount of samples per second successfully transmitted to the sinks. Both area throughput and energy consumption are strictly related to connectivity and MAC issues. The aim of this work is to devise a mathematical model that takes MAC and connectivity issues into account, under a common framework. We study the behavior of these two performance metrics when varying the target rate, defined as the maximum number of samples the network was deployed to deliver. Results show that a tradeoff between the area throughput and the energy consumption must be found. Finally, the impact of different sensors and sinks distributions on the area throughput is evaluated.

We discuss the correctness of one of the main results of about the probability density function of the distance between two audible nodes in an infinite 2-dimensional scenario. We prove that result is wrong and derive a more general expression, which is valid for an infinite d-dimensional area. It is worth noting that, although the results on the distribution of the distance between two nodes are wrong, the other results and in particular the fact that the number of audible nodes in an arbitrary area is a Poisson r.v. is correct.

In this paper we present a mathematical approach to evaluate the area throughput of a multi-sink wireless sensor network (WSN), where nodes transmit their packets to a sink, selected among many. Sensors and sinks are both Poisson distributed in a bounded domain. A carrier sensing multiple access (CSMA) based protocol is used by nodes to access the channel. We denote as area throughput the amount of samples per second successfully transmitted to the sinks. This performance metric is strictly related to both connectivity and MAC issues: it depends, in fact, on the probability that a given sensor node is not isolated and that it succeeds in transmitting its packet (i.e., the packet does not collide). The aim of this work is to devise a mathematical model that takes CSMA and connectivity issues into account under a joint approach. Through this model some network optimisation strategies could be derived. As an example, sensors could perform an aggregation procedure, responding sporadically to queries with a single packet composed of all samples taken since the previous transmission. Our model allows the evaluation of the optimum size of the packet that should be transmitted, so that the area throughput is maximised. Finally, the effects of the connectivity on the area throughput are evaluated.

The hybrid hierarchical architecture (HHA) represents a particular case of wireless hybrid network, where sensor nodes transmit their samples to an infrastructure network through multiple hops. In the HHA, gateway terminals implementing both cellular and infrastructure-less air interfaces, allow integration of the two separate paradigms characterising the wireless sensor network (WSN) and the cellular network. In this paper, in particular we study a hierarchical network where an IEEE 802.15.4 WSN is connected, through a mobile gateway, to an infrastructure network using a cellular air interface like UMTS. In such scenario, the mobile gateway receives data from sensors with an inter-arrival time distribution which depends on the WSN topology, the sensor node density, and the parameters set in the 802.15.4 MAC protocol, such as the superframe order, the number of guaranteed time slots, etc. Such distribution is analysed in this paper through simulation. The outcome of this work provides useful hints to the characterisation of the traffic generated by the mobile gateway and provided to the infrastructure network. The design of the scheduling techniques implemented at the infrastructure side requires suitable knowledge of the characteristics of such traffic.

This paper aims at designing tree-based topologies for Multi-Sink Wireless Sensor Networks (MS-WSNs). Tree height and the average number of children per parent, are the degrees of freedom of such a topology. The objective is to maximise the amount of information gathered at the sinks, that is, network reachability, having fixed node and sink densities and the air interface capacity. Once the number of levels for trees is fixed, network reachability is maximised by a proper choice of the average number of nodes at each level (i.e. the average number of children per parent). The study is performed by means of a statistical model and simulations. A mathematical framework taking into account connectivity aspects, radio channel fluctuations, random node and sink deployment, and the capacity of the air interface is considered; the mathematical analysis is based on a deterministic multiple access control (MAC) scheme. The framework has some limits: it does not consider a contention-based MAC protocol and assumes that nodes are distributed over an infinite plane. To overcome these limits we have developed a multi- sink simulator, which implements an IEEE 802.15.4 standard compliant network, based on a three-level tree based topology. Simulation results show trends very similar to the ones obtained through the mathematical model; the causes of the differences between the two are discussed in detail. Finally, the difference between a mono-sink scenario, as usually considered in the literature, and the case with multiple sinks, is discussed by means of numerical comparisons.

This paper investigates the topology formation mechanisms provided by IEEE 802.15.4 to create tree-based topologies, in the context of a multi-sink wireless sensor network. the topology formation protocol is simulated starting from a random deployment of a given number of nodes, to obtain statistics of the position of those nodes belonging to given tree levels. It is shown that under some circumstances the nodes belonging to any level can be assumed to be uniformly distributed over the plane. Under such assumption, a mathematical formulation is used to optimize the average number of children per parent and the number of levels in the tree (that is, the tree height), through maximization of the network association probability

In this paper, the estimation of a scalar field over a bidimensional scenario (e.g., the atmospheric pressure in a wide area) through a self-organizing wireless sensor network (WSN) with energy constraints is investigated. The sensor devices (denoted as nodes) are randomly distributed; they transmit samples to a supervisor by using a clustered network. This paper provides a mathematical framework to analyze the interdependent aspects of WSN communication protocol and signal processing design. Channel modelling and connectivity issues, multiple access control and routing, and the role of distributed digital signal processing (DDSP) techniques are accounted for. The possibility that nodes perform DDSP is studied through a distributed compression technique based on signal resampling. The DDSP impact on network energy efficiency is compared through a novel mathematical approach to the case where the processing is performed entirely by the supervisor. The trade-off between energy conservation (i.e., network lifetime) and estimation error is discussed and a design criterion is proposed as well. Comparison to simulation outcomes validates the model. As an example result, the required node density is found as a trade-off between estimation quality and network lifetime for different system parameters and scalar field characteristics. It is shown that both the DDSP technique and the MAC protocol choice have a relevant impact on the performance of a WSN.

This paper investigates a three-level tree-based network topology using IEEE 802.15.4 for wireless sensor networks, where some nodes randomly elect themselves cluster heads (CHs): the CHs (level one) communicate with the other sensor nodes gathered in clusters (level two) on one side and with the sink (level zero), on the other. In particular, the percentage of samples taken from nodes that are correctly received at the sink is maximised, by optimising the number of CHs. Simulation results show that this optimum value exists, as a trade off between the load within clusters, which depends on their size and is controlled by increasing the number of CHs, and the probability of collisions among CH packets, that can be minimised by decreasing the number of CHs. To clarify the role of the different network parameters, such as the network size, the IEEE 802.15.4 superframe order, etc. in the determination of this optimum value, a simple and approximate statistical analysis of the PER is also included

We investigate a hierarchical wireless hybrid network, implementing two different network paradigms: ad hoc and infrastructure-based systems, where mobile gateways, equipped with both cellular and infrastructure-less air interfaces, allow integration of the two separate paradigms. The paper reports results achieved through a mathematical analysis of the topology of wireless links, providing the distribution of the number of lowest-level nodes attached to the highest level. Nodes have a maximum capacity, and seek service at the upper level through a retry mechanism where the nodes are addressed according to a ranked list based on the level of received power. The mathematical analysis provides a means to allow access control, depending on the maximum number of radio resource units available at each node and the maximum number of retries at each level. Wireless nodes at different levels are uniformly distributed over the bi-dimensional plane with different densities; path loss and shadowing are included with different propagation parameters, different air interfaces are used, and different hard capacities characterise the nodes at the different levels. With respect to other papers recently presented by the authors to other conferences, this work focuses on the role of the node capacity limitations and the maximum number of retries

In this paper, the results reported in (J. Orriss et al, IEEE Trans. on Comm., vol.51, no.4, 2003), where some probability distributions for the number of nodes which can communicate with one another are derived, are extended from the 2D to the 3D case. The nodes are assumed to be randomly and uniformly distributed over an area, and subject to random channel fluctuations. The analytical handling of the problem leads to closed form solutions, showing that, among the other results, the number of audible nodes from the infinite space, or a given volume, is Poisson with mean depending on the propagation parameters and the maximum loss. The model can be useful to investigate networks of tiny nodes (such as RFIDs, or wireless sensors) where the vertical dimension of the environment (a building, or a room, or a container) becomes very significant with respect to the short range of the single communication links.

A frequency- and time-division multiple-access (F-TDMA)-based mobile radio system using both dynamic channel allocation (DCA) and frequency hopping (FH) is investigated. We propose a new interference adaptive DCA (IA-DCA) algorithm that is suitably designed for a network implementing FH. The role played by the power control algorithm in this DCA-FH context is also investigated. We compare the performance of our proposal to that of fixed channel allocation (FCA) with and without FH and the well-known IA-DCA schemes investigated in the literature in the absence of FH. The performance results show that interesting synergic effects can be obtained in terms of forced termination and user satisfaction probability by using both DCA and FH. The results we show in the paper have been achieved by means of a system-level simulation tool which takes propagation, user mobility, interference, traffic, and channel allocation into account. The advantages of using FH are accounted for by using a suitable analytical model that gives the frame error rate as a function of the carrier-to-interference ratio and the number of hopping frequencies, at link level; this model is taken from the literature where it was presented for FCA, and here it is modified in order to be applicable to the DCA case.

An analytical framework for the performance evaluation of a dense energy-efficient wireless sensor network (WSN), enabling distributed collaborative environment monitoring, is developed. We address the estimation of a target multidimensional process by means of samples captured by nodes randomly and uniformly distributed and transmitted to a collector through a self-organizing clustered network. The estimation in the presence and in the absence of collaborative signal processing with different types of data interpolators are compared in terms of both process estimation error and network life-time. Our analytical model, aimed at providing useful information for WSN design, takes many aspects into account, such as distance-dependent path loss and shadowing, energy consumption, information routing, process estimation quality, node density, transmission protocol and system parameters. As an example result, fixing requirements on estimation errors and network life-time, the node density is found as a function of the system/protocol parameters.

In this paper a network of sensors uniformly and randomly distributed over a plane large enough to be considered infinite is considered. The author formalises a particular problem, where a supervisor wants to collect the sensor data from a specific region around him/her. This region is assumed to be small, so that no routing aspects have to be considered, as direct communication between sensor nodes and supervisor can take place. The losses characterising the communication links between them follow a power law affected by a random component. The system constraints are related to an application-driven maximum value of latency and the requirement of a minimum life duration of sensors. Propagation, physical and multiple access problems are jointly addressed through a complete analytical description. The evaluation of the statistics of the number of packets successfully received by the supervisor is carried out through the use of the results of a statistical study, taken from the literature, on the connectivity of nodes uniformly and randomly distributed over the infinite plane and subject to log-normal distributed loss. These results have been suitably applied to the sensor network, which requires the investigation of aspects related to the packet capture effect, the probability distribution of audible sensors, etc.

This work deals with the problem of cellular network reconfiguration. In particular, we focus on the development of a very complete simulation tool that allows an accurate performance analysis of realistic mobile radio systems. The results we show in the paper have been achieved by means of an integrated approach taking both the system and link level aspects into account. Since the accuracy of the simulation results depends strongly on the mobility model considered, we propose a methodology to assign a given class of mobility to the cells. Finally, to validate the simulation tool we have compared the results with those obtained in a realistic network The results show that the agreement between simulation and the measured values is very good.

This paper investigates the effects of directed retry (DR) in cellular networks characterized by a nonuniform environment, that is, with unequal distribution of users and/or radio resources. We propose an analytical model able to take both propagation and network aspects into account. The nonuniform environment is modeled assuming the superposition on a uniform scenario of a circular area characterized by different offered load or radio capacity. The case of a hot spot of users is included. Path loss and lognormal shadowing have been considered. This is done through a statistical approach which is very general, and provides an expression for the probability of receiving a suitable power level from a given number of base stations within a fixed distance from the mobile. A comparison to simulation results is performed to validate the model. We show that the impact of DR on the blocking probability depends largely on the propagation parameters considered.

In this paper we propose a semi-analytical approach, to evaluate WCDMA downlink capacity. New results are provided, with respect to literature, related to the role of the soft handover process and SSDT Mode (site selection diversity transmission power control) in network dimensioning. Moreover, the impact of sectorization has been taken into account in determining capacity, developing the model in case of three sectorial antennae. We show that, depending on soft handover algorithm parameters, capacity can be improved or worsened by enlarging the maximum active set size.

In this paper we propose an analytical approach, to provide a statistical method for estimating WCDMA downlink capacity. Starting from the model proposed in [H. Holma et al., 2001] and [K. Sipila et al., May 2000]. which has been illustrated in [C. Balzanelli et al., September 2003], we have provided an analytical extension to define capacity from a statistical point of view. The new results obtained from this improvement, have been compared with those obtained by a more complex semi-analytical model, proposed in [C. Balzanelli et al., September 2003], taking a scenario composed of many hexagonal cells, positions of users and a more precise path loss model into account. The comparison between this different approaches has pointed out similar results, in terms of capacity curves.

In this paper a link adaptation (LA) strategy to be implemented in a Bluetooth piconet is investigated: the performance level that can be achieved is derived and the comparison with the case of no LA is performed. Suitable definitions of the performance figures are given and starting from the knowledge of the physical level performance obtained via simulation, their analytical evaluation is performed in both the cases of LA and no LA. All aspects related to the propagation characteristics of the environment, the modulation/demodulation and coding/decoding techniques, and the multiple access strategy are taken into account in an integrated way.

This paper considers two sets of points which can communicate with one another and which are uniformly and randomly distributed, one set within a confined area, a hot spot, on the plane, and the other with a different density over the rest of the plane: an inverse power law with lognormal shadowing is assumed for attenuation. Communication is assumed to be possible if the attenuation of a signal transmitted from one point, say a mobile telephone, does not exceed some specified value by the time it reaches the other, say a base station. The probability distribution of the number of base stations which can hear such a signal from a given mobile is found, taking account of the effect of the hot spot. The conditional distribution of the number of such base stations within the hot spot, given the total number over the whole plane, is deduced, and this result is applied in an example to finding the blocking probability for a directed retry algorithm. The results also apply to the number of mobiles within range of a base station and, in an ad hoc network, to the number of nodes within range of a given node.

The most recent development in the field of mobile wireless ad-hoc networks is constituted by the Bluetooth wireless technology which allows users to make effortless, wireless, instant and low cost connections between various communication devices. Even if Bluetooth has not been specifically designed for WLAN, the possibility to establish low cost wireless connections makes this technology attractive also for the world of WLAN. In this paper we assess by means of a simulative approach, the performance of a Bluetooth based WLAN adopting a token ring MAC protocol in realistic channel conditions.

In this paper, the power allocation strategy in the downlink of a W-CDMA system is investigated, with the aim of assessing the network capacity. It is known that the downlink capacity for a base is limited by the maximum transmit power, which is a common resource to be shared by all users; if one user requires a large fraction of the allowed transmit power, the overall capacity could be decreased: in the work herein we analyse the impact on the capacity of a power allocation strategy which limits the maximum fraction of transmit power per link. A semi-analytical model is developed, considering a classical propagation model and taking soft handover and a multi-service scenario into account. The results show cell capacity as a function of some interesting parameters, such as the cell radius and the number of bases included in the active set. Both the cases of omni and sectorised cells are considered.

In this paper we present an analytical model for the estimation of the blocking probability as a function of the offered traffic per user in a cellular environment, where capacity is determined by hard blocking and the average number of users per cell is small. By the term "hard" capacity we mean the maximum number of users that can be served by a base station. Using a statistical model, the paper shows that the number of mobiles audible to a base that have that base as the strongest one has a Poisson distribution whose mean is given in terms of the mean densities of mobiles and base stations and the parameters of the attenuation law. The model proposed here does not represent a tool for detailed network planning, but it is a helpful (and from the computational viewpoint simple) means for network dimensioning.

An F-TDMA (frequency and time division multiple access) based mobile radio system using both dynamic channel allocation (DCA) and frequency hopping (FH) is investigated in this work. This paper follows the work of Verdone, Zanella and Zuliani (see IEEE PIMRC'01, San Diego, CA, September 2001), where the same authors proposed a new interference adaptive DCA algorithm that is suitably designed for a network implementing FH. The role played jointly by the power control and handover algorithm in this mixed DCA-FH context is investigated in this paper. We compare the performance of our proposal to that of fixed channel allocation with FH. The performance results show that interesting synergic effects can be obtained in terms of user satisfaction probability by using both DCA and FH. The results we show in the paper have been achieved by means of an integrated approach taking both system level and link level aspects into account.

In this paper a F-TDMA based mobile radio system using both dynamic channel allocation (DCA) and frequency hopping (FH) is investigated. We propose and evaluate the performance of a new interference adaptive DCA (IA-DCA) algorithm that is suitably designed for a network implementing FH. The results we show in the paper have been achieved by means of a simulation tool which takes propagation, mobility, interference, traffic and channel allocation into account. Moreover, the advantages of using FH are accounted for by using suitable analytical models that give the frame error rate as a function of the carrier-to-interference ratio and the number of hopping frequencies. We compare the performance of our proposal to that of fixed channel allocation (FCA) with and without FH, and the well known IA-DCA schemes investigated in the literature in the absence of FH. The performance results show that interesting synergic effects can be obtained in terms of forced termination and user satisfaction probability by using both DCA and FH in a F-TDMA based mobile radio network.

An analytical model to evaluate the mean packet delivery time in a WLAN implementing a token ring MAC protocol is presented and validated through simulation. Both the cases of an ideal and a faded channel are considered in the analysis: the model allows the introduction of the wireless channel characteristics, in terms of received power statistics of the first and second order. To this aim, a proper channel model is also presented that takes the typical characteristics of the wireless indoor channel into account. The analytical model gives a closed form derivation of the maximum offered load which keeps the system stable, and, thus, allows the assessment of the number of users to be admitted in the system. Through simulation the impact of the channel conditions on the maximum token holding time is also shown. It is found that, depending on the average signal-to-noise ratio experienced by the wireless terminals, the optimum maximum token holding time can be finite or not.

A channel adaptive scheduling algorithm for TDD/TCDMA radio interfaces was proposed by Ferracioli et al. (2000). This algorithm is investigated in this paper in order to assess its ability to improve the performance of the radio interface in the presence of fading and both real time and non real time traffic. More precisely, we estimate the maximum fading rate which gives performance improvements.

The blocking probability in a cellular network implementing directed retry (DR) is analyzed as a function of the main characteristics of the scenario (the density of base stations), the propagation model (path loss and shadowing), and the speed of mobiles. The analytical approach allows the evaluation of the blocking probability as a function of the maximum number of cells that can be considered at call setup by the DR algorithm. It is shown that the effectiveness of the DR strategy depends on the values of the variance of shadowing and the path loss model. Moreover, the blocking probability is found to be approximately independent from the mobile speed. The analytical results are compared to those obtained by means of simulation: a good agreement has been found, and the analytical model has been shown to be able to reflect the main effects of propagation and mobility on the advantages of DR in a real cellular network.

In this letter, a wireless local multipoint distribution system (LMDS) at millimeter waves for the last-mile broad-band distribution to users of interactive services is investigated. The system analyzed employs a coded orthogonal frequency-division multiplexing transmission scheme with frequency-division multiplexing (FDM) and/or time-division multiplexing techniques and adaptive carrier allocation to counteract the effects of the wireless communication channel. The idea of deploying the reverse channel (exploited by the user for interactivity purposes) to provide channel information to the broadcasting transmitter is introduced. System performance is evaluated for an urban microcellular radio system in an actual propagation environment and the choices of the optimum multiplexing technique and carrier allocation algorithm are discussed in the case of ideal feedback. It is found that a pure FDM technique combined with an adaptive carrier allocation algorithm giving priority to users having the largest path loss leads to the best performance.

A semi-analytical approach is proposed to evaluate the performance of TCP-IP based applications on wireless channels. The physical, data link and IP layers are analytically treated; the TCP is simulated. Additive Gaussian noise, slow and fast impairments due to the mobile radio channel are taken into account, with particular attention focused on a careful description of the different time scales to be used for the different processes introduced by the wireless link. It is shown that the fast fading and the spatial correlation due to shadowing can be analytically studied by means of a suitable combination of the link-level bit error rate (BER) vs. signal-to-noise ratio (SNR) curves and the Gilbert-Elliott model. Results are first given for wireless TCP-IP based on generic physical and data link layers; then, the application to IP over GPRS is considered. A validation of the approach is carried out by comparisons with pure simulation at the logical link control (LLC) level.

In this paper, the investigation of the effects of directed retry (DR) in cellular radio networks is carried out through an analytical model which takes both propagation and network aspects into account. In this model, path loss as well as lognormal shadowing have been considered. The paper shows that the impact of DR on the blocking probability depends largely on the number of cells which can be considered by the mobile during the call set-up procedure; this number depends on the propagation environment considered. The analytical results have been compared with those obtained through a simulation tool and the results have shown a good agreement. Moreover, with the aim to take non-uniform scenarios into account, a generalization of the analytical method, through a statistical approach, has been proposed.

In this paper a wireless local multipoint distribution system (LMDS) at 30 GHz for the last mile broadband distribution to users of interactive services is investigated. The system we propose users a coded OFDM transmission scheme with FDM and/or TDM multiplexing techniques and adaptive carrier allocation to counteract the effects of the wireless communication channel. An urban microcell in an actual propagation environment is analyzed by means of a ray-tracing tool. Coverage probability is defined and investigated and the choices of the optimum multiplexing technique and carrier allocation algorithm are discussed. It is found that a pure FDM technique with adaptive carrier allocation and priority to users having the largest path loss leads to the best performance.

A framework for the performance characterization of short-range communications systems is developed with the intention of investigating the feasibility of new multimedia wireless services at millimeter waves (MMWs). Both narrow- and wide-band systems are considered for mobile and/or fixed users. This paper aims at defining and evaluating proper metrics to characterize the service quality for the user and jointly takes the propagation characteristics, the transmission techniques, and the multiple access protocols into account. The definition of service-oriented metrics is emphasized. Three different real scenarios operating at MMW are investigated with a unified perspective: intelligent transport systems, wide-band local-area networks, and local multipoint distribution systems for interactive video services. The role played by the MMW band in the development of these services is discussed. In each scenario, accurate propagation analysis is carried out and suitable countermeasure techniques are pointed out in order to join suitable service-quality levels. The methodology considered is based on both analytical and semianalytical tools for performance evaluation.

Packet radio systems with random multiple access where the geometry of the scenario determines the presence of a maximum of two simultaneous transmitters, are analysed. Packet capture probability is evaluated in order to investigate the possible improvements of the random transmission power strategy first proposed by Lee (1987). Propagation is characterised by a path-loss exponent. The capture effect is taken into account by means of a threshold model. Averaging over interfering distance is accomplished. After an analytical development, the packet capture probability is found to be determinable by means of closed-form expressions. It is shown that the random transmission power strategy improves system performance in certain conditions. Some examples of application of this technique are given.

This paper proposes a semi-analytical approach to characterise the performance of digital cellular systems when cochannel interference is determined by a small number of interferers. Linearly modulated signals (such as BPSK, QPSK, M-ASK, M-QAM, MSK, O-QPSK, etc.) with linear receivers are investigated by considering the probability density function of the interference at the input of the decision device. Non-linear (continuous phase) modulation signals (such as GMSK) and non-coherent detection are also investigated by means of simulation. As a consequence, a general approach to outage probability definition considering the protection ratio as a function of the interferers number and useful signal-to-noise ratio is introduced.

The suitability of the R-ALOHA protocol for short range inter-vehicle communications in highway environments is investigated. In the context of road transport information (RTI) applications (such as, e.g., cooperative driving), in Europe the 63.5 GHz band has been recommended for inter-vehicle communications. Hence, network performance has to be evaluated by assuming a suitable propagation model; the joint impact of random access, interference, thermal noise, propagation and packet capture effect, is taken into account. The results obtained by means of a software tool that is able to simulate a one-lane highway scenario are presented. The simulation approach allows the evaluation of the impact of protocol parameters (such as, e.g., the number of slots per frame) on network performance. Only non-steady-state situations are analysed.

A new analytical methodology for outage probability evaluation of a vehicle-to-vehicle packet radio communication system at 60 GHz, is presented. Propagation characteristics, co-channel interference and thermal noise are taken into account, in the context of an highway traffic model. Both code division and time division multiple access schemes are analysed: results as a function of offered traffic and useful link distance are discussed. The analytical procedure is general, and can also be applied to bi-dimensional cellular structures.>

Doppler and frequency selectivity effects on the performance of vehicle-to-vehicle highway communication systems at 60 GHz, are evaluated. The environment under investigation is typical of PROMETHEUS applications. Performance evaluation for both omnidirectional and directional antennas is carried out. An analytical approach to evaluate average bit error probability for an MSK system with limiter-discriminator-integrator detection in a Rayleigh fading channel taking filter distortion, noise, time and frequency selectivity into account, is exploited. The optimum bit rate can be determined as a trade-off between time and frequency selective effects.>