Search Results (208)

Wireless sensor networks (WSNs) face a fundamental challenge: their sensors run on batteries, making energy efficiency crucial. While researchers have tried to extend network lifespans by improving routing and access control protocols across different layers, this remains a complex issue. One promising solution is opportunistic routing (OR), which uses multiple nodes to relay data. This approach reduces how long senders must wait for a specific next-hop node and helps prevent data loss from collisions. That said, choosing which nodes should act as forwarders can greatly affect how well the network performs. To tackle this problem, we developed a new approach called FCM-OR, a local density-based forwarder selection algorithm for opportunistic routing in WSNs. Our algorithm is particularly effective in networks where sensors are not evenly spread out and are densely packed. It uses fuzzy c-means (FCM) clustering to smartly pick the best forwarders based on how many nodes are nearby. By focusing on the sender’s immediate surroundings, FCM-OR helps solve the problems that arise when different parts of the network have varying densities of nodes. We also created a new way to measure routing effectiveness called “forwarding rank”. To test how well our protocol works, we ran extensive simulations comparing it to existing methods, including opportunistic routing with duty-cycled WSNs and load-balanced opportunistic routing. The results are clear: FCM-OR significantly improves both network performance and energy efficiency, especially in networks where nodes are not uniformly distributed. Full article

Edge computing minimizes data transmission delay and fulfills the requirements of real-time applications for the wireless network coverage problem in edge computing. This paper proposes a resource optimization algorithm based on service caching, and designs a hybrid computing model of UAV-assisted computation offloading and service programs, in which the UAV (Unmanned Aerial Vehicle) not only carries a server for computation offloading but also serves as a relay to assist in the downloading of service programs. The objective is to minimize the system’s total energy consumption while adhering to constraints such as delay, cache capacity, and other relevant factors. Because of the non-convex nature of the objective problem, it is divided into three sub-problems for more effective handling. Through analysis, the relationship between the resource allocation ratio and total system energy consumption is established, and the simulated annealing algorithm, greedy algorithm, and genetic algorithm are used to solve these sub-problems, respectively. Finally, the global optimal solutions for location deployment, service caching, resource allocation, and offloading decisions are obtained through iteration. The simulation results show that the proposed algorithm successfully reduces the total energy consumption of the system while maintaining task completion delay constraints. Full article

Semantic communication technology, as it allows for source data meaning extraction and the transmission of appropriate semantic information only, has the potential to extend Shannon’s paradigm, which is concerned with the reproduction of a message from one location to another, regardless of its meaning. Nevertheless, some user terminals (UTs) may experience inadequate service due to their geolocation in reference to the base stations, which may entirely affect the accuracy of transmission and complicate deployment and implementation. A High-Altitude Platform Station (HAPS) serves as a key enabler for the deployment of wireless broadband in inaccessible areas, such as in coastal, desert, and mountainous areas. This paper proposes a novel HAPS relay-based semantic communication scheme, named DeepSTAS, which leverages deep learning techniques to enhance transmission accuracy. The proposed scheme focuses on attention-based semantic signal decoding, denoising, and forwarding modes; thus, called a CSA-DCGAN SDF HAPS relay network. The simulation results reveal that the proposed system with attention mechanisms significantly outperforms the system without attention mechanisms, both in peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index (MS-SSIM); the proposed system can achieve a 2 dB gain when leveraging the attention mechanisms, and a PSNR of 38.5 dB can be obtained, with an MS-SSIM exceeding 0.999 at an approximate SNR of only 20 dB. The system provides considerable performance, more than 37 dB, and a corresponding MS-SSIM close to 0.999 at an estimated SNR of 20 dB when the CIFAR-100 dataset is considered and an MS-SSIM of 0.965 at an approximate SNR of only 10 dB on the Kodak dataset. The proposed system holds promise to maintain consistent performance even at low SNRs across various channel conditions. Full article

The marine environment’s complexity poses considerable difficulties for the stability and reliability of communication links. The restricted coverage of onshore base stations in marine areas makes relay technology a critical solution for extending the communication coverage. Here, connectivity analyses help nodes select the optimal forwarding links, reducing transmission failures and improving the network performance. However, the rapid changes in marine wireless channels and the complexity of hydrological conditions make it challenging to acquire precise channel state information (CSI). In particular, dynamic environmental factors like tides, waves, and wind speed lead to substantial variations in the channel parameters over time. In response to these challenges, this paper puts forward a ship-to-shore communication system using relay ships to extend the coverage of terrestrial base stations. A novel channel modeling method is designed to capture the characteristics of marine wireless channels accurately. Additionally, a machine learning (ML)-based approach is introduced to predict the dual-hop link connection probability at future time points by analyzing historical time-series data on oceanic environmental and ship movement parameters. The proposed model consists of a convolutional-layer-based feature extractor and a bidirectional long short-term memory (BiLSTM) estimator. The CNN module extracts effective high-level features from the input data, while the BiLSTM module further explores the dependencies and dynamic patterns along the temporal dimension. The attention mechanism is introduced to distinguish the importance of the information through a weighted approach. The experimental results show that compared to traditional methods and other deep learning approaches, the proposed CNN-BiLSTM-AM model performs better in terms of its prediction accuracy and fitting ability. The model’s mean squared error (MSE) is as low as 0.0126. Full article

In recent years, the demand for maritime wireless communication has been increasing, particularly in areas such as ship operations management, marine resource utilization, and safety assurance. However, due to the difficulty of deploying base stations(BSs), maritime communication still faces challenges in terms of limited coverage and unreliable communication quality. As the number of users on ships and offshore platforms increases, along with the growing demand for mobile communication at sea, conventional terrestrial base stations struggle to provide stable connectivity. Therefore, existing maritime communication primarily relies on satellite communication and long-range Wi-Fi. However, these solutions still have limitations in terms of cost, stability, and communication efficiency. Satellite communication solutions, such as Starlink and Iridium, provide global coverage and high reliability, making them essential for deep-sea and offshore communication. However, these systems have high operational costs and limited bandwidth per user, making them impractical for cost-sensitive nearshore communication. Additionally, geostationary satellites suffer from high latency, while low Earth orbit (LEO) satellite networks require specialized and expensive terminals, increasing hardware costs and limiting compatibility with existing maritime communication systems. On the other hand, 5G-based maritime communication offers high data rates and low latency, but its infrastructure deployment is demanding, requiring offshore base stations, relay networks, and high-frequency mmWave (millimeter-wave) technology. The high costs of deployment and maintenance restrict the feasibility of 5G networks for large-scale nearshore environments. Furthermore, in dynamic maritime environments, maintaining stable backhaul connections presents a significant challenge. To address these issues, this paper proposes a low-cost nearshore wireless communication solution utilizing tethered balloons as coastal base stations. Unlike satellite communication, which relies on expensive global infrastructure, or 5G networks, which require extensive offshore base station deployment, the proposed method provides a more economical and flexible nearshore communication alternative. The tethered balloon is physically connected to the coast, ensuring stable power supply and data backhaul while providing wide-area coverage to support communication for ships and offshore platforms. Compared to short-range communication solutions, this method reduces operational costs while significantly improving communication efficiency, making it suitable for scenarios where global satellite coverage is unnecessary and 5G infrastructure is impractical. Additionally, conventional uniform power allocation or channel-gain-based amplification methods often fail to meet the communication demands of dynamic maritime environments. This paper introduces a nonlinear dynamic power allocation method based on channel gain information to maximize downlink communication efficiency. Simulation results demonstrate that, compared to conventional methods, the proposed approach significantly improves downlink communication performance, verifying its feasibility in achieving efficient and stable communication in nearshore environments. Full article

Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, and energy inefficiency. In changing underwater environment to make routing possible among nodes or/and base station (BS) an adaptive receiver-initiated deep adaptive with power control and collision avoidance MAC (DAWPC-MAC) protocol is proposed to address the challenges of interference, collisions, and energy inefficiency. The proposed framework is based on Deep Q-Learning (DQN) to optimize network performance by enhancing collision avoidance in a varying sensor locations, conserving energy in changing path loss with respect to time and depth and reducing number of relaying nodes to make communication reliable and ensuring synchronization. The dynamic and unpredictable underwater environment, shaped by variations in environmental parameters such as temperature (T) with respect to latitude, longitude, and depth, is carefully considered in the design of the proposed MAC protocol. Sensor nodes are enabled to adaptively schedule wake-up times and efficiently control transmission power to communicate with other sensor nodes and/or courier node plays vital role in routing for data collection and forwarding. DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission, improving the packet delivery rati (PDR) by 14%, throughput by over 70%, and utility by more than 60% compared to existing methods like TDTSPC-MAC, DC-MAC, and ALOHA MAC. These enhancements significantly contribute to network longevity and operational efficiency in time-critical underwater applications. Full article

We introduce a robust distributed collaborative beamforming (RDCB) approach for addressing channel estimation challenges in dual-hop transmissions within wireless sensor and actuator networks (WSANs) of K nodes. WSANs enhance wireless communication by reducing data transmission, latency, and energy consumption while optimizing network load through integrated sensing and actuation. The source S transmits signals to the WSAN, where nodes relay them to the destination D using beamforming weights to minimize noise and preserve signal integrity. These weights depend on channel state information (CSI), where estimation errors degrade performance. We develop RDCB solutions for three first-hop propagation scenarios—monochromatic [line-of-sight (LoS)] or “M”, bichromatic (moderately scattered) or “B”, and polychromatic (highly scattered) or “P”—while assuming a monochromatic LoS or “M” link for the second hop between the nodes and the far-field destination. Termed MM-RDCB, BM-RDCB, and PM-RDCB, respectively (“X” and “Y” in XY-RDCB—for X $\in \{\mathrm{M},\mathrm{B},\mathrm{P}\}$ and Y $\in \left\{\mathrm{M}\right\}$—refer to the chromatic natures of the first- and second-hop channels, respectively, to which a specific RDCB solution is tailored), these solutions leverage asymptotic approximations for large K values and the nodes’ geometric symmetries. Our distributed solutions allow local weight computation, enhancing spectral and power efficiency. Simulation results show significant improvements in the signal-to-noise ratio (SNR) and robustness versus WSAN node placement errors, making the solutions well suited for emerging 5G and future 5G+/6G and Internet of Things (IoT) applications for different challenging environments. Full article

This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) and use routing protocols to forward the sensed data of target(s) to an aerial base station (BS) in real-time through multihop communication, which can then transmit the data to a control center. However, the unpredictability of target locations and the highly dynamic nature of autonomous, decentralized UAV networks result in frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and can incur large control overhead and delays. In addition, their performance suffers from poor network connectivity in sparse networks with multiple objectives (exploration and monitoring of targets), which results in frequent route unavailability. To address these challenges, we propose two routing schemes: Pipe routing and TC-Pipe routing. Pipe routing is a mobility-, congestion-, and energy-aware scheme that discovers routes to the BS on-demand and proactively switches to alternate high-quality routes within a limited region around the routes (referred to as the “pipe”) when needed. TC-Pipe routing extends this approach by incorporating a decentralized topology control mechanism to help maintain robust connectivity in the pipe region around the routes, resulting in improved route stability and availability. The proposed schemes adopt a novel approach by integrating the topology control with routing protocol and mobility model, and rely only on local information in a distributed manner. Comprehensive evaluations under diverse network and traffic conditions—including UAV density and speed, number of targets, and fault tolerance—show that the proposed schemes improve throughput by reducing flow interruptions and packet drops caused by mobility, congestion, and node failures. At the same time, the impact on coverage performance (measured in terms of coverage and coverage fairness) is minimal, even with multiple targets. Additionally, the performance of both schemes degrades gracefully as the percentage of UAV failures in the network increases. Compared to schemes that use dedicated UAVs as relay nodes to establish a route to the BS when the UAV density is low, Pipe and TC-Pipe routing offer better coverage and connectivity trade-offs, with the TC-Pipe providing the best trade-off. Full article

Future generations of wireless networks at high-frequency spectrum suffer from limited coverage and Non-Line- of-Sight signal blockage, challenging emerging applications, such as smart industries and intelligent automation systems. Collaborative and cooperative communications with smart relays via Non-Orthogonal Multiple Access (NOMA) could be a breakthrough solution to this challenge. This paper presents a blockchain-integrated framework for NOMA wireless communication systems that incentivizes cooperation among users serving as relays. By leveraging Ethereum-based smart contracts, we introduce a Service Verification Contract featuring a Proof of Quality of Experience (PQoE) mechanism. The contract uses trust scores, weighted verifications, and dynamic validation thresholds to ensure honest behavior and deter malicious activities. The simulation results show that honest participants gradually increase their trust scores and require fewer verifications, while malicious verifiers lose influence over repeated rounds. Our findings indicate that combining trust-based incentives with a decentralized ledger can effectively promote reliable data-relaying services and streamline payment processes in collaborative and smart wireless networking systems. Full article

In relay-assisted underwater wireless optical communication (UWOC) systems, the traditional time-division-multiplexed relay forwarding strategy faces high latency and low throughput with the increase of relay users. To address these issues, this paper proposes a multiple receiving antenna power allocation-based bit splicing physical layer network coding (MRA-PABS-PNC) method in a three-user asymmetric modulated relay-assisted UWOC scenario. MRA-PABS-PNC reduces the number of multiple access time slots by using multi-antenna reception techniques. At the same time, it employs a bit-splicing method to concatenate the data that would normally be transmitted over two-time slots into a longer data stream transmitted in a single time slot, thus reducing the number of broadcast time slots and ultimately improving throughput. Moreover, this paper models and determines the optimal position and angle of the relay node photodetector. Once the relay node is positioned at the optimal location and angle, the system can allocate power to each user node based on the channel state information to overcome the effect of asymmetric channels on PNC coding, thereby further improving system performance. Simulation results show that the method improves the throughput by 100% compared with the existing four-time slot PNC (FT-PNC) method. Full article

Wireless Sensor Networks are formed by tiny, self-contained, battery-powered computers with radio links that can sense their surroundings for events of interest and store and process the sensed data. Sensor nodes wirelessly communicate with each other to relay information to a central base station. Energy consumption is the most critical parameter in Wireless Sensor Networks (WSNs). Network lifespan is directly influenced by the energy consumption of the sensor nodes. All sensors in the network send and receive data from the base station (BS) using different routing protocols and algorithms. These routing protocols use two main types of clustering: hierarchical clustering and flat clustering. Consequently, effective clustering within Wireless Sensor Network (WSN) protocols is essential for establishing secure connections among nodes, ensuring a stable network lifetime. This paper introduces a novel approach to improve energy efficiency, reduce the length of network connections, and increase network lifetime in heterogeneous Wireless Sensor Networks by employing the K-Nearest Neighbours (KNN) algorithm to optimise node selection and clustering mechanisms for four protocols: Low-Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Threshold-sensitive Energy Efficient sensor Network (TEEN), and Distributed Energy-efficient Clustering (DEC). Simulation results obtained using MATLAB (R2024b) demonstrate the efficacy of the proposed K-Nearest Neighbours algorithm, revealing that the modified protocols achieve shorter distances between cluster heads and nodes, reduced energy consumption, and improved network lifetime compared to the original protocols. The proposed KNN-based approach enhances the network’s operational efficiency and security, offering a robust solution for energy management in WSNs. Full article

Wireless Sensor Networks (WSNs) connected to the Internet of Things (IoT) are increasingly employed in commercial and industrial applications to accomplish various tasks at a low cost. WSNs are essential for gathering diverse types of data within physical environments. A key design objective for WSNs is to balance energy consumption and increase the network’s operating lifetime. Recent studies have shown that mobile base stations (BSs) can significantly extend the lifetime of such networks, especially when their location is optimized using specific criteria. In this study, we propose an algorithm for selecting the optimal BS location in a large network. The algorithm computes a distance metric between sensor nodes (SNs) and potential BS locations on a virtual grid within the WSN. The selection process is repeated periodically to account for dead SNs, allowing the BS to relocate to a new optimal position based on the remaining active nodes after each iteration. Additionally, the inclusion of a relay node (RN) in large networks is explored to improve scalability. The impact of path loss within WSNs is also discussed. The proposed algorithms are applied to the well-known Stable Election Protocol (SEP). Simulation results demonstrate that, compared to other algorithms in the literature, the proposed approaches significantly enhance the lifetime of WSNs. Full article

Indoor Internet of Things (IoT) is considered as a crucial component of Industry 4.0, enabling devices and machine to communicate and share sensed data leading to increased efficiency, productivity, and automation. Increased energy efficiency is a significant focus within Industry 4.0, as it offers numerous benefits. To support this focus, we developed a hybrid switching mechanism to switch between energy harvesting techniques, ambient backscattering and Simultaneous Wireless Information and Power Transfer (SWIPT), which can be utilized within cooperative communications. To implement the proposed switching mechanism, we consider an indoor warehouse environment, where the moving sensor node transmits sensed data to the fixed relay located on the roof, which is then transmitted to an IoT gateway. The relay is equipped with the proposed switch to energize its communication capabilities while maintaining the expected quality of service at the IoT gateway. Simulation results illustrate the improved energy efficiency within the indoor communication setup while maintaining QoS at varying signal-to-noise (SNR) conditions. Full article

This study proposes a novel optical wireless communication system for high-speed, large-capacity data transmission, supporting underwater IoT devices in shallow seas. The system employs a mirror-equipped aerial drone as a relay between underwater drones and a terrestrial station, using 850 nm optical signals for low atmospheric loss and enhanced confidentiality. Adaptive modulation optimizes transmission capacity based on SNR, accounting for air and underwater channel characteristics. Experiments confirmed an exponential SNR decrease with distance (0.6–1.8 m) and demonstrated successful 4K UHD video streaming in shallow seawater (turbidity: 2.2 NTU) without quality loss. The design ensures cost-effectiveness and stable optical alignment using advanced posture control. Full article

In recent years, wireless sensor networks have been widely used, especially in three-dimensional environments such as underwater and mountain environments. However, in harsh environments, wireless sensor networks may be damaged and split into many isolated islands. Therefore, restoring network connectivity to transmit data effectively in a timely manner is particularly important. However, the problem of finding the minimum relay nodes is NP-hard, so heuristics methods are preferred. This paper presents a novel connectivity recovery strategy based on boundary nodes and spatial triangle Fermat points for three-dimensional wireless sensor networks. The isolated islands are represented as the boundary nodes, and the connectivity recovery problem is modeled as a graph connectivity problem. Three heuristics algorithms—the variant Kruskal algorithm, the variant Prim algorithm, and the spatial triangle Fermat point algorithm—are proposed to solve this problem. The variant Kruskal algorithm and the variant Prim algorithm connect the isolated islands by constructing the minimum spanning tree to link all the boundary nodes and placing relay nodes along the edges of this tree. We derive an accurate formula to determine the coordinates of spatial triangle Fermat points. Based on this formula, the spatial triangle Fermat point algorithm constructs a Steiner tree to restore network connectivity. Extensive simulation experiments demonstrate that our proposed algorithms perform better than the existing algorithm. Full article