Unmanned aerial base stations (UABSs) are a promising solution for improving coverage and capacity in vehicle-to-everything (V2X) communications, particularly in dense urban areas. However, their operation is constrained by onboard energy consumption, required for both flight and communication. Beamforming, while enhancing network performance, adds to this challenge due to its energy-intensive nature.This paper proposes a sequential and hierarchical decision-making framework for UABS operations, considering trajectory planning, dynamic beamforming, and radio resource assignment (RRA). While heuristic and optimal solutions are employed for trajectory planning and RRA respectively, the beamforming model is modeled as Markov decision process (MDP) to maximize served user demand, weighted by time-varying priorities, under strict energy constraints. Leveraging a dueling double deep q-network (3DQN) algorithm that penalizes energy budget violations, an agent policy for the beamformer is then trained. Simulation results demonstrate that the proposed approach outperforms static beamforming benchmarks and closely matches an ideal step-wise oracle, achieving a balance between energy efficiency and served user demand while adapting to dynamic V2X traffic conditions.

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.

This paper addresses the joint design of Unmanned Aerial Vehicles (UAVs) trajectory and radio resource management (RRM) in dynamic wireless environments by leveraging a multi-agent deep reinforcement learning (MADRL) framework. In contrast to prior works that either assume constant synchronization between agents and the controller or overlook the communication cost, we explicitly model the interaction between UAVs and the central controller. We propose an adaptive synchronization strategy that selectively transmits model parameters and experience data based on their relevance, enabling a resource-aware RRM algorithm that optimally balances learning performance and communication overhead. The MADRL agents optimize their trajectories based on rewards that incorporate priorities derived from the RRM layer, which jointly manages both uplink and downlink communications. Simulation results demonstrate that our event-driven synchronization strategy outperforms periodic baselines in both convergence speed and communication overhead, towards scalable deployment in realistic urban environments.

In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations, particularly for demanding vehicle-to-everything (V2X) applications. In this scenario, one of the most challenging problems is the design of trajectories for multiple UAVs, cooperatively serving the same area. Such joint trajectory design can be performed using multi-agent deep reinforcement learning (MADRL) algorithms, but ensuring collision-free paths among UAVs becomes a critical challenge. Traditional methods involve imposing high penalties during training to discourage unsafe conditions, but these can be proven to be ineffective, whereas binary masks can be used to restrict unsafe actions, but naively applying them to all agents can lead to suboptimal solutions and inefficiencies. To address these issues, we propose a rank-based binary masking approach. Higher-ranked UAVs move optimally, while lower-ranked UAVs use this information to define improved binary masks, reducing the number of unsafe actions. This approach allows to obtain a good trade-off between exploration and exploitation, resulting in enhanced training performance, while maintaining safety constraints.

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.

Unmanned Aerial Vehicles (UAVs), functioning as Unmanned Aerial Base Stations (UABSs), hold considerable potential for augmenting vehicular network performance through on-demand enhanced radio coverage. A pivotal challenge lies in devising algorithms that efficiently optimize UABS trajectories under strict Radio Resource Management (RRM) and coverage gap discovery. This can be tackled using Deep Reinforcement Learning (DRL) models. However, their effectiveness relies on the relevance of acquired knowledge to the current scenario, posing a challenge when the underlying dynamics or governing rules undergo modifications. To address this issue, we propose a framework integrating a deep meta-learning algorithm to enhance the adaptability of our DRL-based trajectory design to newly encountered scenarios. Scenarios may vary in mobile users’ movement profiles, UABS take-off zones, or new service maps. Our numerical results demonstrate that an agent that leverages information from prior tasks achieves target performance in fewer episodes compared to a conventional DRL agent, while also ensuring superior long-term training proficiency.

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.

It is expected that future 6G vehicular networks will rely on Unmanned Aerial Vehicles (UAVs) acting as flying Base Stations, namely Unmanned Aerial Base Stations (UABSs), in order to provide a wide range of services that current terrestrial networks cannot manage. Vehicles may exploit strong links with the UABS, enabling applications such as advanced driving and extended sensing. In this context, if vehicular users are satisfied with an appropriate Quality of Experience (QoE), they are able to upload a given amount of data for a given time window, continuously. To allow this, an efficient path planning is fundamental. This paper presents a Deep Reinforcement Learning (DRL)-based solution, where a novel reward function is proposed with the aim of offering a continuous service to vehicles. Results are presented in terms of the percentage of satisfied users and both, a continuous action space as well as a discrete action space, are considered exploiting two different DRL algorithms (i.e., Double Dueling Deep Q Network (3DQN) and Deep Deterministic Policy Gradient (DDPG)) in order to compare the two and select the best one according to the described scenario of interest.

Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless networks to support applications such as extended sensing via vehicle-to-everything (V2X) services. A key problem in such systems is designing algorithms that can efficiently optimize the trajectory of the UABS in order to maximize coverage. In existing solutions, such optimization is carried out from scratch for any new traffic configuration, often by means of conventional reinforcement learning (RL). In this paper, we propose the use of continual meta-RL as a means to transfer information from previously experienced traffic configurations to new conditions, with the goal of reducing the time needed to optimize the UABS's policy. Adopting the Continual Meta Policy Search (CoMPS) strategy, we demonstrate significant efficiency gains as compared to conventional RL, as well as to naive transfer learning methods.

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.

In recent years, the use of unmanned aerial vehicles (UAVs) for wireless communications has been shown promising in a plethora of different applications. Their flexible deployment and mobility make them key enablers for the next generation of networks, provided that system design is properly addressed. In this article, we analyze a beyond-5G network where a UAV, acting as an unmanned aerial base station (UAB), is employed to collect data from reduced capability user equipments (UEs), deployed in an urban area. Specifically, we study a cluster-based scenario, where UEs are deployed following a Thomas cluster process, and the UAB flies over cluster centers according to the traveling salesman problem solution. Through the use of a stochastic approach, we mathematically devise the system performance accounting for uplink transmission protocol constraints, random access procedure, finite number of radio resources available, and coverage issues during the UAB flight. The mathematical model, validated via comparison with simulations, allows to optimize some system parameters, like the UAB speed, the number of UEs per cluster, and the number of radio resources to be used for the access and for data transmissions.

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.

Low-power wide-area networks (LPWANs) have become an important enabler for the Internet of Things (IoT) connectivity. Application domains, such as smart cities, smart agriculture, intelligent logistics, and transportation, require communication technologies that combine long transmission ranges and energy efficiency. Recent and future trends make the long-range wide-area network (LoRaWAN) and narrowband-IoT (NB-IoT) the most prospective drivers of the IoT business. In this article, after discussing the main features of the two technologies, we carry out a fair quantitative comparison between the two, investigating different performance indicators, in order to guide designers in the selection of the most appropriate technology, depending on the application requirements.

In this paper, we consider a NarrowBand-Internet of Things (NB-IoT) network where an Unmanned Aerial Vehicle (UAV), acting as Unmanned Aerial Base Station (UAB), is employed to collect data from IoT nodes deployed in a service area. Due to the UAB inherent mobility and NB-IoT protocol constraints, the design of proper parameters setting is not an easy task. To try and solve this problem, we analyse the scenario through stochastic tools, allowing to introduce an appropriate relation between protocol and UAB flight parameters, together with application requirements. Specifically, we study a cluster-based scenario, where IoT nodes are assumed to be deployed according to a Thomas cluster process, and we apply a Traveling Salesman Problem to design the UAB trajectory over the area. Notably, our model considers the protocol constraints, in terms of resource units available in the NB-IoT NPUSCH channel, the random access procedure implemented in the NPRACH channel and the data rate that can be provided to IoT nodes. Results allow to quickly deduce optimal design parameters for achieving the maximum network throughput.

Low-power wide-area network technologies are used to interconnect a number of devices in a simple and efficient way. One of these technologies, LoRaWAN, is deemed as one of the most promising due to its capability to allow long-range communications with very small energy consumption. LoRaWAN networks are managed by a network server implementing an adaptive data rate (ADR) algorithm to allocate proper data rates to end devices (EDs). However, the standard ADR solution focuses only on the link-level performance and assigns transmission parameters to EDs one-by-one in an independent way. In this article, we propose a novel and more efficient ADR algorithm, denoted as collision-aware ADR (CA-ADR), which tries to minimize the collision probability when assigning data rates by considering the entire set of EDs in the network and keeping the link-level performance under control. The performance of CA-ADR is characterized and benchmarked against the standard solution as well as another proposal presented in the literature. An integrated simulation-experimental approach is used to assess results for large-scale networks and to compare two architectures based on cloud and fog computing. Results show that CA-ADR outperforms standard solutions when connectivity is good, whereas it behaves similarly in large areas. It is also shown that the improvement with respect to the benchmark solutions does not depend on the channel model considered (no shadowing, uncorrelated, and correlated shadowing). Finally, a fog-based architecture is proved to be feasible, with the advantage of reducing the end-to-end latency.

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.

Industrial Internet of Things (IIoT) networks are considered the large-scale deployment of IoT devices for industrial applications such as smart manufacturing, harvesting and supply chain management. The Internet of Things (IoT) devices are typically connected over a wireless medium, given the large geographical distribution area and the increasing demand for flexible installations. In some cases, a combination of wired and wireless connectivity can be assumed as common practice. In both scenarios, wireless communications for IIoT networks is a fundamental component of the system architecture that needs to satisfy stringent requirements such as reliable connectivity and minimal delays. Therefore, the dependability of wireless communications for IIoT networks should be carefully studied to provide new solutions, which can guarantee that applications can meet their real-time and reliability requirements while optimizing the control capability of the overall network. This paper focuses on the dependable wireless communications in the IIoT networks, where wireless control and monitoring tasks need to meet stringent real-time and reliability constraints. After reviewing recent solutions and discussing their suitability for IIoT networks, we highlight the yet open challenges that needs to be tackled by both academia and industry.

Among the significant advances in mobile network technology, as evident in the latest 3GPP releases, one of the most notable is the possibility to do aggregation between licensed and unlicensed carriers. With LTE transmitting over unlicensed bands, obvious concerns of a fair co- existence with other preexisting technologies have risen up. In this study, we aim to evaluate the impact of LTE transmission on the key mechanism of Clear Channel Assessment (CCA), which is common to several unlicensed systems, amongst which Wi-Fi is the most notable. Relying on the statistical tool of stochastic geometry and a semianalytical approach, we will obtain the probabilities of Wi-Fi preamble false alarm and detection under a wide set of realistic propagation effects, such as path-loss and Rayleigh distributed fading. Above all, we will model the effect of a single LTE downlink interfering transmission, as well as the aggregate interference effect. Hence, we shall be able to evaluate the modified energy detection threshold that has been long debated between 3GPP and IEEE 802.11 Working Groups.

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.

In this paper, we address the problem of scheduling and routing design in a multi-hop heterogeneous radio network. The scenario consists of two types of nodes: scheduled nodes, which depend upon a centralised resource scheduling scheme, and uncoordinated nodes, which employ an asynchronous Carrier Sense Multiple Access (CSMA) based medium access control protocol. Both types of nodes co-exist on the same spectrum and may interfere each other. In fact, scheduled nodes are synchronized to the Base Station (BS) and communicate to it in a multi-hop fashion, while uncoordinated users are asynchronous with respect to the BS and the other nodes. Our work focuses on the problem of routing and scheduling for the scheduled set of nodes, with the aim of avoiding interference caused by CSMA nodes. In particular, we propose a Coexistence-Aware (CA) routing scheme, based on the definition of a novel link cost metric accounting for the number of potential uncoordinated nodes interfering. The output of the CA routing scheme serves an input to a Multi-Link Proportional Fair (MLPF) scheme, where a new scheduling metric, accounting for the number of hops needed to reach the BS, is designed. The proposed algorithm is compared with a benchmark solution, where routing is based on the level of received power over the links and where the MLPF algorithm presented in the literature is applied. Results show the improvement achieved with the proposed solution.

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.

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.

This paper presents a cooperative multiple-input multiple-output (MIMO) scheme for a wireless sensor network consisting of inexpensive nodes, organised in clusters and transmitting data towards sinks. The transmission is affected by hardware imperfections, imperfect synchronisation, data correlation among nodes of the same cluster, channel estimation errors and interference among nodes of different clusters. Within this setting, we are interested in determining the number of nodes per cluster that maximises the energy efficiency of the network. The analysis is conducted in the asymptotic regime in which the number N of sensor nodes per cluster grows large without bound. Numerical results are used to validate the asymptotic analysis in the finite system regime and to investigate different configurations. It turns out that the optimum number of sensor nodes per cluster increases with the inter-cluster interference and with the number of sinks.

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.

The Internet of Things (IoT) research activities are oriented towards the standardisation of communication protocols and platforms for globally interconnected smart objects. 6LoWPAN is a standardised protocol stack aiming at providing the seamless interconnection between IPv6 Wireless Sensor Network (WSN) and Internet, while maintaining low-power consumption. In this paper, we present and investigate the performance of 6LoWPAN, being one of the most promising solutions for the implementation of IoT paradigm. The evaluation is performed through experimentation on the “European Laboratory of Wireless Communications for the Future Internet” (EuWIn) facilities developed within the Network of Excellence, NEWCOM#, at the University of Bologna. We report results in terms of average round-trip-time, packet loss rate and throughput for different payload sizes and number of hops, both for unicast and multicast traffics.

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 an implementation of a Low Power Listening-based (LPL) Medium Access Control (MAC) protocol on a platform for Body Area Network (BAN) applications. LPL exploits the transmission of a burst of short packets, called preambles, to synchronize the transmitter and the receiver. In this way, devices are able to spend most of the time in sleeping mode, providing longer lifetime and energy saving. Experiments on the field have been conducted by considering different scenarios and results, in terms of average energy consumed per packet, packet loss rate and average delay, have been investigated. Conclusions regarding the proper parameters setting depending on the application requirements were derived. This work has been performed in the framework of the FP7 Integrated Project, WiserBAN.

In this paper we propose and test, through experimentation on the field, a novel routing protocol for Zigbee networks. The proposed solution aims at reducing traffic congestion, causing delays, packets losses and unfair use of the energy resources of nodes in the network. The protocol exploits, at the network layer, the information related to medium access control losses, therefore collisions, to select the best path to reach the destination, accounting for both, connectivity and congestion on links. The protocol has been implemented on the MC1322x platform produced by Freescale and a comparison between the proposed protocol and Zigbee has been performed. Results demonstrate the significant improvement achieved in terms of packet loss rate and of nodes usage balance, the latter resulting in longer network lifetime.

Wireless sensor networks are often deployed for monitoring purposes: when nodes detect the occurrence of a significant event, they transmit an information to a control entity in a multi-hop fashion. When data rate increases, congestion becomes a fundamental issue, especially when an emergency situation generates alarm messages originating from a specific area of the network. Indeed, congestion increases delays and packet losses, and yields to an unfair use of the energy of nodes. In this paper, we propose to improve the ZigBee routing protocol, aiming at reducing traffic congestion. The proposed solution uses intermediate nodes, denoted as pivots, which are selected by the data sources in order to reduce congestion on paths. Simulation results highlight the significant improvement achieved in terms of packet losses and average delays, with respect to the ZigBee routing protocol, while the overhead generated in the network is maintained under control. A mathematical model to derive the average path length and the number of pivots is also provided.

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.

Energy efficiency in buildings is one of the promising fields of application of wireless sensor networks. Simple affordable sensors can be used to monitor the power consumption of appliances in buildings to implement smart algorithms in order to conserve energy. The ARTEMIS Joint Undertaking project eDIANA (Embedded Systems for Energy Efficient Buildings) funded by the European Commission aims to improve energy efficiency in buildings by using embedded technologies. In the eDIANA platform the energy consumed by household or office appliances is monitored by using IEEE 802.15.4/Zigbee-compliant devices. In this paper we describe the prototype that we have developed by using ZigBee Smart Energy Profile in order to realize the eDIANA application scenarios. Moreover we provide latency statistics and packet error rate obtained through our test-bed in different monitoring scenarios.

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.

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.

Realizing energy efficient control strategies in the buildings is one of the innovative and challenging field of application for wireless sensor networks. To achieve the goal of reduced energy consumption and optimized use of energy in the buildings, the deployment of sensors and actuators is crucial. Simple affordable sensors can be used to monitor the power consumed by each appliance in the building in order to turn them on/off when it is efficient in the use of energy. The ARTEMIS Joint Undertaking project eDIANA (Embedded Systems for Energy Efficient Buildings) funded by the European Commission aims to improve energy efficiency in buildings by using embedded technologies. In the eDIANA platform the energy consumed by household or office appliances is monitored by using IEEE 802.15.4/Zigbee-compliant devices. The aim of the study is to show the feasibility of the IEEE 802.15.4/Zigbee technology to the eDIANA application scenario and to provide guidelines for the network design. For this purpose two studies have been carried out: (i) a simulation analysis of a large apartment where many nodes are deployed; (ii) a real test-bed composed of IEEE 802.15.4/ZigBee-compliant devices. In the first case performance, in terms of packet error rate, average delay and energy consumption, is evaluated by varying the number of nodes in different traffic conditions. In the second case, instead, the statistics of the delay is provided. The study shows that simulation results are in line with the results achieved through the measurements. Moreover results fulfil the eDIANA requirements.

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.

In this paper a Personal Area Network (PAN) composed of multiple nodes, which transmit data to the PAN coordinator through the beacon-enabled mode of the IEEE 802.15.4 Medium Access Control (MAC) protocol, is considered. Upon reception of the beacon transmitted by the PAN coordinator, the nodes transmit their packets using the slotted Carrier Sense Multiple Access with Collision Avoidance algorithm defined by the standard. A mathematical model to describe the access delay statistics and the probability of packet successful reception, is provided. Performance is evaluated by changing the number of nodes, the superframe order and the packet size. The mathematical model is validated through simulation results. A comparison in terms of success probability and throughput with the non beacon-enabled mode of IEEE 802.15.4, is also provided.

In this paper we investigate the performance of two-hop virtual multiple-input-multiple-output systems with random location of nodes. In the scenario we consider the communication between source and destination through the use (following a decode-and-forward approach) of a relay node. A number of ancillary nodes, distributed according to a Poisson point process, are supposed to be distributed around source, relay and destination, with the possibility to create clusters of cooperating nodes. Random fluctuations are accounted for in the channel model which considers both slow and fast fading. Performance is evaluated in terms of outage probability, defined as the probability that the achieved capacity between source and destination is smaller than a given threshold. Finally, the total power consumed by the network for delivering the data is evaluated. Results obtained in the two-hop communication protocol are compared with the single-hop case.

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.

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