Ensuring reliable and secure authentication techniques is essential in the rapidly growing Internet of Things (IoT) environment to protect sensitive data and maintain network integrity. This paper focuses on edge computing environments and proposes Edge-Optimized Hybrid Anneal-Bound Authentication, a novel approach to IoT network security that incorporates the positive aspects of Branch and Bound and Simulated Annealing optimization approaches. This approach reduces latency and enhances resource efficiency by deploying edge devices, which have been strategically placed closer to IoT devices. This makes the authentication process faster and energy efficient. The suggested method optimizes energy usage while maintaining security measures between devices and edge nodes by integrating this algorithm into the edge computing architecture. We conducted simulations in a controlled setting to test the proposed method, considering scenarios and workloads. The results show an enhancement in energy efficiency while maintaining security standards. The novel Hybrid Anneal-Bound Authentication approach significantly improves the security and performance of IoT systems; it shows a detection rate of 98.9

In the last few years, the Internet of Things (IoT) has grown significantly due to technological advancements. However, until recently, there has been no universal set of rules applicable to IoT security. This has opened an area for researchers. The IoT environment enables various smart devices to connect and exchange information; thus, ensuring the authenticity of devices in the IoT network is crucial. We have classified the diverse methods used to authenticate IoT devices to access the data they generate. This study conducted a systematic literature review to identify research gaps, recurring patterns, and potential future directions in IoT authentication, with particular attention to the architectures employed. This review analyzed different authentication techniques and presented their advantages and disadvantages using several criteria for categorization. This survey provides researchers and practitioners with a consolidated understanding of the current state of authentication mechanisms in the IoT. Furthermore, the survey examines emerging authentication paradigms, including blockchain-enabled authentication frameworks, machine-learning-augmented authentication models, and lightweight authentication schemes tailored for resource-constrained IoT devices. The goal of this survey is to aid in creating more robust and secure authentication solutions for the developing IoT by highlighting strengths, limitations, and emerging trends.

Delving into the dynamic realm of communication networks, the Network Simulator-2 (NS-2) emerges as a versatile, object-oriented, and event-driven mirror, analysing the intricacies of computer network. NS-2, an open-source, is a strong tool for protocol innovation that supports numerous routing protocols designed for both wired and wireless networks in addition to the TCP/IP protocol suite. However, the security frontier has remained a latent challenge amidst its diverse protocol array. This research defiantly explores the security void in NS-2 and presents a novel solution by adding a state-of-the-art security module. Elevating NS-2's prowess, this module seamlessly integrates message integrity and sender authentication features, injecting a much-needed security boost. We meticulously detail the inner workings of the security modules, the innovative processes deployed, and the simulation and implementation intricacies within NS-2. The limelight is on the propagation of sender authentication protocols and the embodiment of message integrity in wired communication networks. Network Animator steps onto the stage to make the simulation journey more vivid, providing an engaging visual narrative. At its core, this module strives to democratize the integration of Digital Signature features, carving a path toward a more secure NS-2 landscape. This paper augments NS-2's resilience and charts new territories in the dynamic intersection of communication networks and cutting-edge security protocols.

The rising frequency of cyberattacks has made Network Intrusion Detection Systems (NIDS) essential for securing IoT environments. However, NIDS performance largely depends on the learning mechanism and quality of training data. Traditional rule-based methods struggle to process the massive volume of network traffic generated within seconds, prompting the adoption of data mining techniques to enhance detection accuracy. Effective feature selection is crucial for improving detection rates and computational efficiency but still faces challenges like high dimensionality, false positives, and reduced accuracy. To overcome these limitations, this study introduces a modified Boruta Search and Krill Herd Optimization-based (BS-KHA) classification model for precise intrusion detection across multiple IDS datasets. The proposed model achieved binary classification accuracies of 99.85%, 99.96%, and 99.92%, and multiclass accuracies of 99.93%, 99.99%, and 99.87% on NSL-KDD, UNSW-NB15, and CICIDS2017 datasets, respectively-surpassing existing IDS frameworks.

In the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a prevalent form of cybercrime. These attacks are relatively easy to execute but can cause significant disruption and damage to targeted systems and networks. Generally, attackers perform it to make reprisal but sometimes this issue can be authentic also. In this paper basically conversed about some deep learning models that will hand over a descent accuracy in prediction of DDoS attacks. This study evaluates various models, including Vanilla LSTM, Stacked LSTM, Deep Neural Networks (DNN), and other machine learning models such as Random Forest, AdaBoost, and Gaussian Naive Bayes to determine the DDoS attack along with comparing these approaches as well as perceiving which one is about to give elegant outcomes in prediction. The rationale for selecting Long Short-Term Memory (LSTM) networks for evaluation in our study is based on their proven effectiveness in modeling sequential and time-series data, which are inherent characteristics of network traffic and cybersecurity data. Here, a benchmark dataset named CICDDoS2019 is used that contains 88 features from which a handful (22) convenient features are extracted further deep learning models are applied. The result that is acquired here is significantly better than available techniques those are attainable in this context by using Machine Learning models, data mining techniques and some IOT based approaches. It’s not possible to completely avoid your server from these threats but by applying discussed techniques in the present juncture, these attacks can be prevented to an extent and it will also help to server to fulfil the genuine requests instead of sticking in the accomplishing the requests created by the unauthentic user.

Due to the reliance of the modern community on networks, the significance of efficient intrusion detection systems (IDS) cannot be ignored. As network intrusions are frequently and critically emerging, exhibiting unknown patterns, smart systems practicing machine learning approaches, have been readily explored to deal with certain issues. In this paper, we acknowledge a different ensemble-oriented approaches for detecting numerous types of outliers. The assets of ensembled techniques over common machine learning mechanisms is the competence to train an unlabeled data. Thus, they are applicable for observing unfamiliar attacks. The pivotal objective of the proposed scheme is to train and test the data for achieving high accuracy rate and minimal false positive rate. The experiment is implemented on NSL-KDD dataset which show that how effectively ensemble algorithms generate highly accurate models with low false positive rates. And outperforms in case of predicting unknown anomalies.

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Consumer-centric IoT (Internet of Things) opportunistic networks refer to communication networks that prioritize the needs and preferences of end-users or consumers. In the context of IoT, opportunistic networks are characterized by the ability of devices to establish communication links opportunistically based on availability and proximity. These kinds of networks deploy by seed and grow based on the invitation. Because of their different characteristics, routing in Consumer-centric IoT opportunistic networks has become very challenging. Thus, in these conditions, it becomes imperative to use the available resources efficiently. Considering all these issues, this article proposes a novel message forwarding scheme, BeRout, for Consumer-centric IoT opportunistic networks. The proposed scheme is based on the benevolence behavior of nodes. To achieve high delivery ratio in IoT opportunistic network scenarios, BeRout, to node’s past behavior and activities. To use buffer space effectively and efficiently, a buffer management scheme has been proposed. To show the effectiveness and efficiency of the proposed BeRout scheme simulation is performed on the ONE simulator and results are compared with the existing state-of-the-art routing protocols.

Intrusion detection systems (IDSs) protect computer systems by analyzing internet traffic and activities to identify possible threats. This study aimed to create an advanced IDS using feature selection and ensemble learning techniques that achieve high accuracy. The study gains relevance by using the TON_IoT dataset for training and testing. The study was divided into two main phases, each adding their significance. This strategy maximizes the IDS's performance by choosing the most informative features and utilizing the advantages of various classifiers. The second stage involved applying the ensemble learning approach, which yielded a potent model combining the present algorithms' advantages. The study's findings show how it affects attack detection quality and false alarm rate reduction. According to experimental analysis and the TON_IoT benchmark dataset findings, the proposed approach outperformed several existing deep learning techniques. It displayed a maximum accuracy of 99.63% in binary classification and 99.78% in multiclass classification scenarios for network attack identification.

The proliferation of the Internet-of-Things (IoT) paradigm has brought about transformative changes in various real-life applications and revolutionized how we interact with technology. However, the exponential growth of global IoT implementations has also intensified cybersecurity concerns. Breaches in IoT compromise not only the associated technology but also the information it handles. Therefore, developing IoT intrusion detection systems to handle and tackle these security issues has become imperative. Intrusion detection systems (IDS) have gained popularity due to their real-time intrusion detection capabilities, evolving into signature-based and anomaly-based detection technologies over the decades. In this article, the authors propose an intrusion detection framework to enhance the security of the IoT environment. The proposed model uses the modified stacking ensemble classifier to detect anomalies in a real-time framework. Cuckoo search optimization is used to reduce data dimensionality. The performance of the developed framework is tested over several datasets, including KDD Cup 99, CSE-CIC-IDS2018, and CICIoT2023; it excels in detecting both known and evolving cyber-attack patterns with an accuracy rate of 99.87

In the last few years, road accidents have been emerged as a very big problem worldwide in develop and underdeveloped countries due to enormous increase in number of vehicles. Large number of deaths due to road accidents has been recorded which has become a big challenge for developing nations like India. Due to the increase in number of vehicles, traffic on roads has increased at a rapid rate; due to this, peoples traveling without vehicles are also becoming victims of road accidents. Various road accident occur because of many diverse factors like driving fast, drink and drive, and devastation to drivers, crossing red lights, and bypassing safety features like airbag, driving in wrong lane, and passing other vehicles in false. Road accident can lead to severe injuries but sometimes it can lead to death. Using state-of-the-art machine learning techniques like decision trees, K-nearest neighbors (KNN), Naive Bayes, and AdaBoost, this study aims to showcase the various efforts of its contributors in the area of traffic accident analysis. Various researchers have used these machine learning methods to predict the cluster of areas that are prone to accident or trouble spot areas and various factors that cause the road accident in that areas.

A volume penalization-based immersed boundary technique is developed and thoroughly validated for fluid flow problems, specifically flow over bluff bodies. The proposed algorithm has been implemented in an Open Source Field Operation and Manipulation (OpenFOAM). For capturing the fluid-solid interface more accurately, the grid is refined near the solid surface using topoSetDict and refineMeshDict utilities in OpenFOAM. In order to avoid any numerical oscillation, the present volume penalization method (VPM) is integrated with a signed distance function, which is also referred to as a level-set function. Benchmark problems, such as flows around a cylinder and a sphere, are considered and thoroughly validated with the results available in the literature. For the flow over a stationary cylinder, the Reynolds number is varied so that it covers from a steady 2D (two-dimensional) flow to an unsteady 3D (three-dimensional) flow. The capability of the present solver has been further verified by considering the flow past a vibrating cylinder in the cross-stream direction. In addition, a flow over a sphere, which is inherently three-dimensional due to its geometrical shape, is validated in both steady and unsteady regimes. The results obtained by the present VPM show good agreement with those obtained by a body-fitted grid using the same numerical scheme as that of the VPM, and also with those reported in the literature. The present results indicate that the VPM-based immersed boundary technique can be widely applicable to scientific and engineering problems involving flow past stationary and moving bluff bodies of arbitrary geometry.

Continuous monitoring of drinking water quality is essential for safeguarding public health, particularly in densely populated urban areas like the National Capital Region (NCR) of Delhi. In this study, 47 water samples were randomly collected from various public sources across Delhi and its surrounding areas using a standardized sampling technique. An Arduino-based sensor system was employed to measure critical water quality parameters, including pH, total dissolved solids (TDS), and turbidity. While pH levels were consistently within the acceptable limits across all samples, approximately 14% of the samples exhibited elevated TDS and turbidity levels, raising concerns about potential contamination. In contrast, commercially packaged drinking water samples were found to have significantly lower TDS and turbidity levels, suggesting a higher level of quality control in packaged water compared to public sources. These findings emphasize the importance of continuous water quality monitoring to identify and mitigate potential health risks in real time. This study demonstrates the feasibility and effectiveness of Arduino-based systems as a cost-effective, scalable solution for real-time water quality assessment in urban environments. By leveraging sensor technology, this approach offers a practical means of ensuring safe drinking water in Delhi and similar densely populated regions.

A lack of physical activity stands out as a modifiable risk factor contributing to the development of depression. Exercise proves effective in alleviating depression symptoms across both clinical and non-clinical populations. Its impact extends to influential systems such as neurotransmitters and functions akin to antidepressants in the brain. Exercise fosters elevated neurotrophin levels while curtailing cortisol release through inhibition of the hypothalamic–pituitary–adrenal axis, resulting in a diminished psychological stress response. The positive outcomes derived from exercise parallel the effects of antidepressants among individuals grappling with depression. In light of these observations, this paper undertakes an analysis of exercise as a viable alternative to antidepressant treatments. The exploration encompasses a review of the depression concept, including its causes, signs, and symptoms. Additionally, the paper delves into the mechanisms and transformative effects of exercise on depression. Multiple studies underscore the merits of exercise for individuals with depression, suggesting that its effects may indeed serve as a plausible alternative to antidepressants in certain scenarios.

In the modern era, high-speed vehicles are emerging out and driver want to run them at the full designed speed. But sometimes, the driver is not able to do so because of potholes, cracks, and other bad road conditions. Further, these factors are also responsible for wear and tear of the automobile, inconvenience of passengers, more fuel consumption of fuel, and as well as loss of human life in road accidents. So, detection of bad road conditions, mainly potholes, is very important to improve and maintain the road. Several image processing approaches were implemented to automatic monitoring the pavement surface to detect the potholes. But various road conditions and different scale, shape, size, and illumination effect of potholes led to unacceptable stability of approaches. Therefore, in this paper, convolution neural network is used to automatically detect and analyses the road conditions using digital images. It determines the very precise and accurate depth, area, and shape from digital images.

The Internet of Things (IoT) is a worldwide community of networked computers, gadgets, structures of interrelated computing devices, machinery, products, animals, and those with precise IDs and the capability to alternate information without the intervention of human beings or computers. Another issue to not forget at the same time as making use of the internet is that IoT devices are related to human beings and computer systems via internet protocol addresses and may ship and get hold of information via a network or from any other man-made object. We reviewed the utility of IoT, IoT technologies, IoT structure for design, present-day research and solutions, unsolved demanding situations for future IoT studies, and analysed many kinds of IoT designs making use of the IoT machine model's key crucial levels: Continue to discuss the challenges that the IoT will confront in terms of information security at the perception, network, and application levels.

Over the recent years, billions of IoT devices that do not have adequate security features have been created and deployed over internet, and with the increased bandwidth and lower latency of 5G networks these numbers expected to grow exponentially. Consequently, there is a pressing requirement of reliable methods to identify malware infected IoT devices present in a network. Traditional Centralized Deep Learning approach was used to train a model capable of detecting infected IoT devices, but this approach faces major challenges like data privacy violation, scalability and high network latency. A more efficient process Federated Deep Learning was proposed which aims to improve privacy and security by keeping data on the devices and transmitting only model parameters to a central location for aggregation and then this aggregated model reflected to the edge devices. Because of the centralized aggregation this approach is prone to adversarial attacks. To resolve these vulnerabilities, we presented a decentralized framework consisting of a chain of Federated blocks at each edge devices with cohere-consensus mechanism (FBCC). The framework makes use of blockchain technology both for updating local models and to store global models. We also came up with a novel cohere-consensus technique to facilitate the suggested FBCC, which efficiently cut down on the quantity of consensus computing while simultaneously lowering the risk of malicious attacks. At last, we conducted comprehensive testing of the frameworks with real-world datasets, in both benign and malicious environments. The results revealed that our proposed framework outperformed the other frameworks in the malicious environment, while demonstrating comparable efficiency in the benign environment. Additionally, we conducted a thorough comparison of the frameworks in terms of memory requirements and training time. FBCC framework, due to its core architecture, exhibited slightly lower performance than FDL. However, it maintained privacy as a top priority.

The Internet of Things, which is in the next phase of communication, is quickly overtaking all other technologies. IoT enables seamless data exchange, interaction, and communication between various physical objects. IoT brings automation and intelligence to a variety of industries and fields, including agriculture, transportation, industry, and health. Improving user efficiency and comfort is the goal of IoT applications. The security of internet-connected devices has recently become more important because of global cyber-attacks. Authentication is one of the most important network security principles, whether for small networks like local servers or large networks like central cloud servers. For IoT applications, several solutions have also been put forth, but they are not at all efficient and as well secure. In this paper, we have proposed a trust-based authentication method, which is not only lightweight but as well is secure. For verification of the security of the protocol AVISPA tool has been used in various modes.

Device-to-device communication attracts the research community due to its diversifying range of applications. Device-to-device communication occurs between two devices without any aid from the base station. Thus, it helps in providing connectivity in low connectivity areas. In this article, authors proposed a new technique named as C lustering B ased O pportunistic T raffic Offloading (CBOT), for Device-to-device communication. The proposed CBOT technique divided the network into small clusters and used a hybrid scheme for data transmission. To improve the energy consumption and throughput of the system, authors have proposed cluster formation, cluster head selection, and rotation techniques. The simulation results show that CBOT improves network lifetime by achieving high energy efficiency and increased system performance by achieving higher throughput. Further, the performance of CBOT tested under opportunistic networks scenario and simulation results demonstrate that the proposed approach improves energy consumption and throughput in a better way than the existing approaches.

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized leastmean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l1-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.