The rise of the Internet of Things (IoT) has led to the integration of various devices into smart homes, significantly increasing the complexity and vulnerability of home networks. Consequent network performance issues often lead to complaints directed at Broadband Service Providers (BSPs), which may arise from either legitimate usage or malicious cyber attacks. BSPs, however, lack visibility into client-side networks, which is partly due to privacy concerns. This makes it hard to identify the true cause of performance problems. While previous research has tackled these challenges using Machine Learning (ML) techniques, few studies have approached the problem from the perspective of BSPs. They need a solution that is scalable, accurate, and privacy-preserving. Existing centralized ML models fail to generalize across these heterogeneous environments and provide low accuracy. We address this gap by introducing a novel Federated Learning (FL) framework for smart home device classification and attack detection. The proposed approach offers a privacy-preserving, scalable framework that can achieve accuracies of more than 80%. This framework can be installed inside the existing resource-constrained home gateways, making it suitable for large-scale deployment by BSPs.

Smart homes are increasingly vulnerable to cyberattacks that lead to network instability, causing homeowners to lodge complaints with their Broadband Service Providers (BSPs). Therefore, effective and timely detection of cyberattacks is crucial for both customers and BSPs. Address Resolution Protocol (ARP) spoofing is one of the most common attacks that can facilitate larger and more severe follow-up attacks. Unfortunately, there are currently no methods that can effectively detect and mitigate ARP spoofing in smart homes from a BSP’s perspective. Current Machine Learning (ML)-based methods often rely on a single dataset from a controlled lab environment designed to mimic a single home, assuming that the results will generalize to all smart homes. Our findings indicate that this assumption is flawed. They are also unsuitable for smart homes from a BSP’s perspective, as they require custom applications, introduce additional overhead, and often rely on the injection of probing traffic into the network. To address these issues, we developed an algorithm that can detect ARP spoofing in smart home networks, regardless of the network structure or connected devices. It uses a cross-protocol strategy by correlating ARP packets with Dynamic Host Configuration Protocol (DHCP) messages to validate address bindings. We evaluated our method using four public datasets and two real-world testbeds, achieving 100% detection accuracy in all scenarios. In addition, the algorithm requires only little computational overhead, confirming its suitability for use by BSPs to detect and mitigate ARP spoofing attacks in smart homes.

Software-defined wireless networking (SDWN) offers new methods for managing wireless and heterogeneous systems, but equally poses new security challenges. Here, we investigate how the emerging paradigm of trust may be able to address security challenges in the SDWN data plane through trust management and trust-based routing. This paper presents a Proof-of-Concept (PoC) trust management system for securely managing heterogeneous software-defined networks, with a supporting threat model and trust framework. Evaluation demonstrates that the essential capabilities of a trust-based routing model are functional in our PoC, and also points to opportunities for future research.

Wi-Fi is still by far the most common and cheapest wireless technology to deliver Internet services to users and digital devices, which are dramatically increasing in terms of quantity and the quality of services they require. As a result, Wi-Fi networks are the most congested wireless technology. This issue incentivised many researchers to propose radio resource management solutions in unlicensed frequency bands to alleviate that problem. Nonetheless, these solutions lack coordination among the network operators who utilise wireless spectrum and hence, they do not efficiently solve the problem. In this work we propose a ‘win-win’ platform based on Software Defined Wireless Networking (SDWN) that facilitates a trusted and accountable Wi-Fi network access trading among networks operators, to solve the congestion problem in a collaborative manner that is beneficial for everyone. The platform enables Wi-Fi networks operators to both improve their users' satisfaction and earn incentives hence, achieving the ‘win-win’ equilibrium. Evaluation results in a dense Wi-Fi network environment show how the ‘win-win’ platform significantly improves satisfaction and achieved data rates for the cooperating networks operators' users in comparison to the standard approach.

Until today, Physical Layer Security (PLS) has been a concept from information theory without practical implementations. We here describe a demonstration of how PLS can be achieved with common off-the-shelf WiFi equipment and open-source software. The key principle is that we make intelligent use of the architecture of a wireless network instead of trying to control every link individually on the physical layer.

We investigated if independent wireless operators can effectively collaborate using Software-Defined Wireless Networking (SDWN) to achieve secret wireless communications and optimize useful throughput under the condition of Physical-Layer Security enabled secrecy in heavily contended spectrum. We studied this on the level of useful throughput, in addition to Signal-to-Interference-plus-Noise Ratio. The investigation was carried out by means of a series of discrete event simulations of a realistic Wi-Fi network deployment. We found that the difference between the user throughputs of the legitimate station and the eavesdropper could be a reasonably good proxy for secrecy capacity and conclude that SDWN controllers should ideally optimize the network based on empirical feedback from the system and active learning mechanisms, rather than on hard-programmed pre-calculated network models.

Physical Layer Security (PLS) is widely recognized as a promising approach to secure wireless communications through the exploitation of the physical properties of the wireless channel. However, until today, PLS has mostly been a concept from information theory without practical implementations. In this paper, we present an actual implementation of PLS in a wireless network. We achieved this by leveraging the flexibility and control granularity offered by the relatively new concept of spectrum programming, by which we were able to control and degrade the quality of the eavesdropper’s channel by virtually manipulating the connectivity of the legitimate station. The key feature of our design is that we make intelligent use of the architecture of a wireless network instead of trying to control every link individually on the physical layer. The PLS implementation is built using off-the-shelf hardware and open-source software which makes it cost-effective and easy to replicate.

The broadcasting nature of the wireless medium makes exposure to eavesdroppers a potential threat. Physical Layer Security (PLS) has been widely recognized as a promising security measure complementary to encryption. It has recently been demonstrated that PLS can be implemented using off-the-shelf equipment by spectrum-programming enhanced Software-Defined Networking (SDN), where a network controller is able to execute intelligent access point (AP) selection algorithms such that PLS can be achieved and secrecy capacity optimized. In this paper we provide a basic system model for such implementations. We also introduce a novel secrecy capacity optimization algorithm, in which we combine intelligent AP selection with the addition of Friendly Jamming (FJ) by the not-selected AP.

Cooperation to access wireless networks is a key approach towards optimizing the use of finite radio spectrum resources in overcrowded unlicensed bands and to help satisfy the expectations of wireless users in terms of high data rates and low latency. Although solutions that advocate this approach have been widely proposed in the literature, they still do not consider a number of aspects that can improve the performance of the users’ connections, such as the inclusion of (1) cooperation among network operators and (2) users’ quality requirements based on their applications. To fill this gap, in this paper we propose a centralized framework that is aimed at providing a “win‐win” cooperation among Wi‐Fi and cellular networks, which takes into account 5G technologies and users’ requirements in terms of Quality of Service (QoS). Moreover, the framework is supported by smart Radio Access Technology (RAT) selection mechanisms that orchestrate the connection of the clients to the networks. In particular, we discuss details on the design of the proposed framework, the motivation behind its implementation, the main novelties, its feasibility, and the main components. In order to demonstrate the benefits of our solution, we illustrate efficiency results achieved through the simulation of a smart RAT selection algorithm in a realistic scenario, which mimics the proposed “win‐win” cooperation between Wi‐Fi and cellular 5G networks, and we also discuss potential benefits for wireless and mobile network operators.

Current interference management solutions for dense IEEE 802.11Wireless Local Area Networks (WLANs) rely on locally measuring the cumulative interference at the Acess Point (AP) in charge of adjusting the spectrum resources to its clients.These solutions often result in coarse-grained spectrum allocation that often leaves many wireless users unsatisfied and increases the spectrum congestion problem instead of easing it.In this paper, we present a centralized interference management algorithm that treats the network-wide interference impact of each channel individually and allows the controller to adjust the radio resource of each AP while it is utilised.This coordinated allocation takes into account the Quality of Service (QoS) requirements of downlink flows while minimizing its effect on neighbouring APs.Therefore, this paper proposes a novel approach for quantifying the interference impact of each employed channel and jointly addressing the user-side quality requirements and the network-side interference management.The algorithm is tailored for operatoragnostic Software-Defined Networking (SDN)-based Radio Resource Management (RRM) in dense Wireless Fidelity (Wi-Fi) networks and adopts a fine-grained per-flow approach.Simulation results show that our algorithm outperforms existing solutions in terms of reducing the overall interference, increasing the capacity of the wireless channel, and improving the users' satisfaction.

Cyber-physical systems rely more and more on wireless technologies that operate in unmanaged, unlicensed frequency bands. Already today, this leads to security issues and a significant degradation of network performance due to interference and traffic congestion. Only collaboration between stakeholders leads to a sustainable solution. To facilitate collaboration, we designed a platform that heavily relies on the concept of Software-Defined Networking, introducing a central controller for programmable radio parameters. Part of this solution is a Brokering Platform that automates negotiation for spectral resources between access point (AP) operators. In this article we present a novel architecture for a Brokering Platform. We also propose secure distribution of Brokering Platform functionality over the APs using blockchain.

This paper presents a novel Access Point (AP) selection strategy based on a potential game played at a centralized controller. The proposed approach relies on Software Defined Networking (SDN), which has long been considered in the literature as a method to control management functionalities for Wi-Fi networks. The use of SDN provides a global view of the network, which guarantees an efficient distribution of Wi-Fi users among the APs. The centralized potential game proposed in this paper is based on the Fittingness Factor (FF) concept, which is a metric reflecting the suitability of the available spectrum resources to the application requirements. This paper describes the development of a new SDN-based framework that implements the potential game-based algorithm relying on the FF for efficient AP selection. The simulation campaign illustrates the important gains, both in terms of data rates assigned to the Wi-Fi users and their satisfaction compared against the AP selection suggested by the IEEE 802.11 standards and another algorithm proposed in the literature.

This paper addresses the problem of Access Point (AP) selection in large scale IEEE 802.11 Wireless Local Area Networks (WLANs) through the study of a shopping mall use-case. We investigate through simulations the impact of a smart AP association algorithm including two strategies based on the Fittingness Factor (FF) concept, which considers the suitability of the requirements for different stations accessing the network, on the performance of the WLAN studied in this use-case. The proposed AP selection algorithm is implemented in a framework based on Software Defined Network (SDN), which handles the APs distributed in the WLAN of the considered shopping mall use-case. The simulation campaign illustrates the important gains, both in terms of users' satisfaction and assigned throughput compared against the AP selection suggested by the IEEE 802.11 standards. Moreover, this result gives to the service providers a trade-off between efficiency and complexity delivered by the proposed strategies included in our smart AP selection algorithm when the SDN framework is used to manage large WLANs.

In spite of the enormous popularity of Wi-Fi-enabled devices, the utilisation of Wi-Fi radio resources (e.g. RF spectrum and transmission power levels) at Access Points (APs) is degraded in current decentralised Radio Resource Management (RRM) schemes. Most state of the art centralised control solutions apply configurations in which the network-wide impacts of the involved parameters and their mutual relationships are ignored. In this paper, we propose an algorithm for jointly adjusting the transmission power levels and optimising the RF channel assignment of APs by taking into account the flows' required qualities while minimising their interference impact throughout the network. The proposed solution is tailored for an operator-agnostic and Software Defined Wireless Networking (SDWN)-based centralised RRM in dense Wi-Fi networks. Our extensive simulation results validate the performance improvements of the proposed algorithm compared to the main state of the art alternative by showing more than 25% higher spectrum efficiency, satisfying the users' demands and further mitigating the network-wide interference through flow-based and quality-oriented power level adjustment.

This paper addresses the problem of access point (AP) selection in large Wi-Fi networks. Unlike current solutions that rely on received signal strength to determine the best AP that could serve a wireless user's request, we propose a novel framework that considers the quality of service (QoS) requirements of the user's data flow. The proposed framework relies on a function reflecting the suitability of a Wi-Fi AP to satisfy the QoS requirements of the data flow. The framework takes advantage of the flexibility and centralised nature of software defined networking (SDN). A performance comparison of this algorithm developed through an SDN-based simulator shows significant achievements against other state-of-the-art solutions in terms of provided QoS and improved wireless network capacity.

Cognitive radio is considered as a new paradigm of designing wireless communication systems that aims at improving the spectrum utilization. Spectrum sensing is one of the most important elements in cognitive radio networks. Many spectrum sensing algorithms have been proposed in the literature. Among these algorithms, energy detection has been widely used in practice because of its computational and implementation simplicities. However, the performance of conventional energy detection is greatly deteriorating due to the impact of noise uncertainty, especially in low signal to noise ratio SNR environments. On the other hand, the inappropriate setting of the detection threshold could leads to a significant decline in the performance of detection. In this paper, we explore these situations and we propose an energy detection based optimal threshold scheme. We derived an optimal threshold level based on the tradeoff between misdetection probability and false alarm probability. We also developed an adaptive threshold factor with optimal algorithm in order to combat the noise uncertainty which is compatible with the real world communications and that allows gaining better spectrum sensing performance especially in low SNR.

The smart grid will deliver electricity from suppliers to consumers using digital technology to save energy, reduce cost and increase reliability and transparency. The realization of the smart grid requires the modernization of the control system that governs the electricity supply process. The power grid will be instrumented with thousands of new digital control devices, sensors, and smart meters. Although the introduction of these technologies will bring many benefits to the control of the power networks, it will add more complexity to the communications. These new control entities will generate a huge amount of data and consume a portion of the communication bandwidth that may be needed for other control operations. In this paper we propose to use data aggregation techniques that allow reducing the communication overhead resulting from the introduction of the wireless sensor networks. We show that by using data aggregation functions, the communication traffic generated by smart sensors can be reduced, hence saving precious transmission bandwidth for other control operations.

In wireless sensor networks, sensor nodes are expected to gather specific information about the environment and send it to the user. The data dissemination usually involves many nodes in a multi-hop transmission from the source towards the user base station. Since sensor nodes are energy limited and may fail at any moment, this data delivery is far from secure. Therefore, it is important to design new solution to allow a robust and reliable data dissemination. In this paper we propose a node recovery scheme that exploits the network density and the broadcasting nature of the wireless medium to replace the energy depleted or the failed nodes in the communication. We evaluate this work by simulations and show that our approach improves the communication reliability and extend the routing path time.

The paper presents a new clustering scheme for wireless sensor networks based on semantic properties. Unlike current clustering schemes where signal strength and neighbouring are the most used criteria in the cluster formation, in this work, semantic properties are also taken in consideration in the process of cluster formation. Nodes inside a same cluster are organized like a hyper tree where the cluster-head is the root. This hyper tree organization allows a layered data aggregation, avoids the clusterhead overload and energy dissipation, and makes the network more robust against links and nodes failures. We evaluate this work by simulations and show that our approach is more efficient and more energy conservative than other clustering schemes.