The increasing demand for IoT applications has driven the need for precise and scalable indoor positioning systems (IPS). Among existing techniques, Wi-Fi round trip time (RTT), introduced by the IEEE 802.11mc standard, offers high accuracy but suffers from scalability limitations due to the frames specifically sent for localization purposes. While fingerprinting can improve system scalability compared to multilateration, it relies on computationally intensive machine learning algorithms, which can be challenging to deploy on resource-constrained IoT devices. This article proposes a novel approach to improve the scalability and efficiency of Wi-Fi RTT fingerprinting solutions. The proposed system aims to simplify clustering and feature selection tasks by sorting RSSI measurements, thus eliminating the need for more complex algorithms. Experimental evaluations, in scenarios with varying densities of access points (APs), demonstrate significant performance gains compared to state of the art approaches. In high-density scenarios (e.g., one AP per 22 m2), the proposed system achieves an RMSE below 50 cm, a decrease of almost 98% in the memory used by the device, and supports location requests of up to 124 simultaneous Wi-Fi users, representing a 7.8% increase in scalability compared to previous proposals. These results highlight the potential of the proposed system to achieve a good compromise among accuracy, scalability, and complexity, making it a viable solution for IPS implementation in IoT environments.

Indoor localization has emerged as a critical enabler for next-generation location-based services and the evolving 6G ecosystem, particularly where satellite localization is unavailable. Among indoor technologies, Wi-Fi fingerprinting offers an attractive trade-off between deployment cost, infrastructure availability, and localization accuracy. Recent advances in IEEE 802.11mc have enabled the use of round-trip time (RTT) measurements, providing more stable and accurate observables than received signal strength (RSS). However, RTT fingerprinting still suffers from two major limitations: high overhead from exhaustive site surveys and the difficulty of maintaining dense radio maps in dynamic environments. This paper proposes a Wi-Fi RTT fingerprinting framework that minimizes offline calibration effort by restricting empirical measurements to a sparse set of reference locations associated with deployed access points (APs). A Gaussian Process Regression (GPR)-based reconstruction model synthetically generates dense virtual reference point fingerprints, while physical-signal fitting models improve RTT estimation during reconstruction. Unlike previous studies focused on RTT/RSS fusion, ranging compensation, or generic interpolation, this work systematically investigates GPR-based synthetic radio map reconstruction for Wi-Fi RTT fingerprinting, evaluating the impact of AP geometry, grid density, temporal averaging, and reconstruction models on localization performance. Experimental results from real-world RTT measurements show that the framework maintains sub-meter localization accuracy in deployments where the target area lies within the convex hull of the APs. Conversely, the observed RMSE is approximately 2 m in asymmetric extrapolation scenarios. Nonetheless, it significantly reduces the calibration complexity and signaling overhead associated with dense RTT fingerprint collection.

Non line of sight (NLOS) conditions significantly degrade the accuracy of Wi-Fi-Based indoor positioning systems (IPS). Although round-trip time (RTT) is generally more reliable than the received signal strength indicator (RSSI), it remains vulnerable to attenuation and multipath; moreover, dense RTT-compatible AP deployments may threaten scalability. This paper proposes a fingerprinting-based IPS that achieves high accuracy in NLOS scenarios, while remaining scalable and lightweight for Internet of Things (IoT) devices. By combining RTT, its standard deviation, and RSSI, accurate position estimates are obtained with only two RTT requests, reaching 93% accuracy and a 62 cm root mean squared error (RMSE) despite harsh conditions.

The wide availability of the Wi-Fi infrastructure together with the recent integration of the IEEE 802.11mc capability in common-off-the-shelf (COTS) devices, have contributed to increasing the interest of the research community in the fine time measurement (FTM) technique, which allows two Wi-Fi devices to estimate distances between each other. However, one of the main issues yet to be solved is how it scales with an increasing number of Wi-Fi devices injecting location-specific traffic in the shared medium. While the recently released IEEE 802.11az standard will still take time before being integrated in COTS devices, this paper aims at assessing the impact of the burst size on the ranging performance of COTS Wi-Fi Android devices running release 12. While increasing the burst size is expected to bring higher stability in the observed distance, on the other hand a longer transmission period for location-only purposes may jeopardize the transmission of data traffic among Wi-Fi users. Several models of smartphones and APs, and different frequency bands, have been considered in order to evaluate the behavior of the FTM procedure in real devices, showing that not always the newest or most expensive ones perform better. Also, it is shown that using the minimum burst size significantly decreases the performance and it is thus not recommended. While bursts longer than 8 may no be always supported by all the models and/or frequency bands, the small improvements in ranging estimations obtained when they are used do not always justify the increased location traffic injected in the network.

The field of Indoor Positioning Systems (IPS) is rapidly expanding due to the increasing need for accurate indoor localization. This research delves into the fingerprinting technique, a commonly used method in IPS, and advocates for coupling the Received Signal Strength Indicator (RSSI) to the Round-Trip Time (RTT) to boost its effectiveness. The primary incentive for this integration is to alleviate the network burden caused by the Wi-Fi RTT method while maintaining the system's precision. Our goal is two-fold: firstly, we aim to find the ideal combination of RTT and RSSI features that a specific machine learning algorithm requires to supply precise and prompt position estimations for real-time applications. Secondly, we aim to reduce the number of RTT features needed, as they demand the addition of location traffic, which may saturate the network when multiple stations try to locate themselves. To meet these goals, a variety of machine learning models and several combinations of the available metrics (RTT and RSSI) have been assessed. Initial findings indicate that this combined approach significantly diminishes network overhead and enhances the scalability and effectiveness of the fingerprinting method, paving the way for further exploration in indoor localization.

Since its integration in common-off-the-shelf (COTS) devices, the IEEE 802.11mc standard for the ranging estimation between a Wi-Fi card and an Access Point (AP) has grown interest in the research community. In 2021, Android released the new version 12 of its operating system, which adds some new configurable features in the Wi-Fi fine time measurement (FTM): the possibility for the user to set different burst sizes to the FTM procedure and a new one-sided FTM mode that can work with legacy APs. This paper assesses the impact of the burst size on the performance of the Wi-Fi FTM ranging. Intuitively, one may expect a higher stability in the ranging observation when the burst size is increased; however, it would imply an increase in the overhead of the ranging procedure. The impact has been studied with several models of smartphones and APs, and working in different frequency channels. The results show that, while using the minimum size allowed is not recommended because of a significant decrease in the performance, using burst sizes higher than 8 does not bring significant advantages that justify the additional traffic injected in the network.

Next-generation cellular networks, i.e., beyond fifth generation (B5G) and sixth generation (6G) networks, aim at significantly increasing capacity by deploying a massive number of small cells (SCs) and leveraging millimeter wave (mmWave) links that have large bandwidth availability. However, as optical connections to a large number of base stations (BSs) are not cost effective, wireless backhaul (BH) links may be used to connect SCs to the core network using mmWave frequencies and forming multihop mesh BH paths. While very flexible in deployment, the uncertainty in user demand, which varies over time and place, makes network planning and management challenging. In this work, we propose novel algorithms to solve the joint problem of energy-efficient user association, BH traffic routing and BS/BH link on/off switching. We first formulate an exact model assuming user traffic demand uncertainty leveraging Γ-robustness theory. As the model is intractable for large-scale instances, we develop a novel greedy robust heuristic (P-HEUR), which includes a robustifying step to effectively cope with demand uncertainty. Our evaluation demonstrates that P-HEUR provides robust and energy-efficient solutions quickly for different scenarios and traffic demand uncertainty levels, and it significantly outperforms state-of-the-art approaches while achieving up to 83% of the optimal capacity, even in the most demanding scenarios in terms of traffic load and demand uncertainty, with up to 200k times lower complexity.

AODVv2 is a well-known routing protocol used in MANETs (Mobile Ad hoc Networks). Formerly known as DYMO (DYnamic MANET On-demand), it is frequently used as a reference for routing protocols assessment. However, implementations of these protocols are scarce and the few ones available are currently outdated, no longer maintained and hardly upgradeable. This paper provides the details of a new AODVv2 implementation to be used in embedded devices working with the ARM microprocessor architecture. A user-space approach has been followed so both the upgradability and platform-independence are favored. A WiFi ad hoc network, modeling representative real scenarios, has been deployed to verify the correctness of the developed AODVv2 code and assess the performance of the protocol under realistic traffic conditions. A virtual machine has been used to perform a cross-compilation of a the code that implements the DYMO protocol in the Intel x86 computer architecture. Once compiled for being used in ARM-based devices, the code has been tested in Raspberry Pi devices to verify the proper behaviour. Simple scenarios and scenarios with high density of nodes have been deployed and data have been collected and analysed.

Since IEEE 802.11 defined a couple of enhancements that allow accurate time measurements in COTS Wi-Fi devices, the possibility of achieving precise low-cost distance estimations in Wi-Fi has become a reality. However, many sources of error, such as bandwidth limitations of the Wi-Fi signal, limited clock rate in the device, multipath propagation due to the obstacles in the indoor environment, etc., may add noise to the time measurements and therefore distort the estimated ranging. This study aims at covering the gap existing in the literature by assessing the performance of the ranging estimation in real IEEE 802.11mc stations in a typical NLOS environment. The impact on the accuracy is also explored when the station is held in different positions with respect to the floor.

Indoor positioning is still considered an open issue, especially when general-purpose communication networks are used to observe the metrics for positioning. IEEE 802.11mc aims at providing positioning capabilities to Wi-Fi networks. Google recently developed a feature aimed at making easy to run the standard in any Android device; however, only few access points (APs) officially support and advertise it. It is proven, though, that a bunch of APs are able to attend the location requests from 802.11mc devices despite not announcing their positioning capabilities. An application is proposed here that effectively gathers a list of all the APs that may collaborate in the indoor positioning process. This work is aimed at studying the performance of the location error in a real scenario where Android Wi-Fi devices run the IEEE 802.11mc positioning facility. The results demonstrate that, although errors are smaller than 2 meters as promised, the performance greatly depends on the AP and the frequency band used.

MANETs have been focusing the interest of researchers for several years. The new scenarios where MANETs are being deployed make that several challenging issues remain open: node scalability, energy efficiency, network lifetime, Quality of Service (QoS), network overhead, data privacy and security, and effective routing. This latter is often seen as key since it frequently constrains the performance of the overall network. Location-based routing protocols provide a good solution for scalable MANETs. Although several location-based routing protocols have been proposed, most of them rely on error-free positions. Only few studies have focused so far on how positioning error affects the routing performance; also, most of them consider outdated solutions. This paper is aimed at filling this gap, by studying the impact of the error in the position of the nodes of two location-based routing protocols: DYMOselfwd and AODV-Line. These protocols were selected as they both aim at reducing the routing overhead. Simulations considering different mobility patterns in a dense network were conducted, so that the performance of these protocols can be assessed under ideal (i.e. error-less) and realistic (i.e. with error) conditions. The results show that AODV-Line builds less reliable routes than DYMOselfwd in case of error in the position information, thus increasing the routing overhead.

With the advance of fifth generation (5G) networks, network density needs to grow significantly in order to meet the required capacity demands. A massive deployment of small cells may lead to a high cost for providing fiber connectivity to each node. Consequently, many small cells are expected to be connected through wireless links to the umbrella eNodeB, leading to a mesh backhaul topology. This backhaul solution will most probably be composed of high capacity point-to-point links, typically operating in the millimeter wave (mmWave) frequency band due to its massive bandwidth availability. In this paper, we propose a mathematical model that jointly solves the user association and backhaul routing problem in the aforementioned context, aiming at the energy efficiency maximization of the network. Our study considers the energy consumption of both the access and backhaul links, while taking into account the capacity constraints of all the nodes as well as the fulfillment of the service-level agreements (SLAs). Due to the high complexity of the optimal solution, we also propose an energy efficient heuristic algorithm (Joint), which solves the discussed joint problem, while inducing low complexity in the system. We numerically evaluate the algorithm performance by comparing it not only with the optimal solution but also with reference approaches under different traffic load scenarios and backhaul parameters. Our results demonstrate that Joint outperforms the state-of-the-art, while being able to find good solutions, close to optimal, in short time.

A dense deployment of small cells is one of the key characteristics envisioned for future 5G mobile networks in order to provide the required capacity increase. To cut cabling costs, small cells are foreseen to form multihop topologies using high capacity backhaul wireless links in the mmWave bands. Such small cells are deployed within the coverage area of a macro cell (eNB) to provide localized capacity where needed according to the well known Heterogeneous Network concept (HetNet). However, green networking will be very important because powering on unnecessarily a massive amount of small cells may lead to increased OPEX and CO2 emission. In this paper, we propose an optimization model that minimizes the total power consumption of 5G HetNets deployments while providing the required capacity and coverage. Our model jointly optimizes the user association, routing in the multihop backhaul and decides to power on or off the small cells to serve the user demands. Our numerical evaluation show significant power savings over a large range of traffic demand distributions while keeping the blocking probability low.

Fifth generation (5G) wireless networks will target at energy and spectrum efficient solutions to cope with the increasing demands in capacity and energy efficiency. To achieve this joint goal, dense networks of small cells (SCs) are expected to overlay the existing macro cells. In parallel, for the SC connection to the core network, a promising solution lies in a mesh network of high capacity millimeter wave backhaul (BH) links. In the considered 5G architecture, each SC is able to forward its BH traffic to the core network through alternative paths, thus offering high BH network reliability. In this context, the joint problem of user association and BH routing becomes challenging. In this paper, we focus on this problem targeting at energy and spectrum efficient solutions. A low-complexity algorithm is proposed, which bases its user association and BH routing decision i) on minimizing the spectrum resources to guarantee the user rate, so as to provide high spectrum efficiency, and ii) on minimizing both the access network and BH route power consumption to provide high energy efficiency. Our results show that our solution provides better trade-offs between energy and spectrum efficiency than the state-of-the-art in 3GPP scenarios.

Spectrum scarcity together with high capacity demands make the use of millimeter wave (mmWave) frequencies an interesting alternative for next generation, i.e., fifth generation (5G), networks. Although mmWave is expected to play a key role for both access network and backhaul (BH), its initial use in the BH network seems more straight-forward. This stems from the fact that, in the BH case, its deployment is less challenging due to the fixed locations of BH transceivers. Still, provided that mmWave spectrum consists of several subbands, each one with different characteristics and thus different deployment constraints (e.g., channel bandwidth, maximum transmission power), a comparison is required in order to gain a better insight into the potentials of each solution. To that end, in this paper, the main mmWave candidate frequency bands are compared in terms of range, throughput and energy consumption. In our results, the bandwidth availability, the maximum transmission power as well as the antenna gains of each BH technology are taken into account, as defined by the Federal Communications Commission. The results are also compared with current industry-oriented state-of-the-art transceiver characteristics in order to gain further insights into the maximum achievable gains of each subband.

Reducing the energy consumption of data centers and the Cloud is very important in order to lower CO_2 footprint and operational cost (OPEX) of a Cloud operator. To this extent, it becomes crucial to minimise the energy consumption by consolidating the number of powered-on physical servers that host the given virtual machines (VMs). In this work, we propose a novel approach to the energy efficient VM consolidation problem by applying Robust Optimisation Theory. We develop a mathematical model as a robust Mixed Integer Linear Program under the assumption that the input to the problem (e.g. resource demands of the VMs) is not known precisely, but varies within given bounds. A numerical evaluation shows that our model allows the Cloud Operator to tradeoff between the power consumption and the protection from more severe and unlikely deviations of the uncertain input.

Topics on network simulation are often offered as elective topics or short seminars of programs on computer sciences and telecommunications engineering. In other programs simulation is offered as part of a general core topic (e.g. probability and statistics, network performance, etc.). It is not frequent to find simulation as a core topic. At the Faculty of Telecommunications in Barcelona it was decided to include network simulation as a core topic in the former integrated master (5 academic years) on telecommunication engineering. This paper deals with the reasons after that decision. The goal of the paper is to present and discuss how the topic evolved through the years and how it has been migrated to the new careers with separated degree and master after the Bologna process.

Together with the trend towards engineering designs more accessible to handicapped people there is an increasing concern at the engineering faculties to include topics related to accessibility in their programs. Engineers must be aware that handicapped people use technology and designs must take into account this fact. This paper explains how a topic on accessibility was started at a technical faculty as a cross topic to five different engineering degrees: Electrical, Electronics and Automatic Control, Industrial Product Design, Electronic Systems and Computer Science. The introduction process is explained along with the teaching methodology. Results after the first two courses are good in general but present some issues. The main issue to be corrected is that students from different branches achieve different academic performances.

Users in a cellular network are allowed to move freely while the network handoffs their calls or data transfers among different access points. Studies demonstrate that the mobility pattern followed by the users has an impact on some metrics strongly related to the performance (i.e., handoff rate, cell residence time, etc). Some aspects of the Random Waypoint mobility model have been studied in depth, but relating those studies with different possible layouts for the access points still remains an open issue. The interest in forecasting the next cell where the device may be handed off is twofold: 1. it gives a deeper insight into the statistical study of the mobility model; 2. it is useful to manage the resource allocation to improve the performance. The goal of this paper is to provide an analytical framework for these predictions in a symmetric layout composed of four circular cells covering a circular area. Equations are provided along with numerical examples for a given scenario.