International Journal of Sensors Wireless Communications and Control - Volume 11, Issue 1, 2021

Background & Objective: Internet of things devices managed by wireless networks so that higher data rates can be achieved. With increase in demand of usage of Internet microwave signals are capable for delivering the demand. However these high frequency waves needs a waveguide to propagate otherwise they get affected due to atmospheric attenuation and rain fade. A modified structure of rectangular waveguide is used named as Substrate Integrated Waveguide (SIW). Methods: In this paper, the work has been carried out taking SIW and Leaky Wave Antenna. The dielectric substrate was taken as carbon. Modeling of SIW Leaky wave Antenna is done by making C shaped Slots. Design steps for modeling LWA were orderly pursued also optimized with various equations followed by Finite Element Method based modeling. A range of frequency as input is taken from 7 GHz to 11 GHz to take the analysis of design modeled. Results: The result shows the electric field intensity and radiation pattern is directly proportional with frequency while the return loss is acceptable for applied frequency range. Conclusion: The proposed antenna can radiate in omni directional radiation pattern hence can be used to connect devices with IoT.

Background & Objective: Networks on chip are being developed as a communication infrastructure in the design of multiprocessor SOCs. With the reduction in feature size, transient faults on the links are becoming a major issue on the performance of NOCs. Methods: In this paper, two fault-tolerant algorithms are proposed. In the first algorithm, a faulty link tolerant algorithm is designed which by measuring network loads on the links will reduce transient faults and balances the load. To address the effect of hardware faults, fault and congestion controlled algorithm is designed that not only control the congestion, but also the faults on both links and the nodes. Results: The packet size is taken as 8 flits and data width of 32 bits are considered for all switches. Input buffers are having 8 slots for storing data. The simulations are carried over two synthetic traffic scenarios that are, hotspot and transpose. Conclusion: The proposed strategies are evaluated on two different synthetic traffic patterns and the results so obtained shows better network and hardware performance of both the routing in comparison with non-fault-tolerant routing.

Background: Energy Efficient wireless routing has been an area of research particularly to mitigate challenges surrounding performance in category of Wireless Networks. Objectives: The Opportunistic Routing (OR) technique was explored in recent times and exhibits benefits over many existing protocols and can significantly reduce energy consumption during data communication with very limited compromise on performance. Methods: Using broadcasting nature of the wireless medium, OR practices to discourse two foremost issues of variable link quality and unpredictable node agility in constrained WSNs. OR has a potential to reduce delay in order to increase the consistency of data delivery in network. Results: Various OR based routing protocols have shown varying performances. In this paper, a detailed conceptual and experimental analysis is carried out on different protocols that uses OR technique for providing more clear and definitive view on performance parameters like Message Success Rate, Packet Delivery Ratio and Energy Consumption.

Background: Acoustic waves have a large range of applications in UWSNs from underwater monitoring to disaster management, military surveillance to assisted navigation. Acoustic waves are primarily used for wireless communication in water. But radio waves are more suitable than acoustic waves for many underwater applications (e.g. real-time applications, shallow water applications). Objectives: A propagation model is required to effectively design a radio wave based UWSN. Propagation model predicts the average received signal strength at a given distance from the transmitter and the variability of the signal strength in close spatial proximity to a particular location. Various radio propagation models are developed for air. Methods: The performance of RF-EM waves underwater is not the same as that in the air. Many parameters which have real-value in the air becomes complex valued in seawater. Thus, propagation models for air cannot be directly used to calculate propagation loss underwater. Various radio propagation models are developed for water by Al-Shamaa’a et al., Uribe and Grote, Jiang et al., Elrashidi et al., Hattab et al. Each model has some merits and demerits. Path loss model developed by Al-Shamma’a et al. is a simple model based on attenuation only. Results: Uribe and Grote have introduced distance-dependent attenuation coefficient in path loss calculation. Path loss model by Jiang et al. calculates path loss for freshwater. Model by Hattab et al. is specifically designed for UWSN. According to the authors, it is the first path loss model developed for UWSN. Elrashidi et al. have calculated path loss for freshwater and seawater at 2.4 GHz. The model includes the effect of the reflected signals on the received signal by the receiver node. Conclusion: The paper presents a comparative analysis of these various radio propagation models developed for underwater. Among these models, the radio propagation model by Hattab et al. is more realistic and covers both propagation loss and interface loss. According to the authors, it is the first radio propagation model developed for UWSNs.

Background & Objective: DDoS attack poses a huge threat to the communication and security of mobile nodes in MANETs. The number of approaches proposed to defense against DDoS attacks in MANETs is much less as compared to those for the wire-based networks. The aim of this paper is to test the effectiveness of the proposed cluster-based DDoS attacks mechanism with various reactive routing protocols. Methods: The scheme proposed here is the clustering-based DDoS defense mechanism, in which the cluster heads monitor the incoming traffic to identify the presence of suspicious behaviour. After the successful identification of suspicious behavior, the flow responsible behind it will be identified and confirmed whether it is related to DDoS attack or not. Once the DDoS attack is confirmed, all the packet related to it will be discarded. Results & Discussion: OMNeT++ along with the INET framework is used to evaluate the effectiveness of the proposed defense scheme with different routing protocols. In attack situations, DYMO exhibited higher throughput and able to deliver approximately 95% legitimate packets. DYMO, in comparison to AODV and DSR, managed to control end-to-end delay at its best levels (i.e. 0.40 to 0.70 seconds). In terms of packet delivery ratio, AODV and DYMO both perform better than DSR and able to maintain PDR at their highest levels (i.e. 0.90 to 0.94). Conclusion: The attack detection mechanism proposed here performs various tasks like monitoring, characterization, and identification of attack traffic from the incoming flow with the help neighbouring cluster heads. The flow identified as the attack is discarded and attack-related information would be shared with neighbouring cluster heads to achieve distributed defense. The performance of the proposed defense system is assessed with different reactive routing protocols and identified that DYMO protocols perform better than AODV and DSR.

Background: Automatic licence plate recognition system is used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. Objective: In this paper, a convolutional neural network and support vector machine-based licence plate recognition system is proposed. Method: Firstly, from the input image of the vehicle, the characters are extracted and segmented. Then features of the segmented characters are extracted. The extracted features are classified using convolutional neural networks and support vector machine for the final recognition of the licence plate. Results: The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The obtained results for the proposed hybrid automatic licence plate system is compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. Conclusion: The proposed hybrid ALPR system performs better than NN, SVM, and CNN based ALPR systems.

Background: Routing protocols in Wireless Sensor Networks (WSN) are of major concern because of the important factors like energy efficiency and reliability. Opportunistic Routing (OR) is the simplest and reliable routing technique for ad-hoc networks and WSN. The OR protocol guarantees data delivery in the network. As WSN need energy-efficient routing, therefore, energy- efficient OR protocols gained popularity in the last three years. Objectives: Opportunistic routing improves the performance of the network by utilizing the broadcasting abilities of wireless channels. In this paper, a new energy-efficient and reliable routing protocol has been proposed that is based on the opportunistic routing technique. Methods: The proposed protocol is based on the energy-efficient routing metric. The proposed OR protocol prioritizes among the candidate forwarders on the basis of energy-efficient routing metric and this will lead to the achievement of improved network lifetime. Results: The end-to-end delay is reduced because of the selection of candidate forwarders from the neighbor list of each node. The proposed protocol uses selective acknowledgment mechanisms. The simulation results of the proposed protocol depict the improved performance as compared to other recent OR protocols. Conclusion: The proposed protocol can directly be used with existing WSN applications. Further improvements can be made by utilizing more parameters like distance and the quality of transmission.

Background: The determination of the tumor extent is a major challenging task in brain tumor planning and quantitative evaluation. Magnetic Resonance Imaging (MRI) is one of the non-intellectual technique that has emerged as a front-line diagnostic tool for a brain tumor with non-ionizing radiation. Objectives: In Brain tumors, Gliomas is the very basic tumor of the brain; they might be less aggressive or more aggressive in a patient with a life expectancy of not more than 2 years. Manual segmentation is time-consuming, therefore we use a deep convolutional neural network to increase the performance, which is highly dependent on the operator's experience. Methods: This paper proposed a fully automatic segmentation of brain tumors using deep convolutional neural networks. Further, it uses high-grade gliomas brain images from BRATS 2016 database. The suggested work achieves brain tumor segmentation using tensor flow, in which, the anaconda frameworks are used to execute high-level mathematical functions. Results: Hence, the research work segments brain tumors into four classes like edema, nonenhancing tumor, enhancing tumor and necrotic tumor. Brain tumor segmentation needs to separate healthy tissues from tumor regions, such as advancing tumor, necrotic core, and surrounding edema. We have presented a process to segment 3D MRI image of a brain tumor including their separate sub-areas. We have applied an SVM based classification. Categorization is complete using a soft-margin SVM classifier. Conclusion: Deep convolutional neural networks have been used to present the brain tumor segmentation. Outcomes of the BRATS 2016 online judgment method assure us to increase the performance, accuracy, and speed with our best model. The fuzzy c-mean algorithm provides better accuracy and trains the SVM based classifier. We can achieve the finest performance and accuracy by using the novel two-pathway architecture, i.e.., encoder and decoder, as well as the modeling local label that depends on stacking two CNN’s.

Background & Objective: Cognitive Radio Network (CRN) is an emerging technology that provides ability of adapting operating parameters to wireless devices in order to overcome spectrum scarcity problems. However, involvement of numerous pervasive smart wireless devices introduces many security threats in CRN. The research work specifically highlights the different kinds of attacks that occur in cognitive networks. Methods: The algorithm is designed which focuses on vulnerabilities that occur in any single or an individual node. The proposed algorithm will take many factors into consideration like integer programming, trust and Byzantine failure model. Though there are various security parameters especially the five major attributes like integrity, confidentiality, availability, authorization, access control and nonrepudiation, which should be considered for developing any alogotithm. Conclusion: Nevertheless, certain security parameters are kept into consideration, which are embedded into algorithm to make it of its unique kind.

Aims: We consider the Direction of Arrival (DOA) estimation for code division multiple access (CDMA) signals. Background: Solving this problem requires non-linear optimization and thus the speed of convergence becomes crucial. Objective: A novel Modified Artificial Bee Colony (MABC) has been proposed. We use secondorder Taylor series expansion of the cost function to ameliorate the searchability of artificial bee colony (ABC) for finding the globally optimal solution. Methods: The main idea is to harness the exploration and exploitation features. The optimum point of second-order Taylor expansion of cost function is used as a velocity factor of the ABC algorithm. Results: The proposed technique is used for solving the DOA estimation problem of a CDMA system. Simulation results confirm the performance improvement of our proposed algorithm. Conclusion: The cost function of the DOA estimation usually leads to a non-linear optimization problem. Using evolutionary algorithms can improve convergence rate of such problems.

Background: Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. Objective: Motivation of this work is to use most out of the data generated from sensors and use them for recognition of classes. Recognition of actions from videos sequences is a growing field with the upcoming trends of deep neural networks. Methods: Automatic learning capability of Convolutional Neural Network (CNN) make them good choice as compared to traditional handcrafted based approaches. With the increasing demand of RGB-D sensors combination of RGB and depth data is in great demand. Results: This work comprises of the use of dynamic images generated from RGB combined with depth map for action recognition purpose. We have experimented our approach on pre trained VGG-F model using MSR Daily activity dataset and UTD MHAD Dataset. We achieve state of the art results. To support our research, we have calculated different parameters apart from accuracy such as precision, F score, recall. Conclusion: Accordingly, the investigation confirms improvement in term of accuracy, precision, F-Score and Recall. The proposed model is 4 Stream model is prone to occlusion, used in real time and also the data from the RGB-D sensor is fully utilized.

Background: WSN is a network of smart tiny electromechanical devices named sensors. Sensors perform various tasks like sensing the environment as per its range, transmitting the data using transmission units, storing the data in the storage unit and performing an action based on the captured data. As they are installed in an unfriendly environment, recharging the sensors is not possible every time which leads to a limited lifetime of the network. To enhance the life of a sensor network, the network requires energy-efficient protocols. Various energy-efficient MAC protocols have been developed by the research community, but very few of them are integrated with the priority-based environment which performs priority-based data transmission. Another challenge of WSN is that most of the WSN areas are delay-sensitive because it is implemented in critical fields like military, disaster management, and health monitoring. Energy, delay, and throughput are major quality factors that affect the sensor network. Objective: In this paper, the aim is to design and develop a MAC protocol for a field like military, where the system requires energy efficiency and priority-based data transmission. Methods: In the proposed model, a cluster-based network with priority queues is formed that can achieve higher power efficiency and less delay for sensitive data. Results: In this research, the simulation of proposed MAC, TMAC and SMAC is done with different numbers of nodes, same inter-packet intervals, and variant inter-packet intervals. Based on the script simulation, result graphs are generated. Conclusion: The proposed work achieves a greater lifetime compared to TMAC and SMAC using priority-based data transmission.

Background: There are mainly three types of banking includes Traditional banking, online banking, and mobile banking. The use of the latter two i.e, online banking and mobile banking, over traditional methods should rise exponentially among the people. This will be the implication only if the customers are made aware of the new technologies and advancements in the banking sector. Therefore, appropriate securities to be implied on the digital methods of banking for reliability and trust of the customers. Objective: In this paper, we formulate a model that represents a means for detecting and describing the transmission of malicious objects through various individual nodes in a server of banking. Methods: For a strong banking infrastructure, the challenge is to those concerned with information security to place a monetary value on the protection of information resources. In addition, a trust model for the banking system in online or offline modes is also provided. Results: The results of this model can help in understanding the mechanism by which malicious object spread, to predict the future course of an outbreak and to evaluate the strategies to control. Conclusion: The paper can be concluded to achieve its objective that how to maintain Trust in the banking system in different scenarios along with predicting the outbreak of a perticular malicious object.