International Journal of Sensors Wireless Communications and Control - Volume 11, Issue 7, 2021

Background: Every organization generally uses a VPN service individually to bypass the filters that hide the actual communication. Such communication filtration is not allowed by the organizational monitoring network. But these institutes are not in a position to spend a considerable amount of funds on a secure sockets layer to monitor traffic flow over their computer networks. Objective: Our work suggests a simple technique to block or detect annoying VPN clients inside the network activities. This method does not require the network to decrypt or even decode any network communication. Methods: The proposed solution selects two machine learning techniques Feature Tree and K-means as classification techniques that work on time-related features. First, the DNS mapping with the ordinary characteristic of the transmission control protocol / Internet protocol computer the network stack is identified, and it is not to be considered as a regular traffic flow if the domain name information is not available. The process not only examines non-standard utilization of hypertext transfer protocol security but also conceals such communication from hypertext transfer protocol security dependent filters in the firewall to detect as an anomaly in large. Results: We define the traffic flow as normal traffic flow and VPN traffic flow. These two flows are characterized by taking two machine learning techniques, Feature Tree and K-means. We executed each experiment 4 times. As a result, eight types of regular traffics and eight types of VPN traffics were represented. Conclusion: Once the traffic flow is identified, it is classified and studied by machine learning techniques. Using time-related features, the traffic flow is defined as normal flow or VPN traffic flow.

Background: Road accidents are a major cause of deaths worldwide. This is enormously due to fatigue, drowsiness, and microsleep of the drivers. This does not just risk the life of the driver and co-passengers but also a great threat to the vehicles and humans moving around that vehicle. Methods: Research, online content, and previously published papers related to drowsiness are reviewed. Using the facial landmarks in DAT file, the prototype locates and identifies the eye coordinates, and then calculates Eye Aspect Ratio (EAR). The EAR indicates whether the driver is drowsy or not based on the result of various sensors that get activated, such as an alarm generator, LED indicators, LCD message scroll, message sent to the owner, and the engine that gets locked. Results: The prototype is able to locate eyes in the frame and detect whether the person is sleepy or not. Whenever the person is feeling drowsy, an alarm is generated in the cabinet, and afterward , LED indicators will start glowing, messaging will be scrolling at the rear part of the vehicle so that other vehicles and humans get cautioned. After this, the vehicle slows down, and the engine gets locked. Conclusion: This prototype will help in the reduction of road accidents due to human intervention. It is not only helpful to the person who installs it in their vehicle but also for the other vehicles and humans moving around it.

Backgroud: The major objective of resource management systems in the cloud environments is to assist providers in making consistent and cost-effective decisions related to dynamic resource allocation. However, because of the demand changes of the applications and the exponential evolution of the cloud, the resource management systems are constantly called into question with regard to their ability to guarantee effective resource provisioning. Objective: To tackle these challenges, future demand prediction is a practical solution that has been adopted in the literature. The prediction has widely relied on CPU utilization since it is considered a leading cause of the Quality of Service dropping. Methods: The successful application of artificial intelligence techniques in forecasting problems motivated us to use the Kohonen Self Organizing Maps that try to capture the gathered empirical CPU load time series in regular behaviors to perform an accurate forecast. The proposed solution is a two-step approach that first classifies the collected data and then predicts the future CPU load. Results and Conclusion: The experimental results show that our proposed system outperforms other models reported in the literature. In addition, we proved that Self Organizing Maps known for their strength in classification are also effective for prediction.

Aims: To design a new variant of the Shuffled Frog Leaping Algorithm in which memeplexes formation is modified with a new strategy. Background: Shuffled Frog Leaping (SFL) is a memetic meta-heuristic algorithm that inherits two other algorithms’ features. Its intensification component of search is similar to Particle Swarm Optimization, while the inspiration for diversification is inherited from the global exchange of information in Shuffled Complex Evolution. A basic variant has been applied to solve many optimisation problems. SFLA suffers from a slow acceleration rate. Objective: To propose a robust hybrid SFLA that accelerates convergence. Methods: Two modifications are proposed in the structure of basic SFLA. Firstly, memeplexes formation is modified to handle continuous optimization problems. Secondly, in the basic SFL algorithm, the position of the worst frog is improved by moving it towards the best frog in the respective memeplex. With the progress of execution, the difference between best and worst frog position reduces; there may be more chances to trap in local minima. To improve convergence and avoid trapping in local optima, a parent-centric operator is embedded in each memeplex while performing a local search. The proposed algorithm is named PC-SFLA (Parent Centric - Shuffled Frog Leaping Algorithm). Results: The improved efficiency of PC-SFLA is validated on a robust and diverse set of standard test functions defined in CEC 2006 and 2010. Further, its efficacy is verified to optimize the total cost of supply chain management of a system. Non-parametric statistical result analysis demonstrates the efficiency of the proposal. Conclusion: PC-SFLA performed better than PSO, DE, PESO+, Modified DE, ABC, and SFLA at 5% and 10% level of significance whereas at par with Shuffled-ABC for g01-g07 functions of CEC 2006 in terms of NFE’s. Similarly, PC-SFLA performed better than SaDE, SFLA, CMODE at both levels of significance (5% & 10%) and par with MPDE in terms of mean function value for 17 problems taken from CEC 2006. Further, PC-SFLA is investigated on 18 problems from CEC 2010, and Wilcoxon signed ranks test is performed at a 5% level of significance. PC-SFLA performed better than SFLA and CHDE and at par with PESO. The computational results present the competency of the proposed method to solve quadratic, nonlinear, polynomial, linear, and cubic functions efficiently. The simulated results show that the proposed algorithm can solve mix integer constrained continuous optimization problem efficiently.

Background: COVID 19 created a challenging situation for many of the industries across the globe. Our primary focus is on the most affected airline industry. In this paper, the connectivity and profits of an airline company were analyzed with the theoretical approach and by proposing a novel model to increase the performance of the above parameters. In our previous work, two airlines were investigated, and it was observed that adding trips to a non-profit airline concerning profit Airlines is one of the optimal techniques to improve the performance. In this paper, multi-airlines have been considered. Methods: In the first step, identify the appropriate data sets for three airline companies and the collected data set in image format, to convert them into graph format consisting of nodes and edges. In the next step, an analysis has been conducted on data set graphs by considering the parameters like diameter, density, average degree, clustering coefficient and the shortest path generated to identify the profitable airlines. The proposed algorithms will apply either trimming or adding operations on lowprofit airline operators with respective profitable airlines. In the last step, the proposed algorithm will generate an output with better connectivity and profits. Results: In this research, other interesting findings, which are relatively contrasted to the previous findings, were observed. In the present research findings, trimming of trips to non-profit airlines concerning the profit airlines can also be an optimal solution for better performance. Discussion: In this research, complex multigraph airlines were analyzed by using the graph analytics technique for the optimum solution. Standard parameters like edges, nodes, degree, clustering, and shortest path on indigo, spice jet, and AirAsia airline systems were also compared. Conclusion: The proposed algorithm analyzes the connectivity of airline systems and applies either trimming or enhancing techniques. Indigo airlines have the best-connected network as compared to the other two models. Trimming operations will be performed on Indigo, whereas on Air Asia and spice jet, both cutting and enhancing will be served.

Aim: Today, Internet of Things (IoT) applications are extended from smart homes to ehealth, cyber security, data analytics, logistics and management of assets. There are many upcoming IOT solutions and platforms like ThingWorx, Xively, and Yaler. However, the existing eco-systems are not vibrant because of the high entry-level barrier and low potential for any stakeholder. Especially, the academic community requires a comprehensible way to create IoT services, develop their skillsets and build applications around them. In this regard, this work presents an affordable and scalable IoT eco-system with an easily programmable hardware platform, a private web server on the cloud and a user-friendly mobile application. Background: Home automation controls the devices and appliances in the home environment to increase the comfort and convenience. To design a typical immune home automation system, we need to incorporate different sensors, wireless networking and a central node that can collect data and act as a gateway for the internet connection. Objective: Delivering an IoT solution involves the use of multiple technologies that cut across traditional engineering stream boundaries - sensors, microcontrollers, wireless networking, network protocols, web programming, and mobile app development. The open challenge is to put the entire ecosystem together either through new development or through configuartion of existing components. Methods: In this work, we incorporated a suitable hardware platform that can be easily programmed. The platform is open so that new sensors and actuators can be added as per requirement. The existing web services are used to post and retrieve data from the cloud. In addition, mobile apps can be developed to make data available to the user. Results: A custom-built GISMO based IoT cloud system is developed with sensors and nodes to form an infrastructure. The framework will assure standard design that establishes a functional link between hardware, software and web applications. A private web service using HTTP server and MQTT broker is designed with access from anywhere with a public IP. The web services are coded in PHP and since it is an in-house development, the addition of new services and maintenance of existing services are easy. Conclusion: The IoT eco-system developed provides a platform for a quick out-of-the-box implementation of an IoT project. Other sensors such as a PIR sensor, an RTC module, an ultrasonic sensor, a soil moisture sensor have been interfaced to the GISMO module using the IOs brought out onto the expansion header. The GISMO thus can serve as a generic hardware platform for sensor/actuator interface in the IoT scheme. The eco-system can be replicated in other institutes and can serve as a base to implement applications like sensor-to-cloud interface, cloud-to-actuator interface, cloud-based alerts and notifications, HTTP and MQTT protocols usage.

Introduction: In IOT, very few problems were solved by using optimal n-edge connected networks; however, this is the first attempt to increase the performance of IOT by optimal n-edge. The major focus of this study was to design an optimal edge connectivity because it plays an important role in the fault tolerance of a network. These models are designed by studying standard network models like Static, Generative, and Evolving network models. Methods: In this model, initially, take a mesh network, i.e., Gmesh, which needs to be optimized. Divide Gmesh into clusters were i=1, 2..., n and n=number of possible clusters for Gmesh. Edges Ei, j which are not in Clusters, add them into list Rem. Choose the optimal networks like 1-edge, 2-edge, 3- edge, and Trimet of clusters, which are required by the user. Results: Edges count is less for 1-edge compared to 2-edge, 3-edge, and random mesh. Whereas the number of edges for Trimet of clusters lies between 1-edge and 2-edge. The diameter of 1-edge is high compared to all other networks, as the number of nodes is increased. As the diameter is used to find the shortest distance between the two most distant nodes in the network, it is low for Trimet of clusters compared to 1-edge, 2-edge, 3-edge, and mesh network. Conclusion: Proposed models 1-edge, 2-edge, 3-edge, and TGO-edge are compared with invariant network science parameters of random mesh. The 1-edge connected model has a high diameter and average shortest path length. The 2-edge connected model has a high diameter and average shortest path length. In a 3-edge connected model, diameter and average shortest path length are high, whereas it has a low density and average degree as compared to random mesh. TGO-edge model has optimal results over random mesh in all aspects except average degree.

Aims: This study aimed to propose a routing protocol for IoT-based WBANs that is reliable, power-efficient, and has a high throughput. Background: A variety of services and applications that use wireless connections such as LTE, 3G, Wi-Fi, Bluetooth, and ZigBee communication technologies have become popular in daily life as a result of the rapid development of network hardware technology. Remote medical monitoring and care is one such service. Governments have developed new policies in response to the healthcare needs of aging populations. Their goal is to create a comprehensive medical network based on new wireless technologies like sensor networks and cloud computing. Their purpose is to take the medical industry and the Internet of Things (IoT) to the next level of development. Objective: The goal of our proposed study is to improve the network nodes' ability to stay alive for a longer period of time and to maintain stability. A longer stability period contributes to high packet delivery of the node to the sink that enhances the efficiency of the WBAN network. Methods: The Wireless Body Area Network (WBAN) Internet of Things (IoT) for healthcare applications has attracted attention from various fields of study in the last few years. In this paper, we propose a routing protocol for IoT-based WBANs that is reliable, power-efficient and has a high throughput. To achieve low energy consumption and a longer network lifetime, we used a multi-hop topology. To choose a parent node or forwarder, we propose a cost function that selects a parent node with the highest residual energy and the shortest distance to sink. The residual energy parameter balances energy consumption among sensor nodes, while the distance parameter ensures packet delivery to the sink. Our key goal is to increase WBAN’s total network by raising cumulative energy usage. The residual energy parameter governs the usage of energy by the sensor nodes, while the distance parameter ensures that the packet is effectively transmitted to the sink. Result: Simulation results demonstrate that our proposed protocol is energy efficient and maximizes network stability for longer periods. Conclusion: Real-time health and activity recognition with wearable sensors is a prerequisite for assistive paradigms. In this paper, we suggest a method for routing data to WBANs. The proposed scheme employs a cost function to determine the best route to the sink. The residual energy of nodes and their distance from the sink is used to calculate the cost function. Nodes with a lower cost function value are selected as the parent node. Other nodes are children nodes and send their data to the parent node. The proposed routing scheme improves network stability time and packet delivery to sink, according to our simulation results. Path loss is also investigated in this protocol and will be considered in future work.