International Journal of Sensors Wireless Communications and Control - Current Issue

The Internet of Things offers ubiquitous automation of things and makes human life easier. Sensors are deployed in the connected environment that sense the medium and actuate the control system without human intervention. However, the tiny connected devices are prone to severe security attacks. As the Internet of Things has become evident in everyday life, it is very important that we secure the system for efficient functioning.

This paper proposes a secure federated learning-based protocol for mitigating BH attacks in the network.

The experimental result proves that the intelligent network detects BH attacks and segregates the nodes to improve the efficiency of the network. The proposed techniques show improved accuracy in the presence of malicious nodes.

The performance is also evaluated by varying the attack frequency time.

This research aims to explore and evaluate various strategies for improving energy efficiency within wireless sensor networks (WSNs). Specifically, the study focuses on the critical challenge of extending network lifespan through energy conservation by establishing balanced clusters within the WSN architecture.

In wireless sensor networks (WSNs), ensuring prolonged network operation while conserving energy resources is a significant concern. One promising approach to address this challenge is the implementation of equalized clusters, which requires an effective selection of cluster heads (CHs). However, this task presents considerable complexity and demands innovative solutions to overcome.

The primary objective of this study is to develop and assess a novel methodology for selecting precise cluster heads (CHs) within WSNs. This methodology is based on the utilization of Bluetooth low energy (BLE) sensors deployed in a randomly distributed manner across the study area. By employing an enhanced artificial neural network and greedy approach (EANN-GA), the proposed technique seeks to identify CHs with optimal proximity to the cluster center and substantial remaining energy reserves.

The proposed methodology involves the deployment of BLE sensors distributed randomly throughout the study region, which are then organized into clusters. Using the enhanced artificial neural network and greedy approach (EANN-GA), the sensor node nearest to the cluster center with the highest remaining energy is selected as the cluster head (CH). Additionally, a mobile sink (MS) is introduced to harness the power of CHs, and the number of paths utilized by the MS is estimated through a genetic approach. Based on this path information, the MS enters each cluster to initiate the data-gathering process.

Performance analysis of the presented methodology demonstrates significant improvements in energy efficiency and the extension of network lifetime. By employing the proposed EANN-GA technique for CH selection and optimizing MS path utilization, the study showcases enhanced operational effectiveness within WSNs.

The findings of this research underscore the effectiveness of the proposed methodology in enhancing energy efficiency and prolonging the lifespan of wireless sensor networks. Through the innovative integration of BLE sensors, EANN-GA CH selection, and genetic-based MS path estimation, the study contributes valuable insights toward addressing the critical challenges of energy conservation in WSNs.

Over the last decade, Wireless Sensor Networks (WSNs) have found applications across various domains, such as mines, agricultural sectors, healthcare, etc. These networks consist of multiple sensor nodes responsible for collecting and relaying data to a central gateway. Consequently, the integration of sensor devices bears the potential to influence the operational effectiveness of these systems.

This study concentrates on scrutinizing coverage and connectivity within a WSN deployed for monitoring purposes. This research delves into determining the most efficient deployment pattern requiring the minimum number of sensors.

For attaining thorough coverage and uninterrupted connectivity, adopting a non-deterministic strategy in sensor deployment is crucial. This study utilizes a model inspired by the salp swarm optimization method, a technique rooted in swarm-based optimization principles. In this methodology, clusters are defined as groups of sensor nodes meeting connection criteria and ensuring adequate coverage. Adequacy is achieved when at least one of these nodes transmits monitored data to the central hub.

The results offer compelling evidence that the discrete salp swarm optimization algorithm is better than state-of-the-art algorithms. The results are interpreted in three different metrics, namely the number of deployed sensors, total computational time, and the ratio between potential sensors available and the number of sensors deployed to cover the region. As a result, on average, the proposed model achieved an overall 87% of coverage and connectivity that are simulated with different iteration numbers on a 50X50 grid. For the 100X100 grid, a total of 89% of coverage and connectivity among sensors were achieved.

The application of the discrete salp swarm optimization model presents a promising approach to addressing the coverage connectivity problem in WSNs. Through the utilization of salp-inspired behaviors, this model effectively optimizes network coverage while ensuring robust connectivity, thus enhancing the overall performance and reliability of WSNs. By harnessing the collective intelligence of salp swarms, the proposed algorithm demonstrates superior convergence speed and solution quality compared to traditional methods.

To address the vulnerability of Multi-Hop Wireless Network Systems (MHWNs) to jamming attacks and propose an effective solution to maintain communication integrity and Quality of Service (QoS).

In MHWNs, the open-access nature makes them susceptible to jamming attacks, which disrupt communication by interfering with authenticated nodes in the wireless medium. Existing methods primarily focus on tracking and countering jammers but lack effectiveness in preventing communication disruptions.

The objective of this study is to introduce a novel algorithm, Optimized Transmission Mechanism (OTM), to mitigate the impact of jamming attacks on MHWNs. OTM aims to optimize node handover and packet routing to bypass jammed areas, ensuring uninterrupted packet transmission and preserving QoS.

The proposed OTM algorithm determines the optimal transmission route based on radio transmitter location and connection quality. It prioritizes routes with the highest connection quality to maintain QoS even in jammed conditions. Additionally, it incorporates mechanisms for packet redirection away from jammed areas to ensure successful transmission.

Evaluation of the Extended Optimized Transmission Mechanism (EOTM) demonstrates significant improvements in packet delivery performance compared to existing algorithms. The enhanced algorithm effectively mitigates the impact of jamming attacks, ensuring reliable communication and preserving QoS in MHWNs.

The proposed OTM algorithm presents a promising approach to counter-jamming attacks in MHWNs by dynamically routing packets to avoid jammed areas and maintain communication integrity. The results highlight the effectiveness of EOTM in improving packet delivery performance and ensuring uninterrupted communication in the face of jamming threats.

Unmanned Aerial Vehicles (UAVs) pose significant security risks to critical infrastructures, potentially leading to accidents, attacks, or illicit surveillance. Robust prevention measures are essential around strategic facilities. The navigation and control of UAV systems fundamentally depend on the Global Positioning System (GPS). Consequently, jamming or spoofing the GPS navigation system can significantly hinder the control and direct UAVs to their specified destinations. This paper introduces a novel hybrid spoofing/spot jamming system aimed at fortifying security against UAV threats.

This study aims to propose and evaluate the efficiency of a hybrid spoofing/spot-jamming system to bolster security against UAVs.

Leveraging a Software-Defined Radio (SDR) and complementary software tools, we generated spoofing and jamming signals targeting the civilian-use signals of GPS satellites at the L1 frequency (1575.42 MHz), incorporating the Coarse Acquisition (C/A) code. The system enables flexible timing adjustments within the navigation message to simulate fictitious locations.

Through comprehensive testing involving multiple commercial UAVs across three distinct scenarios—reactive spoofing, proactive spoofing, and spot jamming followed by spoofing—the experiments demonstrated superior efficacy in proactive spoofing and spot jamming followed by GPS spoofing, compared to reactive spoofing alone.

Proactive spoofing and spot jamming, followed by GPS spoofing, emerge as more effective strategies for countering UAV threats, offering enhanced security measures for critical infrastructures vulnerable to UAV intrusions. This hybrid approach holds promise in augmenting existing defense mechanisms against evolving UAV-based security risks.

A vehicular ad hoc network (VANET) is a combination of wireless and mobile networks that comprises connected vehicles, and immobile nodes placed on the roads called roadside units (RSUs). RSUs help vehicles find information, improve communication amongst vehicles, and provide help with signal acknowledgement and violation, traffic safety and driving awareness.

The main obstacle for VANET though is the deployment of RSUs. In order to lower the cost of the VANET and increase coverage, the goal is to deploy the fewest possible RSUs.

A virus optimization algorithm (VOA) has been implemented on the VANET architecture to help achieve this goal.

The major accomplishment of this article is the optimal deployment of RSUs in VANET and through the simulation it was found that with the application of VOA, the number of RSUs deployed could be reduced by 58.3%. In addition, this work focuses on enhancing the VANET performance on various metrics such as throughput, energy consumption, packet delivery ratio, end delay and packet loss ratio. The performance of VOA is also compared with some latest predominant algorithms used for optimization such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

According to the findings, VOA outperforms PSO and GA in terms of enhanced VANET performance and the most effective RSU deployment.

A key technological alternative to WiFi technology is Li-Fi (Light Fidelity), which is associated with the binary data executing mechanism that can prevail over the disadvantages of Wi-Fi due to its high-speed data transmission capacity, superior bandwidth and reliability.

Keeping these dominant factors of Li-Fi technology in mind, we proposed a prototype of the system which is named BLi-Fi: Li-Fi-based indoor navigation for the blind. The proposed idea is to develop a system that enhances the easiness of indoor navigation for blind persons and this navigation technology uses visible light communication.

In the proposed system, we utilized an LED (light emitting diode) fitted at our home as an information transmitter and an LDR (light dependent resistor) fitted on the walking stick of the vision impaired person at residence as a data receiver for indoor navigation.

Indoor location data information like room, hall, kitchen, etc. is embedded into the light rays transmitted by LED and the embedded system with a microcontroller having a visible light receiver at a walking stick collects the data, demodulates it and communicates through audio with the actual location to the vision impaired person.

Digital image forgery has emerged as a significant threat in an era where visual content plays a crucial role in communication and authentication. The rise of sophisticated manipulation techniques demands innovative approaches for reliable detection.

This research introduces a novel methodology for Digital Image Forgery Detection using Noise Cancellation in Feature-Map Convolutional Neural Networks (NC-FM-CNN). Our approach focuses on exploiting the inherent patterns of manipulated images by integrating a noise cancellation mechanism within the CNN architecture. The use of feature maps enables the network to discern subtle alterations in image content, offering enhanced sensitivity to forged regions. By selectively filtering out noise patterns introduced during the forgery process, the model can more accurately pinpoint areas of manipulation. The proposed NC-FM-CNN architecture undergoes extensive training on diverse datasets encompassing various types of image manipulations, ensuring its adaptability to a wide range of forgery techniques. The network's ability to learn and differentiate between authentic and manipulated features is enhanced through advanced optimization techniques and regularization methods.

Our experimental results, showcasing an accuracy of 97%, demonstrate the superior performance of the NC-FM-CNN compared to traditional forgery detection methods. The model exhibits robustness in detecting forged content even in cases where manipulations are subtle or deeply embedded. Moreover, its efficiency in handling diverse forgery scenarios positions it as a versatile tool for forensic analysis in digital image authenticity verification.

As image manipulation techniques continue to evolve, the proposed NC-FM-CNN framework offers a proactive and reliable solution for combating digital forgery, contributing to the establishment of a trustworthy digital ecosystem.