No. 3 (2020)
Published:
2020-09-30
ARTICLES FROM THIS ISSUE
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Differentiated Service Model-Supported Cluster-Based Routing in Wireless Sensor Networks
Abstract
Wireless Sensor Network finds its extensive use in healthcare applications for the transfer of time-critical data through wireless connectivity. The primary cause of network failure is the transfer of time-critical multimedia data. The article presents a new differentiated service modelsupported (DSM) cluster-based routing in wireless sensor networks (WSNs) that overcomes the above issue. DSM prioritizes the transfer of different flow types based on packet type and packet size. The employment of computational offloading minimizes delay for critical and small-sized data packets and by carrying out data reduction of large-sized packets at proxy server. It outperforms the existing protocols in terms of energy efficiency, throughput, and reliability by prioritizing the transfer of time-critical health application data
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Modeling and Analysis of Additional Clear Channel Assessment to Improve Performance of IEEE 802.15.4-based MAC Protocol
Abstract
Design of the MAC protocol is crucial in all wireless sensor networks (WSNs) due to its influence on the performance of the transceiver, i.e. the most energy-consuming component of each sensor node. A mechanism known as “carrier sense multiple access with collision avoidance” (CSMA/CA) is used for accessing the wireless channel in the IEEE 802.15.4 standard-based MAC protocol in order to avoid collisions between the network’s communicating nodes. CSMA/CA relies on two clear channel assessments (CCA=2) for checking the status of the channel. In this paper, we develop an additional CCA algorithm for the two scenarios encountered in star topology-enabled WSNs. Next, we investigate the impact of an additional clear channel assessment (CCA=3) on performance in IEEE 802.15.4. We develop a Markov chain model for the proposed methodology, and validate it using Matlab. Simulation results show that there is a significant improvement of performance metrics in the IEEE 802.15.4 standard-based MAC protocol with an additional CCA
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EARPC – Energy Aware Routing Protocol for Cooperative MIMO Scheme in WSNs
Abstract
Wireless sensor networks are typically operated on batteries. Therefore, in order to prolong network lifetime, an energy efficient routing algorithm is required. In this paper, an energy-aware routing protocol for the co-operative MIMO scheme in WSNs (EARPC) is presented. It is based on an improved cluster head selection method that considers the remaining energy level of a node and recent energy consumption of all nodes. This means that sensor nodes with lower energy levels are less likely to be chosen as cluster heads. Next, based on the cooperative node selection in each cluster, a virtual MIMO array is created, reducing uneven distribution of clusters. Simulation results show that the proposed routing protocol may reduce energy consumption and improve network lifetime compared with the LEACH protocol
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ICBCA – Improved Cluster Based Channel Allocation in Cognitive Radio Sensor Networks
Abstract
Wireless sensor networks (WSNs) operate in an overcrowded electromagnetic environment, as the spectrum is shared by various wireless communication technologies. This gives rise to various challenges related to optimized and efficient spectrum utilization. Cognitive radio (CR) has emerged as a solution satisfying this requirement, as it is capable of adapting to the dynamic radio spectrum. Thanks to the deployment of cognitive radio in WSNs, the spectrum may be utilized in a more efficient manner. CR may identify the vacant channels dynamically, allowing the sensor nodes to effectively communicate with each other. In this paper a clustering algorithm known as improved cluster-based channel assignment (ICBCA) is implemented, forming clusters of CR sensor nodes and then selecting vacant channels for data transmission purposes. Simulation results show that ICBCA outperforms existing clustering algorithms in CR sensor networks
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Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods
Abstract
The continuous growth of demand experienced by wireless networks creates a spectrum availability challenge. Cognitive radio (CR) is a promising solution capable of overcoming spectrum scarcity. It is an intelligent radio technology that may be programmed and dynamically configured to avoid interference and congestion in cognitive radio networks (CRN). Spectrum sensing (SS) is a cognitive radio life cycle task aiming to detect spectrum holes. A number of innovative approaches are devised to monitor the spectrum and to determine when these holes are present. The purpose of this survey is to investigate some of these schemes which are constructed based on machine learning concepts and principles. In addition, this review aims to present a general classification of these machine learningbased schemes
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Performance Improvement for Vehicular Communications Using Alamouti Scheme with High Mobility
Abstract
The IEEE 802.11p standard is the basic protocol for wireless access in a vehicular environment (WAVE), providing high throughput for multimedia and high quality for vehicular transmissions. However, IEEE 802.11p fails to offer any multi-antenna approaches. In this paper, a multipleinput single-output (MISO) implementation with orthogonal frequency division multiplexing (OFDM), aiming to improve the performance of IEEE 802.11p, is proposed. The authors investigate the impact of time-varying channel on the performance of Alamouti space-time block codes (STBC) in OFDM systems. The Alamouti STBC approach shows good performance in slow time-varying environments, while its Alamouti space frequency block codes (SFBC) counterpart performs better over fast time-varying environments. An adaptive switching scheme is proposed to select appropriate spaceblock coding (STBC or SFBC) in vehicular channels with high mobility levels. It is shown that the proposed adaptive scheme provides better performance compared with traditional spaceblock codes
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Fuzzy-based Description of Computational Complexity of Central Nervous Systems
Abstract
Computational intelligence algorithms are currently capable of dealing with simple cognitive processes, but still remain inefficient compared with the human brain’s ability to learn from few exemplars or to analyze problems that have not been defined in an explicit manner. Generalization and decision-making processes typically require an uncertainty model that is applied to the decision options while relying on the probability approach. Thus, models of such cognitive functions usually interact with reinforcement-based learning to simplify complex problems. Decision-makers are needed to choose from the decision options that are available, in order to ensure that the decision-makers’ choices are rational. They maximize the subjective overall utility expected, given by the outcomes in different states and weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using the Bayes’ law. Fuzzy-based models described in this paper propose a different – they may serve as a point of departure for a family of novel methods enabling more effective and neurobiologically reliable brain simulation that is based on fuzzy logic techniques and that turns out to be useful in both basic and applied sciences. The approach presented provides a valuable insight into understanding the aforementioned processes, doing that in a descriptive, fuzzy-based manner, without presenting a complex analysis
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Application of Graph Theory Algorithms in Non-disjoint Functional Decomposition of Specific Boolean Functions
Abstract
Functional decomposition is a technique that allows to minimize Boolean functions that cannot be optimally minimized using other methods, such as variable reduction and linear decomposition. A heuristic method for finding nondisjoint decomposition has been proposed lately. In this paper, we examine how the usage of different graph theory techniques affects the computation time and the quality of the solution obtained. In total, six different approaches were analyzed. The results presented herein prove the advantages of the proposed approaches, showing that results obtained for standard benchmark M-out-of-20 functions are better than those presented in previous publication. Results obtained for randomly generated functions prove that time complexity and scalability are significantly better when using the heuristic graph coloring algorithm. However, quality of the solution is worse, in general
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Review of Experimental Verification Methods of Gyrotron Quasi-optical Mode Converters
Abstract
This survey presents a review of experimental methods relied upon while implementing gyrotron higher mode generation techniques and performing near electromagnetic field measurements in launcher and quasi-optical mode converters. In particular, the paper focuses on low power (cold) testing of gyrotron quasi-optical mode converters outside of the gyrotron, without the presence of high electromagnetic power and electron beams
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Electromagnetic Scattering of Inhomogeneous Plane Wave by Ensemble of Cylinders
Abstract
The interaction between an ensemble of cylinders and an inhomogeneous plane wave is introduced and is determined, in the present paper, through a rigorous theoretical approach. Scattered electromagnetic field generated by an indefinite number of infinite circular cylinders is analyzed by the application of the generalized vector cylinder harmonics (VCH) expansion. The exact mathematical model relied upon to represent this scenario considers the so-called complex-angle formalism reaching a superposition of vectorial cylindrical-harmonics and Foldy-Lax Multiple scattering equations (FLMSE) to account for the multiscattering process between the cylinders. The method was validated by comparing the numerical results obtained with the use of the finite element method and a homemade Matlab code
