Cognitive Internet Of Things Network Research Articles

Multi-Channel Cooperative Spectrum Sensing and Scheduling for Cognitive IoT Networks

This paper presents a novel multi-channel cooperative spectrum sensing and scheduling (MC$_2$S$_3$) framework for spectrum and energy harvesting cognitive Internet of Things (IoT) networks. We address the challenge of maximizing network throughput by formulating a combinatorial problem that jointly optimizes the sensing scheduling of primary channels (PCs), the assignment of IoT devices for sensing scheduled PCs, and the clustering and allocation of IoT nodes to efficiently use discovered idle PCs; subject to spectrum utilization and collision avoidance constraints. Recognizing the inherent complexity of the underlying NP-hard mixed-integer non-linear programming (MINLP) problem, we propose a decomposition strategy that decouples the master problem into PC exploration and exploitation sub-problems. In the exploration phase, we derive closed-form solutions for optimal sensing durations and detection thresholds that satisfies spectrum utilization and collision avoidance constraints, which are then used to develop a priority metric to rank PCs. The proposed PC ranking informs a sequential PC scheduling and IoT sensing assignment approach that exploits a linear bottleneck assignment (LBA) strategy, proceeding until further scheduling does not enhance network utility. For the exploitation phase, we leverage a non-orthogonal multiple access (NOMA) strategy to multiplex multiple IoT nodes on a single PC, employing an iterative linear sum assignment (LSA) method for efficient allocation to maximize utilization of idle PCs. Numerical results validate the efficacy of our proposed methodologies, reaching an accuracy of approximately 99% in the order of milliseconds, significantly outperforming time complexity of brute-force benchmarks.

Deep Learning approach for Co-operative Spectrum Sensing under Congested Cognitive IoT networks

Deep Learning approach for Co-operative Spectrum Sensing under Congested Cognitive IoT networks

Imperfect CSI-Based Resource Management in Cognitive IoT Networks: A Deep Recurrent Reinforcement Learning Framework

Imperfect CSI-Based Resource Management in Cognitive IoT Networks: A Deep Recurrent Reinforcement Learning Framework

Radio resource management in energy harvesting cooperative cognitive UAV assisted IoT networks: A multi-objective approach

Cooperative communication through energy harvested relays in Cognitive Internet of Things (CIoT) has been envisioned as a promising solution to support massive connectivity of Cognitive Radio (CR) based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems. In this work, a cooperative CIoT system is contemplated, in which a source acts as a satellite, communicating with multiple CIoT devices over numerous relays. Unmanned Aerial Vehicles (UAVs) are used as relays, which are equipped with onboard Energy Harvesting (EH) facility. We adopted a Power Splitting (PS) method for EH at relays, which are harvested from the Radio frequency (RF) signals. In conjunction with this, the Decode and Forward (DF) relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices. We developed a Multi-Objective Optimization (MOO) framework for joint optimization of source power allocation, CIoT device selection, UAV relay assignment, and PS ratio determination. We formulated three objectives: maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission. The MOO formulation is a Mixed-Integer Non-Linear Programming (MINLP) problem, which is challenging to solve. To address the joint optimization problem for an epsilon optimal solution, an Outer Approximation Algorithm (OAA) is proposed with reduced complexity. The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search (NOMAD) algorithm.

Feature-Based Spectrum Sensing of NOMA System for Cognitive IoT Networks

With the rapid increase of the demand for the Internet of Things (IoT), spectrum resources have incremental challenges. Nonorthogonal multiple access (NOMA) and spectrum sensing (SS) are considered key candidate technologies for next-generation wireless communications to improve spectrum utilization. Nevertheless, using both technologies at the same time makes the system more complex and brings new challenges to user differentiation. In order to make better use of these advantages, we creatively propose a feature detection-based SS method for NOMA systems. To better distinguish the relationship between the presence or absence of signals from different NOMA users, we employ feature detection to obtain the feature values of each user. We propose workflows and transceiver architectures combining the two technologies. Based on the relationship among users’ priorities, power, and transmission in common scenarios, we design a downlink mode and two uplink modes and deduce the threshold settings of the corresponding modes. Meanwhile, we also customarily propose enhanced algorithms, to have a marked increase in the performance for the proposed method in various modes. Experimental results illustrate that the proposed technique is feasible and has prominent detection performance and satisfying throughput performance.

Energy Efficient Cooperative Spectrum Sensing with Energy Harvesting for Optimal Node Selection and Channel Access in Cognitive IoT Networks

Energy Efficient Cooperative Spectrum Sensing with Energy Harvesting for Optimal Node Selection and Channel Access in Cognitive IoT Networks

Joint bandwidth and power resource allocation technique in multi‐carrier non‐orthogonal multiple access‐based cognitive Internet of Things networks

AbstractThe recent development of the Internet of Things (IoT) devices offers an intelligent concept for integrating massive number of interconnected wireless devices. However, supporting such massive number of IoT connections (hundreds of billions) requires an efficient and dynamic spectrum access strategy. Accordingly, the integration of non‐orthogonal multiple access (NOMA) in the cognitive radio (CR) systems, referred to as a multi‐carrier NOMA CR‐enabled system, is envisioned as a potential spectrum‐efficient networking paradigm that can support massive energy‐efficient IoT connectivity in B5G networks while maintaining the quality of services (QoS) of the IoT devices. However, the power consumption issue of the multi‐carrier NOMA CR‐based system becomes as one of key challenges, especially when considering the expected massive connectivity. Accordingly, several power‐aware optimization frameworks have been considered to address this issue. While most of the existing CR‐based multi‐carrier NOMA power allocation mechanisms only optimize the allocated per‐user power, this article proposes a novel joint bandwidth and power allocation problem over each available idle channel with the aim of minimizing the overall transmission power under a set of QoS constrains. Specifically, the bandwidth of each channel is adaptively subdivided into two variable‐width sub‐channels, each is capable to serve a group of CR users using power‐domain NOMA. This novel design is formulated as a joint optimization problem that is shown to be a non‐convex. Therefore, an iterative algorithm with a second‐order cone programming is deployed to handle the non‐convexity of the problem, and thus, determine the solution of it. Simulation results reveal that the developed variable bandwidth adaptive power allocation mechanism outperforms the conventional equal sub‐channel allocation in terms of the required transmission power, which significantly improves the energy efficiency in the system.

Overlay Cognitive IoT-Based Full-Duplex Relaying NOMA Systems With Hardware Imperfections

Energy harvesting with cognitive radio and nonorthogonal multiple access (NOMA) techniques offer a promising solution to enhance the spectral and energy efficiency in Internet of Things (IoT) networks. This article investigates the performance of an overlay cognitive NOMA (OCNOMA) system tailored for IoT applications. Herein, the primary network includes a primary transmitter-receiver pair, whereas the secondary network comprises an energy-constrained full-duplex (FD) relaying-based secondary transmitter (ST) with its intended multiple receivers. Accordingly, ST employs a time-switching (TS)/power-splitting (PS)-based receiver architecture to harvest the energy from the radio-frequency signal of primary transmission and, thereby, uses this energy to relay the primary signal and to transmit its own signals simultaneously using the OCNOMA principle. For this, we propose a cooperative spectrum-sharing transmission (CSST) scheme using the decode-and-forward relaying strategy, while considering the realistic assumptions of FD-based loop self-interference, NOMA-based imperfect successive interference cancelation, and the transceiver hardware impairments in IoT devices. Adopting Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading environments, we comprehensively analyze the performance by deriving the expressions of the outage probability for the primary and secondary networks for the FD-based CSST scheme under both TS and PS protocols. Thereby, we disclose some apposite ceiling effects and provide an insight on deciding the value of the OCNOMA power allocation factor for efficacious spectrum-sharing cooperation. We further quantify throughput and energy efficiency for the overall system. Our results demonstrate the performance advantages of the proposed FD CSST scheme over the benchmark schemes and provide useful guidelines for the practical design of cognitive IoT networks.

Spectrum Sensing and Allocation Strategy for IoT Devices Using Continuous-Time Markov Chain-Based Game Theory Model

Spectrum sensingis a strategic technology in ascertaining idle spectrum in cognitive radio (CR)-based IoT networks. Adaptability, receptiveness, and accuracy are the steeplechases in the Internet-connected device networks because of the broad spectrum of applications and wide usage of them across the global. CR helps in dynamically allocating unlicensed frequency bands to the Internet of Things (IoT) devices there by providing effective utilization of spectral holes. This letter proposes a nonoperative game theoretic model to implement a spectrum-sensing mechanism and analyze the spectral usage rates of a CR-based IoT networks. The strategy for spectrum access and utilization is developed using a noncooperative game theory, and a utility function in terms of spectral usage rate is developed using the 2-D continuous-time Markov chain model. A utility function is designed to analyze the performance of the proposed algorithm in terms of mean arrival rate, average throughput, and mean delay as a function of increasing load. The results show that the system is consistent and scalable and enhances the detection performance in cognitive IoT networks.

Swarm Intelligence-Based Uplink Power Control in Cognitive Internet of Things (CIoT) for Underlay Environment

Internet of things (IoT) aims to shift intelligence to things and tends to increase the spectrum utilization efficiency. However, in doing so, it might generate high interference to the primary users (PUs) due to massive data flow into the networks. Cognitive radio smartly addresses this challenge by enabling different spectrum sharing modes while guaranteeing the quality of service. Motivated by this fact, the incorporation of cognitive abilities in IoT has given birth to a new sub-domain in IoT, known as Cognitive IoT (CIoT). This paper considers a single cell scenario in which multiple CIoT users (CUs) coexist with a PU in an underlay environment, and their communication performance has been optimized while adhering to the transmit power and interference constraints. Furthermore, two swarm intelligence-based implementations of the proposed algorithm have been provided, one based on Artificial Bee Colony (ABC) and the other based on Particle Swarm Optimization (PSO), and their effectiveness to solve the constrained power allocation problem for CIoT networks has been proved through simulations.

Short-Packet Communications in Wireless-Powered Cognitive IoT Networks: Performance Analysis and Deep Learning Evaluation

In this paper, we study short-packet communications in wireless-powered cognitive Internet-of-Things (IoT) networks with multiple primary receivers (PRs). The considered system can be applied for small factory automations, where a source and multiple relays harvest energy from a multi-antenna dedicated power beacon (PB) to send short packets to a robot destination for controlling purposes under cognitive radio constraint imposed by PRs. We propose an opportunistic relay selection (ORS) scheme to maximize the end-to-end signal-to-noise ratio in cognitive IoT systems. Closed-form expressions for the average block error rate (BLER) of the proposed system are obtained, based on which the performance floor analysis, goodput, and energy efficiency (EE) are also carried out. Relying on analytical results, we develop a deep learning framework for the BLER prediction with high accuracy and short execution time. Simulation results show the BLER, goodput, and EE improvements of the ORS scheme over conventional relay selection schemes. Moreover, the developed deep learning-based evaluation model achieves the equivalent performance as the ORS scheme in terms of BLER, goodput, and EE, while remarkably reducing the execution time in cognitive IoT systems.

A Deep-Neural-Network-Based Relay Selection Scheme in Wireless-Powered Cognitive IoT Networks

In this article, we propose an efficient deep-neural-network-based relay selection (DNS) scheme to evaluate and improve the end-to-end throughput in wireless-powered cognitive Internet-of-Things (IoT) networks. In this system, multiple energy harvesting (EH) relays are deployed randomly to assist data transmission from a source node to multiple users under practical nonlinearity of the EH circuits. We first design an incremental relaying protocol, where a selected user will request the help from relays if the direct transmission is not favorable. In such a protocol, we develop a deep neural network framework for relay selection and throughput prediction with high accuracy, less channel feedback amount, and short execution time. Simulation results show that the proposed DNS scheme achieves higher throughput than the conventional relay selection methods, while it considerably reduces computational complexity, suggesting a real-time configuration for IoT systems under complex scenarios. Moreover, the proposed DNS scheme achieves the root-mean-square error (RMSE) of 6.6×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> on the considered dataset, which exhibits the lowest RMSE as compared to the state-of-the-art machine learning approaches.

Particle Swarm Optimization in the Presence of Malicious Users in Cognitive IoT Networks with Data

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.

SWIPT Cooperative Spectrum Sharing for 6G-Enabled Cognitive IoT Network

Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.

Context Aware Data Perception in Cognitive Internet of Things - Cognitive Agent Approach

In the past, the existing Internet of Things caused traffic congestion and receiver uncertainty problems due to insufficient data transfer between the nodes or devices for data perception. The authors have proposed the method for context-aware data perception in the cognitive internet of things environment. The proposed context-aware data perception is described in the following stages, initially nodes in Cognitive Internet of Things network are clustered effectively using adaptive pillar ‘K' means clustering algorithm. After the formation of effective clusters, the cognitive agent performs the effective context-aware data learning using support-based convolutional neural networks. Finally, adaptive fuzzy logic defines the effective decision for data perception. The experimental results show that the proposed method outperforms the cognitive agent approaches of data perception in terms of network lifetime, energy consumption, data perception accuracy, and throughput in the cognitive internet of things.

Incentive Mechanism Based Cooperative Spectrum Sharing for OFDM Cognitive IoT Network

Internet of Things (IoT) is considered as the future network to support machine-to-machine communications. To realize IoT network, a large number of IoT devices need to be deployed, which will lead a problem of allocating sufficient spectrum for the rapidly increasing devices. Through cooperative spectrum sharing, the unlicensed IoT devises (UIDs) help forward the signal of primary users (PUs) to exchange for dedicated spectrum to transmit their signals, which can effectively improve the spectrum utilization for the IoT network. However, UIDs are selfish and rational. They may not be willing to take participate in the cooperative spectrum sharing after considering the potential costs. It is necessary to provide effective incentives to encourage them to take part in the cooperative spectrum sharing. In this paper, we use contract theory to model the incentive mechanism in OFDM-based cognitive IoT network under a practical scenario with incomplete information, where UIDs’ private information (i.e., transmission cost and wireless channel characteristics) are not known by PUs. By using contract theory, the negotiations between PUs and UIDs are modeled as a labor market, where PUs and UIDs act as the employer and employees, respectively. We first study the optimal contract design to maximize PUs’ utility as well as the social welfare, in which the contract consists of a menu of expected signal-to-ratios and payments over each subcarrier. Then, we propose heuristic UIDs’ selection method with a finite PUs’ budget. Finally, simulation results demonstrate the efficiency of our proposed contract design and UIDs selection method.

Spectrum Sharing Technologies for Cognitive IoT Networks: Challenges and Future Directions

Spectrum Sharing Technologies for Cognitive IoT Networks: Challenges and Future Directions

A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users

Internet of Things (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the future-connected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. Therefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. The main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipath-affected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithm-based soft decision fusion scheme to determine the optimum weighting coefficient vector against SUs’ sensing information. The weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. Through extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.

Performance Analysis of Cooperative Underlay Spectrum Sharing System in Co-channel Interference Limited Environment

In this paper, performance analysis of an asynchronous distributed space time block coded system with optimum combining at the cognitive radio receiver (CR-Rx) has been done in a cooperative underlay cognitive Internet of Things network under the influence of multiple co-channel primary users (PUs) interferers. The transmit power strategy being employed at the CR-Tx considers the PIP (peak interference power) at the PU-Rx and peak transmit power constraints at the CR-Tx, respectively. We have derived the probability density function of the signal-to-interference ratio (SIR) at the destination for the proposed system. The system is numerically and mathematically analyzed in terms of average post processed SIR (APPSIR), ergodic capacity ($${\text{C}}_{\text{erg}}$$), average bit error rate (ABER) and outage probability. All the numerical outcomes are in corroboration with its simulation counterpart.

Resource Management for Cognitive IoT Systems With RF Energy Harvesting in Smart Cities

The number of Internet of Things (IoT) nodes is increasing in modern cities which demands spectrum and energy efficiency. Fifth-generation (5G) networks are considered as a key paradigm for the realization of future IoT applications. Particularly, cognitive radio and non-orthogonal multiple access are candidate technologies for 5G networks that can improve spectral efficiency and accommodate a large number of IoT devices. Furthermore, radio frequency (RF) energy harvesting can increase the energy efficiency of IoT networks. In this paper, we propose a resource management scheme for cognitive IoT network with RF energy harvesting in 5G networks. The objective is to maximize the throughput while assuring quality-of-service requirements in terms of data rate and minimum residual energy constraint on each IoT node. We use mixed integer linear programming and greedy approaches to solve the optimization problem. We then present the simulation results of the proposed scheme to exhibit the significant positive impact on the performance of the IoT network.