Asset tracking solutions have proven their significance in industrial contexts, as evidenced by their successful commercialization (e.g., Hilti On!Track). However, a seamless solution for matching assets with their users, such as operators of construction power tools, is still missing. By enabling asset-user matching, organizations gain valuable insights that can be used to optimize user health and safety, asset utilization, and maintenance. This article introduces a novel approach to address this gap by leveraging existing Bluetooth low energy (BLE)-enabled low-power Internet of Things (IoT) devices. The proposed framework comprises the following components: 1) a wearable device; 2) an IoT device attached to or embedded in the assets; 3) an algorithm to estimate the distance between assets and operators by exploiting simple received signal strength indicator (RSSI) measurements via an extended Kalman filter (EKF); and 4) a cloud-based algorithm that collects all estimated distances to derive the correct asset-operator matching. The effectiveness of the proposed system has been validated through indoor and outdoor experiments in a construction setting for identifying the operator of a power tool. A physical prototype was developed to evaluate the algorithms in a realistic setup. The results demonstrated a median accuracy of 0.49m in estimating the distance between assets and users, and up to 98.6% in correctly matching users with their assets.

Unmanned aerial vehicles (UAVs) equipped with full-duplex relays (FDRs) are pivotal in overcoming connectivity challenges by dynamically establishing effective communication channels. However, despite their potential in network performance via trajectory optimization, integrating energy consumption models for UAV-mounted FDRs remains unexplored, crucial for trajectory design adhering to existing energy constraints. To this end, we introduce an energy-aware trajectory optimization framework to maximize network performance and user fairness within the UAV’s energy constraints. Specifically, we present a detailed energy consumption model describing the operational needs of UAV-mounted FDRs and formulate a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem considering the power dynamics of UAV-mounted FDRs. Finally, our simulation results highlight the role of energy awareness in achieving optimal trajectory and scheduling, contributing to UAV-mounted FDRs’ performance in future networks.

In the evolving landscape of sixth-generation (6G) wireless networks, unmanned aerial vehicles (UAVs) have emerged as transformative tools for dynamic and adaptive connectivity. However, dynamically adjusting their position to offer favorable communication channels introduces operational challenges in terms of energy consumption, especially when integrating advanced communication technologies like reconfigurable intelligent surfaces (RISs) and full-duplex relays (FDRs). To this end, by recognizing the pivotal role of UAV mobility, the paper introduces an energy-aware trajectory design for UAV-mounted RISs and UAV-mounted FDRs using the decode-and-forward (DF) protocol, aiming to maximize the network’s minimum rate and enhance user fairness, while taking into consideration the available on-board energy. Specifically, this work highlights their distinct energy consumption characteristics and their associated integration challenges by developing appropriate energy consumption models for both UAV-mounted RISs and FDRs that capture the intricate relationship between key factors such as weight, and their operational characteristics. Furthermore, a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem is formulated, considering the power dynamics of both systems, while assuring that the UAV energy is not depleted mid-air. Finally, simulation results underscore the importance of energy considerations in determining the optimal trajectory and scheduling and provide insights into the performance comparison of UAV-mounted RISs and FDRs in UAV-assisted wireless networks.

A primary objective of the forthcoming sixth generation (6G) of wireless networking is to support demanding applications, while ensuring energy efficiency. Programmable wireless environments (PWEs) have emerged as a promising solution, leveraging reconfigurable intelligent surfaces (RISs), to control wireless propagation and deliver exceptional quality-of-service. In this paper, we analyze the performance of a network supported byzero-energy RISs (zeRISs), which harvest energy for their operation and contribute to the realization of PWEs. Specifically, we investigate joint energy-data rate outage probability and the energy efficiency of a zeRIS-assisted communication system by employing three harvest-and-reflect (HaR) methods, i) power splitting, ii) time switching, and iii) element splitting. Furthermore, we consider two zeRIS deployment strategies, namely BS-side zeRIS and UE-side zeRIS. Simulation results validate the provided analysis and examine which HaR method performs better depending on the zeRIS placement. Finally, valuable insights and conclusions for the performance of zeRIS-assisted wireless networks are drawn from the presented results.

Data collection in massive Internet of Things networks requires novel and flexible methods. Unmanned aerial vehicles (UAVs) are foreseen as a means to collect data rapidly even in remote areas without static telecommunication infrastructure. In this direction, UAV-mounted reconfigurable intelligent surfaces (RISs) aid in reducing the hardware requirements and signal processing complexity at the UAV side, while increasing the network’s energy efficiency and coverage. Hence, in this paper, we propose the utilization of a UAV-mounted RIS for data collection and study the coverage probability in such networks. Additionally, we propose a novel medium access control protocol based on slotted ALOHA and Code Combining to handle the communication of multiple sensors. To account for the crucial energy issue in UAVs, we devise an energy model that considers both the UAV and the RIS weight, as well as the environmental conditions and the UAV’s velocity. Finally, we characterize the performance of the proposed data collection scheme by analyzing the average throughput and the average collected data per flight, while providing useful insights for the design of such networks.

Massive IoT deployments in smart cities pose a significant challenge to the data collection due to the harsh wireless channel conditions of dense urban environments. Aerial reconfigurable intelligent surfaces (RIS) carried by Unmanned Aerial Vehicles (UAVs) can improve the communication thanks to their high mobility that provides line-of-sight propagation. In this paper, we investigate the impact of the aerial RIS in the data collection by deriving the outage probability of the randomly-deployed devices, while taking into account the imperfect channel state and the UAV fluctuations. Furthermore, we study the effects on the network reliability of two hybrid automatic repeat request protocol types, i.e., incremental redundancy and code combining, as well as on the average throughput. Finally, we provide useful insights regarding the RIS characteristics that guarantee the optimal network performance.

As the demand for Network Function Virtualization accelerates, service providers are expected to advance the way they manage and orchestrate their network services to offer lower latency services to their future users. Modern services require complex data flows between Virtual Network Functions, placed in separate network domains, risking an increase in latency that compromises the offered latency constraints. This shift requires high levels of automation to deal with the scale and load of future networks. In this paper, we formulate the Service Function Chaining (SFC) placement problem and then we tackle it by introducing SCHEMA, a Distributed Reinforcement Learning (RL) algorithm that performs complex SFC orchestration for low latency services. We combine multiple RL agents with a Bidding Mechanism to enable scalability on multi-domain networks. Finally, we use a simulation model to evaluate SCHEMA, and we demonstrate its ability to obtain a 60.54% reduction of average service latency when compared to a centralised RL solution.

The deployment of fifth generation (5G) mobile networks along with the massive penetration of multimedia applications render the edge cache capacity a valuable but limited network resource. Edge caching has been lately widely adopted, as it can bring to the network important benefits, such as better network utilization and higher Quality of Experience (QoE). However the cache sharing among different Content Service Providers (CSPs) is a non-trivial problem due to: i) the limited nature of cache resources, and ii) the network neutrality concept that requires equal treatment of the CSPs. In this paper, we study the cache sharing problem in the presence of multiple CSPs, focusing both on the fairness and the network performance. Taking into account the importance of network neutrality and the scarcity of the cache resources, we formulate the cache sharing as a bankruptcy problem and we propose a set of different approaches for its solution. The performance of the proposed approaches is assessed through a series of simulation experiments in different scenarios (i.e., assuming different popularity distributions and market shares), identifying the potential trade-offs.

The standard for 5G communication exploits the concept of a network slice, defined as a virtualized subset of the physical resources of the 5G communication infrastructure. As a large number of network slices is deployed over a 5G network, it is necessary to determine the physical resource demand of each network slice, and how it varies over time. This serves to increase the resource efficiency of the infrastructure without degrading network slice performance. Traffic prediction is a common approach to determine this resource demand.State-of-the-art research has demonstrated the effectiveness of machine learning (ML) predictors for traffic prediction in 5G networks. In this context, however, the problem is not only the accuracy of the predictor, but also the usability of the predicted values to drive resource orchestration and scheduling mechanisms, used for resource utilization optimization while ensuring performance. In this paper, we introduce a new approach that consists on including problem domain knowledge relevant to 5G as regularization terms in the loss function used to train different state-of-the-art deep neural network (DNN) architectures for traffic prediction. Our formulation is agnostic to the technological domain, and it can obtain an improvement of up to 61,3% for traffic prediction at the base station level with respect to other widely used loss functions (MSE).

Future mobile networks will offer high data rates based on high-capacity fronthaul. Current fronthaul design has two main components that communicate via the common public radio interface and fiber links, i.e., remote units (RUs) that implement simple signal processing and centralized baseband units (CBBUs) in high power-consuming data centers that perform complex network functions. Various functional splits between CBBUs and RUs are feasible, inducing trade-offs between centralization gains and bandwidth demands. This design lacks in capacity and flexibility, motivating the use of converged fiber-wireless (Fi-Wi) fronthaul with high-bandwidth fiber and millimeter-wave links, and splits that move functionalities to RUs reducing the delay demands. Further flexibility is offered by analog radio-over-fiber fronthaul that supports dynamic functional splitting via software-defined networking (SDN). Ensuring acceptable delay for all RUs, i.e., minimizing fronthaul grade-of-service (GoS), requires selection of CBBUs, channel bandwidth and functional splits of RUs. The split type affects fronthaul power consumption determining which fronthaul components are active and their processing power. Using a simulated annealing-based dynamic fronthaul resource allocation (DFRA) scheme, we jointly optimize GoS and power consumption in a novel SDN Fi-Wi fronthaul. Our results show that DFRA minimizes GoS and power consumption for all load levels outperforming baseline approaches.

As technology progresses, cars can not only be considered as a transportation medium but also as an intelligent part of the cellular network that generates highly valuable data and offers both entertainment and security services to the passengers. Therefore, forthcoming 5G networks are said to enhance Ultra-Reliable Ultra-Low-Latency that will allow for a new breed of services that will disrupt the industry as we know it today. In this work, we devise a unique fusion of Deep Learning based mobility prediction and Genetic Algorithm assisted service orchestration to retain the average service latency minimal by offering personalized service migration, while tightly packing as many services as possible in the edge of the network, for maximizing resource utilization. Through an extensive simulation based on real data, we evaluate the proposed mobility orchestration combination and we find gains in low latency in all examined scenarios.

The increasing demand for fast, reliable, and robust network services has driven the telecommunications industry to design novel network architectures that employ Network Functions Virtualization and Software Defined Networking. Despite the advancements in cellular networks, there is a need for an automatic, self-adapting orchestrating mechanism that can manage the placement of resources. Deep Reinforcement Learning can perform such tasks dynamically, without any prior knowledge. In this work, we leverage a Deep Deterministic Policy Gradient Reinforcement Learning algorithm, to fully automate the Virtual Network Functions deployment process between edge and cloud network nodes. We evaluate the performance of our implementation and compare it with alternative solutions to prove its superiority while demonstrating results that pave the way for Experiential Network Intelligence and fully automated, Zero touch network Service Management.

Network Slicing (NS) represents a key technology enabler for advanced connectivity and data processing tailored to customers' specific requirements. While significant progress has already been achieved for Core NS, Radio Access Network (RAN) slicing still presents limitations in terms of sharing infrastructure, Service Level Agreement (SLA) guarantees, isolation, resource scheduling and allocation. In this context, this paper firstly introduces a novel slices configuration framework for the 5G New Radio (5G NR) infrastructure able to dynamically migrates the radio resources among the slices, while preserving the Quality of Service (QoS) of the served users. Our solution is illustrated in detail and tested on top of a real case 5G scenario, using a software-based simulator. Finally, this paper investigates the flexibility, scalability, and real-time properties of the proposed method, as required in the future 5G cloud-based architectures.

The upcoming fifth generation (5G) mobile communications urge software-defined networks (SDNs) and network function virtualization (NFV) to join forces with the multiaccess edge computing (MEC) cause. Thus, reduced latency and increased capacity at the edge of the network can be achieved, to satisfy the requirements of the Internet of Things (IoT) ecosystem. If not properly orchestrated, the flexibility of the virtual network functions (VNFs) incorporation, in terms of deployment and lifecycle management, may cause serious issues in the NFV scheme. As the service level agreements (SLAs) of the 5G applications compete in an environment with traffic variations and VNF placement options with diverse computing or networking resources, an online placement approach is needed. In this article, we discuss the VNF lifecycle management challenges that arise from such heterogeneous architecture, in terms of VNF onboarding and scheduling. In particular, we enhance the intelligence of the NFV orchestrator (NFVO) by providing: 1) a latency-based embedding mechanism, where the VNFs are initially allocated to the appropriate tier and 2) an online scheduling algorithm, where the VNFs are instantiated, scaled, migrated, and destroyed based on the actual traffic. Finally, we design and implement an MEC-enabled 5G platform to evaluate our proposed mechanisms in real-life scenarios. The experimental results demonstrate that our proposed scheme maximizes the number of served users in the system by taking advantage of the online allocation of edge and core resources, without violating the application SLAs.

As industries are under pressure for shorter business and product life cycles, there is an extensive effort from the research community for novel and profitable automation processes. This effort has given rise to the fifth-generation (5G) Tactile Internet, which is characterized by extremely low latency communication in combination with high availability, reliability, and security. In this paper, we discuss the key technologies to support the Tactile Internet characteristics in industrial environments and then showcase the implementation of a novel 5G network function virtualization enabled experimental platform. Given that ultra-reliable low-latency communication is crucial for the manufacturing process, we demonstrate that, in our setup, submillisecond end-to-end communication is attainable, proving the suitability of our platform for Tactile Internet industrial applications.

The 5G networks are expected to satisfy diverse use cases and business models with significant advancements in terms of capacity, reliability, and latency. The allocation and provisioning of network resources pose a challenge for this novel architecture to guarantee higher flexibility and quality of service. As a potential enabler, network slicing was proposed as an innovative approach for the control of the network resources. Although a static slicing approach can be suitable for the transport and core network, the stochastic behavior of the wireless channel requires fast and secure slicing techniques for resource allocation. In this paper, we propose a dynamic slicing approach for the radio access network, where the network resources are carefully assigned to guarantee the service level agreements and increase the number of served users. To prove the performance of our approach, we implemented a fronthaul testbed to emphasize the strength of our method in terms of throughput and resource utilization, compared to static slicing.

Considering that cities are typically divided in residential and business districts, massive population migrations between different areas introduce considerable discrepancies in the traffic distribution. However, although novel technologies are constantly introduced to fulfill the 5G requirements, the current cellular networks raise some limitations due to their static deployment. Aerial networks with unmanned aerial vehicles (UAVs) that carry access points can flexibly overcome this issue by controlling this traffic effectively in an ad hoc manner, but their limited power storage is their major drawback. In this paper, we propose the use of solar-powered charging stations to satisfy the energy needs of UAVs and formulate a theoretic matching problem to ensure the best performance in terms of energy, communication, and safety. Then, we evaluate the network performance by comparing the coverage and utilization between the two scenarios: i) the static infrastructure, and ii) the aerial network, and we present useful insights regarding the benefits of aerial networks.

Emerging 5G communication paradigms, such as machine-type communication, have triggered an explosion in ad-hoc applications that require connectivity among the nodes of wireless networks. Ensuring a reliable network operation under fading conditions is not straightforward, as the transmission schemes and the network topology, i.e., uniform or clustered deployments, affect the performance and should be taken into account. Moreover, as the number of nodes increases, exploiting natural energy sources and wireless energy harvesting (WEH) could be the key to the elimination of maintenance costs while also boosting immensely the network lifetime. In this way, zero-energy wireless-powered sensor networks (WPSNs) could be achieved, if all components are powered by green sources. Hence, designing accurate mathematical models that capture the network behavior under these circumstances is necessary to provide a deeper comprehension of such networks. In this paper, we provide an analytical model for the connectivity in a large-scale zero-energy clustered WPSN under two common transmission schemes, namely, unicast and broadcast. The sensors are WEH-enabled, while the network components are solar-powered and employ a novel energy allocation algorithm. In our results, we evaluate the tradeoffs among the various scenarios via extensive simulations and identify the conditions that yield a fully connected zero-energy WPSN.

As wearables provide new health-related functionalities, they can be employed in hospitals to monitor patients and notify the medical personnel regarding their status. However, in order to be approved by the medical community, wearables need to have reliable communication and high lifetime. In such scenarios, it is important to know the probability of correct notification which is affected mainly by the deployment of the wireless wearables and their energy supply. Typically, rooms in hospitals host multiple people and, thus, a clustered communication model should be adopted for more trustworthy results. Moreover, by employing wireless charging, it is possible to provide an uninterrupted operation with high reliability. Therefore, in this paper, we study the aforementioned probability in a clustered network while the wearable devices are wirelessly charged. We provide an analytical model for the wearables' ability to inform quickly the medical personnel and discuss different trade-offs via extensive simulations.

The emerging Internet of Things paradigm has triggered an explosion in ad-hoc applications that require connectivity among the nodes of wireless networks. However, the channel randomness and the random deployment of such networks could cause missed detections by isolated (i.e., unable to disseminate their messages) or inactive (i.e., without enough energy to transmit) nodes. Moreover, as the number of nodes increases, the use of “green” solutions such as wireless energy harvesting for powering battery-less devices could eliminate the maintenance costs and boost immensely the network lifetime. In this paper, we study the connectivity in a wireless-powered sensor network (WPSN) with battery-less devices under two common routing schemes, namely unicast and broadcast. We provide an analytical model for the ability of a WPSN to be reliable and evaluate the trade-offs among the different scenarios via simulation.

Contemporary technologies as implemented in the field of healthcare have provided the everyday clinical practice with a plethora of tools to be used in various settings. In this field, distributed and networked embedded systems, such as Wireless Sensor Networks (WSNs), are the most promising technology to achieve continuous monitoring of aged people for their own safety, without affecting their daily activities. WSN4QoL is a Marie Curie project involving academic and industrial partners from three EU countries, which aims to show how new WSNs-based technologies suit the specific requirements of pervasive healthcare applications. In particular, in this paper, the WSN4QoL's system architecture is presented as designed to exploit the Network Coding (NC) mechanisms to achieve energy efficiency in the wireless communications and distributed positioning solutions to locate patients in indoor home environments. The system has been validated through experimental activities using commercial off the shelf (COTS) WSN testbeds and medical devices prototypes offered by a commercial partner. Results demonstrate that NC helps in achieving substantial gains in terms of energy efficiency as compared to traditional relay mechanisms, while the proposed positioning solution is able to locate people in indoor environments at a sub-room accuracy level, without requiring any extra dedicated hardware.

As large-scale dense and often randomly deployed wireless sensor networks (WSNs) become widespread, local information exchange between colocated sets of nodes may play a significant role in handling the excessive traffic volume. Moreover, to account for the limited life-span of the wireless devices, harvesting the energy of the network transmissions provides significant benefits to the lifetime of such networks. In this paper, we study the performance of communication in dense networks with wireless energy harvesting (WEH)-enabled sensor nodes. In particular, we examine two different communication scenarios (direct and cooperative) for data exchange and we provide theoretical expressions for the probability of successful communication. Then, considering the importance of lifetime in WSNs, we employ state-of-the-art WEH techniques and realistic energy converters, quantifying the potential energy gains that can be achieved in the network. Our analytical derivations, which are validated by extensive Monte-Carlo simulations, highlight the importance of WEH in dense networks and identify the tradeoffs between the direct and cooperative communication scenarios.

As the number of nodes in wireless sensor networks (WSNs) increases, new challenges have to be faced in order to maintain their performance. A fundamental requirement of several applications is the correct transmission of the measurements to their final destinations. Thus, it is crucial to guarantee a high probability of connectivity, which characterizes the ability of every node to report to the fusion center. This network metric is strongly affected by both the fading characteristics and the different routing protocols that are used for the dissemination of data. In this paper, we study the probability of a network to be fully connected for two widely employed routing mechanisms, namely unicast and K-anycast. The analytical derivations and the simulations evaluate the trade-offs among the different routing mechanisms and provide useful guidelines on the design of WSNs.

Random dense deployment of heterogeneous networks (HetNets), consisting of macro base stations (BS) and small cells (SC), can provide higher quality of service (QoS) while increasing the energy efficiency of the cellular network. In addition, it is possible to achieve lower deployment cost and, therefore, maximize the benefits for the network providers. In this paper, we propose a novel method to determine the minimum deployment cost of a two-tier heterogeneous cellular network using random deployment. After deriving the coverage probability of the two-tier deployment by using stochastic geometry tools, we identify the tier intensities that provide the minimum deployment cost for a given coverage probability. Extensive simulations verify the existence of a unique set of intensities for different coverage constraints.

In smart homes, it is essential to reliably detect events including water leakages. A control action, such as shutting the water pipes, relies on reliable event detection. In this demo, a wireless sensor network for detection and localization of events in smart homes is presented. The demo is based on novel distributed detection-estimation and localization algorithms. A graphical user interface to visualize in real-time the network status is developed. Upon a detected event, the user is alerted through a Twitter notification. In the experiments the false alarm probability is improved by 30% and the average relative localization error is 1.7%.

Medium access control (MAC) and routing protocols are fundamental blocks in the design of low power and lossy networks (LLNs). As new networking standards are being proposed and different existing research solutions patched, evaluating the performance of the network becomes challenging. Specific solutions that can be individually efficient, when stacked together may have unexpected effects on the overall network behavior. In this paper, we provide an analysis of the fundamental MAC and routing protocols for LLNs: IEEE 802.15.4 MAC and IETF RPL. Moreover, a characterization of their cross-layer interactions is presented by a mathematical description, which is essential to truly understand the protocols mutual effects and their dynamics. Novel metrics that guide the interaction between MAC and routing are compared to existing metrics. Furthermore, a protocol selection mechanism is implemented to select the appropriate routing metric and MAC parameters given specific performance constraints. Analytical and experimental results show that the behavior of the MAC protocol can hurt the performance of the routing protocol and vice versa, unless these two are carefully optimized together.

Life expectancy is projected to increase significantly in the coming years. This fact has pushed the need for designing new and more pervasive healthcare systems. In this field, distributed and networked embedded systems, such as Wireless Sensor Networks (WSNs), are the most suitable technology to achieve continuous monitoring of aged people for their own safety, without affecting their daily activities. This paper proposes recent advancements in this field by introducing WSN4QoL, a Marie Curie project which involves academic and industrial partners from three EU countries. The project aims to propose new WSN-based technologies to meet the specific requirements of pervasive healthcare applications. In particular, in this paper, a Network Coding (NC) mechanism and a distributed localization solution are presented. They have been implemented on WSN testbeds to achieve efficiency in the communications and to enable indoor people tracking. Preliminary results in a real environment show good system performance that meet our expectations.

In this paper, routing metrics for low power and lossy networks are designed and evaluated. The cross-layer interactions between routing and medium access control (MAC) are explored, by considering the specifications of IETF RPL over the IEEE 802.15.4 MAC. In particular, the experimental study of a reliability metric that extends the expected transmission count (ETX) to include the effects of the level of contention and the parameters at MAC layer is presented. Moreover, a novel metric that guarantees load balancing and increased network lifetime by fulfilling reliability constraints is introduced. The aforementioned metrics are compared to a routing approach based on backpressure mechanism.