WITHDRAWN: Grasshopper algorithm based channel assignment for cognitive radio networks

The wireless mesh network is a new emerging technology exploiting the advantages of multiple radios and multiple channels. In this paper we have studied the channel assignment problem which is to assign channels to links in the network, and aimed at minimizing the network interference so as to improve the network throughput. The channel assignment problem is proved to be NP-hard. In this paper, we have developed a new approach based on the particle swarm optimization (PSO) algorithm and a heuristic algorithm to solve channel assignment problem in multi-radio multi-channel wireless mesh networks. Meanwhile, the crossover operation is employed in the discrete particle swarm optimization (DPSO) algorithm to handle the discrete channel assignment problem. Experimental results have shown that the rate of interference in the DPSO algorithm descends by 67.67% compared with the Tabu-based algorithm when there are 12 channels and 5 radios. And the heuristic algorithm can also yield good solutions with good quality in both dense and sparse networks.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

In this paper, channel assignment for spectrum sensing is studied in multi‐channel cognitive radio (CR) networks to maximize the number of channels satisfying sensing performance (called available channels). Beginning with a nonlinear integer programming problem, we derive the upper bound of optimal value through many‐to‐many assignment problem and then propose three algorithms for both centralized and distributed scenarios. In centralized case, a heuristic scheme is proposed based on the signal‐to‐noise ratios (SNRs) over all primary channels (PCs). Then, a greedy scheme is proposed to reduce the reported information from the CRs. In distributed case, a novel scheme with multi‐round operation is designed following the coalitional game theory. In each round, each CR selects some PCs based on SNRs. Then, the CRs selecting the same channel play coalitional game, and thereby, multiple games are played concurrently over multiple channels. Finally, the best coalition for each channel is chosen among the formed coalitions to perform the cooperative spectrum sensing. The simulation results show that the proposed schemes can significantly increase the number of available channels. Copyright © 2013 John Wiley & Sons, Ltd.

As the proliferation of wireless handheld devices such as smart phones and tablets grow exponentially, new technologies have to be adopted to efficiently use the radio frequency spectrum. Cognitive Radio(CR) network offers a promising solution to improve spectrum utilization and efficiency by harnessing the available spectrum resources in real-time. However, participants in the CR network can comprise of malicious adversaries who adopt dishonest and non-cooperative strategies to attack the network. In our research, we focus on a critical attack on the CR network: Primary User Emulation (PUE). In a PUE attack, the malicious adversary violates spectrum sharing etiquette by impersonating the primary user at the expense of other unselfish secondary users. To thwart such an attack, we present a strategy to authenticate the primary user, which involves collaborative learning among CR users.

We focus on a specific class of denial-of-service (DoS) attacks that is executed through Channel Eviction Triggering (CET), whereby adversary nodes unduly invoke mechanisms inherent in a cognitive radio (CR) network (CRN) operation to protect the licensed users and thus disrupt secondary access to the otherwise idle licensed bands. Skewing the spectrum sensing decision of CRN through sensing misreports is a manifestation of CET attacks. Whereas most studies in the literature focus on making the cooperative sensing more robust against such sensing misreports, we tackle the problem from the novel perspective of incentive alleviation. We distinguish two classes of such DoS attacks, which we refer to as CET and CET-jamming attacks. In the former case, the incentive of adversary CRs is to remove the competition of truthful CRs in accessing the licensed spectral ranges. The latter class of DoS attack deals with scenarios in which the adversary nodes are mainly interested in denying the chances of communication of CRN over primary bands and as such their incentive cannot be modelled by the same utility maximisation model as truthful CRs. We propose a solution for each class of attacks, and our numerical results verify the effectiveness of the proposed CET defence scheme in both cases. Copyright © 2012 John Wiley & Sons, Ltd.

The channel assignment problem is one of the most important issues in cognitive radio networks (CRNs). Under a SINR-driven model, we consider channel assignments in a network using dynamic source routing (DSR). In this unicasting model, channel assignments are conducted in a relatively small scale of nodes, which are on the chosen route. In addition, we can make use of the route reply (RREP) message in DSR to estimate the SINR and the maximum data transmission rate of nodes on the chosen route. In this way, the source node can conduct the channel assignment in a more efficient way. We propose two algorithms for the single route and multi-route channel assignments, where the multi-route scheme uses alternative nodes to help transmitting. We give a complexity analysis of two algorithms and an extension of reducing complexity for the multi-route channel assignment algorithm. Finally, we conduct simulations of our two algorithms under networks with different densities and show that the performance of our algorithms is efficient.

Cognitive radio networks and communications is an emergent paradigm to address the spectrum scarcity challenge in the wireless community. Currently, different wireless systems are regulated by a fixed spectrum assignment strategy. This policy partitions the whole spectrum into a large number of different ranges. Each piece is exclusively used by a specific wireless system. This leads to an undesirable situation that some systems may only use the allocated spectrum to a very limited extent while others have very serious spectrum insufficiency situation. Cognitive radio is believed to be a highly potential technology to address these issues. However, it is still in the very early stage of research and development. There are a number of technical, economical, and regulatory challenges to be addressed. This special issue aims to foster the dissemination of high quality research in cognitive radio networks and communications. Original papers are solicited in all aspects of cognitive radio including physical layer issues, architectures, protocol designs, theoretical studies, practical applications, and experimental prototypes. To guarantee the quality, in this special issue, we only include the following eight papers at this special issue. The first paper, entitled “Evaluation of Dynamic Channel and Power Assignment for CognitiveNetworks”, introduces a unifying optimization formulation to describe the dynamic channel and power assignment (DCPA) problem and an evaluation method for comparing DCPA algorithms. The second paper is entitled “Joint Bandwidth and Power Allocations for Cognitive Radio Networks with Imperfect Spectrum Sensing”. In this paper the authors study the joint

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

The non-uniform inflow caused by the elbow inlet is one of the main reasons for the low actual operation performance of a centrifugal pump. Orthogonal experiment and GA_PSO algorithm are used to improve the head and efficiency of a centrifugal pump with an elbow inlet based on the method combining numerical simulation and prototype experiment in this paper. The effects of the design parameters, including elbow inlet radius ratio, blade inlet angle, blade number, blade wrap angle, blade outlet angle, impeller outlet diameter, blade outlet width and flow area ratio, on the pump head and efficiency are studied in the orthogonal experiment. The blade inlet angle is the major factor to match the non-uniform inflow and reduce the flow loss in the impeller inlet to contribute to enhancing the pump performance and cavitation characteristics. The particle swarm optimization (PSO) algorithm is optimized by integrating the genetic algorithm (GA), which ensures that the PSO-calculation result avoids falling into the local optimization and the global optimal solution is obtained as quickly as possible. The centrifugal pump with an elbow inlet is optimally designed by the GA_PSO algorithm. According to the performance test results, the efficiency of the optimized pump is 4.7% higher than that of the original pump.

The objective of the research work is to examine energy-efficient routing in wireless sensor networks for moving nodes by contrasting new evolutionary algorithms with the Particle Swarm Optimization (PSO) Algorithm. Comparative analysis of energy efficient routing is performed by a novel genetic algorithm where there are several samples (N = 10) and a PSO algorithm where there are several samples (N = 10) techniques with pre-test power of 80% using NS2. When routing wireless sensor networks with moving nodes, Various metrics, including delay, energy use, and packet delivery ratio, are used to compare PSO and genetic algorithms. The evolutionary algorithm outperforms the PSO algorithm in these ways in terms of packet delivery ratio, energy usage, and delay. (p < 0.05) indicates a statistically significant difference. The research's findings comprised the three metrics of delay, energy consumption and packet delivery ratio. The method shows that the novel Genetic algorithm outperforms the PSO algorithm for wireless sensor network energy-efficient routing for moving nodes by identifying a set of routes that can satisfy the delay restrictions.KeywordsNovel genetic algorithmParticle Swarm Optimization (PSO)Wireless sensor networkDelayEnergy consumptionPacket delivery ratio

With growing concern about environmental issues and an emerging green communications paradigm, cognitive radio (CR) networks (CRNs) have to be considered from the energy efficiency perspective. In this paper, we focus on scheduling in CRNs, in which a cognitive base station (CBS) makes frequency allocations to the CRs at the beginning of each frame. A cognitive scheduler must consider the diversity among CRs' queues and channel capacities in terms of number of bits and the channel switching cost from one frequency to another. Taking all these into account, we formulate the scheduling problem as an energy efficiency maximization problem, which is a nonlinear integer programming (NLP) problem and is thereby hard to solve. We seek alternate computationally easier solutions. To this aim, we propose a polynomial-time heuristic algorithm, i.e., the energy-efficient heuristic scheduler (EEHS), which allocates each idle frequency to the CR that attains the highest energy efficiency at this frequency. Next, we reformulate the original problem first as a throughput maximization problem subject to energy consumption restrictions and then as an energy consumption minimization problem subject to minimum throughput guarantees. These two schedulers also have the power to provide fairness in resource allocation. We analyze the energy efficiency and successful transmission probability of the proposed schedulers under both contiguous and fragmented spectrum scenarios. Performance studies show that, compared with a pure opportunistic scheduler with a throughput maximization objective, proposed schedulers can attain almost the same throughput performance with better energy efficiency.

Wireless networks communicate with each other using radio spectrum bands which are assigned to license owners. Due to the fixed spectrum assignment policy, a large portion of the spectrum stays unused. The aim of cognitive radio is enabling users which do not hold a license to be able to access the spectrum assigned to license owners. In the channel assignment problem, the objective is to assign channels to unlicensed users in order to maximize channel utilization without causing any interference to licensed users. In this study, we propose a hyper-heuristic approach to solve the channel assignment problem in cognitive radio networks. Results show that our approach gives high channel utilization rates by allowing unlicensed users to access the channels owned by licensed users. The results are promising and promote further study.

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

Cognitive radio (CR) is a promising solution to the lack of spectrum, as well as spectrum inefficiency, in current communication networks. A major demand of this technology is for efficient and fair spectrum assignment to unlicensed (secondary) users. This paper presents a novel method to address this challenge in CR networks, based on the graph colouring problem (GCP) and gravitational search algorithm (GSA). In addition, the chaotic search process has been designed in the method to avoid falling into local optimal solution. Last, we compare the performance of the proposed algorithm with particle swarm optimisation (PSO), genetic algorithms (GA) and quantum genetic algorithm (QGA).