Cognitive Radio Internet Of Things Research Articles

OntoBlock: a novel ontological-based and blockchain enabled spectrum sensing framework for detection of malicious users in cognitive radio internet of things (CR-IoT) networks

OntoBlock: a novel ontological-based and blockchain enabled spectrum sensing framework for detection of malicious users in cognitive radio internet of things (CR-IoT) networks

ProMETHEUS: A Secure Lightweight Spectrum Allocation Protocol against SSDF Attacks in Cognitive Radio IoT Networks

ProMETHEUS: A Secure Lightweight Spectrum Allocation Protocol against SSDF Attacks in Cognitive Radio IoT Networks

A novel resource allocation method based on supermodular game in EH-CR-IoT networks

Internet of Things (IoT) allows the connectivity of smart devices embedded with sensors, but with the growing problem of overcrowding in unlicensed bands, the data exchange in the network is severely disrupted. Besides, because Cognitive Radio IoT (CR-IoT) networks are composed of many small sensor devices, there is a great need for energy utilization efficiency. Building a new kind of Energy Harvesting enabled Cognitive Radio IoT (EH-CR-IoT) networks by applying EH technology and CR functions to IoT becomes an existing technical solution that can better solve problems such as scarce spectrum resources and valuable energy resources. In order to efficiently and reasonably manage resources such as energy and spectrum for EH-CR-IoT networks, Supermodular Game (SG) theory based resource allocation methods are proposed for both perfect spectrum sensing and imperfect spectrum sensing. The proposed methods first model the resource allocation problems in EH-CR-IoT networks as Bertrand game competition models, then design the utility functions of the consumers and the entire networks in terms of network pricing, next prove that the proposed Bertrand game competition models strictly comply with the theory of SG and the function solutions are Nonlinear Optimization Problems (NOP), after that the Nash Equilibrium (NE) solutions and the optimal network utility are obtained, finally simulation results are presented and prove the validity and the superiority of the proposed methods. Compared with conventional game methods, the proposed methods can better improve the network resource utilization and network benefits.

Multi-Dimensional Resource Allocation for Throughput Maximization in CRIoT with SWIPT

To solve the power supply problem of battery-limited Internet of Things devices (IoDs) and the spectrum scarcity problem, simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technology were integrated into the Internet of Things (IoT) network to build a cognitive radio IoT (CRIoT) with SWIPT. In this network, secondary users (SUs) could adaptively switch between spectrum sensing, SWIPT, and information transmission to improve the total throughput. To solve the complicated multi-dimensional resource allocation problem in CRIoT with SWIPT, we propose a multi-dimensional resource allocation algorithm for maximizing the total throughput. Three-dimensional resources were jointly optimized, which are time resource (the duration of each process), power resource (the transmit power and the power splitting ratio of each node), and spectrum resource, under some constraints, such as maximum transmit power constraint and maximum permissible interference constraint. To solve this intractable mixed-integer nonlinear program (MINLP) problem, firstly, the sensing task assignment for cooperative spectrum sensing (CSS) was obtained by using a greedy sensing algorithm. Secondly, the original problem was transformed into a convex problem via some transformations with fixed-power splitting ratio and time switching. The Lagrange dual method and subgradient method were adopted to obtain the optimal power and channel allocation. Then, a one-dimensional search algorithm was used to obtain the optimal power splitting ratio and the time switching ratio. Finally, a heuristic algorithm was adopted to obtain the optimal sensing duration. The simulation results show that the proposed algorithm can achieve higher total system throughput than other benchmark algorithms, such as a greedy algorithm, an average algorithm, and the Kuhn–Munkres (KM) algorithm.

On the Statistical Delay Performance of Large-Scale IoT Networks

Large-scale Internet of things (IoT) networks have shown great advantages and attracted much attention in 5G networks due to its high-speed Internet connectivity, high data rate, ultra reliability and low latency. However, the diversity of ubiquitous IoT devices and complex transmission environment lead to the difficulty of theoretical analysis of delay performance. In order to capture the characteristics of large IoT networks, a novel integrated analysis framework is developed to build the performance model and analyze the statistical delay performance in this paper. Specifically, we model the spatial distribution of IoT devices with heterogeneous Poisson point processes. With the Laplace transform of signal-to-interference-plus-noise ratio, the service capability of the wireless channel with multiple interferes is determined with a mathematical stochastic-network-calculus aided approach in the exponential domain for Poisson bipolar and cellular networks. Then, the upper bound of delay is achieved by the Mellin transform of the service incremental process. We extend our work to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -tier heterogeneous network and apply the analysis results to a practical two-tier cognitive radio IoT network. Simulation results validate the theoretical analysis of delay performance with various network parameters.

Low complexity multichannel spectrum prediction algorithm based on optimized neural network for spectrum allocation in cognitive radio internet of things

AbstractCognitive radio has been nominated as a key technology for the internet of things (IoT), due to its intelligent functionalities ensuring continuous connectivity for IoT objects. Spectrum prediction, as one of the core CR functions, has emerged as a leading tool to alleviate the spectrum scarcity problem. Spectrum prediction minimizes sensing and decision‐making delays, and thereby it reduces collisions with primary users and guarantees safe access for secondary users (SUs). Thus, it became an inseparable part of many new spectrum allocation and mobility methods. In this work, the proposed cognitive radio IoT model consists of local sensors (LS) that perform sensing instead of SUs, and a cognitive base station that receives sensing results from different LSs to predict the next occupancy information and allocate frequencies for SUs. The predictor is followed by a channel extraction block for efficient spectrum allocation. Then, a low complexity spectrum prediction and preallocation system based on optimized neural network architecture is presented. Two nonlinear neural network models, time delay neural network and nonlinear autoregressive with exogenous input, that are trained on a real spectral occupancy dataset, are optimized using the Bayesian optimization algorithm then compared. The best predictor forecasts the next occupancy rate of multiple channels simultaneously based on three dimensions, area, time, and frequency. Performance evaluation was conducted through accuracy, mean squared error (MSE), and regression fit. The highest prediction accuracy was93.5%, the regression coefficient was0.98, and a reduced MSE of0.0013obtained. Results show that the considered scheme is efficient in forecasting the spectrum availability of different bands within the IoT spectrum resources.

Energy-aware spectrum coordination with intelligent frequency-hopping for software defined networks

Software-defined Cognitive radio (CR) networking is considered a promising communication paradigm that can deliver huge spectrum opportunities for enabling massive deployment for wireless Internet-of-Things (IoT) applications and services. An important challenge in this domain is how to provide effective energy-efficient distributed spectrum coordination mechanisms (known as Spectrum Rendezvous), through which the CR IoT users can opportunistically coordinate their transmissions over the idle licensed frequency channels without relying on the existence of a dedicated common control channel (CCC). This paper proposes a novel frequency hopping (FH) distributed rendezvous mechanism for CR-capable IoT (CR-IoT) networks using grid-based quorum systems, referred to as adaptive grid-based quorum channel-hopping scheme. The proposed quorum-based scheme divides the time into equidistant cycle segments, referred to as quorum-based FH intervals. Each quorum interval consists of equal-duration beaconing intervals, each associated with a licensed channel. According to the proposed algorithm, each CR-IoT device randomly selects awake-sleep schedules based on the adopted quorum system structure. Any two devices can exchange their control information over the assigned channels of their common awake beaconing intervals. The proposed algorithm provides probabilistic guarantees on the successful rendezvous (RDV) between any two CR-IoT users within a single quorum-based FH interval (cycle) and dynamically adjusts the adopted QS structure based on the time-varying traffic load in the CR-IoT network such that the RDV probability and the expected average time to rendezvous improve with the least possible energy consumption. Compared to reference design schemes, the simulation results demonstrate that the proposed algorithm significantly increases the RDV probability, reduces the average rendezvous time, and reduces the energy consumption per successful RDV.

Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network

IoT network-connected devices are increasing day by day. It is impossible to allocate a spectrum for all IoT devices. This spectrum scarcity can be solved by cognitive radio-based dynamic spectrum sharing, which is referred to as Cognitive Radio Internet of Things (CRIoT). The jamming physical layer attack in CRIoT resulting in a denial of cognitive radio services and make spectrum underutilization. Continuous jamming can be detected quickly based on time delay on spectrum access, but discrete random jamming detection is challenging. This article proposes an autoencoder-based jamming attack detection in cognitive radio. The jamming detection problem is modeled as anomaly detection. The autoencoder is used to detect the anomaly signal component and the jammer. The simulation of random jamming attack detecting with time instant of attack is carried out to mitigate it. The proposed mechanism able to detect the jammer with 89% of accuracy.

Machine learning-based malicious user detection for reliable cooperative radio spectrum sensing in Cognitive Radio-Internet of Things

The Cognitive Radio based Internet of Things (CR-IoT) is a promising technology that provides IoT endpoints, i.e., CR-IoT users the capability to share the radio spectrum otherwise allocated to licensed Primary Users (PUs). Cooperative Spectrum Sensing (CSS) improves spectrum sensing accuracy in a CR-IoT network. However, its performance may be degraded by potential attacks of the malicious CR-IoT users that send their incorrect sensing information to the corresponding Fusion Center (FC). This study presents a promising Machine Learning (ML)-based malicious user detection scheme for a CR-IoT network that uses a Support Vector Machine (SVM) algorithm to identify and classify malicious CR-IoT users. The classification allows the FC to make a more robust global decision based on the sensing results (i.e., energy vectors) which are reported only by the normal CR-IoT users. The effectiveness of the proposed SVM algorithm based ML in a CR-IoT network with the malicious CR-IoT users is verified via simulations.

Enhanced Spectrum Sensing Techniques in Cognitive Radio Based Internet of Things

The use of open Spectrum broadcasting, which is a restricted and valuable resource, has been put under a range of constraints due to the growing interest in wireless applications. There is a huge spectrum requirement nowadays but radio spectrum is an extremely terrifying resource in a normal system. The frequency detection challenge has gained new angles with cognitive radio and artful spectrum allocation principles. However a fixed spectrum allocation needs to result in the use of spectrum as an extraordinary bit of enabled spectrum is not effectively used. To remain away from interference and optimise spectrum immersion, the Cognitive Radio (CR) based Internet of Things (IoT) concept was developed. The best strategy for effective spectrum usage is CR IoT. The fundamental goal of CR IoT would be to use the small set of resources without interfering with the main users (PUs). The different spectrum detection techniques used in CRs are explained in this paper and the advantages and disadvantages of each sensing technique are also discussed. We explained the detection of the cooperative spectrum and all its structures, and also the multi-dimensional dynamic spectrum method. From a cognitive radio point of view, different aspects of a problems of spectrum identification are concentrated and a multi-dimensional idea of spectrum sensing is introduced. Spectrum management problems are provided and techniques to empower spectral detection are presented.

Efficient Channel Allocation for Cognitive Radio Internet of Things

Recently, multiple wireless technology has arisen to provide effective and flexible IoT networking. Cognitive radio (CR) technology that facilitates software-defined radio is one of the key technology that gives IoT connectivity to a multitude of linked IoT devices in an opportunistic way. Unlicensed bandwidth has congested latest creation and study in the area of networking technology. The effect is unchecked and unregulated intrusion in the Internet of Things with low-powered wireless sensors (IoT). On the other hand, these technologies allowed low-energy IoT architecture at low cost, low energy usage and efficient spectrum use. Cognitive radio (CR) network, a low-cost approach for effective spectrum use, discusses the issue of spectrum use. Unlicensed users use underused bandwidth in CR networks. Due to its opportunistic existence, these networks' output relies on the observed spectrum pattern of the primary consumer. In these networks, frequency detection and usage must be accurately modelled. In this article, we propose the main Cognitive Radio Internet of Things (CR IoT) user identity mechanism using Markov's secret model. We implemented two algorithms: one for free channel identification and one for successful channel allocation. Simulation results suggest that CR-IoT surpassed traditional networking systems.

Wireless‐powered CR‐IoT with ambient backscattering: a new transmission mode

In this study, a new hybrid secondary transmitter (ST) mode is proposed for cognitive radio Internet of Things (CR-IoT) networks with energy harvesting (EH) and ambient backscatter (AmBack) communication capabilities. The authors firstly describe the proposed hybrid mode with wireless-powered ST. The ST, consisting of EH, Amback, and conventional active transmission modules, adapts itself under different primary user operation states, i.e. active or idle. The overall throughput of the secondary system is formulated and the system settings for achieving optimal performance are derived and validated through numerical simulations.

Mobile Edge Computing Against Smart Attacks with Deep Reinforcement Learning in Cognitive MIMO IoT Systems

In wireless Internet of Things (IoT) systems, the multi-input multi-output (MIMO) and cognitive radio (CR) techniques are usually involved into the mobile edge computing (MEC) structure to improve the spectrum efficiency and transmission reliability. However, such a CR based MIMO IoT system will suffer from a variety of smart attacks from wireless environments, even the MEC servers in IoT systems are not secure enough and vulnerable to these attacks. In this paper, we investigate a secure communication problem in a cognitive MIMO IoT system comprising of a primary user (PU), a secondary user (SU), a smart attacker and several MEC servers. The target of our system design is to optimize utility of the SU, including its efficiency and security. The SU will choose an idle MEC server that is not occupied by the PU in the CR scenario, and allocates a proper offloading rate of its computation tasks to the server, by unloading such tasks with proper transmit power. In such a CR IoT system, the attacker will select one type of smart attacks. Then two deep reinforcement learning based resource allocation strategies are proposed to find an optimal policy of maximal utility without channel state information(CSI), one of which is the Dyna architecture and Prioritized sweeping based Edge Server Selection (DPESS) strategy, and the other is the Deep Q-network based Edge Server Selection (DESS) strategy. Specifically, the convergence speed of the DESS scheme is significantly improved due to the trained convolutional neural network (CNN) by utilizing the experience replay technique and stochastic gradient descent (SGD). In addition, the Nash equilibrium and existence conditions of the proposed two schemes are theoretically deduced for the modeled MEC game against smart attacks. Compared with the traditional Q-learning algorithm, the average utility and secrecy capacity of the SU can be improved by the proposed DPESS and DESS schemes. Numerical simulations are also presented to verify the better performance of our proposals in terms of efficiency and security, including the higher convergence speed of the DESS strategy.

A new strategy for packets scheduling in cognitive radio internet of things

The usage of Cognitive Radio Networks (CRNs) to establish Internet of Things (IoT) smart Networks is an effective and economical solution to the IoT spectrum scarcity problem. CRNs is merged with the IoT technology to be named Cognitive Radio Internet of Things (CRIoT). Spectrum sharing/scheduling in CRIoT has not attracted a lot of attention as it should do. This paper proposes a new packets' scheduling strategy for CRIoT. It is a centralized time slotted algorithm that uses discrete permutation particle swarm optimization for scheduling packets among predicted primary network empty time slots. An objective function is formulated to maximize the fairness index among fog nodes, minimize the packets' queuing delay and minimize the number of dropped packetsat each time interval. In order to evaluate the proposed protocol, it is implemented along with another protocol named spectrum auction. Using the fairness index, the average queuing delay and the number of dropped packets as evaluation metrics, simulation results proved that the proposed protocol outperforms the spectrum auction protocol. It also has a lower time and space complexity.

A multi-stage resource-constrained spectrum access mechanism for cognitive radio IoT networks: Time-spectrum block utilization

Extravagant demands for wireless communications have resulted in the shortage of spectrum, such that unlicensed spectrum bands are overcrowded, whereas licensed bands are not utilized efficiently. Moreover, content discovery and retrieval using the traditional host-centric approach of IP-based networks adds more burden on the wireless spectrum, such that, each time a request is initiated by the mobile service requester, a new routing path is discovered to retrieve the service from the service provider. Cognitive radio (CR) technology has been proposed to enable efficient and opportunistic spectrum band usage through the utilization of vacant licensed channels. This can offer huge spectrum to enable efficient large-scale deployment for IoT networks. Moreover, Information Centric Networking (ICN) has been proposed to decouple the service requester from the provider such that in-network content caching is used to allow for the retrieval of services within a mobile node’s proximity. The integration of CR and ICN will be essential for enabling envisioned IoT services within smart cities. In this article, we consider the multi-user single-transceiver coordinated spectrum access problem in CR-IoT networks under the overlay spectrum sharing model. We formulate the spectrum access problem as a multi-stage rate/channel assignment optimization problem. The objective is to maximize the overall network throughput by maximizing the achieved sum-rate over all contending CR-IoT devices. Specifically, we propose a novel resource-constrained channel assignment policy that provides a proper utilization of the available time–frequency units. The proposed policy also exploits the packet fragmentation capability to further enhance network throughput. Moreover, we envision a scenario where our proposed solution can be adapted to information-centric cognitive radio-based networking for IoT smart city applications. We showcase the significant improvement achieved by our proposed solution over state-of-the-art schemes through simulation results.

Interference Sources Localization and Communication Relationship Inference With Cognitive Radio IoT Networks

With the widespread application of Internet of things (IoT), the interference problem becomes more and more serious, which results in not only the poor network performance but also the increased energy consumption of IoT nodes. Therefore, in this paper, we investigate the problem of how to locate the interference sources and infer their communication relationships in the cognitive radio IoT (CR-IoT) deployment scenario. First, we utilize MDS-MAP(P) algorithm with dynamic power control to realize cooperative self-localization of the CR-IoT nodes, which is more energy-efficient than all the IoT nodes equipped with global positioning system (GPS) receivers. Then, we propose a non-cooperative localization method to determine the inference sources with the angle of arrivals (AoAs) measured by the CR-IoT nodes. Finally, the communication relationship between interference sources can be inferred based on the association rule of signals. The network simulation results validate that the proposed methods can locate the interference sources accurately with low energy consumption and correctly infer their communication relationships, which is helpful for the interrupted CR-IoT nodes to take a specific opportunistic transmission policy to reduce their energy consumption.

Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks

Due to the growing demand for spectral resources with the emergence of IoT networks and applications, cooperative cognitive radio IoT networks (CCR-IoTN) are expected to play an essential role in the world of smart technology. In this paradigm, the benefits of cooperative communication and cognitive radio networks (CRN) are merged to meet IoT networks' needs. In this work, we employ the hybrid overlay-underlay CRN to guarantee both the secondary user (SU) stability and provide acceptable total throughput. In our CCR-IoTN, SUs may act as a relay to help the primary transmission. If the primary user (PU) is active, the secondary non-relay nodes transmit concurrently with the PU in underlay mode. In return for their cooperation, SUs are allowed to access the spectrum to transmit their data in overlay mode. In this context, we propose several fair resource allocation frameworks and compare well-known problems such as spectrum efficiency, energy efficiency as an objective function for CCR-IoTN. This kind of problem is inherently nonconvex. To solve the problems, therefore, we reformulate them as a convex problem using quadratic transform. Finally, we use simulation to evaluate the performance of the proposed hybrid resource allocation method. The resource allocation framework based on proportional fairness policy is shown to have acceptable performance in terms of throughput, fairness, and energy efficiency.

Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

With the rise of the Internet-of-Things (IoT), new requirements have been brought into communication networks to make them more efficient, sustainable, and self-sufficient. Requirements, such as availability and ultra-reliability combined with the solutions of energy-harvesting and dynamic spectrum access, make the analyses of such networks more complex, while imposing different performance trade-offs. This paper analyzes the performance of ultra-reliable energy-harvesting cognitive radio Internet-of-Things (UR-EH-CR-IoT) networks, and provides analytical derivations for different IoT network metrics, such as GoodPut, reliability, collision probability, availability, and stability, so as to investigate their trade-offs. A new metric for network availability is defined based on energy availability and spectrum accessibility for UR-EH-CR-IoT networks, while incorporating transmission diversity. The effect of IoT network parameters, such as sensing time, diversity transmission, and number of packets in a data frame, is examined on the IoT network performance metrics. Lastly, the derived expressions are utilized to optimize the GoodPut, subject to various practical constraints.

Deep Reinforcement Learning Based Resource Allocation For Narrowband Cognitive Radio-IoT Systems

Abstract Narrowband Internet-of-Things (NB-IoT) is a low-power wide area (LPWA) technology developed by the Third-generation Partnership Project (3GPP) with objective to enable a wide range of IoT devices, low cost device and low power in the 5G era. As the number of IoT devices continue to increase, the demand for the spectrum allocation grows proportionately. The NB-IoT spectrum allocation is limited from 180 KHz to 200 KHz and is not sufficient to accomodate the exponential surge in the size of the NB-IoT devices.Thus, the need to efficiently allocate the available spectrum to the NB-IoT devices. Furthermore, in an attempt to enhance the coverage in NB-IoT network, recent relevant studies (3GPP release 13) have introduced the concept of repeated transmission. Since repeated transmissions ensure coverage enhancement but cause spectrum wastage, the traditional resource allocation is not appropriate for NB-IoT network. Motivated by this research gap we propose a NB-Cognitive Radio-IoT (NB-CR-IoT) technique which integrates Cognitive Radio (CR) techniques into the operation of the conventional NB-IoT. The resulting architecture seeks to foster an efficient opportunistic spectrum access in distributed heterogeneous networks.We further formulate the resource allocation problem as a deep Q-learning solved by reducing the number of repeated transmissions and allocating more IoT devices in NB-IoT network. The results in this contribution indicate that DQN outperforms the traditional Q-learning algorithm.

Beamforming Design for Physical Layer Security in a Two-Way Cognitive Radio IoT Network With SWIPT

In this article, we study the secure beamforming design for a two-way cognitive radio (CR) Internet of Things (IoT) network aided with the simultaneous wireless information and power transfer (SWIPT). Located at the center of secondary network, the IoT controller helps to provide relay assistance and cooperative physical layer security (PLS) for two primary users (PUs) against an eavesdropper, while transmitting information and power to the other IoT devices (IoDs) with primary spectrum. To enhance the information security, we aim to maximize the secrecy sum rate (SSR) for PUs by jointly designing the beamforming matrix and vectors at the central controller. To efficiently solve the nonconvex problem, we first propose the branch-reduce-and-bound (BRB)-based algorithm to obtain an upper bound for the SSR and offer a feasible solution by Gaussian randomization, which demands two-level iteration and thus has high complexity. To strike a balance between the complexity and the performance, we then propose iterative algorithm based on constrained-convex-concave programming (CCCP) and a zero forcing (ZF)-based noniterative algorithm, the latter of which with lowest complexity is suitable for the central controller with limited-power supply. The simulation results are provided to demonstrate the effectiveness of our proposed optimization algorithms in comparison to the traditional schemes.