Worst-Case Cooperative Jamming for Secure Communications in CIoT Networks

The Internet of Things (IoT) represents a bright prospect that a variety of common appliances can connect to one another, as well as with the rest of the Internet, to vastly improve our lives. Unique communication and security challenges have been brought out by the limited hardware, low-complexity, and severe energy constraints of IoT devices. In addition, a severe spectrum scarcity problem has also been stimulated by the use of a large number of IoT devices. In this paper, cognitive IoT (CIoT) is considered where an IoT network works as the secondary system using underlay spectrum sharing. A wireless energy harvesting (EH) node is used as a relay to improve the coverage of an IoT device. However, the relay could be a potential eavesdropper to intercept the IoT device’s messages. This paper considers the problem of secure communication between the IoT device (e.g., sensor) and a destination (e.g., controller) via the wireless EH untrusted relay. Since the destination can be equipped with adequate energy supply, secure schemes based on destination-aided jamming are proposed based on power splitting (PS) and time splitting (TS) policies, called intuitive secure schemes based on PS (Int-PS), precoded secure scheme based on PS (Pre-PS), intuitive secure scheme based on TS (Int-TS) and precoded secure scheme based on TS (Pre-TS), respectively. The secure performances of the proposed schemes are evaluated through the metric of probability of successfully secure transmission (), which represents the probability that the interference constraint of the primary user is satisfied and the secrecy rate is positive. is analyzed for the proposed secure schemes, and the closed form expressions of for Pre-PS and Pre-TS are derived and validated through simulation results. Numerical results show that the precoded secure schemes have better than the intuitive secure schemes under similar power consumption. When the secure schemes based on PS and TS polices have similar , the average transmit power consumption of the secure scheme based on TS is lower. The influences of power splitting and time slitting ratios are also discussed through simulations.

With the increasing applications in the domains of ubiquitous and context-aware computing, Internet of Things (IoT) is gaining importance. The study to efficiently exploit and manage a spectrum resources for industrial IoT (IIoT) applications is currently in the interest of research community. As increasing number of IIoT devices is heading towards the future-connected society with the cost of high system complexity, to meet the growing demands of wireless communication in future, cognitive IoT (CIoT) technology is considered as a choice. Reliable detection of the vacant spectrum holes is a vital task in the CIoT network with data. However, the performance of spectrum sensing severely degraded with the existence of malicious users (MUs) which falsifies the sensing results by reporting false data to the fusion center (FC). In this paper, we focus on the use of particle swarm optimization (PSO) to safeguard the cooperative spectrum sensing (CSS) from the negative effects caused by the MUs. The effectiveness of the proposed scheme is verified numerically in various scenarios with different types of MUs through analysis and simulations.

Wireless mesh networks (WMNs) can provide flexible wireless connections in a smart city, internet of things (IoT), and device-to-device (D2D) communications. The performance of WMNs can be greatly enhanced by adopting a multi-radio technique, which enables a node to communicate with more nodes simultaneously. However, multi-radio WMNs face two main challenges, namely, energy consumption and physical layer secrecy. In this paper, both simultaneous wireless information and power transfer (SWIPT) and cooperative jamming technologies were adopted to overcome these two problems. We designed the SWIPT and cooperative jamming scheme, minimizing the total transmission power by properly selecting beamforming vectors of the WMN nodes and jammer to satisfy the individual signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) constrains. Especially, we considered the channel estimate error caused by the imperfect channel state information. The SINR of eavesdropper (Eve) was suppressed to protect the secrecy of WMN nodes. Due to the fractional form, the problem was proved to be non-convex. We developed a tractable algorithm by transforming it into a convex one, utilizing semi-definite programming (SDP) relaxation and S-procedure methods. The simulation results validated the effectiveness of the proposed algorithm compared with the non-robust design.

In this paper, we investigate the roles of cooperative jamming (CJ) and noise forwarding (NF) in improving the achievable secrecy rates of a Gaussian wiretap channel (GWT) when the helper node is equipped with multiple antennas. We decompose the channel from the helper to the eavesdropper into two orthogonal components: one is aligned in the direction of the channel between the helper and the legitimate receiver (direct component) and the other is in the orthogonal direction to the channel between the helper and the legitimate receiver (orthogonal component). We then propose a strategy in which the helper uses the orthogonal component to transmit pure Gaussian noise as in the CJ strategy while he uses the direct component for either CJ or NF depending on the given channel conditions. We explicitly derive the optimal power control policy for this strategy and give the achievable secrecy rates when the direct component is used to perform CJ or NF. We hence derive the channel conditions where CJ is better than NF over the direct component and vice-versa. Finally, we consider the reversely degraded multiple antenna relay-eavesdropper channel. We show that a simple strategy in which the relay jams with full power along the orthogonal component and transmits nothing in the direct component achieves a secrecy rate that approaches the secrecy capacity of this channel as the relay's average power goes to infinity. Moreover, we show that this result holds almost surely even if the relay-eavesdropper's channel state information is unavailable.

In this paper, we investigate the roles of cooperative jamming (CJ) and noise forwarding (NF) in improving the achievable secrecy rates of a Gaussian wiretap channel (GWT) when the helper node is equipped with multiple antennas. We decompose the channel from the helper to the eavesdropper into two orthogonal components: one is aligned in the direction of the channel between the helper and the legitimate receiver (direct component) and the other is in the orthogonal direction to the channel between the helper and the legitimate receiver (orthogonal component). We then propose a strategy in which the helper uses the orthogonal component to transmit pure Gaussian noise as in the CJ strategy while he uses the direct component for either CJ or NF depending on the given channel conditions. We explicitly derive the optimal power control policy for this strategy and give the achievable secrecy rates when the direct component is used to perform CJ or NF. We hence derive the channel conditions where CJ is better than NF over the direct component and vice-versa.

A network-independent tool-based usable authentication system for Internet of Things devices

Increasing system complexity poses significant difficulties for green energy management and predictive maintenance, primarily due to limited adaptability and centralization of AI systems. Such difficulties are eliminated in this work by synthesizing Cognitive IoT (CIoT) and bio-inspired embedded Artificial Intelligence, as a consequence of which a self-adaptive and energy-efficient industrial ecosystem is being designed. The proposed industrial ecosystem utilizes swarm intelligence, neural adaptation, and self-organizing feedback loops for decentralized decisions and real-time fault identification. Embedded AI modules embedded at IoT edge nodes learn automatic behavior during operation, predict likely faults, and optimize scheduling for repairs. Moreover, a hybrid framework implementing evolutionary optimization and deep neuro-symbolic learning maintains dynamic resource allocation and energy-aware computation. The novelty is dependent upon bio-inspired cognitive architecture, and self-evolving and environmentally adaptable operation is achieved by means of multi-agent coordination and hybrid learning paradigms. The end objective is improved reliability of a system, decreased downtime, and ecological sustainability for next-generation industrial networks.

We propose a framework for cooperative spectrum sensing in cognitive radio (CR) networks with adaptive energy threshold control for local spectrum sensing and an efficient approach for data fusion. Each secondary user (SU) carries out energy detection based local spectrum sensing, where the energy threshold is adaptively adjusted in real time according to both the SINR (signal to interference plus noise ratio) observed at the SU receiver and the statistics of the PU transmissions. A distinct linear threshold control function is adopted at each SU, which formulates the dynamic energy threshold as a function of the realtime SINR. The threshold control function is determined by solving a MAR optimization problem, which maximizes the achievable average rate of the SU under a given missed detection probability constraint. We also propose an efficient data fusion mechanism to well balance the impacts of the local sensing beliefs and the number of SUs supporting each hypothesis. As far as we know, this is the first work that adopts adaptive energy threshold control for cooperative spectrum sensing, through which data fusion can directly interplay with not only the diversified time-varying channel states of individual SUs but also the statistics change of PU transmissions. Numerical results show that the proposed framework significantly reduces the false alarm probability while keeping a low missed detection probability as compared with other existing works.

The proliferation of the Internet of Things (IoT) technology for wide range of wireless applications increase raw data, leads to spectrum scarcity, and also burdens available spectrum resources. Cognitive Radio (CR) is one of the key enablers to build future IoT systems, which has the ability to exploit dynamic networking characteristics to optimize network performance. One of the critical challenges is to obtain perfect Channel State Information (CSI) to efficiently allocate the transmission power and frequency resources among IoT nodes. In this paper, we focus on resource management optimization problem which is transformed into Markov Decision Process (MDP) and solved with proposed Deep Recurrent Reinforcement Learning (DRRL) based scheme with the consideration of energy-efficiency to deal with partial observability from limited information. Furthermore, to heighten the performance of proposed scheme, cooperative framework is introduced with Gated Recurrent Unit (GRU) layer to improve the resource allocation policy by accessing the historical information. Finally, extensive simulations corroborate the superiority of proposed scheme in terms of faster convergence and higher reward. The results also show that CSI imperfections is non-negligible, thus highlights the importance of robust and intelligent design that maintains users’ QoS.

Spectrum sharing between license holders (primary users) and unlicensed secondary users has been proposed as a solution to frequency wastage. Anyhow, the coexistence among these two types of users should not affect the quality of service of primary users. In this paper, we propose an interference control approach when one primary and multiple secondary users are simultaneously transmitting on the same frequency band. We first evaluate the Signal to Interference-plus-Noise Ratio (SINR) of the primary user in the presence of multiple secondary transmissions. Assuming a constraint on the primary SINR, we explicitly compute the probability that this SINR exceeds a certain threshold in the case of cohabitation. These precise probabilities are afterwards used in the transitions between different states of the Continuous Time Markov Chain that we proposed to model the system. A new model with additional outage probabilities is then built, leading to do a cross layer design of dynamic spectrum access, by considering both the traffic queuing problem and the channel outage in physical layer. Finally, some performance metrics of the system are analyzed, namely the primary throughput, the mean number of secondary users accessing the spectrum, and the secondary deprivation rate.

In this work, we investigate cooperative jamming schemes for the transmission of confidential messages in ad-hoc networks. By letting mobile units (the helpers) in the vicinity of a legitimate receiver send out jamming signals while a transmitter communicates with the receiver, an environment that is hostile to any eavesdroppers can be established. Meanwhile, the jamming signals are designed in an intelligent way such that the interference that the legitimate receiver experiences is kept low. Two particular approaches are considered, namely, Uncoordinated Cooperative Jamming (UCJ) and Coordinated Cooperative Jamming (CCJ). For both approaches, we present simulation results of the secrecy rate with respect to the node density of the network, the area the helpers occupy, and the number of transmit/receive antennas.

LPTD: Achieving lightweight and privacy-preserving truth discovery in CIoT

Temporal traffic smoothing for IoT traffic in mobile networks

We consider a system where a licensed radio spectrum is shared by two networks, namely the primary network and the secondary network. The spectrum is licensed to the primary network, however the secondary network makes opportunistic access of the unused and/or underutilized portions of it by using cognitive radio technology. For such a system, we study the problem of joint power control and beamforming in the downlink of the cognitive radio network and formulate two iterative algorithms considering two different scenarios of spectrum sharing. In the first case, the primary network cooperates with the secondary network to jointly optimize the transmit beamforming weights and total transmitted power by both networks subject to signal to interference plus noise ratio (SINR) constraints of both primary and secondary users. In the second case, the secondary network jointly optimizes the transmit beamforming weights and its total transmitted power satisfying the SINR constraint of the secondary users and maximum tolerable interference constraint to the primary user. Simulation results are provided to show convergence behavior of the iterative algortihms.

In this work, physical layer secure transmission of a cooperative wireless communication system over a three-phase amplify-and-forward (AF) two-way relaying channels is studied. A cooperative jamming and multi-antenna relay beamforming scheme that improves physical layer security of the system is proposed. Specifically, it is suggested that in the first and second timeslots when the legitimate users transmit alternatively, a jamming signal can be transmitted by the legitimate party that is not transmitting information-bearing signals This scheme, compared with the approach using a dedicated jammer, achieves a similar jamming effect with reduced complexity. In the third time slot of the protocol, the relay uses two beamforming matrices to process the bidirectional information signals. The jamming signal components at the relay are projected onto the null space of the legitimate relay-destination channels such that they do not affect legitimate communications. Through joint optimization, the sum throughput of the legitimate communication is maximized. In the numerical examples, the proposed scheme is shown to improve the system's secrecy transmission performance.