Abstract The escalating impacts of marine pollution and climate change demand reliable underwater monitoring systems capable of operating under harsh subsea conditions, including high pressure, low visibility, and constrained communication bandwidth. This paper proposes a Hybrid Underwater Wireless Sensor Network (UHWSN) that integrates acoustic and optical sensing with advanced deep learning to enable efficient and accurate underwater monitoring. At the sensor layer, mean-shift tracking is employed for robust object localization, while arithmetic coding performs lossless compression, reducing transmitted data volume by up to 60% without information loss. At the cluster-head layer, Convolutional Neural Networks (CNNs) conduct hierarchical feature extraction, and Deep Belief Networks (DBNs) provide probabilistic object classification and anomaly detection. The framework was evaluated using a video-group-aware stratified 80/20 split on a combined dataset of 7,212 unique images from the TrashCan and J-EDI underwater benchmarks. Experimental results demonstrate strong performance, achieving 98.0% Average Precision (AP) for object tracking, 99.1% anomaly detection accuracy, and overall classification accuracies of 98.63% during training and 99.30% on a strictly held-out test set. Regression analysis further confirms high predictive reliability, yielding a perfect linear correlation (R = 1) between predicted and ground-truth values with uniformly distributed residuals. Statistical validation using a paired t-test (p < 0.001) demonstrates significant superiority over baseline and ablation models. Compared with state-of-the-art approaches, the proposed framework consumes 1.25 J per processed image in simulation, representing a 35–55% reduction relative to ablations and corresponding to 30–40% communication overhead savings. These results highlight the effectiveness of the proposed UHWSN framework for energy-efficient, high-precision underwater monitoring in resource-constrained environments.

Institutions must improve their IT infrastructure to accommodate their rising activities and prevent service disruptions caused by the growing threat of malware and cyberattacks. The proposed approach focuses on improving communication institutions’ network infrastructure by creating a network topology that creates an on-premises configuration with two sites and three different networks. This concept takes a new approach to network infrastructure upgrades by adopting a dual-site, on-premises design with three specialized networks: a LAN for client devices, a server data center, and a DMZ for hosting public-facing services like websites and DNS. This architecture is built around a layered security framework based on FortiGate firewalls is key to this design, as it not only protects critical data and resources from unwanted access but also incorporates advanced trap and IP security technologies for optimal protection. Furthermore, the proposed infrastructure is designed to be scalable, allowing for future organizational expansion and technological advancement. This solution creates a resilient and future-proof basis for important business processes by combining robust on-premises architecture with cutting-edge security measures, resulting in uninterrupted service delivery and increased operational efficiency.

The increasing prevalence of marine pollution and the escalating impacts of climate change have intensified the need for advanced underwater monitoring systems capable of operating reliably in harsh subsea environments. However, underwater environmental monitoring remains a challenging task due to factors such as high hydrostatic pressure, limited visibility, dynamic water conditions, and the inherent constraints of underwater communication channels. To address these challenges, this study proposes a Hybrid Underwater Wireless Sensor Network (UHWSN) that integrates acoustic and optical sensing modalities with advanced deep learning techniques to enable efficient underwater debris detection, tracking, and classification. The proposed framework combines mean-shift-based object tracking, lossless arithmetic coding, Convolutional Neural Networks (CNNs), and Deep Belief Networks (DBNs) within a hierarchical processing architecture. At the sensor-node level, mean-shift tracking is employed for region-of-interest (ROI) localization, while arithmetic coding provides efficient lossless data compression, achieving a data-volume reduction of approximately 55–60

Wireless sensor networks (WSNs) have several uses and provide endless future possibilities. Nodes in wireless sensor networks are prone to failure owing to energy depletion, communication link problems, malicious attacks, and so on. As a result, self-recovery mechanisms are one of the most important challenges in WSNs. Fault detection is the primary strategy in the self- recovery mechanism in wireless sensor networks (WSNs), with each cluster head frequently checking the readings of its members. According to previous research, most comparing approaches will fail if more than half of a sensor's nearby nodes are incorrect. Furthermore, these comparing approaches cannot discover common mode failures. The suggested fault self-recovery method works by comparing the pulse sequence number generated by surrounding nodes and disseminating the choice made regarding each node. This paper presents an approach which can both locate and recover malfunctioning nodes in sensor networks. The proposed model is integrating capabilities of isolating the defective cluster sensors, which cause WSN malfunctions, from the cluster cycling and advertising the new path coordinates for the base station (BS). The simulation findings reveal that the suggested Effective Fault Clustering Management (EFCM) approach is very precise in discovering malfunctioning nodes and very fast in finding a cover free of such nodes when using the NS3 simulator

The explosive growth of the Internet of Things (IoT) has highlighted the urgent need for strong network security measures. The distinctive difficulties presented by Internet of Things (IoT) environments, such as the wide variety of devices, the intricacy of network traffic, and the requirement for real-time detection capabilities, are difficult for conventional intrusion detection systems (IDS) to adjust to. To address these issues, we propose DCGR_IoT, an innovative intrusion detection system (IDS) based on deep neural learning that is intended to protect bidirectional communication networks in the IoT environment. DCGR_IoT employs advanced techniques to enhance anomaly detection capabilities. Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. Furthermore, complex gated recurrent networks (CGRNs) are used for the temporal feature extraction module, which is utilized by DCGR_IoT. Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. The effectiveness of the DCGR_IoT was proven through extensive evaluations of the UNSW-NB15, KDDCup99, and IoT-23 datasets, which resulted in a high detection accuracy of 99.2%. These results demonstrate the DCG potential of DCGR-IoT as an effective solution for defending IoT networks against sophisticated cyber-attacks.

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering–learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring.

To avoid overloading a network, it is critical to continuously monitor the natural environment and disseminate data streams in synchronization. Based on self-maintaining technology, this study presents a technique called self-configuration management (SCM). The purpose is to ensure consistency in the performance, functionality, and physical attributes of a wireless sensor network (WSN) over its lifetime. During device communication, the SCM approach delivers an operational software package for the radio board of system problematic nodes. We offered two techniques to help cluster heads manage autonomous configuration. First, we created a separate capability to determine which defective devices require the operating system (OS) replica. The software package was then delivered from the head node to the network’s malfunctioning device via communication roles. Second, we built an autonomous capability to automatically install software packages and arrange the time. The simulations revealed that the suggested technique was quick in transfers and used less energy. It also provided better coverage of system fault peaks than competitors. We used the proposed SCM approach to distribute homogenous sensor networks, and it increased system fault tolerance to 93.2%.

Abstract As institutions expand, and grow, surging interference malware and attacks; large portions of the Internet at a time impinge and create large amounts of service disruption. So, their IT infrastructure needs to be updated and expanded to accommodate the changing demands of their business. The project's main objective is to improve and enhance the network infrastructure design for communication institutions. In this work, we tried to present a meliorating plan for the infrastructure of enormous communication systems inside institutions. It implemented the network topology so it has established an on-premises infrastructure constituting two sites with three different networks: LAN, data center, and DMZ. The LAN network was reserved for client devices. In contrast, the data center network is intended for servers such as the domain controller, additional domain controller, file storage server, network-attached storage, DHCP server, DNS server, and WDS server (used for deploying operating systems on devices). The DMZ network included servers for hosting public websites (IIS server) and public DNS, as well as a DFS server for replicating files between the two sites. To ensure the security of the infrastructure, a Fortigate firewall is used to separate the DMZ network from the LAN and data center networks. It connected firewall A to Firewall B to enhance security and filter traffic. It connected firewall B to a router, which is then connected to the ISP network.The on-premises infrastructure provides a reliable, secure, and scalable solution for institutions. The layered security approach with Fortigate firewalls helps to ensure that data and resources are protected from unauthorized access. Also, we performed the trap and IP security technology on the sites. The infrastructure is flexible enough to accommodate the organization's future growth and expansion needs. The combination of on-premises infrastructure and layered security measures provides a solid foundation for critical business operations. The infrastructure is well-suited to meet the needs of a modern enterprise with its advanced threat protection capabilities and ability to scale up or down as required.

Wireless sensor networks (WSNs) have conquered comprehensive survey progressions in the regular control and management fields. Although WSN allows the spatial monitoring of real-world events, the mobility action depletes a huge part of a sensor’s energy cost in wireless communication. WSN sensors are often prone to various faults as frequent crashes and temporary or permanent failures. This is because it propagates them in very complex and harsh environments. So, we tend to design a Self-Adaptive based Autonomous Fault-Awareness (SAAFA) model, to limit the impact of such failures and filter them. In this paper, we incorporate the two of adaptive-filters FIR with RLS through three adaptive two-stages performed at the level of cluster head, for independent fault-correction during the propagation platform. The proposed model (SAAFA) included two stages, the first stage comprised self-detection the failure and self-aware for the lost scales, in which relied on responses of delay port and prior-knowledge of absent sensor-signals throughout monitoring, through adjusting the filter weights in the adaptive feedback loop for awarding convergent signals for the lost ones. The second stage is adaptive filtering the registered signals from the above stage for gaining pure measures and free of interferences. Compared to the state-of-the-art methods, the scheduled model attained a speed in diagnosing faults and awareness the missing readings with a rate of accuracy reached 98.8% improving the robustness of performance. Evaluation criteria revealed the progress of SAAFA in reducing the radio communication to ~ 97.47% that kept about 93.7% of battery-energy throughout the picked dataset sample. Hence, it expanded the whole network lifetime.

Wireless Sensor Networks (WSNs) are exposed to various data-deployment faults during the communication action. These faults may impact the behaviour of the sensors that degrade its performance and cuts its life. Therefore, we tend to implement the integration of two independent trends are self-awareness and self-adaptation capabilities along with two integrated adaptive filters, FIR and RLS. The proposed Autonomous Fault-Awareness and Adaptive (AFAA) model composed of three adaptive two-stage executed self-awareness approach to limit the impact of such faults during the propagation process. In this paper, we introduce the operational mechanism of AFAA that manages to identify the failure and aware of the lost signal values autonomously, then filter the perceptive-signals for eliminating the accompanied interference and gaining convergent values. It executed the incorporated autonomous model at the level of Cluster Head (CH) for independent fault-correction using an adaptive feedback model. Compared to the state-of-the-art methods, the proposed model achieved speed in fault diagnosis; also high-accuracy rate in the prediction of the lost signal values as much as 98.63%, thus improving the percentage of performance efficiency to 3:1 times along of duty cycle. Hence, it enhanced the overall network lifetime.

Wireless sensor networks (WSNs) are collecting data periodically by randomly dispersed sensors (motes) that typically exploit high energy in monitoring a specified application. Furthermore, dissemination mode in WSN is resulting noisy or missing information that affects the behaviour of WSN. So, data prediction-based filtering is an important approach to reduce redundant data transmissions, conserve node energy and overcome the defects resulted from data dissemination. Therefore, in this article, a distributed data-reduction model (DDRM) is proposed to prolong the network lifetime by decreasing the energy consumption of sensor nodes. It is built upon a distributive clustering model for predicting diffusion-faults in WSN. The proposed model is developed using the RLS adaptive filter integrated with a FIR filter for minimising the amount of transmitted data and provide high convergence of the signals. A dataset of atmospheric changes was handled. The results clarify that DDRM reduced the rate of data transmission to ~20%. Also, it depressed the energy consumption to ~95% throughout the dataset sample. DDRM effectively upgraded the performance of the sensory network by about 19.5%, and hence extend its lifetime.

Wireless sensor networks (WSNs) are collecting data periodically through randomly dispersed sensors (motes) that typically exploit high energy in monitoring a specified application. Furthermore, dissemination mode in WSN usually produces noisy or missing information that affects the behavior of WSN. Data prediction-based filtering is an important approach to reduce redundant data transmissions, conserve node energy, and overcome the defects resulted from data dissemination. Therefore, this letter introduced a novel model was based on a finite impulse response filter integrated with the recursive least squares adaptive filter for improving the signals transferring function by canceling the unwanted noise and reflections accompanying of the transmitted signal and providing high convergence of the transferred signals. The proposed distributed data predictive model (DDPM) was built upon a distributive clustering model for minimizing the amount of transmitted data aimed to decrease the energy consumption in WSN sensor nodes. The results clarified that DDPM reduced the rate of data transmission to ~20%. Also, it depressed the energy consumption to ~95% throughout the dataset sample. DDPM effectively upgraded the performance of the sensory network by about 19%, and hence extend its lifetime.

Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to ∼20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.

Wireless sensor networks (WSNs) have wide range of applications and provide limitless potential in our life. Unfortunately, they usually are prone to failure due to energy consumption, hardware failure, communication link errors, or malicious attacks. Therefore, fault tolerance mechanism is mandatory while designing the WSN. Fault tolerance includes fault detection, diagnosis, and repair. In the majority of WSN environments, fault tolerance is enforced and managed centrally at cluster head level. In this work, we present a distributed self-healing approach (DSHA), in which the processes of fault detection, diagnosis, and repair are performed at both node level and cluster head level. The proposed mechanism succeeded in locating hardware failures in sensor nodes, diagnosing them and applying countermeasures to ensure reliability and resiliency of the WSN. The countermeasures included isolating malfunction nodes and topology modifications. The experimental results proved that DSHA could tolerate up to 67.3% of hardware components failures and announce 62.6% improvement in the rate of sensor network lifetime.

In Wireless Sensor Networks (WSNs), fault tolerance of a sensor node is a demanding issue since sensors are usually deployed in unattended environments. Limited memory, processing power, and communication range of sensor nodes make conventional fault tolerance schemes infeasible for WSNs. This work introduces a distributed self-healing methodology in which the detection, diagnosis and healing processes were performed at both node and cluster head levels. At node level, battery, sensor and receiver faults were diagnosed. At cluster head level, transmitter and mal-functional nodes were detected and recovered. The simulation results showed that the proposed methodology is precise in locating malfunctioning nodes and fast in finding a cover for such nodes.