In wired networks, Medium Access Control (MAC) spoofing is a well-known and extensively studied class of network security attacks in which an adversary impersonates a legitimate host by falsifying its MAC address. Despite existing defense stan- dards, such attacks remain relevant in contemporary networks. MAC spoofing can enable Man-In-The-Middle (MITM) attacks, allowing an attacker to intercept and inject traffic while posing as the victim whose MAC address has been compromised. Despite its conceptual simplicity, implementing a fully transparent MAC spoofing MITM attack is non-trivial. Existing techniques fail to maintain transparency from the victim’s perspective and may disrupt connectivity. While traffic interception is relatively straightforward, injecting traffic on behalf of the victim without affecting the victim’s normal operation is more challenging. Achieving such transparency typically requires custom code and specialized libraries that may lack portability across platforms or operate in user space rather than kernel space, thereby imposing performance limitations. This demo presents an implementation of a MAC spoofing-based MITM attack that enables transparent interception and injection of packets while impersonating the victim, without requiring custom code. The approach operates entirely in kernel space, leveraging Linux networking names- paces, and ensures complete transparency to both the victim host and its communication endpoint.

5G networks provide low-latency, high throughput, and massive connectivity, yet the control plane remains exposed to several security threats. Among the most common and impactful threats are Denial-of-Service (DoS) attacks, with Radio Resource Control (RRC) signaling storms being particularly effective and difficult to mitigate. In this attack, a malicious User Equipment (UE) aims to exhaust Next Generation Node Base (gNB) resources, preventing legitimate UEs from establishing a connection. Existing defenses are typically limited to detection, only evaluated through numerical simulations, and cannot discern between high-load network conditions and attacks. Most of them also assume static setups and do not take mobility into account. In this paper, we first evaluate the feasibility of the signaling storm attack by using the OpenAirInterface(OAI) 5G protocol stack. Then, we propose StormShield, a signaling storm attack detection and mitigation technique implemented as an xApp on an O-RAN Near-Real-Time (near-RT) RAN Intelligent Controller (RIC). It fingerprints and blocks Malicious UEs (MUEs) before gNB resources are exhausted. We prototyped our solution on an Over-The-Air (OTA) testbed with OAI, NVIDIA Aerial, and two different gNB setups. The first one leverages an USRP X410 Software-defined Radio (SDR) with 8.1 functional split; the second a commercial Foxconn Radio Unit (RU) with 7.2 functional split. Our experimental evaluation demonstrates that StormShield effectively prevents gNB resource exhaustion, identifying and blocking MUEs with an average detection accuracy of 97.6% within 106.5 ms from the beginning of the attack.

5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthaul (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity. In this article, we demonstrate the impact of successful spoofing and replay attacks against PTP synchronization. We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack, necessitating manual intervention to restore full network operations. To counter this, we design a Machine Learning (ML)-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.

The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework that enables research on Deep Reinforcement Learning (DRL) techniques for improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems. Using the open-source simulator ns-O-RAN and the reinforcement learning environment Gymnasium, the framework enables to train and evaluate DRL agents that dynamically control the activation and deactivation of cells in a 5G network. We show how to collect data for training and evaluate the impact of DRL on energy efficiency in a realistic 5G network scenario, including users' mobility and handovers, a full protocol stack, and 3rd Generation Partnership Project (3GPP)-compliant channel models. The tool will be open-sourced upon acceptance of this paper and a tutorial for energy efficiency testing in ns-O-RAN.

Next-generation wireless systems, already widely deployed, are expected to become even more prevalent in the future, representing challenges in both environmental and economic terms. This paper focuses on improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems through the near-real-time dynamic activation and deactivation of Base Station (BS) Radio Frequency (RF) frontends using Deep Reinforcement Learning (DRL) algorithms, i.e., Proximal Policy Optimization (PPO) and Deep Q-Network (DQN). These algorithms run on the RAN Intelligent Controllers (RICs), part of the Open RAN architecture, and are designed to make optimal network-level decisions based on historical data without compromising stability and performance. We leverage a rich set of Key Performance Measurements (KPMs), serving as state for the DRL, to create a comprehensive representation of the RAN, alongside a set of actions that correspond to some control exercised on the RF frontend. We extend ns-O-RAN, an open-source, realistic simulator for 5G and Open RAN built on ns-3, to conduct an extensive data collection campaign. This enables us to train the agents offline with over 300,000 data points and subsequently evaluate the performance of the trained models. Results show that DRL agents improve energy efficiency by adapting to network conditions while minimally impacting the user experience. Additionally, we explore the trade-off between throughput and energy consumption offered by different DRL agent designs.

Remote peering (RP) enables networks to reach Internet Exchange Points (IXPs) without physical presence in the same datacenter/location, offering cost-effective interconnection. However, in the context of anycast, where routing efficiency relies on the assumption that traffic is routed to the geographically nearest instance, RP can undermine this principle by introducing hidden performance costs and inflating paths. This paper presents a hybrid methodology, combining data-plane measurements with control-plane insights, to detect and geolocate RP links at scale using IP-level geolocation, AS-level mapping, and IXP facility data. Using 3 million traceroutes targeting 13,735 anycast /24-prefixes, we quantify the impact of RP on anycast routing.Our analysis shows that whilst RP is widespread, it often connects ASes geographically close to the IXP facility. However, for cases where it is “remote” we find it frequently results in long detours, elevated latency, and sub-optimal anycast site selection. Finally, we identify key ASes and IXP regions that play a central role in driving RP-related detours and locality violations in anycast routing. Our findings demonstrate at Internet scale that RP undermines anycast locality by increasing the likelihood of detours and inflated paths, underscoring the need for improved visibility into interconnection practices that impact latency-sensitive services.

The coordinated scheduling of electric vehicle (EV) charging is a critical challenge for smart cities, particularly in high-density infrastructure such as Mobility Hubs (MHs). This paper evaluates and compares two prominent approaches to the EV Charging Scheduling Problem (CSP): Mixed-Integer Linear Programming (MILP) and Reinforcement Learning (RL). We formulate a shared problem framework and apply both strategies under two structured scenarios: a small-scale deterministic benchmark and a medium-scale, realistic deployment with higher heterogeneity. Results show that MILP achieves optimal cost and state of charge SoC compliance in tractable cases but struggles with scalability. RL, based on Proximal Policy Optimization (PPO), achieves near-optimal performance while scaling to 100 EVs with minimal computation time. Despite occasional SoC deviations, the RL agent exhibits robust and adaptive behavior under dynamic conditions. This study offers actionable insights for selecting and deploying EV scheduling strategies in real-world urban environments.

Although the integration between Unmanned Aerial Vehicles (UAVs) and Radio Access Network (RAN) applications is envisioned to enable a variety of new use cases and services, several practical aspects related to autonomous operations over cellular systems are still largely unexplored due to difficulties in testing and validating such integration in the real world. In this paper, we bridge the gap between simulation and real-world applications by introducing a new framework that combines real-world robotic controllers and 5th generation (5G) cellular stacks with channel and flight simulation. We consider a holistic approach where we use ArduPilot as the flight controller and OpenAirInterface (OAI) and srsRAN as the 5G cellular stacks to provide a unified solution for developing and experimenting with UAV s for cellular applications. We utilize ArduPilot Software-in-the-Loop (SITL) to simulate and control the mobility of UAVs, while OAI-RFSim and srsRAN are used to model channel conditions. Our framework is particularly useful for developing data-driven solutions that require (i) a large amount of data collected under realistic operational conditions to learn effective control policies; and (ii) a sandbox and safe testing environment that enables exploration of the action space. By addressing a UAV coverage problem and developing a greedy heuristic, we demonstrate how our framework can be used to create and test algorithms in a simulated environment, showcasing its potential as a bridge to real-world applications.

In the Internet of Things (IoT) scenario, two of the most important innovations in recent years are Edge Computing and Low Power technologies, like LoRaWAN. Edge Computing facilitates computations to be executed in proximity to users, delivering advantages such as reduced response times, optimized bandwidth usage, and enhanced scalability for IoT applications. On the other hand, LoRaWAN stands out as an IoT communication technology engineered for secure, low data-rate transmissions with high energy efficiency. However, implementing the edge computing paradigm into LoRaWAN presents challenges due to its centralized cloud-based architecture, which conflicts with the principles of edge computing. In previous years, proposals have emerged to decentralize LoRaWAN and integrate it at the edge level. However, these proposals often result in the creation of new protocols that diverge from the standard LoRaWAN framework and are not backward compatible. Furthermore, existing proposals are typically tested on arbitrary testbeds and traffic conditions, failing to account for the diverse applications and traffic characteristics inherent in real-world scenarios. In this study, we enhance and refine DeLoRaN, our system architecture facilitating the decentralized operation of LoRaWannetworks at the edge layer, where network control operations are executed at the gateway level. Additionally, we develop and present a data-driven traffic model for LoRaWAN that reflects real-world scenarios, derived from extensive capturing of LoRaWantraffic over several months. This model leverages packets from devices across multiple networks and serves diverse applications not under our direct control. Using this model, we validate the performance of DeLoRaN by simulating device traffic generation based on our data-driven realistic model.

The security and efficiency of low-power wide area networks (LPWANs) for connecting an ever-growing number of IoT devices, expected to exceed 40 billion by 2030, are becoming increasingly important. LoRaWAN, a leading LPWAN technology, enables long-range, low-power communications but remains susceptible to jamming attacks that degrade network performance at the physical layer. This paper introduces a robust framework for detecting and mitigating jamming in LoRaWAN networks using comprehensive threat modeling and machine learning-based countermeasures. The framework simulates two types of jamming attacks: a channel-oblivious jammer, which transmits continuously to randomly interfere with channels, and a channel-aware jammer, which selectively disrupts active transmissions. We evaluate LoRaWAN's resilience through extended simulations in the ns-3 module, adapted for jamming scenarios. Additionally, we provide a high-precision LSTM-based detection model to identify jamming patterns and a mitigation strategy to counteract the channel-oblivious jammer, including an automatic restoration process for returning devices to normal operation post-disruption. The proposed framework enhances network robustness against jamming, showing that ML-based detection significantly reduces disruptions.

This survey explores the convergence of Internet of Things (IoT) technologies with Water Distribution Systems (WDSs), focusing on large-scale deployments and the role of edge computing (EC). Effective water management increasingly relies on IoT monitoring, resulting in massive deployments and the generation of Big Data. While previous research has examined these topics individually, this work integrates them into a comprehensive analysis. We systematically reviewed 255 studies on IoT in WDS, identifying key challenges such as interoperability, scalability, energy efficiency, network coverage, and reliability. We also examined technologies like LPWAN and the growing use of EC for real-time data processing. In large-scale WDS scenarios, where vast amounts of data are generated, we highlighted the importance of technologies like NB-IoT, SigFox, and LoRaWAN due to their low power consumption and wide coverage. Based on our findings, we provide guidelines for sustainable, large-scale IoT deployment in WDS, emphasizing the need for edge data processing to reduce cloud dependency, improve scalability, and enable smarter cities and digital twins.

Open Radio Access Networks (RANs) leverage disaggregated and programmable RAN functions and open interfaces to enable closed-loop, data-driven radio resource management. This is performed through custom intelligent applications on the RAN Intelligent Controllers (RICs), optimizing RAN policy scheduling, network slicing, user session management, and medium access control, among others. In this context, we have proposed dApps as a key extension of the O-RAN architecture into the real-time and user-plane domains. Deployed directly on RAN nodes, dApps access data otherwise unavailable to RICs due to privacy or timing constraints, enabling the execution of control actions within shorter time intervals. In this paper, we propose for the first time a reference architecture for dApps, defining their life cycle from deployment by the Service Management and Orchestration (SMO) to real-time control loop interactions with the RAN nodes where they are hosted. We introduce a new dApp interface, E3, along with an Application Protocol (AP) that supports structured message exchanges and extensible communication for various service models. By bridging E3 with the existing O-RAN E2 interface, we enable dApps, xApps, and rApps to coexist and coordinate. These applications can then collaborate on complex use cases and employ hierarchical control to resolve shared resource conflicts. Finally, we present and open-source a dApp framework based on OpenAirInterface (OAI). We benchmark its performance in two real-time control use cases, i.e., spectrum sharing and positioning in a 5th generation (5G) Next Generation Node Base (gNB) scenario. Our experimental results show that standardized real-time control loops via dApps are feasible, achieving average control latency below 450 microseconds and allowing optimal use of shared spectral resources.

The digitalization of Water Distribution Systems (WDSs) is becoming a key objective in modern society. The increasing complexity of contemporary WDSs, driven by urbanization, fluctuating consumer demand, and limited resources, makes their management particularly challenging, especially in large-scale scenarios. This paper proposes the SWI-FEED framework designed to facilitate the widespread deployment of the Internet of Things (IoT) for enhanced monitoring and optimization of WDSs. The framework aims to investigate the utilization of massive IoT in monitoring and optimizing WDSs in different contexts, with a particular focus on four use cases such as optimal node activation, IoT gateways deployment, distributed leakage detection and water demand disaggregation. SWI-FEED has been tested with predefined network models available in the Open Water Analytics community public repository. Specifically, the four use cases are evaluated using a large network consisting of 4,419 sensor nodes, 3 tanks and 5,066 pipes. Overall, this comprehensive framework provides a holistic approach to address possible challenges of a WDS and optimize the efficiency of large-scale IoT deployments. It reduces the energy consumption of IoT devices within the WDS while enhancing leak detection and localization capabilities in real-world water networks. Our adopted theoretical methodology is based on graph theory, which allows IoT gateways to be strategically positioned to maximize network coverage and minimize infrastructure redundancy. This makes it possible to significantly reduce the number of gateways required and, consequently, the overall system energy consumption.

The security of Border Gateway Protocol (BGP) operations at Internet Exchange Points (IXPs) is critical to ensuring the integrity of data exchanges between Internet Service Providers (ISPs). A key challenge in BGP is its trust-based route sharing, which introduces vulnerabilities that attackers can exploit to hijack or disrupt traffic. While mechanisms like Internet Routing Registries (IRRs) and the Resource Public Key Infrastructure (RPKI) have been developed to mitigate these risks, their effectiveness is often undermined by inherent design flaws that limit their reliability. This paper presents a novel tool designed to address these security gaps in IXP infrastructures. By analyzing the Routing Information Base (RIB) of an IXP’s route server, the tool identifies possible prefix hijacking attacks. These prefixes serve as input for calculating a Risk Level metric for each Autonomous System (AS), offering IXP operators insights into anomalous behaviors. The effectiveness of the metric is validated through its application to well-known attack scenarios. Finally, we showcase the application of this tool through an analysis of real-world data from the route server of a major Italian IXP.

The wireless spectrum’s increasing complexity poses challenges and opportunities, highlighting the necessity for real-time solutions and robust data processing capabilities. Digital Twin (DT), virtual replicas of physical systems, integrate real-time data to mirror their real-world counterparts, enabling precise monitoring and optimization. Incorporating DTs into wireless communication enhances predictive maintenance, resource allocation, and troubleshooting, thus bolstering network reliability. Our paper introduces TwiNet, enabling bidirectional, near-real-time links between real-world wireless spectrum scenarios and DT replicas. Utilizing the protocol, MQTT, we can achieve data transfer times with an average latency of 14 ms, suitable for real-time communication. This is confirmed by monitoring real-world traffic and mirroring it in real-time within the DT’s wireless environment. We evaluate TwiNet’s performance in two distinct use cases: (i) enhancing Safe Adaptive Data Rate (SADR) systems by assessing risky traffic configurations of UEs, resulting in approximately 15% improved network performance compared to original network selections; and (ii) deploying new CNNs in response to jammed pilots, where the DL pipeline achieves up to 97% accuracy by training on artificial data and deploying a new model in as low as 2 minutes to counter persistent adversaries. TwiNet enables swift deployment and adaptation of DTs, addressing crucial challenges in modern wireless communication systems.

LoRaWAN networks have become popular for enabling long-range, low-power connectivity in Internet of Things (IoT) applications. Traditional LoRa Wannetworks typically rely on a centralized architecture, which may pose limitations regarding scalability, reliability, and adaptability. In contrast, decentralized LoRaWAN networks offer a compelling alternative with several distinct features. This study explores the advantages of decentralized LoRaWAN networks over their centralized counterparts and presents DeLoRaN, a completely decentralized and fully compatible LoRaWAN network. Firstly, a decentralized network architecture enhances the availability of services by leveraging multiple copies of a LoRaWAN Network Server (NS), here called Network Controller, thereby eliminating the single points of failure. Secondly, the decentralized nature of the network improves data availability and integrity by utilizing shared and decentralized ledgers, such as blockchain technology. This ensures that data remains accessible and tamper-proof even in the presence of malicious actors or network failures. Thirdly, a decentralized network strengthens resilience by tolerating faulty or malicious nodes through the consensus mechanisms employed by the Network Controller. To prove our point, we present an implementation of our distributed approach and test it in different scenarios, to appreciate performance and scalability of DeLoRaN when compared to a centralized approach.

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the user-level. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducingns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps, automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)-driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative$Q$-learning (CQL) algorithm, combined with a state-of-the-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.

Water scarcity is nowadays a critical global concern and an efficient management of water resources is paramount. This paper presents an original approach for monitoring Water Distribution Systems (WDSs) through Internet of Things (IoT) that involves the integration of multiple sensors placed across the distribution network to accurately measure water flow. To enhance energy efficiency for green monitoring and communication process, we harness the power of graph theory and graph signal processing to represent in a tunable and accurate way the water flow and simultaneously minimize the number of IoT sensors communicating those measurements. We propose a graph model where water flow is represented as signal on graph and we introduce an algorithm, named GraphSmart, designed to reconstruct the graph signal when certain measurements are unknown or missing. Our framework is applied on a synthetic realistic environment within the context of LoRaWAN (Long Range Wide Area Network), an infrastructure and protocol designed for ultra-low-power IoT devices. Our findings show that GraphSmart significantly reduces energy consumption while ensuring precise flow estimation. Our research demonstrates high potential for energy-efficient and accurate water flow monitoring, paving the way to improve the management of WDSs and enabling water operators to address water scarcity challenges.

Border Gateway Protocol (BGP) is the standard protocol used for inter-domain routing in the Internet. Since it was designed without built-in security mechanisms, nowadays it results in being vulnerable to various security issues. Although countermeasures exist to secure BGP sessions, they are not widely used due to lack of knowledge and complexity of the setup. The aim of this paper is to raise awareness about routing security in BGP, to provide a methodology to deepen the analysis of BGP incidents and a tool to reproduce them in a sandbox environment, to better understand how these issues arise and why it is crucial to have security countermeasures in place. The paper examines a recent BGP incident in March 2022, where a Russian ISP hijacked an IP prefix belonging to Twitter. A comprehensive analysis of the incident is performed, including how it spread throughout the Internet and presenting the powerful toolkit used for the analysis. In the last section, the paper explains the usage and the potentiality of the tool KathBGPBuilder, which can recreate a real BGP deployment with minimal manual configuration using open data collected from RIPEstat. This tool can be utilized to experiment and recreate real BGP incidents, or to test security mechanisms.

Bluetooth Low Energy (BLE) is a pervasive wireless technology all around us today. It is included in most commercial consumer electronic devices manufactured in the last years, and billions of BLE-enabled devices are produced every year, mostly wearable or portable ones like smartphones, smartwatches, and smartbands. The success of BLE as a cornerstone in the Internet of Things (IoT) and consumer electronics is both an advantage, enabling short range, low cost, and low power consumption wireless communications, and a disadvantage, from a security and privacy standpoint. BLE exposes packets that enable a potential attacker to detect, enquire and fingerprint actual devices despite manufacturers’ attempts to avoid detection and tracking. Medium Access Control (MAC) address randomization was introduced in the BLE standard to solve some of these issues. In this paper we discuss how to detect and fingerprint BLE devices, basing our analysis and data collection on interactions allowed by the standard. In our study, we propose the Bluetooth Low Energy Nodes Detect, Enquire, (and) Recognition (BLENDER) framework for enumerating and fingerprinting BLE devices for crowd monitoring and recognition purposes, based on four different strategies used to analyze BLE-enabled devices. We will show that it is possible to associate BLE randomized MAC addresses to actual devices. We will then describe a proof of concept for large-scale data collection. In addition, to determine the spots where the stations could be optimally positioned, we created a synthetic dataset based on mobility models and then we emulated the BLENDER approach. The latter allowed training Machine Learning models to predict the expected number of devices appearing at any particular position, day, and hour.

The optimization and digitalization of Water Distribution Networks (WDNs) are becoming key objectives in our modern society. Indeed, WDNs are typically old, worn and obsolete. These inadequate conditions of the infrastructures lead to significant water loss due to leakages inside pipes, junctions and nodes. It has been measured that in Europe the average value of lost water is about 26 %. Leakage control in current WDNs is typically passive, repairing leaks only when they are visible. Emerging Low Power Wide Area Network (LPWAN) technologies, and especially IoT ones, can help monitor water consumption and automatically detect leakages. In this context, LoRaWAN can be the right way to deploy a smart monitoring system for WDNs. Moreover, most of the current smart WDNs solutions just collect measurements from the smart metres and send the data to the cloud servers, in order to execute the intended analyses, in centralised way. In this paper, we propose new solutions to improve monitoring, leak management and prediction by exploiting edge processing capabilities inside LoRaWAN networks. Our approach is based on an IoT system of water sensors that are placed at junctions of the WDN to have measurements in correspondence to various smart metres in the network and Machine Learning (ML) algorithms to process the data directly at the edge in order to visualise and predict leakages. We present a numerical simulation tool useful to evaluate the suggested monitoring method. Based on our results, we examine whether it is possible to identify network leaks using the edges without having a complete or accurate overview of the collected measurements of the full WDN. System performance is shown separately at gateways network.

LoRaWAN is a wireless technology developed to transmit over long distances using low power. It runs over the proprietary LoRa radio modulation and provides fundamental IoT requirements such as bi-directional communication, end-to-end security, key management, mobility, and localization services. Despite LoRaWAN guarantees confidentiality and integrity of application payload, the wireless nature of the medium causes that an eavesdropper, listening to the network communications, can collect non-encrypted information stored in the packets. In particular, it can obtain two sensible metadata elements, called DevAddress e DevEUI. Since the association between these elements can involve privacy issues, LoRaWAN forces endpoints to expose their DevEUI only during the association procedure to avoid the association with the corresponding DevAddress. In the first part of this work, we prove how an adversary can link them nevertheless. Then we explain the consequences for the privacy of devices and users that joined the network and propose PIVOT (Privacy-Monitoring), an analyzer system for LoRaWAN that detects in real-time vulnerable endpoints. Furthermore, we explain how the metrics used in PIVOT can support the operator in applying adequate countermeasures. Finally, we test our scheme on a simulated LoRaWAN application and examine the results obtained.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record.This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article.Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

This article describes HIRO-NET, an Heterogeneous Intelligent Robotic Network. HIRO-NET is an emergency infrastructure-less network that aims to address the problem of providing connectivity in the immediate aftermath of a natural disaster, where no cellular or wide area network is operational and no Internet access is available. HIRO-NET establishes a two-tier wireless mesh network where the Lower Tier connects nearby survivors in a self-organized mesh via Bluetooth Low Energy (BLE) and the Upper Tier creates long-range VHF links between autonomous robots exploring the disaster-stricken area. HIRO-NET's main goal is to enable users in the disaster area to exchange text messages to share critical information and request help from first responders. The mesh network discovery problem is analyzed and a network protocol specifically designed to facilitate the exploration process is presented. We show how HIRO-NET robots successfully discover, bridge and interconnect local mesh networks. Results show that the Lower Tier always reaches network convergence and the Upper Tier can virtually extend HIRO-NET functionalities to the range of a small metropolitan area. In the event of an Internet connection still being available to some user, HIRO-NET is able to opportunistically share and provide access to low data-rate services (e.g., Twitter, Gmail) to the whole network. Results suggest that a temporary emergency network to cover a metropolitan area can be created in tens of minutes.

With the rise of the IoT, many protocols have been developed in order to fulfill the need for a wireless connectivity that assures energy efficiency and low-data rates. LoRaWAN is certainly one of the most widely used protocols. The LoRaWAN 1.1 specification aims to fix some serious security vulnerabilities in the 1.0 specification, however there still exist critical points to address. In this paper, we identify an attack that can affect LoRaWAN 1.0 and 1.1 networks, which hijacks the downlink path from the Network Server to an End Device. The attack exploits the deduplication procedure and the gateway selection during a downlink scheduling by the Network Server, which is in general implementation-dependent. The attack scheme has been proven to be easy to implement, not requiring physical layer-specific operations such as signal jamming, and could target many LoRaWAN devices at once. We discuss the implications of this attack and identify the possible mitigations that could be adopted by network providers to address this vulnerability.

LoRaWAN (Long 1 Range Wide Area Network) is an attractive network infrastructure and protocol suite for ultra low power Internet of Things devices. Even if the technology itself is quite mature and specified, the currently deployed wireless resource allocation strategies are still coarse and based on rough heuristics. This paper proposes an innovative “sequential waterfilling” strategy for assigning spreading factors to End Devices. Our design relies on three complementary approaches: i) equalize the Time-on-Air of packets transmitted by the system's End Devices in each spreading factor's group; ii) balance the spreading factors across multiple gateways and iii) keep into account the channel capture, which our experimental results show to be very substantial in LoRa. While retaining an extremely simple and scalable implementation, this strategy yields a significant improvement (up to 38%) in the network capacity over the Adaptive Data Rate used by many network operators on the basis of the design suggested by Semtech, and appears to be extremely robust to different operating/load conditions and network topology configurations.

The introduction of new key features into the core specification of Bluetooth Low Energy (BLE) increased its potentialities, thus paving the way to the development of new networking paradigms. The ability for a single node to participate in more than a piconet and to assume both the role of master and slave makes it possible the formation of multi-hop networks that can be used in several emerging application scenarios. Additionally, the inherent low power consumption at the cost of contained throughput makes this technology appealing for Internet of Things (IoT), where power memory and capacity constrained devices exchange messages at relatively low data rates. In this paper, we devise a two layers BLE mesh-based networking paradigm obtained by generalizing Android BE-MESH for hardware-independent sensor networks. Each node enforces a plug-and-play architecture which makes it able to autonomously switch between client and server role, discover and connect to existing scatternets and relay messages through them, making the network able to extend and self re-organize in a distributed fashion. To have our implementation ready for IoT systems we based it on the ESP32 off-the-shelf board. We describe both the implemented functions as well as some practical results proving the effectiveness of the framework in some tested configurations.

This paper investigates the advantages and design challenges of leveraging Unmanned Aerial Vehicles (UAVs) to deploy 4G/5G femto- and pico-cells to provide quality-aware user service and improve network performance. In order to do so, we combine UAVs dashing flight capabilities with Software-defined Radios (SDRs) flexibility and devise the concept of self-optimizing UAV Base Stations (UABSs). The proposed framework allows for on-the-fly drone repositioning based on rigorous optimization techniques using real-time network metrics to enhance users' service. This makes it possible to offload the traditional cellular infrastructure, or to mend its temporary failure, by deploying UABSs in areas of interest. Cellular connectivity is, then, provided to mobile subscribers through the LTE-compliant OpenAirInterface software interfaced with the on-drone SDR. We first describe the UABS design challenges and approaches. Then, we give details on the devised optimization algorithm and its main requirements. Finally, we illustrate a prototype implementation of the proposed UABS that leverages an SDR device and a PX4 flight controller, and test its effectiveness. Experimental results demonstrate that UABSs are able to autonomously reposition themselves based on cellular network metrics and to improve network performance.

In this paper we present HIRO-NET, Heterogeneous Intelligent Robotic Network. HIRO-NET is an emergency infrastructure-less network tailored to address the problem of providing connectivity in the immediate aftermath of a natural disaster, where no cellular or wide area network is operational and no Internet access is available. HIRO-NET establishes a two-tier wireless mesh network where the Lower Tier connects nearby survivors in a self-organized mesh via Bluetooth Low Energy (BLE)and the Upper Tier creates long-range VHF links between autonomous robots exploring the disaster stricken area. HIRO-NET main goal is to enable users in the disaster to exchange text messages in order to share critical information and request help from first responders. The mesh network discovery problem is analyzed and a network protocol specifically designed to facilitate the exploration process is presented. We show how HIRO-NET robots successfully discover, bridge and interconnect local mesh networks. Results show that the Lower Tier always reaches network convergence and the Upper Tier can virtually extend HIRO-NET functionalities to the range of a small metropolitan area. In the event of an Internet connection still being available to some user, HIRO-NET is able to opportunistically share and provide access to low data-rate services (e.g. Twitter, Gmail)to the whole network. Results suggest that a temporary emergency network to cover a metropolitan area can be created in tens of minutes.

We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (μeNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of μeNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After formulating the optimal problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer as well as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE).

We present a quality-selection policy for Quality of Experience (QoE) demanding video streaming in wireless networks. The proposed policy predicts the TCP throughput and adapts video segment requests in order to assure high QoE by taking into account the client buffer level. We introduce the concept of Affordable delivery Time (AT) and we design a buffer-based algorithm, hereafter referred to as Buffer-based lolyPOP (BPOP). The AT accounts for the ideal chunk download time which equals the chunk playout time, as well as for the client buffer status. In a nutshell, the buffer-based AT favors the download of higher quality video chunks when the client has more buffered data than a pre-established target buffer occupancy. Conversely, AT inhibits higher quality video chunks download when the buffer is below the targeted occupancy.

LoRaWAN is emerging as an attractive network infrastructure for ultra low power Internet of Thing devices. Albeit the technology itself is quite mature and specified, how to effectively allocate wireless resources so as to support a large amount of devices in a same terrestrial area is an open challenge. This paper contributes by proposing two algorithms (of incremental complexity) which are shown to outperform the basic Adaptive Rate Strategy (ADR) so far considered. A first approach, named EXPLoRa-SF, shows the benefits of a simple strategy which does not limit to use (as per ADR) distance/RSSI measurements, but also selects Spreading Factors (SF) based on the total number of connected devices. The advantages attained with EXPLoRa-SF further lead us to propose a more sophisticated algorithm, named EXPLoRa-AT, which employs an innovative “ordered waterfilling” approach which attempts to allocate the spreading factors so as to equalize the Time on Air of the packets transmitted by the system's end devices in each spreading factor's group. Simulation results show that the proposed algorithms significantly outperform the basic ADR strategy, and particularly the EXPLoRa-AT algorithm appears very robust to different operating conditions and consistently guarantees high bit rates in the case of high traffic loads.

We consider the problem of controlling the rate of failures triggered by fatigue processes of Base Stations (BSs) in cellular networks subject to Sleep Modes (SMs). Specifically, the increase of time spent in SM tends to decrease the BS failure rate by following, e.g., the Arrhenius law. However, the transitions between the power states tend to increase the BS failure rate, which can be predicted by the Coffin-Manson model. In this context, the energy savings triggered by SMs would not be economically useful if the BS failure rate were increased too much. Our goal is therefore to tackle the problem of minimizing the BS failure rate in a cellular network subject to SMs. After showing that the optimal formulation of the problem is NP-Hard, we propose a new algorithm, named LIFE, to practically solve it. We run LIFE on different scenarios (driven by LTE and legacy UMTS technologies). Our results show that LIFE outperforms two previous energy-aware algorithms, which instead do not take into account the BS failure rate. Specifically, our solution is able to achieve up to 40 percent of power saving at night, without a strong penalty in the BS failure rate.

In this work, we show that there are two effects impacting cellular networks owned by an operator as a consequence of the application of the green networking via Sleep Modes (SMs) state to Base Stations (BSs). On one side, it is possible to save money from the electricity bill by exploiting SMs. On the other hand, however, the SM state triggers a variation in the failure rate of the BS, and therefore in the replacement/reparation costs to fix it. We therefore derive a simple model to compute the total savings vs. costs for an operator network. Our results, obtained over 3G and 4G scenarios, show that there is a trade-off between the saving and the related costs. Moreover, we show that the costs incurred by the operator depend on different factors, including: i) the energy-aware strategy, ii) the specific technology adopted, and iii) the components used to build the BS.

We propose a dynamic and cooperative approach to support the desired Quality of Experience (QoE) of the mobile users in a heterogeneous cellular network. Specifically, we focus on a scenario in which a mobile video streaming service is provided to users. We then develop a mobile streaming traffic micro-to-macro offloading scheme based on a cross layer approach, in which we exploit the knowledge of the video structure at a proxy side. Our results, obtained over a detailed case study, show that the proposed approach is able to enable high quality video streaming services that are otherwise not feasible. Moreover, even a moderate portion of the macro bandwidth (typically ranging between 15% and 25% of the total bandwidth) is already able to serve users that have been offloaded from the small cell. Finally, we show that, when the offloading is based on a cross-layer approach, the QoE experienced by users (e.g., in terms of delay) is higher than in the case in which the offloading decision is solely based on lower layer parameters like the channel quality.

We consider adaptive-rate scheduling for downlink unicast transmissions of video streams over cellular wireless networks. We study a service under which each mobile client receives requested video streams at a variable Quality of Experience (QoE) level, based on its experienced communication quality condition. We employ a proxy-video manager at the base station node. The manager classifies users into two groups, based on their reported experienced CQIs (Channel Quality Indicators). The manager intercepts a client's video stream request destined to the HTTP server, determines its group classification, and proceeds to transmit a properly encoded version of the requested stream. To effectively regulate inter- cell signal interference, we examine a number of different spectral reuse and fractional frequency reuse scheduling schemes. We calculate, for each scheduling and user group classification scheme, the average bandwidth per stream that is required to provide user groups with their targeted QoE levels. We demonstrate a user classification process that aims to optimize a utility metric based on the definition of a will-to-pay utility function. The derived optimal configuration of FFR based schemes are shown to significantly enhance the system's performance behavior.

We target the problem of managing the Base Stations (BSs) lifetime and their energy efficiency when a sleep mode (SM) is adopted. We first show that the BS lifetime is affected by two opposite effects: the duration of SM, which tends to increase the lifetime, and the SM frequency, which on the contrary decreases the lifetime. After optimally formulating the problem for a cellular network, we propose a new heuristic, called LIFE, to practically solve it. Our solution integrates the BS lifetime when SM decisions are considered. Results, obtained over an UMTS scenario and a LTE one, prove that LIFE outperforms two previous energy-efficient algorithms in terms of lifetime performance, since all the previous solutions tend to drastically reduce the BS lifetime. Moreover, we show that LIFE is able to save up to 40% of power during night in the long-term.

This paper investigates the advantage of adopting a flexible resource control scheme when performing HTTP-based adaptive streaming across LTE systems. To guarantee video fluidity, mobile video streaming is known to require a large bandwidth overhead with respect to the net encoded video rate. The quality of a received video stream is impacted by variations in the size of the transmitted video packets (chunks), and by statistical fluctuations in the data rate at which the allocated downstream wireless channel operates. First, in considering an illustrative video scenario, we show that the chunk size distribution is heavy-tailed, and is well fit by a Gamma distribution. Second, we employ a HAS based proxy video manager and resource controller at the base station node. Based on the channel quality observed and reported by a mobile client, the manager selects the proper channel bandwidth and data rate levels at which to transmit the stream's chunks, in accordance with the selected encoded video rate and the configured Quality of Experience (QoE) level at which the user is targeted to receive the video stream.The communications data rate is also set to assure an acceptable low video reception stall probability. To illustrate the performance of such a dynamic bandwidth allocation scheme, we compare it with an operation that employs a stationary setting of the channel bandwidth, and we compute the gain achieved when such adaptations are performed at the base station node on a chunk by chunk basis. We show by analysis, and confirm by simulations, the improvements achieved in the system's performance behavior through the use of the adaptive resource allocation scheme.

Implementations of data dissemination protocols in Vehicular Ad-Hoc Networks (VANETs) typically use multi-hopping approaches at the network layer, employing timers whose expiration times determine whether a vehicle should relay a message. These protocols, when they interact with lower access layers, such as the IEEE 802.11p MAC, are often affected by phenomena that impair the performance of the dissemination process. In this paper, we highlight the spurious forwarding phenomenon that arises when using timer-based protocols. We demonstrate the degradation incurred in the packet dissemination performance when the VANET is loaded by a high rate of packet flows. We then propose a probabilistic decimation approach, demonstrate its ability to alleviate the spurious forwarding problem, and discuss its performance as a function of vehicular traffic density and packet flow rate. Comparisons with other probabilistic dissemination protocols highlight the significant performance improvement attained by using our approach, assuring the realization of high throughput rate and packet delivery ratio.

A key paradigm enabled by Vehicular Ad hoc NET- works is the support of location aware push-mode info-tainment services, besides the safety services that motivate the deployment of the technology in the first place. Message and advertisements are spread via multi-hop communications from the originating Road Side Units connected to the Internet. Support of such services relies on robust and efficient dissemination protocols. We define a timer-based vehicular backbone network protocol, where each vehicle can take forwarding decisions only based on the information read in the message header, its current state and local measurements. We analyze its performance taking into account the effect of the IEEE 802.11p MAC layer. A comparison with other literature dissemination protocols is carried out in a highway setting.

We develop an empirical model of the Multiview Video Coding (MVC) performance that can be used to identify and separate situations when MVC is beneficial from cases when its use is detrimental in wireless multimedia sensor networks (WMSN). The model predicts the compression performance of MVC as a function of the correlation between cameras with overlapping fields of view. We define the common sensed area (CSA) between different views, and emphasize that it depends not only on geometrical relationships among the relative positions of different cameras, but also on various object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. With these premises, we first experimentally characterize the relationship between MVC compression gain (with respect to single view video coding) and the CSA between views. Our experiments are based on the H.264 MVC standard, and on a low-complexity estimator of the CSA that can be computed with low inter-node signaling overhead. Then, we propose a compact empirical model of the efficiency of MVC as a function of the CSA between views, and we validate the model with different multiview video sequences. Finally, we show how the model can be applied to typical scenarios in WMSN, i.e., to clustered or multi-hop topologies, and we show a few promising results of its application in the definition of cross-layer clustering and data aggregation procedures.

A critical issue of a Cognitive Radio Network is the interference generated by secondary users simultaneously accessing and transmitting over the primary user spectrum band. In this paper, we introduce and study a distributed and opportunistic access scheme for MIMO ad-hoc cognitive radio networks identified as OPTIM-COG (OPporTunistic Interference control for Mimo COGnitive radio). OPTIM-COG is based on simple power stimuli issued by PU transmitters. These stimuli are basic control messages exchanged, at a known power, by both primary transmitter and primary intended receiver for their internal power control. A periodic repetition of this power stimulus and the power control generated in the primary network are used by secondary nodes to get their transmission opportunities. These opportunities are determined with a twofold goal: on the one hand the secondary node will transmit to its secondary receiver only if the quality of the already established primary connection does not decrease below a minimum level, on the other hand the resulting opportunistic access exploits the MIMO performance improvements. We describe the fully distributed version of this access scheme and we theoretically demonstrate, via a game theoretical formulation, that the resulting power allocation is the unique Nash Equilibrium. Performance results present secondary user access maps in the 2D plan, where both MIMO benefits as well as the presence of multiple active secondary links can be represented. Both the cases of a perfect interference measurement and imperfect channel estimation are evaluated.

We experimentally characterize the compression efficiency of Multiview Video Coding (MVC) techniques in Wireless Multimedia Sensor network (WMSN) composed of multiple video cameras with possibly overlapping field of views. We derive an empirical model that predicts the compression efficiency as a function of the common sensed area (CSA) between different camera views. We show that the CSA depends not only on geometrical relationships among the relative positions of different cameras, but also on several object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. We then apply the model to a WMSN, where we create clusters based on the CSA as estimated by exchanging local data. Based on this estimates, we form clusters and measure the resulting transmission rate. Numerical simulation results show that building clusters based on a CSA criterion can bring significant performance gains in terms of bandwidth efficiency. The herein presented promising results pave the way for clustering optimization taking into account different networks constraints and conditions.

Inter-vehicle communications will play an important role in future cars and traffic management in general. Many different services have been proposed in the literature using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. These services include safety applications like collision warning, up-to-date traffic information, active navigation, and also infotainment. Efficient data dissemination in vehicular networks (VANET) is of particular interest for both safety and infotainment services. In case of infotainment services, which are the object of our study, data dissemination is used to extend the radio coverage area of a Road Side Unit (RSU) to reach all users that can be interested in a given service. In this work, we propose a family of algorithms to extend the RSU coverage area in VANETs. These algorithms apply simple geometrical rules based on the position of the sending nodes. The algorithms are used to allow the data to cross road junctions and to propagate towards multiple directions, without using beacon or hello messages. By applying a simple geometrical analysis we show that the proposed solutions are able to increase the number of nodes reached by a broadcast message. Moreover, we study the performance of our solution through message flows injected in a dynamic scenario, where a Manhattan street grid is reproduced and populated through SUMO, which is able to provide realistic vehicle flows. The effect of traffic lights on vehicle flows in relation to network performance is also discussed.

In this paper, we consider the implications of spectrum heterogeneity on connectivity and routing in a Cognitive Radio Ad Hoc Network (CRAHN). We study the Laplacian spectrum of the CRAHN graph when the activity of primary users is considered. We introduce the cognitive algebraic connectivity, i.e., the second smallest eigenvalue of the Laplacian of a graph, in a cognitive scenario. Throughout this notion we provide a methodology to evaluate the connectivity of CRAHNs and consequently introduce a utility function that is shown to be effective in capturing key characteristics of CRAHN paths. This model provides a unique metric that captures network connectivity, path length, and impact of primary users. Moreover, the proposed metric penalizes paths where spectrum band switchings are highly probable. We design all the components of our routing framework, named Gymkhana, and we present a twofold performance verification: one from a topological perspective to show all the potentialities of the proposed routing approach, and the other considering network traffic to evaluate the performance in terms of end-to-end delay and packet delivery ratio.

After the big Internet growing of the past decade, in the current decade a considerable effort is spent to reduce the Internet energy consumption. Actual Internet topologies have space to power off some links and devices in order to reduce energy consumed in off-peak periods still guaranteeing connectivity among terminals. In this paper we propose a methodology to identify less used links in the network in order to have the capability to switch off these network interfaces for energy saving purposes. We describe four different algorithms able to identify this set of links and we show the tradeoff between complexity, and the consequent execution time, and efficiency in powering off a great number of links. We find that, by using our solutions, it is possible to switch off a big percentage of links still keeping the network load under suitable thresholds and still guaranteeing topological characteristics of the resulting network topology.

We propose a distributed routing scheme for cog- nitive radio ad hoc networks which, besides the awareness of the dynamic behavior of the primary users, also considers that some primary users may be hidden to secondary nodes, thus creating asymmetry in the spectrum opportunity availability. We identify this "Hidden Primary User" problem and we introduce in our distributed protocol a mechanism for being aware of it during the routing. To this aim our protocol creates priority lists at the secondary nodes used to have different possibilities to route the data on a path while the primary users activate and deactivate. The tradeoff between packet loss and delay is shown and discussed in the performance analysis.

We design a routing scheme based on an extension of the algebraic connectivity concept for cognitive radio ad hoc networks. We observe that a cognitive radio network topology and its connectivity are highly influenced by the behavior of the primary users. In some cases, even if the physical proximity of secondary nodes would give rise to a connected topology, the primary user behavior could impact the secondary network connectivity. In graph theory the second smallest Laplacian eigenvalue, i.e., the algebraic connectivity, has numerous relationships with the graph characteristics, including connectivity, diameter, mean distance of vertexes. We then propose to elaborate the algebraic connectivity in a cognitive scenario where we derive the form of the average Laplacian matrix of the network, averaged over the random activity of the primary users, and compute the algebraic connectivity. On the basis of this mathematical model we build up an utility function which is shown to be effective for capturing some key characteristics of networks paths and can be used to compare them for routing purposes. We then design a routing scheme which, by modeling a path with a graph and its Laplacian, captures the connectivity characteristics of the path itself and suitably selects the best route in a uncertain and high variable connectivity scenarios.

Wireless Sensor Networks are traditionally composed of a multiplicity of sensor nodes that sense given phenomena and deliver the sensed data to specific sink nodes. In the most of the application scenarios, sensor nodes have been considered motionless. On the contrary, interesting possibilities arise if some sensors are embedded in devices carried by mobile agents as people, cars, animals, etc. If sinks move within the considered sensor field, they are able to provide both sparse sensing and collecting of data measured by static sensors placed at fixed locations. The main goal of this work is to evaluate, through simulations, the impact of sinks' mobility in a wireless sensor network created by using the topology formation mechanism provided by the IEEE 802.15.4 Standard. To this aim, as a practical case study, we consider a wireless sensor network deployed in a museum used to monitor the presence, the localization and other parameters of artworks exposed in it. In this context, we analyze how sinks' mobility affects connectivity and energy consumption for network formation and re-configuration.

The IEEE 802.15.4 standard defines a set of procedures to set-up a Low-Rate Wireless Personal Area Network where nodes self-organize into a logical communication structure through which data can be routed, hop by hop, from sources to destinations. The network formation of the IEEE 802.15.4 does not impose constraints on the topology. The ZigBee Alliance uses the IEEE 802.15.4 layers to build a complete protocol stack for the implementation of wireless sensor networks. ZigBee specifies the network layer for star, tree and peer-to-peer topologies. Starting from these, more complex cluster-tree topologies can be formed. To control the network topology ZigBee fixes the maximum number of routers and end-devices that each router may have as children and also fixes the maximum depth of the tree. To better understand the importance of these constraints we simulate and analyze the IEEE 802.15.4 formation procedure in different network settings (single-sink and multi-sink scenarios). The goal is to provide guidelines for the practical implementation of ZigBee network formation with the aforementioned constraints.

An IEEE 802.15.4-based wireless sensor network is considered, and the relationship between the IEEE 802.15.4 topology formation mechanism and possible routing strategies at the network layer is studied. Two alternative routing schemes proposed in the framework of the ZigBee alliance are analyzed. The first is the well-known ad-hoc on demand distance vector (AODV) routing protocol, which was designed for highly dynamic application scenarios in wireless ad-hoc networks. The second is a tree-based routing scheme based on a hierarchical structure established among nodes during the network formation phase. This latter approach, referred to as HERA (hierarchical routing algorithm) in the paper, routes packets from sensors to sink based on the parent-child relationships established by the IEEE 802.15.4 topology formation procedure. An extensive simulation analysis is carried out to compare HERA and AODV. It is shown that a hierarchical routing scheme based on the MAC association procedures offers several benefits with respect to reactive routing in typical sensor network applications. Moreover, it is to be noted that most sensor network scenarios are concerned with delivery of packets from a series of static sensors to a single, static, sink.

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

Sensor networks exhibit a unique funneling effect which is a product of the distinctive many-to-one, hop-by-hop traffic pattern found in sensor networks, and results in a significant increase in transit traffic intensity, collision, congestion, packet loss, and energy drain as events move closer toward the sink. While network (e.g., congestion control) and application techniques (e.g., aggregation) can help counter this problem they cannot fully alleviate it. We take a different but complementary approach to solving this problem than found in the literature and present the design, implementation, and evaluation of a localized, sink-oriented, funneling-MAC capable of mitigating the funneling effect and boosting application fidelity in sensor networks. The funneling-MAC is based on a CSMA/CA being implemented network-wide, with a localized TDMA algorithm overlaid in the funneling region (i.e., within a small number of hops from the sink). In this sense, the funneling-MAC represents a hybrid MAC approach but does not have the scalability problems associated with the network-wide deployment of TDMA. The funneling-MAC is 'sink-oriented' because the burden of managing the TDMA scheduling of sensor events in the funneling region falls on the sink node, and not on resource limited sensor nodes; and it is 'localized' because TDMA only operates locally in the funneling region close to the sink and not across the complete sensor field. We show through experimental results from a 45 mica-2 testbed that the funneling-MAC mitigates the funneling effect, improves throughput, loss, and energy efficiency, and importantly, significantly outperforms other representative protocols such as B-MAC, and more recent hybrid TDMA/CSMA MAC protocols such as Z-MAC.

Abstract Modeling the resource manager of an ultra wide band (UWB) network is the main object of this paper. The model is tested in two different application scenarios: (i) a UWB WLAN access network to a backbone where the resource management module is implemented at the access points (APs); (ii) a UWB ad hoc network for either local communications or data exchange among sensors, with peer‐to‐peer links with distributed management. The design must include a quality of service (QoS)‐aware strategy and must take into account coexistence issues raised by the use of UWB at the physical layer. Link quality is represented by the maximum end‐to‐end delay and minimum percentage of correct packets. From these parameters, the resource manager jointly selects the values of power and rate which must be adopted at the physical layer. In the model, radiated power by each device is supposed to be limited by an upper bound, reflecting thus the limitation imposed by regulation. QoS awareness and power constraints are satisfied, thanks to the implementation of an admission control function, which is centralized in the AP in the WLAN case and distributed in the ad hoc case. Major innovative aspects include: (i) taking into account UWB specific features; (ii) introducing QoS awareness based on network layer parameters rather than physical layer parameters; (iii) incorporating error protection functionalities for the optimization of transmission efficiency; (iv) considering both centralized and distributed resource management. Performance of the proposed resource manager module is evaluated in the presence of different classes of traffic, that is multimedia, voice, and data traffic. For each class of traffic, performance is expressed in terms of the maximum possible number of simultaneous connections for the WLAN case, and in terms of the effective achievable throughput for the ad hoc case. Results of simulations indicate that the behavior of the proposed scheme is strongly dependent upon the class of traffic in the WLAN scenario, while it is slightly affected by changes in traffic characteristics in the ad hoc case. Copyright © 2005 John Wiley & Sons, Ltd.

In a scenario where different radio technologies cooperate to provide access to the Internet and advanced wireless services to mobile and nomadic users, Bluetooth is considered an enabling technology for the personal area networking segment. To this aim, Bluetooth devices should be able to set-up a wireless multihop network with given topological characteristics and with limited formation delay. In this work SHAPER, a distributed algorithm for tree scatternet formation, is enhanced to work in a dynamic environment where devices enter and leave the personal area network and require a fast interconnection with an optimized topology. We define a procedure (called SHAPER-OPT) that produces a meshed topology applying a distributed scatternet optimization algorithm (DSOA) on the network built by SHAPER. Nodes are shown to be able to easily join or leave the scatternet at any time, without compromising the long term connectivity. Benefits brought by DSOA are shown by performance analysis, while the delay for network set-up and reconfiguration in dynamic environments is shown to be within acceptable bounds.

Bluetooth is an enabling technology for Personal Area Networks. A scatternet is an ad hoc network created by interconnecting several Bluetooth piconets, each with at most eight devices. Each piconet uses a different radio channel constituted by a frequency hopping code. The way the devices are grouped in different piconets and the way the piconets are interconnected greatly affect the performance of the scatternet in terms of capacity, data transfer delay, and energy consumption. There is a need to develop distributed scatternet formation algorithms, which guarantee full connectivity of the devices, reconfigure the network due to mobility and failure of devices, and interconnect them such a way to create an optimal topology to achieve gainful performance. The contribution of this paper is to provide an integrated approach for scatternet formation and quality-of-service support (called SHAPER-OPT). To this aim, two main procedures are proposed. First, a new scatternet formation algorithm called self-healing algorithm producing multihop Bluetooth scatternets (SHAPER) is developed which forms tree-shaped scatternets. A procedure that produces a meshed topology applying a distributed scatternet optimization algorithm (DSOA) on the network built by SHAPER is then defined. Performance evaluation of the proposed algorithms, and of the accordingly created scatternets, is carried out by using ns2 simulation. Devices are shown to be able to join or leave the scatternet at any time, without compromising the long term connectivity. Delay for network setup and reconfiguration in dynamic environments is shown to be within acceptable bounds. DSOA is also shown to be easy to implement and to improve the overall network performance.

This paper deals with scatternet formation in Bluetooth. A scatternet is an ad hoc network of Bluetooth devices. Some works in the literature rely on the single-hop hypothesis, i.e., all devices are in radio visibility of each other. Other works refer to the more likely circumstance that devices are scattered in an area where some of them can not directly communicate. A challenging issue in this latter scenario (often referred to as multi-hop) is the design of a formation algorithm that: i) operates in a distributed way; ii) dynamically adapts the topology to the mobility of devices; iii) forms a scatternet with given topological properties. In this paper a distributed algorithm for scatternet formation that gives rise to a tree-like structure is introduced. The algorithm is shown to present three key properties that make it innovative with respect to the literature in the field: i) it is fully distributed and asynchronous; ii) it can be applied in a multi-hop environment; iii) it operates in order to dynamically adapt the topology to nodes' mobility and failures. The key steps and rules of the algorithm are described and performance results obtained by simulation are discussed.

To fully exploit the capabilities of Bluetooth for the deployment of wireless ad-hoc networks, the scatternet concept has been proposed. A scatternet is constituted by an overlapping of simple structures called piconets, each composed of up to eight devices sharing the same radio channel. A scatternet may present different topological configurations, depending on the number of composing piconets, the role of involved devices and the configuration of the links. This paper presents a general methodology for scatternet formation and proposes metrics that can be used to evaluate scatternet performance. Several numerical examples are presented and discussed, highlighting the impact of metric selection on scatternet performance.

Ultra-wideband (UWB) radio is becoming a promising field for new generation's digital communication systems. This technique, based mainly on the impulse radio paradigm, offers great flexibility and shows enormous potential in view of a future broadband wireless access. We present the main principles to design a multiaccess scheme based on UWB. The potential of UWB is exploited within a distributed ad hoc wireless system, where we describe the principles for the definition of a medium-access control (MAC) for mobile computing applications and we analyze the main performance results derived from simulations. A general framework for radio resource sharing is outlined for classes of traffic requiring both elastic-dynamic and guaranteed-reserved bandwidth. Then, we discuss the issue of supporting the proposed radio resource sharing scheme by means of a distributed MAC protocol.

Wireless data and multimedia are rapidly becoming a dominant issue of radio communications. In this context, the support of Internet applications based on the TCP presents several open issues due to the specific nature of the wireless network segment. The aim of this work is to evaluate by a rather detailed simulation model the performance of TCP data transfer over the TD-CDMA scheme of the UMTS TDD radio interface. The distinctive point of view is exploring the limits of a relatively, simple approach, by adopting the MAC/RLC connection oriented mode of operation for bulk, loss sensitive data. Features and parameters values that optimize TCP data transfer are discussed and their benefit quantified.

In the third generation wireless access systems the objective of integrating service classes with different QoS requirements introduces efficient and fair capacity sharing issues. We present a general packet scheduling, called CHAOS (channel adaptive open scheduling) whose objectives are to be adaptive to traffic and so fair with respect to different users, to be adaptive to channel quality in order to improve the radio resources utilization, and to use simple algorithms to realize the previous two goals. One key point in the definition of such a scheduling is the knowledge of the channel state: in fact on the basis of the adopted channel model a prediction criteria must be identified.

The wireless approach to the last mile access (wireless local loop, known as WLL) is becoming increasingly attractive to network operators and service providers since it offers a flexible and cost-effective solution to enable delivery of even broadband services to end customers. In this paper, a full-blown broadband WLL network is presented. The proposal is based on the OFDM-CDMA technique, to which an added dynamic reservation/request MAC protocol is proposed, fully exploiting the OFDM-CDMA platform. Central to our proposal is the support of different QoS profiles, in the context of QoS aware networks. As a case study, the explicit presentation of the IETF integrated services support over our WLL system is addressed. An extensive performance evaluation focused on the MAC layer is then reported. We prove that our scheme achieves high utilization efficiency, as well as a fair share of the available radio capacity, even in the presence of highly heterogeneous traffic mix. Delay performance is provided for both reference traffic models, as well as for measured IP and MPEG traffic traces offered to the system.

This paper deals with a novel strategy for connection admission control in ATM networks. This strategy, called worst deterministic pattern allocation (WDPA), aims at reaping statistical gain of ATM, through a simple and controllable bandwidth allocation. WDPA is based on the concept of preventively constraining information sources to emit their cells according to a superimposed deterministic mask. Resource allocation is performed taking into account only the parameters of the declared deterministic mask. The same allocation rule is utilized in every network section. Application of WDPA to input and output queueing switches is also discussed and its compatibility with both these architectures is demonstrated. A performance study is presented. A comparison of the efficiency resulting from WDPA application with that relevant to peak allocation and pure statistical allocation is presented. Results show that, by fixing a maximum transit delay, WDPA outperforms statistical allocation if path length exceed few hops.>