New Approaches in Cognitive Radios using Evolutionary Algorithms

Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. Due many techniques have become a topic of discussion on cognitive radios, the aim of this paper was developed a contemporary survey of evolutionary algorithms in Cognitive Radio. According to the art state, this work had been collected the essential contributions of cognitive radios with the particularity of base they research in evolutionary algorithms. The main idea was classified the evolutionary algorithms and showed their fundamental approaches. Moreover, this research will be exposed some of the current issues in cognitive radios and how the evolutionary algorithms will have been contributed. Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. With a more comprehensive and systematic understanding of evolutionary algorithms in cognitive radios, more research in this direction may be motivated and refined

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Spectrum and transmission range aware clustering for cognitive radio ad hoc networks

Cognitive radio network (CRN) is a promising technology to overcome the problem of spectrum shortage by enabling the unlicensed users to access the underutilization spectrum bands in an opportunistic manner. On the other hand, the hardness of establishing a fixed infrastructure in specific situations such as disaster recovery, and battlefield communication imposes the network to have an ad hoc structure. Thus, the emerging of Cognitive Radio Ad Hoc Network (CRAHN) has accordingly become imperative. However, the practical implementation of CRAHN faced many challenges such as control channel establishment and the scalability problems. Clustering that divides the network into virtual groups is a reliable solution to handle these issues. However, previous clustering methods for CRAHNs seem to be impractical due to issues regarding the high number of constructed clusters and unfair load distribution among the clusters. Additionally, the homogeneous channel model was considered in the previous work despite channel heterogeneity is the CRN features. This thesis addressed these issues by proposing two clustering schemes, where the heterogeneous channel is considered in the clustering process. First, a distributed clustering algorithm called Spectrum and Transmission Range Aware Clustering (STRAC) which exploits the heterogeneous channel concept is proposed. Here, a novel cluster head selection function is formulated. An analytical model is derived to validate the STRAC outcomes. Second, in order to improve the bandwidth utilization, a Load Balanced Spectrum and Transmission Range Aware Clustering (LB-STRAC) is proposed. This algorithm jointly considers the channel heterogeneity and load balancing concepts. Simulation results show that on average, STRAC reduces the number of constructed clusters up to 51% compared to conventional clustering technique, Spectrum Opportunity based Clustering (SOC). In addition, STRAC significantly reduces the one-member cluster ratio and re-affiliation ratio in comparison to non-heterogeneity channel consideration schemes. LB-STRAC further improved the clustering performance by outperforming STRAC in terms of uniformity and equality of the traffic load distribution among all clusters with fair spectrum allocation. Moreover, LB-STRAC has been shown to be very effective in improving the bandwidth utilization. For equal traffic load scenario, LB-STRAC on average improves the bandwidth utilization by 24.3% compared to STRAC. Additionally, for varied traffic load scenario, LB-STRAC improves the bandwidth utilization by 31.9% and 25.4% on average compared with STRAC for non-uniform slot allocation and for uniform slot allocation respectively. Thus, LB-STRAC is highly recommended for multi-source scenarios such as continuous monitoring applications or situation awareness applications

An artificial intelligence based quorum system for the improvement of the lifespan of sensor networks.

Artificial Intelligence-based Quorum systems are used to solve the energy crisis in real-time wireless sensor networks. They tend to improve the coverage, connectivity, latency, and lifespan of the networks where millions of sensor nodes need to be deployed in a smart grid system. The reality is that sensors may consume more power and reduce the lifetime of the network. This paper proposes a quorum-based grid system where the number of sensors in the quorum is increased without actually increasing quorums themselves, leading to improvements in throughput and latency by 14.23%. The proposed artificial intelligence scheme reduces the network latency due to an increase in time slots over conventional algorithms previously proposed. Secondly, energy consumption is reduced by weighted load balancing, improving the network’s actual lifespan. Our experimental results show that the coverage rate is increased on an average of 11% over the conventional Coverage Contribution Area (CCA), Partial Coverage with Learning Automata (PCLA), and Probabilistic Coverage Protocol (PCP) protocols respectively

On the performance of STDMA Link Scheduling and Switched Beamforming Antennas in Wireless Mesh Networks

Projecte final de carrera realitzat en col.laboració amb King's College LondonWireless Mesh Networks (WMNs) aim to revolutionize Internet connectivity due to its high throughput, cost-e ectiveness and ease deployment by providing last mile connectivity and/or backhaul support to di erent cellular networks. In order not to jeopardize their successful deployment, several key issues must be investigated and overcome to fully realize its potential. For WMNs that utilize Spatial Reuse TDMA as the medium access control, link scheduling still requires further enhancements. The rst main contribution of this thesis is a fast randomized parallel link swap based packing (RSP) algorithm for timeslot allocation in a spatial time division multiple access (STDMA) wireless mesh network. The proposed randomized algorithm extends several greedy scheduling algorithms that utilize the physical interference model by applying a local search that leads to a substantial improvement in the spatial timeslot reuse. Numerical simulations reveal that compared to previously scheduling schemes the proposed randomized algorithm can achieve a performance gain of up to 11%. A signi cant bene t of the proposed scheme is that the computations can be parallelized and therefore can e ciently utilize commoditized and emerging multi-core and/or multi-CPU processors. Furthermore, the use of selectable multi-beam directional antennas in WMNs, such as beam switched phase array antennas, can assist to signi cantly enhance the overall reuse of timeslots by reducing interference levels across the network and thereby increasing the spectral e ciency of the system. To perform though a switch on the antenna beam it may require up to 0.25 ms in practical deployed networks, while at the same time very frequent beam switchings can a ect frame acquisition and overall reliability of the deployed mesh network. The second key contribution of this thesis is a set of algorithms that minimize the overall number of required beam switchings in the mesh network without penalizing the spatial reuse of timeslots, i.e., keeping the same overall frame length in the network. Numerical investigations reveal that the proposed set of algorithms can reduce the number of beam switchings by almost 90% without a ecting the frame length of the network

Review on Resource Efficient Relay Selection Scheme for Cognitive Radio Network

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Optimising routing and trustworthiness of ad hoc networks using swarm intelligence

This thesis was submitted for the degree of Doctor of Philsophy and awarded by Brunel UniversityThis thesis proposes different approaches to address routing and security of MANETs using swarm technology. The mobility and infrastructure-less of MANET as well as nodes misbehavior compose great challenges to routing and security protocols of such a network. The first approach addresses the problem of channel assignment in multichannel ad hoc networks with limited number of interfaces, where stable route are more preferred to be selected. The channel selection is based on link quality between the nodes. Geographical information is used with mapping algorithm in order to estimate and predict the links’ quality and routes life time, which is combined with Ant Colony Optimization (ACO) algorithm to find most stable route with high data rate. As a result, a better utilization of the channels is performed where the throughput increased up to 74% over ASAR protocol. A new smart data packet routing protocol is developed based on the River Formation Dynamics (RFD) algorithm. The RFD algorithm is a subset of swarm intelligence which mimics how rivers are created in nature. The protocol is a distributed swarm learning approach where data packets are smart enough to guide themselves through best available route in the network. The learning information is distributed throughout the nodes of the network. This information can be used and updated by successive data packets in order to maintain and find better routes. Data packets act like swarm agents (drops) where they carry their path information and update routing information without the need for backward agents. These data packets modify the routing information based on different network metrics. As a result, data packet can guide themselves through better routes. In the second approach, a hybrid ACO and RFD smart data packet routing protocol is developed where the protocol tries to find shortest path that is less congested to the destination. Simulation results show throughput improvement by 30% over AODV protocol and 13% over AntHocNet. Both delay and jitter have been improved more than 96% over AODV protocol. In order to overcome the problem of source routing introduced due to the use of the ACO algorithm, a solely RFD based distance vector protocol has been developed as a third approach. Moreover, the protocol separates reactive learned information from proactive learned information to add more reliability to data routing. To minimize the power consumption introduced due to the hybrid nature of the RFD routing protocol, a forth approach has been developed. This protocol tackles the problem of power consumption and adds packets delivery power minimization to the protocol based on RFD algorithm. Finally, a security model based on reputation and trust is added to the smart data packet protocol in order to detect misbehaving nodes. A trust system has been built based on the privilege offered by the RFD algorithm, where drops are always moving from higher altitude to lower one. Moreover, the distributed and undefined nature of the ad hoc network forces the nodes to obligate to cooperative behaviour in order not to be exposed. This system can easily and quickly detect misbehaving nodes according to altitude difference between active intermediate nodes

AN IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SPECTRUM ALLOCATION IN COGNITIVE RADIO NETWORKS

The seriousness of the spectrum scarcity has increased dramatically due to the rapid increase of wireless services. The key enabling technology that can be viewed as a novel approach for utilizing the spectrum more efficiently is known as Cognitive Radio. Therefore, assigning the spectrum opportunistically to the unlicensed users without interfering with the licensed users, concurrently with maximizing the spectrum utilization is addressed as a major challenge problem in cognitive radio networks. In this paper, an improved metaheuristic optimization algorithm has been proposed to solve this problem that contingent on a graph coloring model. The proposed approach is a hybrid algorithm composed of a Particle Swarm Optimization algorithm with Random Neighborhood Search. The key objective function is maximizing the spectrum utilization in the cognitive radio networks with the subjected constraints. MATLAB R2021a was used for conducting the simulation. The proposed hybrid algorithm improved the system utilization by 1.23% compared to Particle Swarm Optimization algorithm, 5.57% compared to Random Neighborhood Search, 7.9% compared to Color Sensitive Graph Coloring algorithm, and 27.33% compared to Greedy algorithm. Moreover, the system performance was evaluated with various deployment scenarios of the primary users, secondary users, and channels for investigating the impact of varying these parameters on the system performance