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In today's scenario of India most of the youth is preparing for some competitive exam. If we think behind 15–20 years the number of candidates appearing for competitive exams were in thousands but as the population is increasing exponentially in India day by day the number of candidates is increasing in lacks. We can observe by daily newspapers that most of the competitive exams are facing paper leak problems. Although online examination systems are more effective and secure as compared to offline examination systems because it's not easy to open the question paper before the time starts. On the other hand there are also various consequences where the examination process can be hacked online. But the main issue with online examination process is to lack of resources to conduct parallel examination of millions of candidates, lack of techno enabled exam centers in small cities etc. Our focus is to propose a system in which offline examination can be conducted at most of the govt. and private centers in metro cities as well as small techno backward cities with reduced possibility to leak the paper before commencement of examination. [ABSTRACT FROM AUTHOR]

Centralized access control systems are unsuitable for IoT due to their resource-constrained, heterogeneous, and dynamic nature. Blockchain-assisted decentralized access control systems exist for IoT, but those approaches are tokenization-based. In some cases, IoT devices are not part of the blockchain network due to which they cannot interact with the access control system directly. Instead, they need a trusted admin, management hub, or a fog node for permissions verification and access to resources. This paper presents a smart contract and blockchain-assisted framework for the access control systems in the IoT enterprise environment, called ACS-IoT. In the proposed framework, resource-constrained IoT devices belong to the blockchain network. Therefore, these devices can directly access the permitted resources without any centrally administered authority and management hub's verification. We used smart contract and Ethereum blockchain for the new framework. Smart contract allows automated enforcement of access policies and serves as an autonomous agent running exactly as programmed. The proposed framework is validated through implementation of the proof of concept, and implemented prototype is deployed and tested on the Ethereum test network. The obtained results confirm that usage of blockchain and smart contract can be used as access management technology in the IoT enterprise environment. [ABSTRACT FROM AUTHOR]

This paper presents folded cascode Low Noise Amplifier (LNA) designed for Wireless Local Area Network (WLAN) applications. The proposed LNA uses complementary current reuse technique and diode connected forward body biasing technique to lower the power consumption and boost the transconductance for gain enhancement. Moreover, post linearization is done by P-MOS (Metal Oxide Semiconductor) Inter Modulation Distortion (IMD) sinker which reduces inter modulation distortion present at the input and achieves better linearity of the LNA. Under cadence virtuoso environment, using 180 nm Complementary Metal Oxide Semiconductor (CMOS) technology, the designed LNA exhibit high gain (S21) of 25.4 dB followed by noise figure of 1.75 dB at a reduced supply voltage of 0.6 V. The other important parameters including reflection coefficient (S11) − 12 dB, power dissipation (Pdc) 3.23 mW and Third order Input Intercept Point (IIP3) − 4.43 dBm are also achieved. This demonstrates the applicability of proposed LNA for wireless applications functioning in 5.2 GHz. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the spectral efficiency of hybrid precoding (HP) is too low in the current millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system, this paper proposes a digital joint equivalent channel hybrid precoding algorithm. Based on the introduction of digital encoding matrix iteration. First, the objective function is expanded to obtain the relation equation, and the pseudo-inverse iterative function of the analog encoder is derived by using the pseudo-inverse method, which solves the problem of greatly increasing the amount of computation caused by the lack of rank of the digital encoding matrix, and reduces the overall complexity of hybrid precoding. Secondly, the analog coding matrix and the millimeter-wave sparse channel matrix are combined into an equivalent channel, and then the equivalent channel is subjected to Singular Value Decomposition (SVD) to obtain a digital coding matrix, and then the derived pseudo-inverse iterative function is used to iteratively regenerates the simulated encoding matrix. The simulation results show that the proposed algorithm improves the system spectral efficiency by 10–20% compared with other algorithms, and the stability is also improved. [ABSTRACT FROM AUTHOR]

This paper proposes using a WiFi-based identification system, Wi-ID, to identify users from their unique hand gestures. Hand gestures from the popular game rock-paper-scissors are utilized for the system's user authentication commands. The whole feature of three hand gestures is extracted instead of the single gesture feature extracted by the existing methods. Dynamic time warping (DTW) is utilized to analyze the amplitude information in the time domain based on linear discriminant analysis (LDA), while extract amplitude kurtosis (AP-KU) and shape skewness (SP-SK) are utilized to analyze the Wi-Fi signals energy distribution in the frequency domain. Based on the contributions of the extracted features, the random forests algorithm is utilized for weight inputs in the long short-term memory (LSTM) network. The experiment is conducted on a computer installed with an Intel 5300 wireless networking card to evaluate the effectiveness and robustness of the Wi-ID system. The experiment results showed the accuracy of the proposed Wi-ID system has a personal differentiation accuracy rate over 92%, and with an average accuracy of 96%. Authorized persons who performed incomplete hand gestures are identified with an accuracy of 92% and hostile intruders can be identified with a probability of 90%. Such performance demonstrates that the Wi-ID system achieved the aim of user authentication. [ABSTRACT FROM AUTHOR]

The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT). However, a large portion of the household devices and appliances already in use, are not IoT enabled, and therefore, requires their default control mechanisms for the devices to operate. This paper proposes a smart appliance controller that uses Augmented Reality, MQTT, and other up-to-date platforms to control aftermarket home appliances in the most efficient manner. The proposed work integrates mobile AR with IoT, to control household appliances, with the help of infrared (IR) signals. The characteristics of the system are evaluated through a series of tests and performance measures. The results of the test highlight the quick response time of MQTT for the implementation of a Home Automation System, when compared to the request–reply protocol: CoAP (4 times as fast). [ABSTRACT FROM AUTHOR]

In the paper by Dwivedi et al., a compact size wideband circularly polarized 2-port MIMO antenna is designed for 5G with (<= - 10 dB) impedance bandwidth of 900 MHz in the frequency band 3.3-4.2 GHz and 100% 3-dB axial ratio bandwidth. 5G wireless standards will play a vital role in ensuring security, enabling automation on many fronts such as connected vehicles, and facilitating data rates that are orders of magnitude greater than 4G LTE and thereby enabling rapid access to information. [Extracted from the article]

An efficient malicious node detection system in CR networks is proposed in this paper. This proposed system contains features extraction process and optimization algorithm with soft computing framework. This proposed methodology stated in this paper initially abstracts the features of each individual node in CR network and these individual features are now getting optimized using feed forward radial neural network algorithm, which differentiates each individual node in CR network into either normal or malicious/faulty. This paper analyzes the performance of this proposed work with respect to malicious node detection rate, throughput and latency. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a concept that has attracted significant attention since the emergence of wireless technology. The knowledge diffusion of IoT takes place when an individual disseminates his knowledge of IoT to the persons to whom he is directly connected, and knowledge creation arises when the persons receive new knowledge of IoT, which is combined with their existing knowledge. In the current literature, several efforts have been devoted to summarising previous studies on IoT. However, the rapid development of IoT research necessitates examining the knowledge diffusion routes in the IoT domain by applying the main path analysis (MPA). It is crucial to update prior IoT studies and revisit the knowledge evolution and future research directions in this domain. Therefore, this paper adopts the keyword co-occurrence network and MPA to identify the research hotspots and study the historical development of the IoT domain based on 27,425 papers collected from the Web of Science from 1970 to 2020. The results show that IoT research is focused on IoT applications for smart cities, wireless networks, blockchain technology, computing technologies, and AI technologies. The findings from the MPA address the need to explore the knowledge evolution in the IoT domain. They also provide a valuable guide to disseminate the knowledge of IoT among researchers and practitioners, assisting them to understand the history, present and future trends of IoT development and implementation. [ABSTRACT FROM AUTHOR]

With the proposed generalized, hitless, and simple control strategy, the delay tunability and delay bandwidth product of the delay line increases with the increasing order of the Mach-Zehnder-based stage. To support end-to-end wireless-wired deterministic communication in these private, professional environments, it is necessary to introduce TSN features to the wireless network segments. The article demonstrates the multi-stage Mach-Zehnder-based integrated continuously tunable optical delay line with an extensive tunability range increases transmission bandwidth and the vast delay-bandwidth product. [Extracted from the article]

In the software maintenance and development process, software bug detection is an essential problem because it is related to complete software success. It is recommended to begin anticipating defects at the early stages of creation rather than during the assessment process due to the high expense of fixing the found bugs. The early stage software bug detection is used to enhance software efficiency, reliability, and software quality. Nevertheless, creating a reliable bug-forecasting system is a difficult challenge. Therefore, in this paper, an efficient, software bug forecast is developed. The presented technique consists of three stages namely, pre-processing, feature selection, and bug prediction. At first, the input datasets are pre-processed to eliminate the identical data from the dataset. After the pre-processing, the important features are selected using an adaptive artificial jelly optimization algorithm (A2JO) to eliminate the possibility of overfitting and reduce the complexity. Finally, the selected features are given to the long short-term memory (LSTM) classifier to predict whether the given data is defective or non-defective. In this paper, investigations are shown on visibly obtainable bug prediction datasets namely, promise and NASA which is a repository for most open-source software. The efficiency of the presented approach is discussed based on various metrics namely, accuracy, F- measure, G-measure, and Matthews Correlation Coefficient (MCC). The experimental result shows our proposed method achieved the extreme accuracy of 93.41% for the Promise dataset and 92.8% for the NASA dataset. [ABSTRACT FROM AUTHOR]

Around the world, the educational system is evolving. The new trend can be found in traditional classroom systems as well as digitalization systems. Cloud-based Learning Management Systems (LMS) will accelerate the educational industry forward in the next years because they can provide end—user with a versatile, convenient, secure, and cost-effective learning process. The cloud-based LMS approach is the most effective and proper learning model in the worldwide educational sector, particularly if the organization is in a state of depression owing to a global pandemic. It can be utilized over the internet with several users on the same platform. As a result, the initial requirement is important to enable to the LMS model. Despite its many advantages, LMS confronts challenges such as confidentiality, user acceptance, and traffic. In a pandemic like Covid 19, the entire planet depends on a safe LMS platform to establish student and instructor trust. Therefore, with this work, the attempt has been made to explain one LMS model that may provide its users with optimal security, a user-friendly environment, and quick access. This paper discusses the use of the cloud attack, and also cryptographic and steganographic security models and techniques to address these issues. There's also information on what kinds of security vulnerabilities or operations on cloud data are feasible, and also how to deal with them using various algorithms. [ABSTRACT FROM AUTHOR]

Over the last few years there has been explosive growth in the use of the Wireless communications from top to bottom i.e. Satellite transmission to home Wireless personal area networks. The primary advantage of a wireless network is the ability of wireless node to communicate with the universe during mobility. Two basic system models of a wireless system are fixed and Mobile Adhoc Network has been developed for the wireless network paradigm. The first model consists of multiple numbers of mobile workstations and relatively fewer but more powerful, having fixed routers. The second model has been proposed to set up a network on-demand basis. When a collection of wireless mobile nodes are capable of communicating with each other without the use of network infrastructure, centralized administration, or centralized control like mobile switching centers or base stations, that set up is called Mobile Adhoc Network. Here each mobile node operates not only as a host but also as a router, forwards packets to other mobile nodes in the network that may not be within direct wireless transmission range of each other. Each node participates in an Adhoc routing protocol (Yadav et al. in Comput Netw 118:15–23, 2017) that allows it to discover multiple paths through the network to any other node. Institute of Electrical and Electronics Engineers 802.11 (IEEE 802.11) [Kaur et al. in Int J Res Advent Technol 5(8), 2017] is a vital part of pervasive networks, which is a special kind of network where users can join and communicate anytime or anywhere on the fly. It is a popular kind of network because its applications cover a variety of areas. A unique communication paradigm is used which is able to run without fixed infrastructure and relies on wireless terminals for routing and transporting services. A number of security and scalability issues arise due to its wireless transmissions and unpredictable topology changes. Open standard, dynamic topology, scattered arrangements, and multi-hop routing are crucial features of IEEE 802.11 networks that make them vulnerable to various kinds of attacks. A Wormhole attack is one of the serious kinds of attack. Thus, security is the most important concern in IEEE 802.11 networks. This paper presents the refurbishment of the routing structure of Ad hoc On-Demand Distance Vectors (AODV) protocol (Ning et al. in Proceedings of the IEEE systems, man and cybernetics society information assurance workshop (IAW), West Point, New York, USA, pp 60–67, 2003). It will be helpful to safeguard IEEE 802.11 networks from Wormhole attacks by preventing Wormhole Attacks. The DAPS technique had been presented in Saini et al. (Int J Res Advent Technol 6(4), 2018) to detect Wormhole Attacks. Further to DAPS technique, a new Wormhole prevention technique has been introduced in this paper called Proactive Prevention Key Solution (PPKS). [ABSTRACT FROM AUTHOR]

Due to the increasing interest in wireless networks, the availability of spectrum has become a challenge. With the help of cognitive radio, a promising technology, can be overcome this issue. One of the most challenging tasks in this technique is finding the available spectrum holes. The increasing interest in machine learning techniques for spectrum sensing (SS) has led to the development of several novel methods. In this paper, we use the support vector machine with the kernel transformation that are designed to improve the performance of SS, such as such as Linear kernel, Radial Basis Function or Gaussian kernel, Polynomial kernel and Sigmoid kernel. One of the main reasons why the kernel functions are used is due to the possibility of having a non-linear dataset. The performance of kernel functions is compared in terms of accuracy, precision, recall, f1_score and confusion matrix for different number of users such as 100, 500 and 1000. Among all these, Polynomial kernel SVM has shown better performance of 96%, 97% and 100% accuracy for 100, 500 and 1000 number of users. In addition, this paper presents a comparison of the proposed and existing methods, where the proposed method has shown a better performance. [ABSTRACT FROM AUTHOR]

Message-transmission energy expenditure dominates battery lifetime in a Wireless Sensor Network (WSN). This paper newly combines network coding with a brokered WSN architecture to decrease the number of messages by means of message aggregation. It also facilitates low-latency delivery of critical messages and improves the overall energy efficiency of a WSN. Sensor nodes are arranged into subgroups, in each of which a broker separates messages into High Priority (HP) and Best Effort (BE) queues. Both arriving HP and BE messages are separately aggregated through network coding and, according to priority, are forwarded to the next broker or eventually to a data sink, where they are decoded. Service differentiation, together with network coding, prolongs the lifetime of the network by reducing the amount of energy consumed in brokers and increases message throughput by reducing waiting times at intermediate brokers. Best-effort message latency was reduced message as well as for high-priority messages. Without network coding all WSN nodes had run out of energy, whereas with the network-coded approach, twenty percent of the sensor nodes were still alive. This compares with some prior research which provides one or more of low message latency, increased message throughput, reduced WSN energy consumption, and prioritized queueing but not all these features together. [ABSTRACT FROM AUTHOR]

Front-end speech enhancement methods based on microphone array are helpful to improve the front-end signal quality and speech recognition in cochlear implant. However, the size constraint will limit the available quantity of microphones. In this paper, we propose a dual-microphone signal acquisition method based on four acoustic tubes, and analyze the signal acquisition characteristics and beamforming feature. We research four types of beamforming mode to suit to the speech enhancement demand for cochlear implant, and further summarize the corresponding beamforming direction feature and the influence of different inter-microphone distances. The algorithm proposed in this paper has the characteristics of low computation, few microphones, and multiple sound tubes, and can meet the de-noising requirements of various specific application scenarios of cochlear implant. [ABSTRACT FROM AUTHOR]

In recent years, there have been concentrations on the Digital Twin from researchers and companies due to its advancement in IT, communication systems, Cloud Computing, Internet-of-Things (IoT), and Blockchain. The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complication during the life cycle that produces an enormous quantity of the engendered data and information from them. Likewise, with the development of the Blockchain, the digital twins have the potential to redefine and could be a key strategy to support the IoT-based digital twin's applications for transferring data and value onto the Internet with full transparency besides promising accessibility, trusted traceability, and immutability of transactions. Therefore, the integration of digital twins with the IoT and blockchain technologies has the potential to revolutionize various industries by providing enhanced security, transparency, and data integrity. Thus, this work presents a survey on the innovative theme of digital twins with the integration of Blockchain for various applications. Also, provides challenges and future research directions on this subject. In addition, in this paper, we propose a concept and architecture for integrating digital twins with IoT-based blockchain archives, which allows for real-time monitoring and control of physical assets and processes in a secure and decentralized manner. We also discuss the challenges and limitations of this integration, including issues related to data privacy, scalability, and interoperability. Finally, we provide insights into the future scope of this technology and discuss potential research directions for further improving the integration of digital twins with IoT-based blockchain archives. Overall, this paper provides a comprehensive overview of the potential benefits and challenges of integrating digital twins with IoT-based blockchain and lays the foundation for future research in this area. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are a collection of nodes which contain tiny devices having low power and work with minimal cost. In such a network, the functioning of these nodes plays a very important role to sense data and also forward the sensed data to the target observer. For segregation of nodes in effective locations, there is a need to localize these nodes by combining location information with the sensed data for tracking and monitoring of malicious nodes, goods tracking etc. The various approaches towards localization may be classified as range-free or range-based, both having their own pros and cons. This paper presents a discussion on different localization approaches, followed by simulation results of localization using lognormal shadowing model for distance estimation and trilateration for location computation. The results depict the individual impacts of node density and area of sensed region on the localization error. It is observed that with a fixed node count, the errors in location estimates increase with the increase in node density. It is also depicted that the localization error increases towards the boundary of a network. Since the approach discussed in the paper uses loss in received power for distance computation, there is no cost overhead for additional hardware involved in the implementation. Thus the approach used is cost effective. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, issues such as network security and privacy protection have attracted more and more attention. The traditional way to solve these problems is to design complex cryptographic protocols to strengthen security, but the traditional solutions are based on centralized systems. The stronger the control of the central organization of the system, the more vulnerable it is to attackers. However, the blockchain technology that has emerged in recent years has revolutionized the use of decentralized solutions. The privacy and security issues of smart cities have become particularly important. Safety technology research in cities has also become very important. In order to better study the security technology in smart cities, this paper uses asynchronous transmission mode to study the blockchain security technology in smart cities on the premise of the Internet of Things technology. This paper compares and analyzes the data collection, sorting and calculation of the cryptographic elliptic curve algorithm before and after the improvement in the blockchain technology, the throughput and delay of different consensus algorithms, and the percentage of the computing power of the consensus algorithm. Transaction security. The final result shows that, the improved elliptic curve algorithm has more obvious advantages than the previous elliptic curve when the value of k is larger, and the higher the value of k, the faster its single scalar multiplication speed; The improved elliptic curve algorithm has more obvious advantages than the previous elliptic curve when the value of k is larger, and the higher the value of k, the faster its single scalar multiplication speed; The consensus algorithm has the largest percentage of computing power, BTC.com, accounting for 20.4%, and the smallest percentage of BWPool's computing power is only 1%. Therefore, it can be concluded that the decentralization and consensus mechanism of blockchain security technology can well guarantee the communication security of the Internet of Things. [ABSTRACT FROM AUTHOR]

In Vehicular Ad Hoc Networks, reliable information transmission relies on an effective routing strategy. Most existing reinforcement learning-based routing methods are ineffective in dynamic network environments and cannot prevent inefficient network routing. Efficient network routing can be controlled by network traffic management, so this paper proposes an intelligent routing strategy based on Deep Reinforcement Learning to enhance routing performance. By integrating intersection forwarding and traffic awareness capabilities, this paper addresses the problem of local optimality and utilizes the Deep Q Network to make intersection forwarding decisions. The state space of this strategy consists of intersection nodes, road information between intersections, and forwarding packet information. When a vehicle node carrying a packet approaches an intersection based on the state space, the intersection node uses a neural network to select the optimal next-hop relay intersection from past learning experiences. It generates appropriate vehicle routing decisions based on information from the current and candidate relay intersections. Finally, we use the real taxi trajectory data of Beijing City to conduct extensive simulation experiments. Simulation results and analysis demonstrate that the proposed strategy outperforms related research regarding higher average packet delivery ratio, shorter average end-to-end delay, and lower average overhead ratio in dense and sparse traffic periods under real road environments. Consequently, this strategy provides efficient and reliable message transmission services for Vehicular Ad Hoc Networks. [ABSTRACT FROM AUTHOR]

As the world is moving towards the Internet of Things (IoT), more data rate is required with wider coverage and small delay. While the 5G supports the needed increased data rate, it suffers from a decreased coverage per tower. In contrast, the low population areas such as deserts, oceans, forests, and mountains contain most of the world's transportation such as highways, ships, and planes as well as large areas of agricultural lands that are all not supported by 5G. Consequently, LEO satellite constellations have been introduced to cover the earth's surface with a high-speed data rate. Each LEO satellite constellation requires 4000 + satellites and at least one ground base station (BS) for control, tracking, telemetry, and remote sensing functions. However, to support the needed downlink high-speed data rate demand, the LEO satellite constellation must add many BSs to increase the uplink data rate. As a result, the GEO communication satellites face great competitors in their market. This paper introduces the hybrid LEO-GEO satellite communication system to change this competition into cooperation that benefits both systems. In the hybrid LEO-GEO satellite communication system, this paper proposes maximizing the downlink utilization of both LEO and GEO satellites by the traffic-aware Artificial Expectation Detection (AED) technique. In such a technique, the trending multicast data choose the GEO link while the unicast data and control data choose the LEO link to maximize the downlink utilization efficiency. Our results show that using AED is power efficient and delay efficient while increasing the data rate by (100)x to (100k)x or decreasing the needed number of LEO BSs. [ABSTRACT FROM AUTHOR]

Since the outbreak of the novel coronavirus, Covid-19 has continuously spread across the globe briskly. Countries have undertaken different types of measures to blunt this spread varying from lockdowns to curfews to social distancing to compulsory wearing of protective kits, which has been sporadically fruitful. However, despite these stringent measures, which have their own pitfalls, scientists across the globe have been struggling to develop a suitable mathematical model that could depict the existing disease spreading pattern and also predict a trend of numbers in the forthcoming months or years. In this paper, popularly used mathematical models including Polynomial Regression, Auto Regressive Integrated Moving Average (ARIMA) and Deep learning techniques such as Recurrent Neural Network (RNN) have been explored for 5 countries badly affected by this virus. The models were tested from 16th May, 2020 till 22nd May, 2020 and used for predicting future cases and deaths from 23rd May, 2020 to 30th June, 2020. The current research primarily focuses on forecasting the behaviour of total confirmed cases and deaths in each country and further analysing the performance parameters such as Mean Squared Error, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). It has been observed that the polynomial regression model provides a best fit solution at par with actual numbers of confirmed and death cases for India by producing minimum RMSE and MAPE. For South Korea and Italy, the ARIMA and RNN models have shown fidelity with actual numbers. RNN model has shown conformity with US numbers while ARIMA model has found closeness to United Kingdom data. The purpose to perform data analysis is to measure the performance metrics by using different techniques and depict the pattern for each country. Furthermore, the paper also highlights the future predictions for every country to control the spread of disease, save lives, avoid health systems breakdowns and benefit the researchers in this field. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANET) includes groups of stationary or moving vehicles linked by a wireless network. The significant usage of VANET is to offer comfort and safety to drivers in road environments. VANET provides a communication framework that aids in minimizing accidents. Also, sharing data in VANET is time-sensitive and necessitates vigorous and quick network link formation. However, appropriate routing is critical to avoid the streaming issues that occur in VANET applications. This research area is of great concern to the researchers, and this work intends to propose a new Cluster-based VANET routing model with steps like (a) optimal cluster head selection (CHS) and (b) appropriate gateway selection for data transmission. Here, the optimal CHS takes place considering the Packet delivery ratio (PDR), mean routing load, end-to-end (e2e) Delay, Throughput, and packet overhead. This paper introduced a new Customized hunger's foraging honey badger with a Dynamic multi-objective non-sorted genetic algorithm (CHFHB-DMNSGA). Also, the gateway selection is done under two objectives: Vehicular gateway mobility and receiver signal strength, respectively. Finally, the paper presented a comparative assessment to validate the effectiveness of the adopted algorithm CHFHB-DMNSGA over the other algorithms like R-NSGA-II, NSGA-III, NSGA-II, HBA, HGS, ABC, FF, CSO, ACO, LA, MHB-GA, GA-FF, and HFCHBO. [ABSTRACT FROM AUTHOR]

The visible light communication (VLC) technology has been gathering attention recently and as the emerging systems and appliances in this technology are still new, their behavior and performance issues must be taken into the consideration as much as possible. This paper aims to fully model the noise that is emanating from the usage of the Single-photon avalanche diodes (SPADs) in the VLC systems. This noise is either modeled with the sub-Poisson or Gaussian plus sub-Poisson distributions. These noise models are then applied to the diffusion adaptive networks to show the real-world impact of the VLC noisy links on their performances. The radio noisy link impacts have been investigated on the performance of the diffusion adaptive networks, however, the effects of the optical link noise on their performances is the contribution of this paper. Also, using the realistic and precise models for the optical noise is another novelty in this paper. The results show that the diffusion network can be implemented using VLC appliances and the existence of the SPAD noise will not hinder the convergence of this network. [ABSTRACT FROM AUTHOR]

In the present times, the concept of cloud computing has played a significant role at the global level. With this approach, users can able to customize their services as per their needs. By having the connection of internet users can get able to serve various kinds of services like on-demand access, storage space, software building platforms, data recovery, etc., and pay only for that service that they have consumed. Enormous challenges in the cloud domain such as fault tolerance, energy efficiency, scheduling, resource provisioning, load balancing, etc. This paper is focused on load balancing domain. This can be defined as a redistribution of the workload among various available virtual machines in such an identical manner that would lead to a balanced state. This paper presents the evaluative and inclusive review of numerous load balancing (LB) methods. Quality of services(QoS) is vital role that contain various parameters to evaluate the load balancing methods in respect of makespan, speedup, cost, throughput, etc. This paper is highlighted numerous of load balancing methods with their brief explaination, platform used, different simulator and tools used by these methods and based on QoS parameters. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents a network framework comprising identifiable entities that interact through the Internet. One of its applications is the smart home, where household devices can be remotely monitored and controlled. This has led to an increased demand for reliable security solutions in IoT systems. Security presents a significant challenge in IoT smart home devices and must be carefully considered. Unauthorized access to a smart home system, facilitated by means such as jamming or replay attacks, could pose risks by manipulating sensors and controls, potentially allowing unauthorized entry. This review paper concentrates specifically on the security and privacy aspects of IoT smart home access control devices. It begins with a concise overview of smart home security and privacy, then delves into various techniques within the smart home system taxonomy, such as authentication, access control, blockchain, and cryptography-based methods. Furthermore, the paper compares the advantages and disadvantages of these techniques. It also examines various types of attacks on smart home IoT access control systems and evaluates risk factors such as methodologies, attack frequency, severity, probability, and ranking. Finally, the paper discusses challenges, applications, conclusions, and future directions. [ABSTRACT FROM AUTHOR]

In this paper an improved multiple adaptive neuro-fuzzy inference system (MANFIS) to solve the wheeled mobile robots path planning in unstructured environments is proposed. The fuzzy C-means (FCM) clustering method is used in ANFIS to decrease the input data size, which leads to predict the efficiency of the proposed robot path planning model. The FCM clustering method allow classifying the robot sensors extracted input data into clusters; each cluster has similar properties that assists to develop the correlation between data and as a result simplify the proposed model. The design MANFIS architecture takes both the advantages of artificial neural network which has self-learning ability and fuzzy system to describe the uncertain phenomena of the data. Finally, by combining cluster data and MANFIS an optimum velocity for left and right wheel of the robot is determined, which safely navigate the robot in an optimized route. The simulation and experimental results show that the proposed path planning method is effective and can be implemented for any complex environments. The results obtained by the simulation and in physical experiments are equated with each other and noticed a good promise between both the results as the difference in results is less than 9%. The simulation results consistently demonstrate the superiority of the proposed FCM-ANFIS, manifesting performance improvements of up to 4.4% for path length reduction and up to 8.9% for mobile robot time duration reduction when compared to heuristic methods. This paper identifies and describes a new development on path planning technique that will help the robots to navigate in any kind of uncertain navigation environments. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) network is considered to enhance convergence and wireless communications. However, eavesdropper can access an IRS reflection link, therefore, securing IRS-aided networks is an important challenge. In this paper, a secure IRS aided NOMA clustered network is proposed via artificial jamming. In each cluster, NOMA and jamming signals are sent using a multi-antenna base station to their users in the presence of an eavesdropper with assistance of IRS which has the capability of energy harvesting. The purpose of the paper is maximizing the sum-secrecy rate by proper selection of IRS, power allocation coefficients of the users in each cluster, optimizing the IRS reflecting vector, jamming and beamforming vectors with constraints on the quality of service requirement. We propose a low complexity iterative algorithm based on the convex optimization method and Karush–Kuhn–Tucker conditions to determine the best IRS as a relay for transmission and also the IRS reflecting, jamming and beamforming vectors. Simulation results are verified to show the effectiveness of the proposed scheme in different situations. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are the future of networking. They represent one of the best technologies for automation due to their characteristics, such as cost-effectiveness and wireless nature. However, WSNs require explicit programming to perform certain tasks, lacking adaptability. To address this limitation, machine learning (ML) can be highly beneficial as it grants networks the ability to make decisions automatically. This capability can refine the accuracy and efficiency of WSNs. This paper aims to provide a detailed classification of machine learning algorithms, discussing various problems of traditional wireless sensor networks and the solutions that machine learning can offer. It also includes a brief discussion of problems associated with machine learning in WSNs. This review paper is written with the hope of being useful for researchers in a future era where the amalgamation of WSNs and machine learning algorithms will influence many aspects of human life. [ABSTRACT FROM AUTHOR]

Focusing on the tasks collaboration requirements of Unmanned Aerial Vehicle (UAV) swarm in complex battlefield environment, it is especially important to ensure the reliability of routing communication within the formation. Wireless ultraviolet (UV) secret communication can meet the reliable and secret communication needs of UAV swarm in the complex atmospheric environment. Moreover, considering the limited energy carried by UAVs, in order to maximize the flight time of UAVs, this paper proposes an unequal clustering energy consumption balancing routing algorithm (UCEBRA) of UAV swarm based on UV secret communication. Combined the advantages of wireless UV scattering communication, this algorithm improves the selection threshold of candidate cluster heads (CHs) and the clustering conditions of nodes according to the performance of UAV nodes. Furthermore, a function, which can comprehensively reflect both distance and remaining energy of UAVs, is built in the process of inter-cluster routing, to balance the energy consumption of the CHs. The simulation results show that compared with low-energy adaptive clustering hierarchy (LEACH) and energy-efficient unequal clustering (EEUC), the energy efficiency of the UAV network is increased by 21.7% and 4.6% respectively, and the lifetime of the network prolongs by 26.2% and 7.8% under UCEBRA algorithm. Therefore, the algorithm proposed in this paper can efficiently balance the energy consumption of nodes and thus prolong the lifetime of UAV swarm. [ABSTRACT FROM AUTHOR]

A heterogeneous network, also known as a HetNet, is a network made up of numerous distinct wireless network nodes placed throughout the cellular service coverage area. These nodes have differing features and capabilities. In some areas, the user density may be high or a single macro eNB cannot supply good coverage. This problem can be solved by deploying low-power nodes, such as pico-cells, within the range of a macro-cell. A model built of macro eNBs and pico eNBs, used to fulfill machine-to-machine (M2M) and human-to-human (H2H) devices, no matter how different is the quality of service (QoS), is the main topic of this paper. Furthermore, the paper presents a method to associate cells and balance loads on the network for both M2M and H2H devices, by utilizing a new scheme based on Q-learning. The scheme employs two independent algorithms, where both of them are based on Q-learning. The performance of the proposed scheme is assessed by comparison in two ways: traditional comparison and a Q-learning based scheme deployed in the UE devices, to compare the H2H and M2M blocking probability with the M2M uplink transmission power. As concluded from the results of testing the proposed scheme, the blocking probability of M2M and H2H devices is reduced by about 20–30%. Furthermore, the uplink transmission power of M2M devices is reduced by 50% even under high load conditions, which makes this scheme power-efficient. [ABSTRACT FROM AUTHOR]

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms' operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics. [ABSTRACT FROM AUTHOR]

In order to improve the scientific quality of the public, the Chinese Association for Science and Technology has put forward a call to combine popular science education with leisure and entertainment. In view of the fact that online shopping involves a wide range of areas, and the people pay more attention to it, the paper completed the innovation of online shopping risk warning science knowledge, the design of popular science mechanism and the dissemination of popular science knowledge. The paper used complex network's knowledge discovery methods and decision theory to design online shopping risk warning science knowledge; Using the complex network public opinion dissemination trust analysis realize the dissemination of popular science knowledge and promote the improvement of the public's quality of popular science. The spread of risk early warning science knowledge in the network shows that the risk early warning mechanism designed can achieve the purpose of improving public science knowledge when the reward and punishment measures are appropriate. [ABSTRACT FROM AUTHOR]

For the present, many technical problems exist in the convolution neural network for facial expression recognition, such as the complexity of convolutional face feature extraction, the difficulty in accurately recognizing the subtle feature changes of facial expressions, and the low automatic recognition rate of facial expressions. In this paper, a hybrid attention mechanism based on space and channel—Height Performance Module Implement(HPMI) attention mechanism is proposed to realize automatic facial expression recognition. The addition of this attention mechanism can enhance the weight of key features and make the model focused on the features which are useful for expression classification in the training process. An HPMI module based on spatial and channel-based mixed attention mechanism is embedded in VGG-16 network. This can effectively alleviate the overfitting phenomenon of the network, strengthen the useful information, suppress the useless information, promote the information flow between the key information of the image and the network model. At the same time, it can solve the problem of the inconsistency between the input dimension and the output dimension. The accuracy of the method in this paper is 98.97% and 88.44% on CK + and RAF-DB expression datasets. Experimental comparison shows that by embedding the HPMI module, the model can further enhance the learning of spatial and channel feature weight. Our experiments include 2 datasets for expression recognition and show an average improvement of 3.94% in the accuracy. [ABSTRACT FROM AUTHOR]

Molecular Communication (MC) is a multidisciplinary branch that lies at the junction of nano, bio and communication technology. MC is evolving to serve almost every field of humanity, be it biomedical, be it environment or be it security against NBC attack. On the other hand, MC lags in technological demonstration and development level. However, like any communication system, Error Correction Code (EEC) play vital role in MC system, to improve the system performance. Recently, literatures have presented on VLSI implementation of Cyclic Reed–Muller (C-RM) and Hamming code. However, VLSI implementation and demonstrations of Self-Orthogonal Convolution Codes (SOCC) and Low-Density Parity Check (LDPC) codes, which outperform over others, are not available in the literatures. Therefore, this paper implements SOCC and LDPC codes in Tanner EDA Tool followed by presenting the power consumption and delay of respective codes. More specifically, this paper presents design and implementation of encoder and decoder of SOCC for MC system using Tanner EDA Tool and demonstrated the performance in terms of power consumption and delay. In addition, similar analysis is presented for LDPC code. It is important to mention that implemented decoder of each of the LDPC and SOCC codes is employing newly presented MLG circuit in is manuscript. Finally, power consumption and delay of SOCC and LDPC are compared with those of available ECCs in literatures. [ABSTRACT FROM AUTHOR]

Wireless Body Area Network (WBAN) is an emerging and promising specialized area in Wireless networks that deals with crucial health-related datasets. Unlike other wireless networks, as this type of network deals with medical facts, losing it is fatal. WBAN is a highly constrained network. Reducing energy consumption and enhancing lifetime are the two most important challenges of WBANs. One way to achieve these is by deploying relay nodes optimally in WBANs. Generally, a relay node is placed at the midpoint of the line joining the source and the destination (D) nodes. We show that such simplistic deployment of the relay nodes is not the optimal deployment, which can hamper the overall lifetime of WBANs. In this paper, we have investigated the best location to deploy a relay node on a human body. We assume that an adaptive decode and forward relay node (R) can move linearly between the source (S) and the destination (D) nodes. Moreover, the assumption is that a relay node can be deployed linearly and that the body part of a human is a flat surface and hard. We have investigated the most energy-efficient data payload size based on the optimally placed relay location. The impact of such a deployment on different system parameters, such as distance (d), payload (L), modulation scheme, specific absorption rate, and an end to end outage (O ) are examined as well. It is observed that in every aspect optimal deployment of the relay node performs an important role to enhance the lifetime of wireless body area networks. Sometimes linear relay deployment is very difficult to implement, especially on the different body parts of the human body. To address these issues, we have examined the optimal region for the relay node based on a 3D non-linear system model. The paper provides guidance for both linear and non-linear relay deployment along with the optimal data payload size under various circumstances and also considered the impact of specific absorption rates on the human body. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) technology enables mobile devices in communication network systems offloading tasks to edge servers, effectively reducing response time and energy consumption. However, task offloading decisions become a significant difficulty when the system's mobile and service device count rises. In this paper, the problem of response time and energy consumption of the system is modeled as a multi-objective optimization problem, and we design an improved evolutionary algorithm based on immune algorithm, which can effectively obtain a set of solutions between response time and energy consumption. The simulation results show that our scheme can meet the response time requirements and obtain a lower energy consumption strategy when compared to the offloading scheme in the existing literature. [ABSTRACT FROM AUTHOR]

As humans, speech is the common as well as a natural way of expressing ourselves. Speech Emotion Recognition (SER) systems can be defined as an assortment of methods processes and classifies speech signals for the detection of associated emotions. Automatic emotion recognition is the technique of identification of human emotions from various signals like speech, facial expression and text. Collection of such signals and labelling them is often tiresome and needs proficient knowledge. This paper deals with the different types of open source speech emotion datasets of various languages and recent literature survey in the area of speech emotion recognition that employs a number of machine learning approaches with an objective of enhancing the classification accuracy. The paper prudently aims at identifying and synthesizing contemporary pertinent literature associated to the SER systems with different methodologies or design components, thus providing the researchers with an up-to-date understanding of the research topic in the field of SER. [ABSTRACT FROM AUTHOR]

Blockchain is one of the promising technologies nowadays due to its unique characteristics like security, privacy, data integrity, decentralization, immutability, and traceability. Originally used to implement cryptocurrencies, recently numerous applications have employed blockchain in their architectures including applications targeted for the internet of things (IoT) environments. It is expected that by 2025 more than 21 billion IoT devices will be used especially with use of cloud, fog and edge computing architectures. Integrating blockchain in the IoT architecture provides many advantages such as enhancing security and privacy, better speed and costs, traceability and reliability, and elimination of single point of failure. On the other hand, many issues and challenges have arisen and should be addressed. Typically, IoT system consists of lightweight devices with limited hardware resources and constraints. Hence, the energy efficiency is a fundamental challenge in such devices. The main motivation of this paper is to survey designing a secure and energy efficient blockchain-based IoT implementation using a suitable hardware design. The paper classifies, presents and analyzes existing solutions to better implement IoT environment combined with blockchain technology. Our investigation demonstrations that most of lightweight solutions handle either the energy or security issue separately. Moreover, many works are theoretical-based analysis and solutions without considering the real blockchain-based IoT validation design. Energy evaluation for IoT hardware devices is not given the adequate research bandwidth. Additionally, limited works evaluated their techniques from hardware constrained device perspective. It is recommended that the performance of any proposed solution should be validated using real designs. The hardware perspective evaluation should be in mind for efficient blockchain-based IoT hardware implementation. The proposed lightweight solutions should focus more on efficient energy implementation while considering the lightweight security mechanisms. [ABSTRACT FROM AUTHOR]

In the paper, a tunable split-ring based microstrip band-pass filter is proposed and the corresponding measurement and simulation results are presented. The proposed filter has a band-pass performance around 2.2 GHz. The prototype of the proposed filter is fabricated by using Arlon DiClad 880 substrate with an electrical permittivity of ϵ r = 2.2. There is a quite good agreement between the measurement and simulation results with a slight ( ∼ 0.3 GHz) discrepancies. Also, the frequency tuning performance is achieved by using a simplistic varactor diode model of SMV1247-079LF placed on one gap of the ring. By changing the capacitance on the model, the center resonance frequency of the filter can be adjusted in the range of 2.4–3.4 GHz band. The simulation results demonstrate tunable band-pass characteristics of the proposed design. Modelling and the numerical analysis of the filter design are carried out by using CST Microwave Studio simulator. An equivalent circuit modeling of the proposed filter is presented along with the corresponding simulations. The results of the analysis on the effects of the physical dimensions of the filter on the filter performance are presented. Also, the variation of the resonance frequency with respect to the voltage value on the varactor diode was summarized with cubic spline method and displayed. [ABSTRACT FROM AUTHOR]

Particularly, for real time monitoring-IOT has just evolved to support massive capacity Cellular LTE networks. The fundamental issue is the constrained bandwidth that NB-IOT channels allow. Therefore, goal of paper is to enhance the capacity of the NB-IOT physical layer shared channels for uplink and down links. The primary objective of this article is to improve the efficiency of an LTE-OFDM framework for NB-IOT-WAN via BER and BLER assessment. The proposed approach improves performance by selecting suitable cyclic prefix sampling with FFT size. It is planned to adopt M-QAM for increased data transmission capacity and reducing OFDM BER. In order to enhance the capacity approach of diversity reception is considered along with the modified modulation parameters. Increased FFT size is offered for OFDM capacity enhancement for narrowband physical downlink shared channel (NPDSCH) and narrowband physical uplink shared channel (NPUSCH) transceivers. The block error rate (BLER) is used as the performance evaluation parameters for the LTE-OFDM based NB-IOT system. The impact of the higher block transmission length is assessed for the improvement in the BLER rates. It is proposed to use M-QAM for NPUSCH which may offer better capacity of data transmission. The performance is compared with the standard QPSK modulation. For the uplink design under AWGN and frequency selective channels are considered for evaluation. The numbers of receiver antenna are increased for offering the better diversity reception to further enhance the channel capacity. The optimal design parameter selection might improve the performance of the NB-IOT cellular networks. The BLER is evaluated for different case of uplink and downlink designs against the wide range of the SNR. The entire document takes into account all factors in order to get the greatest system efficiency, and it offers capacity enhancement. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a way to extract emotion-based information or users' sentiments and opinions from text data. Sentiment analysis uses text analysis, Natural Language Processing (NLP), and computational linguistics. Text mining research has a strong focus on Sentiment Analysis (SA), which deals with the processing of opinions, attitudes, and the subjective aspect of the text. In today's time, we are seeing the availability of extensive web-based data on the Hindi language, which is a national language of India and also a first language used by the majority of the population in India. So, it has become extremely important to analyze customer's/user's opinions about the product, services or company and find out the key insights, particularly for companies and government organizations. The insights of sentiment analysis give a ray to the organizations to set their product/services or company as a key player in the market to survive to the long time period and get good business. This paper explores how we can effectively use deep neural networks in sentiment analysis to classify Hindi sentiments. To do it, we used word embeddings for Hindi text data and then trained the proposed model using Long Short-Term Memory (LSTM) and, subsequently, employed parameters. [ABSTRACT FROM AUTHOR]

Recently there has been an increasing demand for terahertz technology, especially in imaging. In the past few decades, the applications of terahertz (THz) imaging technology have seen significant developments in the fields of biology, medical diagnosis, food safety, and nondestructive testing. The medical and semiconductor industry has always attracted significant attention worldwide. In particular, the importance of real and perfect inspection technologies has been growing due to an increasing demand for improving the quality of life and developing industries. This paper presents the research of THz super-resolution imaging in transmission mode by using different samples. We have reported transmission measurement at different THz frequency of each sample. The butterfly sample used super-resolution THz imaging. The THz super-resolution is obtained excellent at 1.8 THz, it is near about 1 micrometer. Good resolution images have been obtained. This new THz super-resolution techniques can apply in medical and security purposes. Further applications will be reported in the coming papers. [ABSTRACT FROM AUTHOR]

Successful implementation of efficient mobility management techniques is critical to the success of next-generation wireless systems. Handoff management has become more critical in present wireless networks, to support multimedia communications and associated services. This paper intends to study the handoff management aspect of the general mobility management issue which is prevailing in wireless networks. Multilayer mobility management models such as Hierarchical Mobile IPv6 (HMIPv6) and flatter architectures like Proxy Mobile IPv6 have great potential to serve as a mobility management solution for the next-generation networks. Influenced by the advantages of layered mobility management architectures, in this paper, a Three Layered MIPv6 (TLMIPv6) model has been examined for its performance against various parameters like signaling load, packet delivery ratio, end-to-end delay, packet delivery overhead, jitter, bandwidth consumption, throughput, etc. Execution of TLMIPv6 has been contrasted and that of HMIPv6 and PMIPv6 under Random Walk Mobility and Gauss-Markov Mobility models. It is seen from the experimental results that the TLMIPv6 protocol exhibits more than 15% reduction in signaling cost for handoff frequencies greater than 7/sec and more than 20% reduction in end-to-end delay for hop distances above 18, in comparison to HMIPv6 under identical simulation scenarios. The future scopes of the work are outlined. [ABSTRACT FROM AUTHOR]

The size of the wireless sensor network (WSN) is extending continually with use of IOT networks. The main difficulty for design wide area WSN is to maintain the higher stability period and energy efficiency (EE) for the routing protocols. The creation of clustering-based routing protocols was applied to the optimization of overall network energy. But, traditional clustering methods were unable to produce improved node heterogeneity, and extended network lifetime. Distributed clustering-based routing protocols are specially designed for enhancing the EE of the networks. In addition, EE can be improved by enhancing the heterogeneity of the node distribution. This paper aims to design the extended distributed clustering-based EE routing protocol. The heterogeneity of nodes is improved by introducing the additional intermediate advanced nodes layer in the network. Therefore, paper proposed to design the Multi-Level Heterogynous EDEEC rousing protocol called ML-HEDEEC by adapting optimum energy enhancing parameters. The notes are divided to normal, advance, advance-interdicted and supper nodes based on the energy allocated to them. The probability of nodes is modified for better clustering and cluster head election by introducing additional energy enhancement parameter. In addition, it is proposed to automatically adopt the network initial energy based on the scaling of network dimensions. This may lead to enhance EE of the network and may improve stability period. Finally, the results are evaluated for a case of WSN routing under the dynamic sink locations. Performance is compared for various distributed clustering protocols and other state of art protocols viz. LEACH, SEP, zonal- SEP, DEC considering the network scaling. Various performances of the network stability, packets sent to base station, and lifetime,.are defined for result evaluation. The network dimensions are scaled up to four times and proposed protocol is tested under scaling consideration. In addition, sink locations are also varied for dynamic sink locations performance evaluation. Overall paper efficiently designed and test heterogeneous improved routing protocol with extended lifetime and stability. [ABSTRACT FROM AUTHOR]

Since the advent of cryptocurrency, Blockchain has evolved as a promising technology towards diverse application areas such as education, healthcare, supply chain management, etc. Various institutions, essentially, the financial institutions, use traditional Know Your Customer (KYC) for the identity verification of a customer. However, KYC being manual and labor-intensive process, the repetitive task of onboarding different institutions by any customer makes it cost inefficient. With decentralized Blockchain technology, the overall efficiency of KYC can be increased manifold in terms of improved speed, minimum onboard time, reduced risk and total cost involved. In the paper, we conduct a PRISMA guided systematic review on the use of Blockchain technology for KYC and its application areas from 2014 onwards. Lastly, the Blockchain platforms such as Ethereum and Hyperledger along with related case studies have been presented followed by future directions in KYC. [ABSTRACT FROM AUTHOR]

With the increasing use of smart homes and IoT devices, security has become a significant concern. This paper presents a method to enhance smart home security using Deep Convolutional Neural Networks (DCNN) and multiple cameras. In this approach, three cameras are used to capture images from different angles, and these images are analysed using DCNNs including VGG16, VGG19 and DenseNet to detect potential intruders. Known for their excellent performance in image classification, these DCNN models aim to improve the accuracy and efficiency of the security system, thereby reducing false alarms and missed detections. Additionally, the system allows authorized individuals to remotely disable the security system, increasing convenience and usability. The proposed method has shown significant improvement in human presence detection and facial recognition, achieving 99.79% accuracy in classifying home occupants and intruders. This performance is superior to alternative models such as SVM, KNN, and complex decision trees. This paper introduces a new method that integrates multiple cameras with DCNN to boost the performance of security systems. [ABSTRACT FROM AUTHOR]