Handover based spectrum allocation in cognitive radio networks

ABSTRACTThe rapid expansion of the Internet of Things (IoT) and the increasing demand for wireless communication have intensified the need for efficient spectrum management in cognitive radio networks (CRNs). Traditional approaches to spectrum sensing and allocation often operate in isolation or rely on static methods, which fail to address the dynamic and evolving nature of modern wireless environments. As IoT devices proliferate and spectrum resources become increasingly congested, there is a pressing need for more adaptive and efficient spectrum management solutions. Our approach addresses this need by offering an adaptive framework that responds to the changing spectrum landscape, thereby optimizing spectrum usage and reducing interference. This research suggests improved NN joint spectrum sensing for CR‐IoTNet (INJSS‐CR). This approach leverages cognitive radio (CR) technology to enhance spectrum utilization and mitigate the impact of spectrum shortages. CR technology enables secondary users (SUs) to detect and access unused spectrum through spectrum sensing. Within the CR‐IoTNet framework, joint spectrum sensing and allocation are performed to serve SU‐IoT devices via an interference‐free channel (IFC). The system comprises multiple primary user base stations (PU‐BSs) and SU devices functioning as IoT smart objects. Additionally, we integrate an improved neural network (INN) to adapt to dynamic network conditions and monitor primary user (PU) spectrum utilization using a comprehensive multiclass (J × 8) − D feature set. This combination of advanced techniques and CR technology aims to optimize spectrum management and support the growing IoT ecosystem. In particular, the INJSS‐CR obtained the greatest accuracy of 0.9492 at a training rate of 80%.

Wireless Sensor Network (WSN) have been attracting increasing interest for supporting a new generation of ubiquitous computing systems with great potential for many applications. However, the communication paradigms in WSNs differ from the ones associated to traditional wireless networks, triggering the need for efficient wireless communication technology. Several wireless technologies have emerged ranging from short and medium distance. Bluetooth, ZigBee and Impulse Radio Ultra Wide Band (IR-UWB) are three popular short range wireless communications. Due to its various features and advantages (especially low power consumption and low complexity), IR-UWB is a very promising wireless communication technology for WSN. In this paper, we evaluate the main features and advantages of this new technology (IR-UWB) for WSN in terms of transmission time, data coding and power consumption compared to Bluetooth and ZigBee. To analyze and evaluate the main features and advantages of IR-UWB as an efficient short range wireless communication technology for WSN, we used MiXiM platform under OMNet++ simulator.

This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> focuses on spectrum allocation and medium access in future cognitive radio wireless networks, e.g. IEEE 802.22 networks. For a given network topology, the proposed spectrum allocation method first generates an interference graph (or conflict graph) based on the network topology, traffic flows and interference constraints. Then a graph coloring algorithm is applied to the interference graph and the chromatic number of the graph is obtained. This number is the minimum number of distinct spectrum slices required. Further, spectrum allocation is done based on the max-min fair share algorithm or proportional fair algorithm according to the spectrum demands for different spectrum blocks. For medium access among secondary users, we then describe and implement a MAC protocol for 802.22-type cognitive radio networks. Simulation results are presented to demonstrate its effectiveness.

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use a given licensed spectrum band as long as there are no primary, licensed, users occupying this band in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. Heterogeneity in channel availability forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take this heterogeneity property and its effect on the network performance into consideration. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by the heterogeneity in channel availability. We proposed a

Cognitive radio (CR) technology is designed to improve reliability in communication between users through efficient and dynamic spectrum exploitation. CRs address the problems in spectrum allocation and channel access and improve the rate of radio resource utilization. The flexibility of the CR networks (CRN) and communication medium exposes it to a variety of threats; primary user emulation attack (PUEA) is a malicious and denial-of-service kind of adversary that defaces CRN performance. This manuscript proposes an adaptive learning-based attack detection in CRN for detecting and mitigating PUEA by analyzing the received power of the transmitter. The learning process endorses some beneficial features by distinguishing low spectrum legitimate PU from an adversary. The learning process adopts cyclostationary feature analysis for distinguishing adversaries and low power PU in CR communications. The process of learning is further enhanced by estimating distance variance and communication time-based analysis for improving the rate of signal classification and SU communication rate. The experimental analysis proves the stability of the proposed detection method by improving the SU throughput, with lesser signal classification time and misdetection probability.

In this paper, we investigate the problem of spectrum allocation in Cognitive Radio Networks (CRNs) where a primary user (PU) leases its idle spectrums to a number of secondary users (SUs) who may have different willing to pay for these spectrums. For the PU, its main objective is to gain profit from selling the idle spectrums, and from the perspective of the Sus, they aim to maximize their utility from using spectrums. We model the interaction between the PU and SUs as a two-stage Stackelberg game, where the PU sets the prices for its spectrum in the first stage, and SUs make their spectrum demands decisions in the second stage. We use the backward induction method to solve this game. The numerical results show that the proposed solution method can increase revenue of the PU compared with the uniform pricing scheme.

In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.

Today’s wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. This new networking paradigm is referred to as cognitive radio networks. In this talk, the novel functionalities and current research challenges of the cognitive radio networks are explained in detail. More specifically, an overview of the cognitive radio technology is provided and the network architecture is introduced. Moreover, the cognitive network functions such as spectrum management, spectrum mobility and spectrum sharing are explained in detail. The influence of these functions on the performance of the upper layer protocols such as routing and transport are investigated. Moreover, the network management, operation and maintenance problems are highlighted and open research issues in these areas are also outlined.

Process of spectrum allocation is equivalent of rental market in cognitive radio, in order to rent free spectrum of authorized users to cognitive users. the lessors of Spectrum( between authorized users )use the methods of competition between authorized users, Lessees of Spectrum( between cognitive users ) also used the methods of game, the spectrum allocation between authorized user and cognitive users also have methods of game, which can be analyzed by game theory. Game theory can analyze lots of policy choice, but spectrum allocation in cognitive radio network is related to the problem of allocation choice, To make performance of the whole system optimal, spectrum allocation considers not only strategies of other users have an effect on their own, but also their own strategies cause interference to other users, to solve spectrum allocation in cognitive radio networks, game theory is in an effective way.

This paper presents an overview of Cognitive Radio Spectrum Allocation Based on IoT and Genetic Algorithm (CRSAA). CRSAA was created to solve the problem of spectrum allocation in Cognitive Radio Networks (CRN). The main focus is on how to allocate cognitive radios based on IoT services. CRSAA introduces a novel Genetic Algorithm (GA) and an Internet of Things (IoT) system. GA is the core algorithm that deals with the task of how to allocate spectrum channels. A theoretical model shows that CRSAA can improve the total throughput and reduce the power consumption of CRN by 50% when compared to traditional Spectrum Sensing techniques. The traditional particle swarm optimization algorithm is improved, and the initial population is constructed by chaotic logic mapping so that the population is uniformly distributed in the solution space. Due to the introduction of the idea of particle mutation, the new algorithm is easier to fall into the local extremum than the original algorithm. Experimental results show that the algorithm can quickly converge to the spectrum allocation of cognitive radio, thus obtaining better overall benefits.

The explosive growth of data with the requirements of high reliability and low latency has posed huge challenges to the vehicular networks. One potential solution is to deploy the fog computing servers geographically closer to the vehicles to serve the vehicle-based applications in real time. However, due to the constraint of caching storage space as well as lack of tractable access mode selection and spectrum allocation algorithm, it is very challenging to balance the network transmission performance and fronthaul savings. In this paper, we investigate the joint optimization problem of access mode selection and spectrum allocation in fog computing-based vehicular networks. To solve the problem in an efficient way, the original high complexity optimization problem is divided into two subproblems. Wherein, the vehicle access mode selection problem is solved by Q-learning-based algorithm by considering spectrum allocation profiles and the fronthaul link cost. The randomly vehicular network topologies are modeled as CoX processes and the closed-form payoff expressions are derived by the stochastic geometry tools. On the other hand, the optimal spectrum allocation indicator value can be finally obtained via convex optimization. The analytical results for the proposed algorithm as well as the traditional baseline approaches are evaluated with different weight factors, which verify our theoretical analysis and confirm the proposed approach can achieve significant performance gains.

Opportunistic availability of licensed frequency bands enables the secondary users (SUs) to avail the radio spectrum dynamically. Cognitive radio (CR) paradigm extends the dynamic spectrum access techniques to sense for free channels (called spectrum holes) which can be efficiently redistributed amongst SUs. Motivated by the adaptive technology in CR, this paper introduces a sealed-bid double auction mechanism which aims to obtain an effective allocation of the unused radio spectrum. The proposed auction model adopts multi-channel allocation where one SU can access more than one available channel, while imposing the constraints for dynamics in spectrum opportunities and varying channel availability time amongst SUs. Previously designed double auctions miss out the CR constraints which can further degrade the network performance. Also, multi-winner allocation is induced in the model which encourages spectrum reuse by allowing a common channel to be assigned to multiple non-interfering SUs. A preference list of channels is maintained at each SU using which SUs offer their bid values for the heterogeneous channels which the primary owners are competing to lease. To organize channel specific groups of non-interfering SUs, a bidder group formation algorithm is developed such that members of a winner group get access to a common channel. The auctioneer formulates a winner determination strategy and a pricing strategy which achieves truthfulness while assigning the idle spectrum. Effectiveness of the proposed model is studied by comparing it with an existing work which shows that channel allocation gets significantly improved on deploying the proposed model.

Spectrum has now become a scarce resource due to the continuous development of wireless communication technology. Cognitive radio technology is considered to be a new method to solve the shortage of spectrum resources. The spectrum allocation model of cognitive radio can effectively avoid the waste of spectrum resources. A novel binary version of slime mould algorithm is proposed for the spectrum allocation model to solve the spectrum allocation scheme. In addition, adding unselected factors strategy can make the approach find a better solution. Compared with other algorithms, the novel binary slime mould algorithm and the strategy of adding unselected factors proposed in this paper have a good performance in spectrum allocation. The resulting spectrum allocation scheme can achieve efficient use of network resources.

The spectrum allocation for cognitive radio networks (CRNs) has received considerable studies under the assumption that the bandwidth of spectrum holes is static. However, in practice, the bandwidth of spectrum holes is time-varied due to primary user/secondary user (PU/SU) activity and mobility, which result in non-determinacy. This paper studies the spectrum allocation for CRNs with non-deterministic bandwidth of spectrum holes. We present a novel probability density function (PDF) model through order statistic to describe the non-deterministic bandwidth of spectrum holes and provide a bound to approximate it. After that, a statistical spectrum allocation model based on stochastic multiple knapsack problem (MKP) is established for spectrum allocation with non-deterministic bandwidth of spectrum holes. To reduce the computational complexity, we transform this stochastic programming probleminto a constant MKP though exploiting the properties of cumulative distribution function (CDF), which can be solved via MTHG algorithm by using auxiliary variable. Simulation results illustrate that the proposed statistical spectrum allocation algorithm can achieve better performances compared to the existing algorithms when the bandwidth of spectrum holes istime-varied.

The growing awareness of the strategic value of service along with higher product performance expectations has directed many industries to adopt service centric competitive strategies in managing their supply chains. Service innovation based on currently available advanced technologies has become critical in improving enterprises’ competitive strengths. Wireless sensor network (WSN) technology, which can provide a mobile, scalable, and reliable monitoring solution, is emerging as a tool for service innovation in supply chain management. This paper provides an overview of wireless sensor network technology and discusses how it can benefit modern industries. The fundamental theories of WSN are introduced as well as an overview of the development. The impact of information technologies on supply chain management and service innovation is then briefly discussed. Much emphasis is placed on the feasibility, procedures, and critical challenges of implementing WSN in supply chain management innovation. Current and future applications of WSN are also provided, followed by a case study demonstrating the application potentials of WSN for service innovation in the healthcare industry.