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The introduction of Wi-Fi into the residences is creating a biological havoc among humans. A lot of research has been evolved and presented depicting the various imperfections caused by the radiation of Wi-Fi. To overcome this LiFi technology may be used for indoor communication instead of Wi-Fi. LiFi communication needs line of sight for communication. LiFi transfers the information through visible light. Light cannot travel through opaque objects. The various properties of light like Reflection, Refraction, scattering effects on visible light will lead to data loss. Hence LiFi is preferably used indoors. This article discusses on the effects of biological degradation caused by Wi-Fi, Bluetooth etc. in short, this article enlists the effects of radio waves in accordance with the psychological changes caused in mankind. This in turn will lead to build a system which will also ensure the safety of the ecosystem for the development of mankind.

Numerous mission-critical applications in the field of information technology will rely on fifth generation (5G) networks in the future. The 5G is projected to bring new technological improvements and innovation. Software-Defined Networking (SDN) is the present backbone of 5G. Because of the specific needs of each application that may be met by network slicing, 5G can provide this feature. In comparison to 4G Long-Term Evolution (LTE) and preceded generations, 5G is more adaptable and scalable. However, considerable advances in 5G cyber security are required to minimise the rising threats of hacking. Both the network and the devices linked to 5G are subjected to security problems. 5G communication networks' weaknesses may create many dangerous unknown attacks. However, it depends on the 5G privacy and security key protocol. In addition, several dangerous attacks may be combined to provide a wide range of attack options for hackers. For the 5G communication network, this article provides a complete framework for security analysis. The findings of this study might lead to unique 5G communication exploits. Moreover, artificial intelligence learning has been recently used to create and analyse attack graphs for software-defined and virtualized 5G communication networks.

While the phrase Big Data analytics is not only applicable for a certain realm of technology, diverse business segments like banking also benefit from the use of advanced mathematical and statistical models like predictive analysis, artificial intelligence, and data mining. If it is a query that is data volume generated in a bank or any financial institution is huge, it is absolutely a yes. As per the recent survey, it is observed that banks worldwide aren't just concentrating on improving the asset quality and fulfilling regulatory compliance but on the lookout for a digital convergence strategy to reach customers effectively in delivering services and products. As most of the data generated in internet banking and ATM transactions are unstructured accounting around for 2.5 quintillion bytes useful for fraud detection, risk management, and customer satisfaction, the use of trending Big Data Analytics methodology can be used to tackle the challenges and competition among banks. There are surplus advantages of Big Data strategy in the banking field and in this paper, we have made an analysis over Big Data Analytics on banking applications and their related concepts.

In recent years, both developed and developing countries have witnessed an increase in the number of traffic accidents. Aside from a significant rise in the overall number of on-road commercial and non-commercial vehicles, advancements in transportation infrastructure and on-road technologies may result in road accidents, which generally result in high mortality. More than half of these fatalities are the result of delayed response by medical and rescue personnel. If an accident site receives quick medical treatment, an accident victim's chances of survival may improve considerably. Based on the IoT-based multiple-level vehicle environment, this study proposes a low-cost accident detection and alarm system. Vehicles are equipped with a "Black Box" board unit and an accident location identification module for the Global Positioning System (GPS), in addition to mechanical sensors (accelerometer, gyroscope) for accurate accident detection. This study has evaluated the proposed system with average packet delivery ratio (PDR) vs. relay nodes. Our simulation results have evaluated the evolution of relay nodes in the mobile / sensor node through internet gateway. It has also been demonstrated that the packet delivery ratio is inversely related to the incremental number of relay nodes.

The concept of interconnecting smart vehicles and advancements in automotive automation leads to beneficial outcomes, such as a reduction in road fatalities and congestion. However, including a chain of automation in the attack surface will expand the attack surface and expose the security of automobiles to malicious infiltration. The proposed methodology provides access to specific users while restricting the third party requests. Moreover, it also makes use of data exchange that takes place between the roadside units and vehicle to track the vehicle status without compromising the in-vehicle network. To ensure a valid and authentic communication, vehicles with a proper and verifiable record will only be allowed to exchange messages in the blockchain network. Using qualitative arguments, we have identified that the proposed work is resilient to identified attacks. Similarly, quantitative experimentation indicates that this methodology shows a storage size compatibility and suitable response time in realistic scenarios. Simulation results indicate that, the proposed work shows positive results to secure vehicular networks, vehicular forensics and trust management.

The use of effective clustering algorithms can extend the life of wireless sensor networks. In general, the clustering algorithm should have the ability to reduce the energy requirement. Henceforth, this paper presents the particle swarm optimization (PSO) technique for the selection of master cluster head (MCH) and surrogate cluster head (SCH) and an efficient multidirectional routing is proposed for packet transmission. To reduce the energy consumption between different MCHs, PSO algorithm utilizes clustering method with different parameters. During the sudden failure of MCH, the communication between nodes in the network is maintained by choosing an extra cluster head (CH) called SCH. Using multidirectional routing technique, the packets will be transmitted through nodes other than the selected CH if it is necessary to reduce the routing distance. The experimental result shows that, the proposed method works well when compared to traditional LEACH algorithm.

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.

This research article investigates effective energy protocols for wireless sensor networks (WSN). The newly proposed taxonomic classification and comparison provides the following protocol categories: latency and efficient routing based on energy and hop selection in network and its architecture, communication sensor network, networking structure, procedure functioning, sending and receiving round mode, and route setting. This research work has examined each class to discuss and compare the different parameters of its representative routing protocols (mechanisms, advantages, disadvantages) based on the energy efficient rate along with delivery delay and network time. The simulation results on the NS-simulator of various protocols show that, the routing task has to be built upon different intelligent technologies to improve the network life and ensures better sensory area coverage.

The hybrid system configuration is used for meeting the thermal and electrical load demands of an off-grid network simultaneously with the model proposed in this paper. Li-ion battery, Micro Gas Turbine (MGT), wind turbine and solar photovoltaic configurations are analyzed. Hybrid Optimization of Multiple Electric Renewables (HOMER) software is used for estimating utilization of various strategies for power management, recovered waste heat and excess energy in the model. The heating demand is met and examined by the thermal load controller with and without the options of waste heat recovery. The hybrid system hardware components are sized, compared and analyzed based on cyclic charging (CC) and load following (LF) dispatch strategies. Various electrical to thermal load ratio are considered for examining the system performance. Various uncertainties and their effects are reported on comparison of grid-connected and stand-alone options. The hardware components are reduced in size thereby appreciable cost benefits are observed in the results. In the optimized hybrid system, the renewable energy fraction is increased causing high renewable penetrations and the CO2 emission is reduced by a large value. For all the configurations analyzed, several environmental and cost benefits are offered by the CC strategy.

IoT objects that have a resource constrained nature resulting in a number of attacks in the routing protocol for lossy networks and low-power networks. RPL is very vulnerable to selfish behaviours and internal attacks though they are built with encryption protection to secure messages. To address this vulnerability, in this paper, we propose a novel trustworthiness methodology based on metric for incorporating trust evaluation, enhancing the robustness of security mechanism. Simulation results indicate that the proposed work is efficient in terms of throughput, nodes’ rank changes, energy consumption and packet delivery ratio. Moreover, using mathematical modelling, it has been observed that this methodology meets the demands of loop-freeness, optimality and consistency. This shows that this metic has both monotonicity and isotonicity requirements to enable the routing protocol. Incorporating the concepts of game theory, we can use this technique as a strategy to iterate Prisoner’s Dilemma. Both evolutionary simulation and mathematical analysis indicate that the proposed metric-based routing protocol is an efficient technique in promoting evolution and stability of the IoT network.

In this digital era, a huge amount of money had been laundered via digital frauds, which mainly occur in the timeframe of electronic payment transaction made by first-time credit/debit card users. Currently, Finance organizations are facing several fraud attempts and it likely happens due to the current infrastructure, which only has an older database.. The current infrastructure diminishes the working environment of any finance organization sector with frequent fraud attempts. In this perspective, the roposed research article provides an overview for the development of an automated prevention system for any finance organization to protect it from any fraudulent attacks. The proposed automated case management system is used to monitor the expenses of the behavior study of users by avoiding the undesirable contact. The proposed research work develops a new management procedure to prevent the occurrence of electronic fraud in any finance organization. The existing procedure can predict digital fraud with an old updated database. This creates disaster and destructive analysis of the finance segment in their procedure. The cyber fraud phenomenon prediction is used to predict the fraud attempt with content-based analysis. The lack of resources is one of the enormous challenges in the digital fraud identification domain. The proposed scheme addresses to integrate all safety techniques to safeguard the stakeholders and finance institutions from cyber-attacks.

The early detection or identification of emotional states plays a vital role in today’s world, where the number of internet and social media users are increasing at an unprecedented rate. The psychiatric disorders are very dangerous and it is affecting 300 million people. This is the motivation behind addressing the research problem with novel research articles. Early detection is the key to reduce the number affected individuals due to this disorder potentially. This research study performs an analysis of a standard dataset obtained from online social media, where detection can be based on a machine learning algorithm. This research article proposes a machine-learning algorithm to develop an early prediction from their depression mode, which can be protected from mental illness and suicide state of affairs. The combination of support vector machine and Naïve Bayes algorithm will be used to provide a good accuracy level. The classification model contains many cumulative distribution parameters, which should be classified and identified dynamically. This identification or detection is the features obtained from textual, semantic, and writing content. The evaluation of various Deep Learning (DL) approaches is identifying the early prediction. The sensitivity and accuracy of the method are providing the significant conditions for early detection and late detection. The proposed hybrid method provides better results for early detection and retained good sensitivity and better accuracy of existing methods. The study from results can help to develop a new idea to develop a early prediction of various emotions of people present in social media.

Internet users are largely threatened by abuse and manipulation of several automated chat service programs called as chat bots. Malware and spam is distributed by the popular chat networks using chat bots. The commercial chat network is surveyed in this paper with a series of measurements. A series of 15 advanced to simple chatbots are used for this purpose. When compared to the bot behavior, the complexity of human behavior is high. A classification system is proposed for accurate distinguishing between human user and chatbots based on the measurements obtained from the study. Naïve Bayes Classifier and entropy classifier are used for the purpose of classification. Chat bot detection is performed with improved efficiency and accuracy using these classifiers. The speed of Naïve Bayes Classifier and accuracy of entropy classifier compliments each other in the process of detection of chat bots. The improved efficiency of the proposed system is proved by testing and comparison with the existing schemes.

In the big data era, there is a high requirement for data storage and processing. The conventional approach faces a great challenge, and de-duplication is an excellent approach to reduce the storage space and computational time. Many existing approaches take much time to pinpoint the similar data. MapReduce de-duplication system is proposed to attain high duplication ratio. MapReduce is the parallel processing approach that helps to process large number of files in less time. The proposed system uses two threshold two divisor with switch algorithm for chunking. Switch is the average parameter used by TTTD-S to minimize the chunk size variance. Hashing using SHA-3 and fractal tree indexing is used here. In fractal index tree, read and write takes place at the same time. Data size after de-duplication, de-duplication ratio, throughput, hash time, chunk time, and de-duplication time are the parameters used. The performance of the system is tested by college scorecard and ZCTA dataset. The experimental results show that the proposed system can lessen the duplicity and processing time.

The speed of internet has increased dramatically with the introduction of 4G and 5G promises an even greater transmission rate with coverage outdoors and indoors in smart cities. This indicates that the introduction of 5G might result in replacing the Wi-Fi that is being currently used for applications such as geo-location using continuous radio coverage there by initiating the involvement of IoT in all devices that are used. The introduction of Wi-Fi 6 is already underway for applications that work with IoT, smart city applications will still require 5G to provide internet services using Big Data to reduce the requirement of mobile networks and additional private network infrastructure. However, as the network access begins to expand, it also introduces the risk of cyber security with the enhanced connectivity in the networking. Additional digital targets will be given to the cyber attackers and independent services will also be sharing access channel infrastructure between mobile and wireless network. In order to address these issues, we have introduced a random neural network blockchain technology that can be used to strengthen cybersecurity in many applications. Here the identity of the user is maintained as a secret while the information is codified using neural weights. However, when a cyber security breach occurs, the attacker will be easily tracked by mining the confidential identity. Thus a reliable and decentralized means of authentication method is proposed in this work. The results thus obtained are validated and shows that the introduction of the random neural network using blockchain improves connectivity, decentralized user access and cyber security resilience.

Various industrial, scientific and commercial processes involve wireless mesh networks in the recent days. These technologies improve communication technology to a large extent which has led to an increase in utilization of these systems in various fields. In application with intense and complex data flow, improving the quality of service (QoS) has been a challenge and a focus of research leading to more advanced wireless communication systems. This paper provides a novel optimization algorithm for improving the QoS in hybrid wireless networks while preventing malware and routing attacks. The concept of QoS and hybrid wireless networks are examined at the initial stage. Further, the algorithm for optimizing the service quality in the network is proposed accordingly. The ability of data transfer is benefited by data packets in this algorithm. Load distribution is performed such that overcrowding is prevented and information routing is done efficiently though the nodes. Delay or routing is created and control messages are sent for withholding data when certain nodes are overcrowded. This reduces the delay created by overcrowding by 50% while maintaining the permittivity.

One of the most promising fields of vehicular fog computing (VFC) is by using it as a means to build a parking lot allotment system. Integrating this with parked vehicle assistance system, we can use VFC to its full potential. In this paper, we propose an automatic vehicle allocation system that guides the vehicles on the move to their allotted parking slot, making it simpler to park. Moreover, further add-ons such as monetary rewards are given means to compensate for the cost of service. The VFC is exploited to ensure that there is no delay in parking allotment. Moreover, the payment rule proposed by the system ensures budget balance, individual rationality and incentive compatibility. The performance output is measured using simulation results and it is observed that there is a significant improvement and the same is recorded.

Cyber threat is a major issue that has been terrorizing the computing work. A typical cyber-physical system is crucial in ensuring a safe and secure architecture of a sustainable computing ecosystem. Cyber Threat Intelligence (CTI) is a new methodology that is used to address some of the existing cyber threats and ensure a more secure environment for communication. Data credibility and reliability plays a vital role in increasing the potential of a typical CTI and the data collected for this purpose is said to be highly reliable. In this paper, we have introduced a CTI system using blockchain to tackle the issues of sustainability, scalability, privacy and reliability. This novel approach is capable of measuring organizations contributions, reducing network load, creating a reliable dataset and collecting CTI data with multiple feeds. We have testing various parameters to determine the efficiency of the proposed methodology. Experimental results show that when compared to other methodologies, we can save upto 20% of storage space using the proposed methodology.

Large amount of data is generated each second around the world. Along with this technology is evolving each day to handle and store such enormous data in an efficient way. Even with the leap in technology, providing storage space for the gigantic data generated per second globally poses a conundrum. One of the main problems, which the storage server or data center are facing is data redundancy. Same data or slightly modified data is stored in the same storage server repeatedly. Thus, the same data is occupying more space due to its multiple copies. To overcome this issue, Data deduplication can be done. Existing deduplication techniques lacks in identifying the data in an efficient way. In our proposed method we are using a mixed mode analytical architecture to address the data deduplication. For that, three level of mapping is introduced. Each level deals with various aspects of the data and the operations carried out to get unique sets of data into the cloud server. The point of focus is effectively solving deduplication to rule out the duplicated data and optimizing the data storage in the cloud server.

Water is one of the basic resource need for every human in the world. The improper management of water storage system can lead a human life to any extent. As a result of technology development, the proposed model is developed to manage the water storage systems like dam and lake through remotely placed sensor signals. The sensors which are placed in the storage places gives the strength and storage capacity of the dam and lakes. Similarly the sensors which are placed at the sender dam or lake are used to predict the incoming water level to the receiver lake. This improves the prediction rate of flood in the river paths and this prediction allows the incoming dam to send off some waters outside to allocate some space for incoming waters. The data which are generated by the connected dams are stored in a cloud space for analyzing the water flow management. The sensors connected in a lake or dam is connected with IoT platform to avoid wire connections. Hence this model avoids sudden floods during rainy conditions and it ensures the physical strength of the lake and dam by continuous monitoring process.

Internet of things (IoT) is a promising solution to connect and access every device through internet. Every day the device count increases with large diversity in shape, size, usage and complexity. Since IoT drive the world and changes people lives with its wide range of services and applications. However, IoT provides numerous services through applications, it faces severe security issues and vulnerable to attacks such as sinkhole attack, eaves dropping, denial of service attacks, etc., Intrusion detection system is used to detect such attacks when the network security is breached. This research work proposed an intrusion detection system for IoT network and detect different types of attacks based on hybrid convolutional neural network model. Proposed model is suitable for wide range of IoT applications. Proposed research work is validated and compared with conventional machine learning and deep learning model. Experimental result demonstrate that proposed hybrid model is more sensitive to attacks in the IoT network.

Internet of Things (IoT) is a collection of networks, objects, devices that are connected using the Internet. It interacts with the environment both internally and externally. IoT senses the environment and acts accordingly. It gives the environment with advanced techniques and thus improves the quality of mankind. IoT allows the devices to communicate with each other either physically or virtually. With the help of IoT, the environment becomes smarter and can be connected with any device at any time. Nowadays people want to communicate with all the non-living things via the Internet. IoT is used to collect and analyze the data from various actuators and sensors then it is passed it over smartphones or computers through a wireless connection. There evolve the applications that use IoT and in each of the applications IoT plays a vital role in enhancing the living standards which includes home appliances, Smart energy, environment, and industries, etc. and makes a friendly environment. To make it more professional, IoT is used in the supply chain, transportation, logistics, automation, and remote monitoring. IoT delivers a change in the quality of individual life and it is widely distributed among the network of devices and provides a new ecosystem for the development of applications by understanding the development of the market in the early stages. IoT has the capabilities of delivering the services and demands of the people in this fast-moving world. This paper presents the survey of IoT smart systems applications and usages of the IoT environment.

Intelligent transport system is one of thriving research domain and in particular multimedia vehicular networks gains more attention. The deployment of multimedia services in vehicle ad-hoc networks (VANETs) provides real time support to users and provides better traffic management with high safety measures. Most of the vehicles has multimedia applications such as weather sensors, real time map applications etc., and it requires adequate resources to process. Resource allocation to a static network is simple and various routing models are evolved. In case of dynamic network like VANETs, routing is still in progress in order to obtain a better model. Routing directly related to quality of services of network and user, it is essential to develop a better dynamic routing strategy for multimedia wireless networks. The proposed work aims to provide an optimized dynamic routing strategy for multimedia networks. For efficient routing, k-means clustering is used to organize the clusters and exchanges the routing information and inverted ant colony optimization is used to obtain the optimal path for multimedia access. Proposed model is experimentally verified and compared to conventional ant colony optimization and grey wolf optimization in terms of parameters such as end to end delay, throughput, target used, average computation time and efficiency.

Software testing is an important process in product development of software companies to ensure the product quality. The developed application must satisfy the customer needs and meets the industrial standards without any compromise. Thus, verification of products through manual test engineer and validate the product once it meets all the necessary requirements. The issues in manual testing is its high computation and analysis time, accuracy and reliability. In order to reduce the issues in manual testing automatic testing is introduced. Though it is also an application which requires parameters to test the given product. Efficient tuning of the product could be achieved through automatic testing. This proposed research work provides an optimized automatic software testing model through differential evolution and ant colony optimization as a hybrid model to achieve improved accuracy and reliability in software testing. Conventional models like artificial neural network and particle swarm optimization are compared with proposed model to validate the reliability of proposed model.

In the present research era, machine learning is an important and unavoidable zone where it provides better solutions to various domains. In particular deep learning is one of the cost efficient, effective supervised learning model, which can be applied to various complicated issues. Since deep learning has various illustrative features and it doesn’t depend on any limited learning methods which helps to obtain better solutions. As deep learning has significant performance and advancements it is widely used in various applications like image classification, face recognition, visual recognition, language processing, speech recognition, object detection and various science, business analysis, etc., This survey work mainly provides an insight about deep learning through an intensive analysis of deep learning architectures and its characteristics along with its limitations. Also, this research work analyses recent trends in deep learning through various literatures to explore the present evolution in deep learning models.

Computer based algorithms are used for monitoring and controlling a computers where human, physical, digital and analog interaction occurs. This scheme termed as cyber-physical systems (CPS) is being deployed in a wide array of applications due to its easy deployment feature and connectivity. CPS based on cloud computing technology offers wider application range with enormous amount of storage and computing resources. However, despite the wide application and deployment of CPS in combining the key technologies like big data analytics, cloud computing and IoT, its energy consumption is large. Optimization of this energy is a major field of research and is of due importance. For this purpose, in cloud environment, virtual machines (VMs) are used for hosting the applications and the resources are managed thereby optimizing the energy consumption. The security issues and the quality of service (QoS) requirements of the system is also met by optimizing the system design. The scheduling issue of the system is addressed by implementation of efficient memory-aware scheduling strategy and algorithms. The proposed technique is tested for performance and the results of simulation is presented.

Wireless Sensor Network (WSN) has drawn high attention in the recent years both in the industrial and the research frontier. It integrates multiple application-based services in areas like disaster management, military usage, smart city, habitat monitoring, healthcare etc. It uses the term Internet of Things (IoT) when providing different services and applications. Similarly, when this network associates with industrial revolution, it is commonly addressed as the Industrial Internet of Things (IIoT). Using IPv6, it gives high scalability using the strength of Internet users. Hence it is crucial to ensure efficiency of the protocols and working modules. This will determine the battery lifetime when it is deployed, connected to the battery’s draining short-term. Based on this consideration, many protocols are tested using IIoT in WSN. For this paper we have chosen G.9959 as the protocol and a comparison is drawn with the IPv6 packet delivery rate. Experimental results indicate that the proposed work performs more efficiently when compared to other previous schemes.

The inefficiency in accessing the tenancy status of the parking slots is mainly due to the results of irregular parking regulation/management. The effective parking management enables to avoid unwanted traffic jams and unnecessary fuel wastages. So an efficient parking is necessary for the developing smart cities that aim for a better way of living. So the paper uses the Mobile-Net Classifier to sort out the tenancy state of the parking slots in the cities to assist a proper parking regulation with better proficiency and perfect management. The Mobile-Net classifiers are a sort of light weight deep neural networks that help in identifying the parking slots available accurately based on the image mined from the live camera that feeds the status of the parking lot continuously. The mechanism put forth detects the patches of images form the live recorded video perfectly and determines the vacant slots. The laid out model was applied in an outdoor parking area to determine the systems effective working on the terms of detection accuracy, false positive and the false negative, true positive and the true negative rates along with the average speed of the in identifying the parking slots. As test case, two different mobile-Net network set up were compared to evaluate the swift ness in processing and the perfectness in detecting.