Continuous wearable electrocardiogram monitoring has transformed cardiac assessment into an Internet of Medical Things problem. Yet, existing learning frameworks remain fragmented—addressing robustness, scalability, and cryptographic security separately while neglecting cross-layer deployment constraints. This paper presents a unified cross-layer architecture that jointly designs lightweight wearable representation learning, edge-assisted variational quantum classification, Byzantine-resilient federated optimization, and standardized post-quantum authenticated model exchange within a single IoT framework. The proposed system treats communication limits, latency budgets, and adversarial stability as first-class design variables. We establish non-convex convergence guarantees under robust aggregation, PAC-style generalization bounds under heterogeneous client distributions, and explicit upper bounds on adversarial attack success growth. Evaluation on MIT-BIH with cross-dataset validation on PTB-XL demonstrates consistent macro-F1 gains over centralized and conventional federated baselines while preserving calibration quality and bounded communication cost. The results substantiate a deployment-aware secure cardiac intelligence architecture for next-generation IoT healthcare systems, supported by controlled comparisons against parameter-matched classical heads and measured cryptographic/system overheads. While the current evaluation is conducted under simulated NISQ and resource-constrained IoMT conditions, the framework is designed to support future validation on physical quantum and wearable-edge platforms.

Audio splicing forgery involves cutting specific parts of an audio recording and inserting or combining them into another audio recording. This manipulation technique is often used to create misleading or fake audio content, particularly in digital media environments. The detection of audio splicing forgery is of great importance, especially in forensic analysis, security applications and media verification processes. In this paper, we present a novel noise robust method for detecting audio splicing forgery. The proposed method converts audio signals into cochleagram images, which are then input into SWIN transformer model for training. Following the training process, the model classifies and labels test audio files as either original or fake. In the experiments, the method is tested on data sets of varying durations. The results demonstrate high performance across different datasets, both without and with Gaussian noise, as well as under real-world environmental noise attacks with varying audio durations. For example, under 30 dB noise condition on 2-second data segments, the model achieved an accuracy of 94.33%, precision of 96.46%, recall of 92.90%, and an F1-score of 94.65%. For rain noise condition, the proposed method achieves the highest accuracy of 93.26%, precision of 99.83%, and F1-score of 95.48% .

This paper proposes an intelligent risk management system in stock markets based on indications of social media platforms . Based on a brief survey, we found that the literature focuses on identifying, assessing and optimizing risks in stock markets using classical data sources as well as utilizing mathematical, statistical and machine learning techniques . So, we suggest a trendy data source that is social media and employing natural language processing as an advanced technique to identify and estimate risks in stock markets. This NLP model can help the investors of stock markets to be aware of the potential risks, its types and magnitudes which can help them to take the best decisions. In addition , we describe this model and some important expressions for risk analysis , risk recognition and risk estimation are driven. Besides, a practical experiment is provided to identify and evaluate the potential risks in NASDAQ stock market. Moreover, several performance measures are used to validate the efficiency of the proposed model such as the mean accuracy for the processes of risk analysis , risk identification and risk assessment is 73.33 %.

Advances in the connected vehicle and cloud computing technologies, Big data, and artificial intelligence techniques have opened new research opportunities. We can integrate them to work out the issues originating from transportation complexities and offer improved services. In this work, we present a seamless multi-module multi-layer vehicular cloud computing system developed using resources of parked vehicles, cloud computing facilities, and vehicular networking technologies. It can offer transportation-specific AI and Big data-empowered services to on-road vehicles. As use cases, we present two innovative and improved services, vehicular Big data mining and vehicular route optimization. A physical testbed is formed to show the feasibility of this work. Results analysis shows that the systems perform better than the standalone systems and servers under different scenarios. Relevant fundamental challenges and future outlooks are also highlighted in this work.

Vehicle Edge Computing (VEC)-assisted computational offloading brings cloud computing closer to user equipment (UEs) at the edge of the access network by delivering various services to the UEs with limited processing power and battery. However, in fifth-generation and beyond 5G (B5G) networks, where UEs’ service requests and locations change dynamically, the deployment of static edge server deployments may lead to an increase in latency and total energy consumption. This paper presents a latency-energy-aware, efficient task offloading scheme for connected autonomous vehicular networks. Firstly, vehicles are assembled into clusters, in which vehicle can transmit tasks to the other vehicle, while on the other hand, the VEC server is used for processing the data. We developed a joint resource allocation and offloading decision optimization problem to minimize network latency and total energy usage. Due to the non-convex character of the optimization issue, we employed the Markov decision process (MDP) to convert it to a reinforcement learning (RL) problem. Then, we used a soft-actor critic-based scheme to achieve the optimal policy for resource allocation and task offloading to reduce the total latency and energy consumption for connected autonomous vehicles. Simulation analysis reveals that the proposed scheme attains 46.6% and 17.2% lesser delay, and 28.8% and 20.0% consumes less energy than the Hybrid DRL with Genetic Algorithm (HDRL-GA) and DRL based collaborative Data Scheduling (DRL-CDSS) state-of-art schemes.

The need to evolve a novel feature selection (FS) approach was motivated by the persistence necessary for a robust FS system, the time-consuming exhaustive search in traditional methods, and the favourable swarming manner in various optimization techniques. Most of the datasets have a high dimension in many issues since all features are not crucial to the problem, which reduces the algorithm's accuracy and efficiency. This article presents a hybrid feature selection approach to solve the low precision and tardy convergence of the butterfly optimization algorithm (BOA). The proposed method is dependent on combining the algorithm of BOA and the particle swarm optimization (PSO) as a search methodology using a wrapper framework. BOA is started with a one-dimensional cubic map in the proposed approach, and a non-linear parameter control technique is also implemented. To boost the basic BOA for global optimization, PSO algorithm is mixed with the butterfly optimization algorithm (BOAPSO). A 25 dataset evaluates the proposed BOAPSO to determine its efficiency with three metrics: classification precision, the selected features, and the computational time. A COVID-19 dataset has been used to evaluate the proposed approach. Compared to the previous approaches, the findings show the supremacy of BOAPSO for enhancing performance precision and minimizing the number of chosen features. Concerning the accuracy, the experimental outcomes demonstrate that the proposed model converges rapidly and performs better than with the PSO, BOA, and GWO with improvement percentages: 91.07%, 87.2%, 87.8%, 87.3%, respectively. Moreover, the proposed model's average selected features are 5.7 compared to the PSO, BOA, and GWO, with average features 22.5, 18.05, and 23.1, respectively.

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods.

Sensor-aware distributed workflow applications are becoming increasingly popular underwater. The apps are marine operations that generate data and process it based on its characteristics. Mobile-fog-cloud paradigms, as well as computing such as sensor nodes, have emerged. As previously stated, the nodes can be combined into a single system to achieve several goals. Many factors are considered, including network contents, workload fluctuation, variable execution durations, deadlines, and bandwidth. As a result, scheduling mobile workflow systems with multiple parameters might be challenging. The study suggests a novel content-efficient decision-aware task scheduling (CATSA) method for defining and adapting to complicated environmental changes. The CATSA consists of several components that work together to perform various benchmarks in the system, including a decision planner, sequencing, and scheduling. As evidenced by test findings during evaluation, the suggested architecture outperforms current studies regarding workflow execution quality of services and improved the makespan 30% and deadline meeting 40% in the study.

Abstract Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.

In Industrial-Internet-of-Things (IIoT) applications, fog computing (FC) has soared as a means to improve the Quality of Services (QoSs) provided to users through cloud computing, which has become overwhelmed by the massive flow of data. Transmitting all these amounts of data to the cloud and coming back with a response can cause high latency and requires high network bandwidth. The availability of sustainable energy sources for FC servers is one of the difficulties that the service providers can face in IIoT applications. The most important factor contributing to energy consumption on fog servers is task scheduling. In this article, we suggest an energy-aware metaheuristic algorithm based on a Harris Hawks optimization algorithm based on a local search strategy (HHOLS) for task scheduling in FC (TSFC) to improve the QoSs provided to the users in IIoT applications. First, we describe the high virtualized layered FC model taking into account its heterogeneous architecture. The normalization and scaling phase aids the standard Harris hawks algorithm to solve the TSFC, which is discrete. Moreover, the swap mutation ameliorates the quality of the solutions due to its ability to balance the workloads among all virtual machines. For further improvements, a local search strategy is integrated with HHOLS. We compare HHOLS with other metaheuristics using various performance metrics, such as energy consumption, makespan, cost, flow time, and emission rate of carbon dioxide. The proposed algorithm gives superior results in comparison with other algorithms.

Human activity recognition (HAR) has been regarded as an indispensable part of many smart home systems and smart healthcare applications. Specifically, HAR is of great importance in the Internet of Healthcare Things (IoHT), owing to the rapid proliferation of Internet of Things (IoT) technologies embedded in various smart appliances and wearable devices (such as smartphones and smartwatches) that have a pervasive impact on an individual's life. The inertial sensors of smartphones generate massive amounts of multidimensional time-series data, which can be exploited effectively for HAR purposes. Unlike traditional approaches, deep learning techniques are the most suitable choice for such multivariate streams. In this study, we introduce a supervised dual-channel model that comprises long short-term memory (LSTM), followed by an attention mechanism for the temporal fusion of inertial sensor data concurrent with a convolutional residual network for the spatial fusion of sensor data. We also introduce an adaptive channel-squeezing operation to fine-tune convolutional a neural network feature extraction capability by exploiting multichannel dependency. Finally, two widely available and public HAR data sets are used in experiments to evaluate the performance of our model. The results demonstrate that our proposed approach can overcome state-of-the-art methods.

Evolution of the Internet of Things (IoT) makes a revolution in connecting, monitoring, controlling, and managing things, objects, and almost surroundings through the Internet. To reveal the potential of IoT, rich knowledge has to be extracted, indexed, and shared securely in real time. Recent comprehensive researches on IoT spot the light on main correlative challenges, such as security, scalability, heterogeneity, and big data. Due to the heterogeneity of IoT applications that produce a large volume of a variety of data streams in real time, mining, securing, and analyzing IoT data become tedious and challenging tasks. Indexing sensory data is one of data mining techniques, which ease information retrieval. But ordinary indexing methods are not fit with such massive and dynamic data; where indexes become out-of-date once they are built. Clustering, data reduction, and summarization present promising solutions for enabling low-power security and balanced indexing. This article presents a novel method for dynamic data reduction and summarization using dynamic time warping (DTW), which also presents a balanced architecture for enabling balanced indexing based on similarity data fusion. Data reduction-based prediction models enable real-time search and secure discovery for Smart Things (SThs). The results of the proposed model were proved using real examples and data sets. Using the Szeged-weather data set similar SThs data is reduced by 95%. Thus, indexes sizes could be reduced, and using smart scheduling, crawling cycle length could be expanded.

Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate the risk of DDoS attacks, but only few efforts have been made to differentiate it from FC flooding as FC flooding also causes the denial of service and usually misleads the detection of the DDoS attacks. In this paper, an adaptive agent-based model, known as an Adaptive Protection of Flooding Attacks (APFA) model, is proposed to protect the Network Application Layer (NAL) against DDoS flooding attacks and FC flooding traffics. The APFA model, with the aid of an adaptive analyst agent, distinguishes between DDoS and FC abnormal traffics. It then separates DDoS botnet from Demons and Zombies to apply suitable attack handling methodology. There are three parameters on which the agent relies, normal traffic intensity, traffic attack behavior, and IP address history log, to decide on the operation of two traffic filters. We test and evaluate the APFA model via a simulation system using CIDDS as a standard dataset. The model successfully adapts to the simulated attack scenarios’ changes and determines 303,024 request conditions for the tested 135,583 IP addresses. It achieves an accuracy of 0.9964, a precision of 0.9962, and a sensitivity of 0.9996, and outperforms three tested similar models. In addition, the APFA model contributes to identifying and handling the actual trigger of DDoS attack and differentiates it from FC flooding, which is rarely implemented in one model.

Summary In recent years, massive growth in the number of images on the web has raised the requirement of developing an effective indexing model to search digital images from a large‐scale database. Though cloud service offers effective indexing of compressed images, it remains a major issue due to the semantic gap between the user query and diverse semantics of large‐scale database. This article presents a radix trie indexing (RTI) model based on semantic visual indexing for retrieving the images from cloud platforms. Initially, an interactive optimization model is applied to identify the joint semantic and visual descriptor space. Next, an RTI model is applied to integrate the semantic visual joint space model for finding an effective solution for searching large‐scale sized dataset. Finally, a Spark distributed model is applied for deploying the online image retrieval service. The performance of the proposed method is validated on two standard dataset, namely, Holidays 1 M and Oxford 5 K in terms of mean average precision (mAP) and processing time under varying dataset sizes. During experimentation, the presented RTI model shows the maximum mAP value of 0.83 under the dataset size of 1000. Similarly, under the sample count of 1000, it is noted that the standalone server requires a maximum of 118 minutes to complete the process, whereas the spark cluster requires a minimum of around only 19 minutes to finish the process. The experimental outcome showed improvement in terms of various measures over the best rivals in the literature.

To improve the quality of service (QoS) needed by several applications areas, the Internet of Things (IoT) tasks are offloaded into the fog computing instead of the cloud. However, the availability of ongoing energy heads for fog computing servers is one of the constraints for IoT applications because transmitting the huge quantity of the data generated using IoT devices will produce network bandwidth overhead and slow down the responsive time of the statements analyzed. In this article, an energy-aware model basis on the marine predators algorithm (MPA) is proposed for tackling the task scheduling in fog computing (TSFC) to improve the QoSs required by users. In addition to the standard MPA, we proposed the other two versions. The first version is called modified MPA (MMPA), which will modify MPA to improve their exploitation capability by using the last updated positions instead of the last best one. The second one will improve MMPA by the ranking strategy based reinitialization and mutation toward the best, in addition to reinitializing, the half population randomly after a predefined number of iterations to get rid of local optima and mutated the last half toward the best-so-far solution. Accordingly, MPA is proposed to solve the continuous one, whereas the TSFC is considered a discrete one, so the normalization and scaling phase will be used to convert the standard MPA into a discrete one. The three versions are proposed with some other metaheuristic algorithms and genetic algorithms based on various performance metrics such as energy consumption, makespan, flow time, and carbon dioxide emission rate. The improved MMPA could outperform all the other algorithms and the other two versions.

With the rapid development of in-vehicle communication technology and the integration of big data intelligent technology, intelligent algorithms for vehicle communication used to predict traffic flow and location information have been widely used. Aiming at the problem that the gravitational algorithm is difficult to minimize the complex function and easily fall into the local optimum, this paper proposes an improved IGSA algorithm. First, a gridding algorithm is introduced to initialize the population, and under the premise of ensuring the randomness of the initial individuals, improving the ergodicity of the population is conducive to improving the quality of the solution; then, an adaptive location-based update strategy of decreasing inertia weights is proposed. this strategy inherits the advantages of linearly decreasing weights, and adaptively adjusts the weights according to the fitness value to further improve the optimization performance. The optimization simulation of 8 classic test functions shows that the IGSA algorithm is an effective algorithm for solving complex optimization problems. Finally, the IGSA algorithm is used to predict the geographic location problem in the vehicle GPS data. The IGSA algorithm is used to optimize the extreme learning method to optimize the hyperparameters and establish a vehicle GPS data prediction model. Simulation results verify the feasibility of the method.

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through non-sub-sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them.

Parkinson's disease (PD) diagnostics includes numerous analyses related to the neurological, physical, and psychical status of the patient. Medical teams analyze multiple symptoms and patient history considering verified genetic influences. The proposed method investigates the voice symptoms of this disease. The voice files are processed, and the feature extraction is conducted. Several machine learning techniques are used to recognize Parkinson's and healthy patients. This study focuses on examining PD diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. The MAFT algorithm is designed to select a set of features to improve the overall performance of prediction models and prevent over-fitting possibly due to extreme reduction to the features. Moreover, this algorithm aims to reduce the complexity of the prediction, accelerate the training phase, and build a robust training model. Ten different machine learning methods are then integrated with the MAFT algorithm to form a powerful voice-based PD diagnosis model. Recorded test results of the PD prediction model using the actual and filtered features yielded 86.38% and 86.67% accuracies on average, respectively. With the aid of the MAFT feature selection, the test results are improved by 3.2% considering the hybrid model (HM) and 3.1% considering the Naïve Bayesian and random forest. Subsequently, an HM, which comprises a binary convolutional neural network and three feature selection algorithms (namely, genetic algorithm, Adam optimizer, and mini-batch gradient descent), is proposed to improve the classification accuracy of the PD. The results reveal that PD achieves an overall accuracy of 93.7%. The HM is integrated with the MAFT, and the combination realizes an overall accuracy of 96.9%. These results demonstrate that the combination of the MAFT algorithm and the HM model significantly enhances the PD diagnosis outcomes.

This article presents a novel data encryption technique suitable for Internet of Things (IoT) applications. The cryptosystem is based on the application of a Catalan object (as a cryptographic key) that provides encryption based on combinatorial structures with noncrossing or nonnested matching. The experimental part of this article includes a comparative analysis of the proposed encryption method with the Catalan numbers and data encryption standard (DES) algorithm, which is performed with machine learning-based identification of the encryption method using ciphertext only. These tests showed that it is much more difficult to recognize ciphertext generated with the Catalan method than one made with the DES algorithm. System reliability depends on the quality of the key, therefore, statistical testing proposed by National Institute of Standards and Technology was also performed. Twelve standard tests, the approximate entropy measurement, and random digression complexity analysis are applied in order to evaluate the quality of the generated Catalan key. A proposal for applying this method in e-Health IoT is also given. Possibilities of applying this method in the IoT applications for smart cities data storage and processing are provided.

The sixth‐generation (6G) is envisioned as a pivotal technology that will support the ubiquitous seamless connectivity of substantial networks. The main advantage of 6G technology is leveraging Artificial Intelligence (AI) techniques for handling its interoperable functions. The pairing of 6G networks and AI creates new needs for infrastructure, data preparation, and governance. Thus, Intent‐Based Network (IBN) architecture is a key infrastructure for 6G technology. Usually, these networks are formed of several clusters for data gathering from various heterogeneities in devices. Therefore, an important problem is to find the minimum transmission power for each node in the network clusters. This paper presents hybridization between two Computational Intelligence (CI) algorithms called the Marine Predator Algorithm and the Generalized Normal Distribution Optimization (MPGND). The proposed algorithm is applied to save power consumption which is an important problem in sustainable green 6G‐IBN. MPGND is compared with several recently proposed algorithms, including Augmented Grey Wolf Optimizer (AGWO), Sine Tree‐Seed Algorithm (STSA), Archimedes Optimization Algorithm (AOA), and Student Psychology‐Based Optimization (SPBO). The experimental results with the statistical analysis demonstrate the merits and highly competitive performance of the proposed algorithm.

Summary The cases identified with Brain tumor have increased with respect to time owing to various reasons. One of the major challenging issues can be defined by incorporating image processing along with data mining models as classification approach. There are various procedures as of now exhibited for segmentation of brain tumor effectively. In any case, it is as yet unequivocal to distinguish the brain tumor from MR images. In this new tumor classifying, considering two significant models, such as Feature Selection (FS) and Machine Learning classification techniques, are extremely valuable for distinguishing and visualizing the tumor in the MRI brain images; it is classified using Adaptive Neuro‐Fuzzy Interface System (ANFIS). For better classification of image, Optimal Feature Level Fusion (OFLF) is considered to fuse low and high‐level feature of brain image; from this analysis, the images are classifying as Benign or Malignant. From this implementation of medical images, the experiment results are evaluating performance metrics are compared existing classifiers. From the proposed MRI image classification process the accuracy as 96.23%, sensitivity as 92.3%, and specificity as 94.52%, compared to existing classifier. It is in the working platform of MATLAB that this proposed methodology is implemented.

Modern supervisory control and data acquisition (SCADA) networks represent a challenging domain for forensic investigators who have the responsibility to determine the main causes of the catastrophic incidents that could happen in SCADA systems and to provide precise and logical evidences supported with comprehensive technical reports to the legal organisations. They are characterised to be complex, large-scale, and highly distributed systems comprising diversities of proprietary components such as field devices, embedded control systems, computers, communication networks, etc. Providing forensic investigators with fully or partially automated forensic investigation can be an effective solution against the challenging nature of modern SCADA networks. This review paper discusses the challenges and opportunities towards achieving that goal and highlights the emerging technological paradigms that can be considered as promising in the realisation of such a framework. Finally, this paper proposes a conceptual framework for automated forensic investigation in modern SCADA networks accompanied with a possible realisation architecture based on the multi-agent systems and wireless sensor networks promising technological paradigms.

In recent years, the need for high security with reliability in the wireless network has tremendously been increased. To provide high security in reliable networks, mobile ad hoc networks (MANETs) play a top role, like open network boundary, distributed network, and fast and quick implementation. By expanding the technology, the MANET faces a number of security challenges due to self-configuration and maintenance capabilities. Besides, traditional security solutions for wired networks are ineffective and inefficient because of the nature of highly dynamic and resource-constrained MANETs. In this paper, the researchers focus on improving reliable data transmission with high security in the MANET using an optimization technique. In the proposed MANET system, the nodes are clustered by utilizing an energy-efficient routing protocol. Then, the modified discrete particle swarm optimization is used to select the optimal cluster head. A secured routing protocol and a signcryption model can be used to improve the transmission security of the reliable MANET. The signcryption algorithm encrypts the digital signature, which can enhance the overall efficiency and confidentiality. The security-based analysis is performed on the basis of packet delivery ratio, energy consumption, network lifetime, and throughput. Finally, the result demonstrates that the MANET with optimization techniques achieves a high transmission rate and improves the reliable data security.

Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.

SCADA (Supervisory Control and Data Acquisition) networks are built to efficiently provide supervisory and control of national and international critical infrastructures. SCADA networks represent a challenging domain for forensic investigators who have the responsibility to discover the main causes of the catastrophic incidents that could happen in these critical mission systems and provide precise and logical evidences supported with comprehensive technical reports to the legal organizations. They urgently need technological tools and frameworks that enable them to effectively do their mission without affecting the running state of SCADA networks which must be sustainable and robust against technical and disruptive incidents. This paper discusses the challenges and opportunities towards achieving that goal and highlights the emerging technological approaches and paradigms that can be considered as promising for the realization of such a framework taking into account the efficient consumption of computational resources. Further, this paper proposes a conceptual framework for automated and secure forensic investigation in modern complex SCADA networks accompanied with a possible realization architecture based on the Multi-Agent Systems (MAS) and Wireless Sensor Networks (WSN) promising technological paradigms. The proposed framework is intentionally designed to be compliant with the currently active motivation towards promoting green computing requirements.

Tactile Internet can combine multiple technologies by enabling intelligence via mobile edge computing and data transmission over a 5G network. Recently, several convolutional neural networks (CNN) based methods via edge intelligence are utilized for fire detection in certain environment with reasonable accuracy and running time. However, these methods fail to detect fire in uncertain Internet of Things (IoT) environment having smoke, fog, and snow. Furthermore, achieving good accuracy with reduced running time and model size is challenging for resource constrained devices. Therefore, in this paper, we propose an efficient CNN based system for fire detection in videos captured in uncertain surveillance scenarios. Our approach uses light-weight deep neural networks with no dense fully connected layers, making it computationally inexpensive. Experiments are conducted on benchmark fire datasets and the results reveal the better performance of our approach compared to state-of-the-art. Considering the accuracy, false alarms, size, and running time of our system, we believe that it is a suitable candidate for fire detection in uncertain IoT environment for mobile and embedded vision applications during surveillance.

Abstract Clustering methods have been developed to improve network life of wireless sensor network (WSN), yet the dynamic nature of sensor clusters and limited memory and processing power make security a much more challenging problem, and most conventional cryptography methods are ill suited to WSNs. In this paper, we propose a novel encryption method to secure data transmission in WSN with dynamic sensor clusters. Our method leverages elliptic curve cryptography algorithm to generate binary strings for each sensor and combines with node ID, distance to the cluster head, and the index of transmission round to form unique 176‐bit encryption keys. Using exclusive OR, substitution, and permutation operations, encryption and decryption are achieved efficiently. Compared with the state‐of‐the‐art methods, our simulation results demonstrated that the proposed method exhibited much improved network lifetime and reduced the energy consumption most evenly among all sensor nodes. More importantly, it overcame many security attacks including brute‐force attack, HELLO flood attack, selective forwarding attack, and compromised cluster head attack. Copyright © 2016 John Wiley & Sons, Ltd.