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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.

The existing applications that are associated with the internet produce enormous amount of data according to the requirements of diverse circumstances prevailing. This causes multitudes of challenges in examining the data and as well as in the operation of the system that relies on the cloud. To simply process and manage the execution of the tasks properly with respect to time the workflow scheduling was devised in the cloud. To further enhance the process of scheduling the named entity recognition is used. The NER-named entity recognition is an important chore of more general discipline of internet explorer application. Since the NER- problem is highly challenging in cloud paradigm. An innovative frame work termed as the MC-SVM (Multi Class- Support Vector Machine) is laid out in the paper to devise the scheduling of the workflow in the cloud paradigm. The scheduling of the tasks in the cloud delivers an arrangement setting up the work flows with the named entity recognition using the MC-SVM. The algorithm developed enhances the resource allocation process, by performing a simultaneous and dynamic allocation/reallocation of named entities to the resources of the cloud satisfying the demands in the performance and cost. The results observed on validating the proposed algorithm proves the capability of the system to manage the resources in the cloud effectively optimizing the make span and the cost.

For wireless sensor network (WSN), localization and tracking of targets are implemented extensively by means of traditional tracking algorithms like classical least-square (CLS) algorithm, extended Kalman filter (EKF) and the Bayesian algorithm. For the purpose of tracking and moving target localization of WSN, this paper proposes an improved Bayesian algorithm that combines the principles of least-square algorithm. For forming a matrix of range joint probability and using target predictive location of obtaining a sub-range probability set, an improved Bayesian algorithm is implemented. During the dormant state of the WSN testbed, an automatic update of the range joint probability matrix occurs. Further, the range probability matrix is used for the calculation and normalization of the weight of every individual measurement. Lastly, based on the weighted least-square algorithm, calculation of the target prediction position and its correction value is performed. The accuracy of positioning of the proposed algorithm is improved when compared to variational Bayes expectation maximization (VBEM), dual-factor enhanced VBAKF (EVBAKF), variational Bayesian adaptive Kalman filtering (VBAKF), the fingerprint Kalman filter (FKF), the position Kalman filter (PKF), the weighted K-nearest neighbor (WKNN) and the EKF algorithms with the values of 0.5%, 7%, 14%, 19%, 33% and 35% respectively. Along with this, when compared to Bayesian algorithm, the computation burden is reduced by the proposed algorithm by a factor of over 80%.

The decentralized network formed without any assistance of the fixed infrastructure and just by the employing the volunteering mobile or the portable devices that work in a cooperative manner along with the other devices that are clubbed in the network are inbuilt with the capacity of providing a best data conveyance facility utilizing the unicast, multicast and the broadcast methods of the routing strategies. Despite the capabilities of these type of networks, it faces difficulties in devising a strategy of routing according to the dynamic nature of the distributed network. The reactive type or the on demand routing are highly used over the proactive way of routing in the decentralized network as the performance of the former is better compared to the latter. As the mobile device employed are capable are moving from its position irrespective of time, and they behave as the route and as well as the end node or the initiating node. The important factors to be considered while forming the route is the power required for transmission and the current position of the nodes, this is quiet tedious. The study in the paper provides the importance of the on demand routing distance vector routing for the distributed network that are linked in a adhoc manner and analysis all the existing on demand distance vector routing strategies and form a new manifold route discovery scheme to improve the conventional_AODV. The method devised to improve the conventional_AODV proves to be efficient when simulated using the NS-3 on the basis of the packet delivery rate, load balancing and the energy usage.

The Smart Grid Network (SGN) is one of the fastest developing technology and has been widely used because of its high performance, in power governance system and current power supply industry. The restriction over wired infrastructure has been overcome because of Wireless Smart Grid Networks (WSGN) which offers the best solution for power management. The most commonly used wireless networking approaches used is the Cognitive Radio Network (CRN). However, when the WSGN approach used is CRN, there is a lot of concern over communication security. The major attaches faced are fended using spoofing and hamming in CRN. The proposed work using optimal power distribution in order to fence off spoofing and jamming known as Maximum Attacking Strategy. Both jamming and spoofing will be able to interfere with many signal channels in order to ensure proper functioning of the channels. The attack strategy proposed in our work uses Duo-Attack using Jamming and Spoofing to evaluate the experiment and record the observations.

Wireless sensors are those devices which sense any physical quantity. Group of sensor node working together can be termed as a wireless sensor network (WSN). The lifetime of WSN is critical and is based greatly on energy consumption for data transmission. By using the available energy efficiently, the nodes can operate for a longer time thereby increasing the network lifetime. In this paper, fuzzy-based clustering is used for clustering, which selects an optimal cluster head (CH). Fuzzy clustering is made with the help of residual energy and distance as fuzzy descriptors. However, when the network is heterogeneous, fuzzy-based clustering is found in efficient in many cases. To improve efficiency, a multidirectional routing is proposed along with fuzzy clustering. Using multidirectional routing, possible multiple paths between node and BS will be found out and the path with least hop will be selected as a routing path. The simulation results have been compared with traditional low energy adaptive clustering hierarchy, and a significant improvement in the lifetime of a node has been observed.

The recent advancements in the mobile devices and the growing needs of the mobile users led to the clubbing of mobile devices with the cloud computing developing a platform coined as mobile cloud computing. The main scope of conceiving this clubbed mobile and cloud paradigm is to direct the tasks that are resource and computationally intensive to the cloud for its execution. While the execution takes place in the cloud, the resources of the mobile users that are clubbed with the cloud remains wasted until the responses are received from the cloud. These results in the excess battery drain causing frequent recharges. To put an end to this, the paper puts forward an the multi-tier architectures in-built with the various level of cluster process for processing to properly handle the mobile device participating in the cloud , minimizing the number of idle mobile users and enhances the energy efficiency. The proposed method is validated using the network simulator-II to evince the energy balancing achieved through the proposed multi-tier architecture based MCC-(mobile cloud computing).

Cloud computing is equipped with the numerous of advantageous features to support software and utilities on the Internet of Things (IoT). Cloud-based technology is widely used when offering support for heterogeneous applications integrating specific IoT that follows various semantics. Attaching additional information to raw data sensed with the help of ontology is accomplished in semantic model. The longer distance between the cloud and IoT applications, however, is a bottleneck for vital IoT software. So the paper puts forth a semantic frame work assisted by the fog to enhance the interoperability in the internet of things. The structure put forth moves some of the cloud's commonly used semantic resources sensor networks edge and also offers an effective off-loading technique between fog – fog and fog – cloud devices to diminish total computation time of the task and the energy consumed by the nodes in the fog. The proposed method further follows an efficient mapping technique to transform the data’s sensed into a RDF-format such that it is compatible for processing. The proposed model is evaluated on the basis of delay in the service provision, the energy consumed , and the total cost of the system and further the results obtained are compared with the relevant cloud based computing models , to reveal the proficiency of the proposed.

The modern ways to empower the ecofriendly people also insists the necessity to cutting back of energy consumption. The minimizing the energy consumption would in turn reduce the rate of carbon emission, resulting in a cleaner air quality and higher standard of living by paving way for a cleaner planet. The increasing demand on power requirement is also one of the important reason for minimizing the energy consumption. The paper tries to decrease the energy usage of the street light system as the lighting systems in the street does not have an efficient way of managing and controlling the power flow in them as they are incapable of taking into consideration the prevailing demands on the intensity of light. So the paper puts forwards the idea of power management in the smart street lighting to control efficiently the power consumption by comparing the intensity of the light with the weather conditions. The artificial neural networks is used in power management of the street lighting in the proposed method. The evaluation of the method show up with the results that produce the better management of the power and the reduced power usage in street lights.

The occurrences of forest fires is not only a progressing concern in the lives of the people but also in the deterioration of the environment. Since the emergence of the internet of things, new methodologies are being continuously devised to have an early knowledge about the occurrence of the forest fires. The identifying the areas with the fire risks and the intimating it to the public would minimize the death rate caused due to these types of fire accidents. So the paper utilizes the cost- effective network that is centered on long range technology to automatically assess the degree of fire risky and forest fire rural areas and transmit to the website for public vision using the things network server. The proposed method includes many long range nodes and the sensing element to measure the atmospheric changes and the CO2 level in the environment. The long range based sensor network used in the detection of the fire risky and the forest fire areas is evaluated using the network simulator-2 and was found to provide an enhanced service quality by providing a better coverage, battery life, latency, cost and as well as efficiency.

The edge paradigm that is intended as prominent computing due to its low computation latencies faces multiple issues and challenges due to the restrictions in the computing capabilities and its resource availability especially in the huge populace scenarios. To examine the problems faced during the task scheduling when the edge computing is called up by multiple users at time, the paper puts forward the game theory approach. Utilizing the game theory strategy the paper puts forth the a novel multi-tasking scheduling in the edge computing from the user perception developing an algorithm taking into consideration the consistency of the stable tasks. The analysis of the proposed algorithm used in the allocation of the tasks is done on terms of average time consumed for the execution of the task and the waiting time. The results acquired showed that the proposed method provides a maximized throughput, minimizing the waiting time compared to the conventional methods used in optimizing the parameters of scheduling.

A novel platform of dispersed streaming is developed by the fog paradigm for the applications associated with the internet of things. The sensed information’s of the IOT plat form is collected from the edge device closer to the user from the lower plane and moved to the fog in the middle of the cloud and edge and then further pushed to the cloud at the top most plane. The information’s gathered at the lower plane often holds unanticipated values that are of no use in the application. These unanticipated or the unexpected data’s are termed as anomalies. These unexpected data’s could emerge either due to the improper edge device functioning which is usually the mobile devices, sensors or the actuators or the coincidences or purposeful attacks or due to environmental changes. The anomalies are supposed to be removed to retain the efficiency of the network and the application. The deep learning frame work developed in the paper involves the hardware techniques to detect the anomalies in the fog paradigm. The experimental analysis showed that the deep learning models are highly grander compared to the rest of the basic detection structures on the terms of the accuracy in detecting, false-alarm and elasticity.

The most vital chores of the health care is the proper attention and sharing of the information’s about the patients record maintained whenever it is needed. The failure in proper maintenance of the patient’s particulars and proper sharing of information’s when required would lead to loss of privacy and reliability resulting in the inert and severe effects on the particulars of the patients as well as their lives respectively. So securing of the health care particulars in electronic form becomes a very important necessity nowadays. The constant monitoring of the centralized storage of health records that are prone to security threats are very difficult. So the paper puts forward a block chain technology to safeguard the reliability of the information’s stored and utilizes the digital signature with authentication to protect the private information’s in the patient’s record developing a model utilizing the cloud that enables the authenticity and the reliability of the information. The model put forth in the paper was examined and compared with the traditional techniques used for storing the medical records, on the terms of reply time, along with the cost of storing and retrieving.

The routing in the wireless sensor networks has significant part in enhancing the functioning of the network as the improper routing methodologies and the routing deteriorates the energy of the sensor networks, affecting the lifespan of the network, leading to link failures and the connectivity problems. The necessity to improve the network performance is the main focus of the paper, so the paper presents the performance analysis of the evolutionary algorithms such as Genetic Algorithm, Ant lion optimization, Particle swarm optimization and Ant colony optimization and evaluates the routes obtained using the fuzzy Petri Net model, to find out the optimal route for the wireless sensor networks. The simulation through the python in terms of the some resource validates the optimal path in terms of energy, network life time and the packet delivery ratio.

The maintenance of Wind farms located in the off shores are highly challenging as the preservation and the operational availability are tedious. The insufficiency in the traditional system for monitoring the conditions of the wind turbines located in the off shores has made this topic an attractive area of research. The proposed method in the paper utilizes the multiple intelligent smart sensors to detect the impairments in the structure of the wind turbines. The sensors utilized in the proposed method estimates the damage index in the wind turbine by engaging the embedded software. The effectiveness of the proposed method was measured by applying it to the off shore wind turbines. The results obtained evinces the minimized cost and the down time in maintaining the off shore wind farms.

Nowadays to increase the efficiency, consistency and the quality of the organizations and to further extend the business world wide the digitization is followed in processing, storing and conveying the information. This in turn has also caused huge set of data flow paving way for the data recovery services. The cloud computing with the massive storage capabilities have become a predominantly used paradigm for data storage and recovery due to its on demand network access, elasticity, flexibility and pay as you go. Moreover to secure the information that is stored the information’s are fragmented and stored. However this fragmentation process often occurs in the form of dispersed and scattered packages lacking proper order heightening the time and minimizing the efficiency of the recovery and information collection. To bring down the restoration time and enhance its efficiency the proposed method in the paper tries to reduce the fragmentation by minimizing the number of dispersed and scattered packages for this the paper utilizes the Hybridized Historical aware algorithm (HHAR) along with the cache aware filter to gather the historical information’s associated with the back-up system and the identify the out of order containers respectively. Further the every data package is protected applying the advanced encryption standard by producing a key to authenticate the access of the data. The proposed model is simulated using the network simulator-II and the results obtained shows that the recovery time is enhanced by 95% and the restore performance is improved by 94.3%.

The vehicular network that scopes to reach the internet access to make use of the available service on road along with the improvement in the safety and convenience while travelling has become a very popular area of research as they lay as the foundation for the intelligent transportation system. But due to the high mobility of the vehicles the network topology remains dynamic and the connectivity between the vehicles keep on changing reducing the life expectancy of the links and failures in the delivery of the services. So this paves way for the entailment of the more effective routing protocol that would heighten the vehicular network quality of service. The proposed method in the paper puts forth the modified Ant colony optimization that integrates the clustering technique to frame optimal shortest route for the internet access in the vehicular network to manage its mobility and the topology changes. The process of optimal route selection taking into consideration the mobility and the dynamic topology is evaluated using the network simulator tool to prove the capabilities of the proposed method on the grounds of bandwidth utilization throughput delay and link life expectancy.

The internet connectivity extended by the internet of things to all the tangible things lying around and used by us in our day today life has convert the devices into smart objects and led to huge set of data generation that holds both the valuable and invaluable information. In order to perfectly handle the information’s generated and mine the valuables from them, the analytics are engaged by the cloud. To have a timely access, most probably the fog services are preferred than the cloud as they bring down the service of the cloud to the user edge and reduces the time complexity in accessing of the information. So the paper proposes the big data analytics for the fog assisted health care application to effectively handle the health information’s diagnosed for the aged persons. The proposed model is simulated using the IFogSim toolkit to examine the performance fogassisted smart healthcare application.

The smart grid developments turning out to a promising technology for the two way communication of the electricity and the information are becoming more prominent in the present scenario due to the increasing demands in the power requirements. The smart grid utilizing the modern communication and the computing infrastructure makes this possible. This utilization of the modern communication and the computing systems make them even prone to the vulnerabilities, mishandlings and the miscommunications and necessary for a complex frame work for managing of the power distribution and generation to make them sustainable. So the paper proposes the cloud computing for the proper management and the protection of the smart grids to make them more secure against threats and sustainable by reducing the Co2 emission by the perfect management. The validation of the smart grid frame work using cloud computing by monitoring the power distribution, management and the security achieved over multiple usages show the enhanced power management and the security services provided by the cloud for the smart grid.

Robots are used in a wide extent in automobile manufacturing industry. This system adds to the safety and flexibility of the stages of production. These robots protects the employees from dangerous, difficult and dirty tasks, improve quality, relieve bottlenecks, and improve capacity. In this paper, we propose a novel pick and place robot for sensing, control and manufacture in automobile industry. The payload and mean error rate are analysed and the performance of the robot is analysed and optimized. This technology is more affordable and can be installed for speeding up the manufacturing process and improve the production rate.

The wireless network virtualization for the cloud and the enterprise information system would enable maximum utilization of resources, causing an enhancement in the enterprise information system efficiency, by sharing the infrastructure among multiple applications or service provisioning’s. The physical wireless structure of the network and the radio resources are split and shared among different applications by abstraction and isolation to provide a cost effective and efficient handling of the enormous amount of the services. Further the virtualization also provides a migration to the latest products and the technologies at ease. In spite of all these potentials there are several challenges that are to be handled before embedding the virtual wireless network over the physical infrastructure. The paper focuses on developing a virtual wireless network that are sustainable(VSWN) for the cloud and enterprise information system involving the software defined networks that includes network function virtualizing and deep packet inspection for improving the agility and the flexibility of the Cloud enterprise information system. The further validation of the proposed model using the network simulator shows throughput enhancements with cost efficiency when compared to the other wireless network management of cloud.

Mobile computing gained much attention in current research arena due to the enhancement of services such as mobile learning, health care monitoring and e commerce. The experience and satisfaction which increases the user count in millions on every day. Advantage of using cloud computing for essential multipath communication creates path for multi homing architecture and multi path data transmission. While the technology develops the security breaches are also increases due to the increased number of users and loads. System has to be considered as stable and safe only if breaches are reduced. Based on the applications services has to be enhanced with reliability, less faults, and robustness makes end to end communication much better for the future. The proposed research work describes a secure cloud computing mobile environment which provides meritorious service to the users. Experimental results highlights the proposed model has 98.5% of success ratio on providing secure communication services to the users via cloud computing applications.