The Internet of Behaviour (IoB) extends the capabilities of the Internet of Things (IoT) by analysing and influencing human behaviour through behavioural data collected from connected devices. This evolution presents critical privacy concerns, especially for resource-constrained IoB devices where traditional cryptographic techniques may be impractical. Physical Layer Security (PLS) emerges as a lightweight, channel-aware solution that leverages the inherent characteristics of the wireless medium to safeguard data confidentiality. This paper examines the application of adaptive noise injection and cooperative jamming—two promising PLS techniques—for improving privacy in IoB networks. We propose concrete algorithms for both methods, supported by mathematical formulations and theoretical guarantees. Additionally, we assess their applicability, effectiveness, and implementation challenges in real-world IoB environments. Our findings provide a pathway for integrating PLS into privacy-preserving IoB architectures, enabling robust protection against passive eavesdropping without imposing heavy computational burdens on the devices.
The Internet of Behaviour (IoB) extends the capabilities of the Internet of Things (IoT) by analysing and influencing human behaviour through behavioural data collected from connected devices. This evolution presents critical privacy concerns, especially for resource-constrained IoB devices where traditional cryptographic techniques may be impractical. Physical Layer Security (PLS) emerges as a lightweight, channel-aware solution that leverages the inherent characteristics of the wireless medium to safeguard data confidentiality. This paper examines the application of adaptive noise injection and cooperative jamming—two promising PLS techniques—for improving privacy in IoB networks. We propose concrete algorithms for both methods, supported by mathematical formulations and theoretical guarantees. Additionally, we assess their applicability, effectiveness, and implementation challenges in real-world IoB environments. Our findings provide a pathway for integrating PLS into privacy-preserving IoB architectures, enabling robust protection against passive eavesdropping without imposing heavy computational burdens on the devices.
Establishing robust cybersecurity for Internet of Things (IoT) ecosystems poses significant challenges for system operators due to IoT resource constraints, trade-offs between security and performance, diversity of applications, and their security requirements, usability, and scalability. This article introduces a physical-layer security (PLS) approach that enables IoT devices to maintain specified levels of information confidentiality against wireless channel eavesdropping threats. This work proposes applying PLS active defense mechanisms utilizing spectrum-sharing schemes combined with fair scheduling and power management algorithms to mitigate the risk of eavesdropping attacks on resource-constrained IoT environments. Specifically, an IoT device communicating over an insecure wireless channel will utilize intentional noise signals transmitted alongside the actual IoT information signal. The intentional noise signal will appear to an eavesdropper (EVE) as additional noise, reducing the EVE’s signal-to-interference-plus-noise ratio (SINR) and increasing the EVE’s outage probability, thereby restricting their capacity to decode the transmitted IoT information, resulting in better protection for the confidentiality of the IoT device’s transmission. The proposed communication strategy serves as a complementary solution to existing security methods. Analytical and numerical analyses presented in this article validate the effectiveness of the proposed strategy, demonstrating that IoT devices can achieve the desired levels of confidentiality.
In the sixth-generation (6G) communications, how to deploy and manage massively connected Internet-of-Things (IoT) nodes will be one of the key technical challenges, because 6G is expected to provide 10 times higher connection, compared to 5G. At the same time, due to the sharp growth in connected devices and newly adopted technologies, learning-based attacks and big data breaches are expected to occur more frequently. With the advances of quantum computing in the future, conventional cryptography-based security protocols may be obsolete in the future wireless networks, which makes physical layer security (PLS) an attractive alternative or complement for secure communications. In this context, cooperative beamforming (CB)-based PLS schemes are known to be effective solutions to guarantee high secrecy rate with IoT devices, which have limited power and hardware complexity. However, the existing CB-based PLS algorithms suffer from extremely low secrecy rate, in case that eavesdroppers are close to the intended receiver. To overcome such critical issue, in this paper, we propose a CB-based PLS with artificial noise (AN) injection, which can be realized in a fully distributed manner to minimize the overhead in the IoT networks with a large number of devices. We analyze the array factor using the virtual antenna array (VAA) created by the proposed PLS algorithm. Then, the secrecy rate is derived in a closed-form expression, which can be used to optimize the performance for given system parameters in both the absence and presence of channel state information (CSI) error. The proposed scheme provides considerably higher secrecy rate compared to the conventional CB-based PLS schemes, when an eavesdropper exists near to the intended receiver. Furthermore, through simulation and numerical results, we show that the secrecy rate of the proposed scheme can be maximized by adjusting the ratio between the data beamformer and AN injection beamformer components. As a result, the proposed method shows a performance improvement of up to two times compared to the conventional CB-based PLS schemes, in terms of the secrecy rate. Such performance gain increases as the angular location of Eve becomes closer to that of Bob, which corresponds to the most vulnerable situation of the conventional CB-based PLS algorithms.
An increasing number of devices exchanges data via the wireless medium into large scale, decentralized, and heterogeneous networks, leading to the concept of Internet of Things (IoT). This PhD work proposes a new practical scheme to perform physical layer security (PLS) in this IoT context.Wireless communications in IoT networks are prone to passive eavesdropping. Therefore, this work introduces a scheme that guarantees the reliability of a communication from a transmitter (Alice) towards a legitimate user (Bob), while preventing multiple passive eavesdroppers (Eve) to correctly decode the data in a worst-cases scenario, thereby considering i) Eve to be noiseless, ii) Eve equipped with an arbitrarily large number of antennas, iii) Alice imperfectly estimating Bob's channel state information (CSI). To do so, a frequency domain time-reversal (TR) precoder is proposed using orthogonal frequency-division multiplexing (OFDM) and artificial noise (AN) injection. The scheme exploits the frequency diversity selective behaviour in multipath channels, and can be incorporated into existing standards, such as in LTE or 5G networks. Different configurations are investigated, namely, single-input single-output (SISO), multiple-input single-output (MISO), and single-input multiple-output (SIMO) systems.The handshake procedures between Alice and Bob are described, both in time-division duplexing (TDD) and frequency-division duplexing (FDD) systems, highlighting the amount of CSI acquisition at Eve, which influences the secrecy performances. Depending on the handshake procedures, different decoding structures are investigated at Eve for each system configuration, considering a block-fading environment. Closed-form approximations of the signal-to-noise ratio required at Bob and the maximal CSI error that can be made at Alice, in order to guarantee a communication ergodic secrecy rate (ESR), are derived. Furthermore, the optimal amount of AN energy to inject, as well as the maximal number of eavesdropper's antennas allowed, considering imperfect CSI, are also given as closed-form expressions. A trade-off on the choice of the spreading factor of the TR precoder is established between maximizing the ESR and decreasing the percentage of outage, leading to information leaked to Eve. Finally, thanks to these results, Alice can be a-priori aware of the ESR over which she can establish a secure communication, assuming worst-case assumptions regarding the eavesdroppers. The PLS scheme presented in this PhD work therefore emerges as a promising solution to secure wireless communications in IoT networks.
The increasing demands for Internet of things (IoT) applications and the tremendous increase in the volume of IoT generated data bring novel challenges for the fifth generation (5G) network. Verticals such as e-Health, vehicle to everything (V2X) and unmanned aerial vehicles (UAVs) require solutions that can guarantee low latency, energy efficiency,massive connectivity, and high reliability. In particular, finding strong security mechanisms that satisfy the above is of central importance for bringing the IoT to life. \nIn this regards, employing physical layer security (PLS) methods could be greatly beneficial for IoT networks. While current security solutions rely on computational complexity, PLS is based on information theoretic proofs. By removing the need for computational power, PLS is ideally suited for resource constrained devices. In detail, PLS can ensure security using the inherit randomness already present in the physical channel. Promising schemes from the physical layer include physical unclonable functions (PUFs), which are seen as the hardware fingerprint of a device, and secret key generation (SKG) from wireless fading coefficients, which provide the wireless fingerprint of the communication channel between devices. \nThe present thesis develops several PLS-based techniques that pave the way for a new breed of latency-aware, lightweight, security protocols. In particular, the work proposes: i) a fast multi-factor authentication solution with verified security properties based on PUFs, proximity detection and SKG; ii) an authenticated encryption SKG approach that interweaves data transmission and key generation; and, iii) a set of countermeasures to man-in-the-middle and jamming attacks. Overall, PLS solutions show promising performance, especially in the context of IoT applications, therefore, the advances in this thesis should be considered for beyond-5G networks.
Physical layer security (PLS) has emerged as a new concept and powerful alternative that can complement and may even replace encryption-based approaches, which entail many hurdles and practical problems for future wireless systems. The basic idea of PLS is to exploit the characteristics of the wireless channel and its impairments including noise, fading, interference, dispersion, diversity, etc. in order to ensure the ability of the intended user to successfully perform data decoding while preventing eavesdroppers from doing so. Thus, the main design goal of PLS is to increase the performance difference between the link of the legitimate receiver and that of the eavesdropper by using well-designed transmission schemes. In this survey, we propose a conceptual, generic, and expandable framework for classifying the existing PLS techniques against wireless passive eavesdropping. In this flexible framework, the security techniques that we comprehensively review in this treatise are divided into two primary approaches: signal-to-interference-plus-noise ratio-based approach and complexity-based approach. The first approach is classified into three major categories: first, secrecy channel codes-based schemes; second, security techniques based on channel adaptation; third, schemes based on injecting interfering artificial (noise/jamming) signals along with the transmitted information signals. The second approach (complexity-based), which is associated with the mechanisms of extracting secret sequences from the shared channel, is classified into two main categories based on which layer the secret sequence obtained by channel quantization is applied on. The techniques belonging to each one of these categories are divided and classified into three main signal domains: time, frequency and space. For each one of these domains, several examples are given and illustrated along with the review of the state-of-the-art security advances in each domain. Moreover, the advantages and disadvantages of each approach alongside the lessons learned from existing research works are stated and discussed. The recent applications of PLS techniques to different emerging communication systems such as visible light communication, body area network, power line communication, Internet of Things, smart grid, mm-Wave, cognitive radio, vehicular ad-hoc network, unmanned aerial vehicle, ultra-wideband, device-to-device, radio-frequency identification, index modulation, and 5G non-orthogonal multiple access based-systems, are also reviewed and discussed. The paper is concluded with recommendations and future research directions for designing robust, efficient and strong security methods for current and future wireless systems.
As Internet of Things (IoT) has boomed in recent years, many security issues have also been exposed. Focusing on physical layer security in wireless Internet of Things network communication, a series of security methods have been widely studied. Nevertheless, cooperative jamming methods in physical layer security to fight against collusive eavesdroppers have not been thoroughly studied yet. In this article, we study a cooperative-jamming-based physical layer secure transmission scheme for Internet of Things wireless networks in the presence of collusive eavesdroppers. We design a cooperative jamming strategy without knowing the channel state information of eavesdroppers. Considering the cooperation of multiple nodes with multiple antennas, this strategy can maximize the signal-to-interference-plus-noise ratio at an actuator (legitimate receiver). Meanwhile, the generated cooperative jamming signals can reduce the signal-to-interference-plus-noise ratio at eavesdroppers. To explore the theoretical security performance of our strategy, we perform a secrecy outage probability analysis and an asymptotic analysis. In the cases of cooperative jamming and without cooperative jamming, the closed-form expressions of the secrecy outage probability are deduced, and the influence of system parameters on the secrecy outage probability becomes more intuitive through a strict mathematical asymptotic behavior analysis. In addition, considering the energy limitation of Internet of Things devices, we propose a power allocation algorithm to minimize the total transmission power given the security requirements. The numerical results show the effectiveness of our schemes and are consistent with the theoretical analysis.