December 2020 - IRO Journal on Sustainable Wireless Systems
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63
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Published By Inventive Research Organization

2582-3167
Updated Tuesday, 15 June 2021

2021 ◽  
Vol 3 (2) ◽  
pp. 118-127
Author(s):  
Subarna Shakya

The ability of wireless sensor networks (WSN) and their functions are degraded or eliminated by means of intrusion. To overcome this issue, this paper presents a combination of machine learning and modified grey wolf optimization (MLGWO) algorithm for developing an improved intrusion detection system (IDS). The best number of wolves are found by running tests with multiple wolves in the model. In the WSN environment, the false alarm rates are reduced along with the reduction in processing time while improving the rate of detection and the accuracy of intrusion detection with a decrease in the number of resultant features. In order to evaluate the performance of the proposed model and to compare it with the existing techniques, the NSL KDD’99 dataset is used. In terms of detection rate, false alarm rate, execution time, total features and accuracy the evaluation and comparison is performed. From the evaluation results, it is evident that higher the number of wolves, the performance of the MLGWO model is enhanced.


2021 ◽  
Vol 3 (2) ◽  
pp. 107-117
Author(s):  
Joy Iong Zong Chen

The green communication and large-scale connection issues will be faced by the wireless communication networks with futuristic sixth generation (6G) technology. The radio-frequency (RF) and spectrum sources may be shared simultaneously to achieve optimal communication in these networks by means of backscatter devices (BD) that may function in constrained spectrums as well as the stringent energy scenarios of green Internet-of-things (IoT) by means of the proposed novel modified backscatter communication model (BCM). Unlicensed eavesdroppers may interfere with the BD due to its vulnerability caused by the wireless communication channels and their broadcasting nature. The intrusion of an unlicensed eavesdropper is detected in an efficient manner by means of the proposed BCM. The analytical derivations of intercept probability (IP) and outage probability (OP) are invoked to analyze the security and reliability of the proposed architecture. Under high main-to-eavesdropper ratio (MER) regime, the IP and under high signal-to-noise ratio (SNR) regime, the OP asymptotic behaviors are estimated additionally. Based on the results of performance evaluation, it is evident that there is a decrease in the security of BD with the increase in MER while there is a simultaneous increase in the legitimate user security. Various system parameters may be adjusted for optimizing the security and reliability performance trade-off. For diverse orders, the existence of error floors are indicated by the non-zero fixed constant of BD and the legitimate user’s OP when high SNR value is observed at the system.


2021 ◽  
Vol 3 (2) ◽  
pp. 97-106
Author(s):  
Sivaganesan D

Sustainable smart agriculture with increase in signal to interference or signal to noise ratio (SIR/SNR) for selection of best relay is discussed in a wireless blockchain based network. The overall communication throughput (OCT), power splitting relaying (PSR), time switching relaying (TSR) and transmission success rate (TRS) are also derived during the selection of best relay performance with and without interference. The performance of OCT, PSR, TSR and TRS increases with the increase in the number of potential relay nodes as seen in the results of derivation. The accuracy of the theoretical values are validated by numerical simulations.


2021 ◽  
Vol 3 (2) ◽  
pp. 87-96
Author(s):  
Suma V

Localization is one of the most important aspects of Wireless Sensor Networks that make it applicable in a number of fields and areas. WSN advances in the technological aspects the number of attacks on the nodes of the WSN have also increased proficiently resulting in a number of security issues. One such attack is the Sybil attack which uses multiple pseudonymous identities to disrupt the reputation of the system. This paper is used to analyse the Sybil attacks using a detection and defence algorithm based on distance vector hop. Simulation of the results using the algorithm will be useful in effectively enhancing security of WSN nodes. In this proposed work based on the experimental analysis we have found out that with 50 beacon nodes, we have been able to decrease the average localisation error buy a solid 4% when compared with previous methodologies.


2021 ◽  
Vol 3 (2) ◽  
pp. 68-86
Author(s):  
Smitha T. V. ◽  
Madhura S ◽  
Shreya N ◽  
Sahana Udupa

This paper examines the use of the Finite Element Method (FEM) in the field of optical waveguides and terahertz signals, with the main goal of explaining how this method aids in recent advances in this field. The basics of FEM are briefly reviewed, and the technique's application to waveguide discontinuity analysis is observed. Second-order and higher-order derivatives result from optical waveguide modeling, which is significant for information exchange and many other nonlinear phenomena. The use of FEM in the improvised design of hexagonal sort air hole porous core microstructure fibers, which produces hexagonal structure cladding and rectangular-shaped air holes in the fiber core for excellent terahertz signal transmission, was also observed. These modifications were intended to improve the fiber's properties in comparison to other structures. This approach verifies that the fiber has high birefringence, low material loss, a high-power fraction, and minimal dispersion varia-tion. The features of square-type microstructure fiber are investigated. A folded-shaped po-rous cladding design is recognized for sensing applications. This type of photonic crystal fiber is also known as FP-PCF since it features circular air holes. The most approximate findings of this application are obtained using FEM. In comparison to many other approach-es for various applications, it is evident that FEM is a powerful and numerically efficient tool. This work does a survey of optical waveguides and terahertz signals using the Finite Element Method. Terahertz signals can be used in conjunction with electromagnetic waves to identify viruses. Thus, Terahertz signals are employed in real-world applications such as fuel adulteration, liquid metal synthesis, and virus detection.


2021 ◽  
Vol 3 (2) ◽  
pp. 59-67
Author(s):  
Abul Bashar ◽  
Smys S

This paper presents an analysis of Wireless Sensor Network (WSN) security issues that take place due to eavesdropping. The sensor-eavesdropper channels and the sensor sinks are exposed to generalized K-fading. Based on the physical layer security framework we use cumulative distribution, optimal sensors and round robin scheduling scheme to decrease the probability of interception and to equip secure connection between the nodes. For identifying the interception probability, a novel analytical methodology is present with simple analytical expressions. Moreover, diversity orders of scheduling schemes and asymptotic closed-form expressions are evaluated. Numerical results show the crucial result of shadowing and fading parameters of wiretap and main links, selected schemes on WSN security and network size. We have analyzed the output using Monte Carlo simulation and conclusions show the validation of the proposed work.


2021 ◽  
Vol 3 (1) ◽  
pp. 49-58
Author(s):  
Subarna Shakya ◽  
Joby P P

Wireless Body Sensor Network (BSNs) are devices that can be worn by human beings. They have sensors with transmission, computation, storage and varying sensing qualities. When there are multiple devices to obtain data from, it is necessary to merge these data to avoid errors from being transmitted, resulting in a high quality fused data. In this proposed work, we have designed a data fusion approach with the help of data obtained from the BSNs, using Fog computing. Everyday activities are gathered in the form of data using an array of sensors which are then merged together to form high quality data. The data so obtained is then given as the input to ensemble classifier to predict heart-related diseases at an early stage. Using a fog computing environment, the data collector is established and the computation process is done with a decentralised system. A final output is produced on combining the result of the nodes using the fog computing database. A novel kernel random data collector is used for classification purpose to result in an improved quality. Experimental analysis indicates an accuracy of 96% where the depth is about 10 with an estimator count of 45 along with 7 features parameters considered.


2021 ◽  
Vol 3 (1) ◽  
pp. 40-48
Author(s):  
Sivaganesan D

A network of tiny sensors located at various regions for sensing and transmitting information is termed as wireless sensor networks. The information from multiple network nodes reach the destination node or the base station where data processing is performed. In larger search spaces, the clustering mechanisms and routing solutions provided by the existing heuristic algorithms are often inefficient. The sensor node resources are depleted by un-optimized processes created by reduced routing and clustering optimization levels in large search spaces. Chaotic Gravitational Search Algorithm and Fuzzy based clustering schemes are used to overcome the limitations and challenges of the conventional routing systems. This enables effective routing and efficient clustering in large search spaces. In each cluster, among the available nodes, appropriate node is selected as the cluster head. Reduction in delay, increase in energy consumption, increase in network lifetime and improvement of the network clustering accuracy are evident from the simulation results.


2021 ◽  
Vol 3 (1) ◽  
pp. 31-39
Author(s):  
Bhalaji N

The biggest challenges faced by wireless sensor networks (WSNs) are the network lifetime and consumption of energy. To reduce the amount of energy used by WSNs, high quality clustering proves to be a crucial approach. There are multiple criteria that need to be evaluated depending on the cluster’s quality and incorporating all these criteria will prove to be cumbersome process, leading to high-quality clustering. Hence, in this paper we propose an algorithm that is used to produce high quality clusters. Cluster quality is set as the deciding criterion to determine the quality of the clusters thereby categorizing them as intra- and inter-clusters based on their distances to eliminate error rate. Using fuzzy logic, the optimal cluster head is chosen. Similarly, based on the maximum and minimum distance between the nodes, the maximum and minimum energy present in every cluster is determined. The major advantages of the proposed methodology are large-scale networks with large nodes count, better scalability, independence of key CHs, low error rate and high reliability. Using internal and external criteria, the validity of the clustering quality can be measured. Experimental simulation shows that the proposed methodology will be useful in improving the network lifetime and energy consumption. Hence the proposed node further enhances the death of the last node and first node when compared using other methodology.


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