Search Results (123)

Reliability studies frequently employ progressive censoring schemes that remove surviving units during testing, yet statistical inference under such designs remains vulnerable to parametric model misspecification. When distributional assumptions fail, conventional maximum likelihood estimators converge to systematically biased limits, producing confidence intervals with severely degraded coverage. We develop a flexible inferential framework that models the hazard function through a neural network architecture, avoiding commitment to a parametric family. To quantify uncertainty, we introduce a stratified weighted bootstrap procedure that preserves the dependency structure induced by progressive removals. We establish that the proposed estimator achieves the minimax optimal nonparametric rate $n^{-\alpha /(2\alpha +1)}$ for $\alpha$-smooth hazard functions and prove that the bootstrap consistently approximates the sampling distribution, yielding asymptotically valid pointwise confidence intervals for the survival function. A local asymptotic analysis precisely characterizes the efficiency–robustness tradeoff. Comprehensive simulations comparing against parametric methods, penalized splines, piecewise exponential models, and kernel estimators demonstrate that our method maintains 92–94% coverage under misspecification, whereas parametric alternatives collapse to 40–45% and simpler nonparametric methods achieve only 85–91%. The neural network architecture provides 23–29% lower integrated mean squared error than penalized splines using the same bootstrap, confirming that both components of our framework contribute to performance. Computational requirements remain practical: parallelized bootstrap inference completes in under 25 s on an 8-core processor for typical sample sizes. Application to electronic component lifetime data illustrates how the methodology yields materially different reliability assessments with direct implications for warranty planning. Full article

Wireless sensor networks encounter issues like irregular deployment, node failures, and uneven energy consumption that create coverage holes, leading to a reduction in network lifetime in critical or disaster-based applications. Most existing approaches focus on coverage enhancement during the initial deployment and perform mitigation only at the beginning of the network operation. However, the coverage holes may also occur later due to node failures and energy depletion. To address this issue, a Hybrid Raccoon–Hermit crab optimization algorithm that advocates both initial coverage enhancement and adaptive mitigation due to future coverage holes is proposed. The proposed algorithm uses the global exploration ability of the raccoon optimization algorithm to find optimal cluster heads and the exploitation ability of the Hermit crab optimization to determine the optimal position and to relocate the static nodes logically to mitigate coverage holes. The proposed algorithm is evaluated under different node densities (50, 100, 200, 500, and 1000), with the sink at $(100,100)$. It results in an enhanced network lifetime of $65.20\%$, an improved coverage ratio $(16.94\%)$ from $(77.05\%)$ to $(93.94\%)$, increased throughput by delivering (3,139,293) bits, and a reduced delay of $2.27292$ s for 1000 nodes compared with other existing methods. Full article

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

Wildfire events are increasing in frequency and severity, creating an urgent need for early, accurate, and energy-efficient forest fire prediction systems that can operate at a large scale. A fundamental challenge in edge-level forest fire prediction lies in jointly achieving high detection accuracy while minimizing wireless transmissions and communication-related energy consumption. This paper proposes a communication-aware hierarchical wireless sensor network (WSN) framework that performs fire versus normal environmental state classification directly at the network edge. Multi-modal physical and constrained virtual sensor readings are fused into short-term temporal supervectors and processed locally using lightweight random forest classifiers deployed on sensor nodes and cluster heads. A temporal 2-of-3 voting mechanism is applied at the edge to suppress transient noise and improve prediction reliability before triggering communication. The proposed design enables selective, event-driven transmission, where only temporally validated abnormal states are forwarded through the hierarchy, thereby decoupling detection accuracy from continuous data reporting. Extensive experiments using real multi-modal environmental sensor data and statistically rigorous 5-fold GroupKFold cross-validation—ensuring strict node-level separation between training and testing—demonstrate the effectiveness of the approach. The proposed framework achieves a node-level accuracy of 98.82 ± 1.75% and a scenario-level detection accuracy of 96.52 ± 0.89%. Compared to periodic reporting and the LEACH protocol, the system reduces wireless transmissions by over 66% and communication-related energy consumption by more than 66% across network sizes ranging from 100 to 1000 nodes. The main contributions of this work are summarized as follows: (1) a communication-aware hierarchical Edge-AI framework for early forest fire prediction that performs local inference and temporal validation directly at sensor nodes; (2) a constrained virtual sensing strategy integrated with temporal supervector modeling to enhance spatial coverage while preserving reliability; and (3) a statistically rigorous large-scale evaluation demonstrating joint optimization of prediction accuracy, transmission reduction, and communication energy efficiency across network sizes ranging from 100 to 1000 nodes. These results show that accurate early forest fire prediction can be achieved through edge-level inference and selective communication, substantially extending network lifetime while maintaining statistically reliable detection performance. Full article

Efficient clustering and cluster head (CH) selection are the critical parameters of wireless sensor networks (WSNs) for their prolonged network lifetime. However, the performances of the traditional clustering algorithms like LEACH and HEED are not satisfactory when they are implemented on a dense WSN due to their unbalanced load distribution and high contention nature. In the traditional methods, the cluster heads are selected with respect to the residual energy criteria, and often create a circular cluster shape boundary with a uniform node distribution. This causes the cluster heads to become overloaded in the high-density regions and the unutilized cluster heads gather in the sparse regions. Therefore, frequent cluster head changes occur, which is not suitable for a real-time dynamic environment. In order to avoid these issues, this proposed work develops a density-aware adaptive clustering (DAAC) protocol for optimizing the CH selection and cluster formation in a dense wireless sensor network. The residual energy information, together with the local node density and link quality, is utilized as a single cluster head detection metric in this work. The local node density information assists the proposed work to estimate the sparse and dense area in the network that results in frequent cluster head congestion. DAAC is also included with a minimum inter-CH distance constraint for CH crowding, and a multi-factor cost function is used for making the clusters by inviting the nodes by their distance and an expected transmission energy. DAAC triggers re-clustering in a dynamic manner when it finds a response in the CH energy depletion or a significant change in the load density. Unlike the traditional circular cluster boundaries, DAAC utilizes dynamic Voronoi cells (VCs) for making an interference-aware coverage in the network. This makes dense WSNs operate efficiently, by providing a hierarchical extension, on making secondary CHs in an extremely dense scenario. The proposed model is implemented in MATLAB simulation, to determine and compare its efficiency over the traditional algorithms such as LEACH and HEED, which shows a satisfactory network lifetime improvement of 20.53% and 32.51%, an average increase in packet delivery ratio by 8.14% and 25.68%, and an enhancement in total throughput packet by 140.15% and 883.51%, respectively. Full article

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

Heterogeneous wireless sensor networks (HWSNs), comprising super nodes and normal sensors, offer a promising solution for monitoring diverse environments. However, their deployment is constrained by the limited battery life of sensors. To address this issue, clustering and routing techniques have been employed to conserve energy. Nevertheless, existing approaches often struggle with suboptimal energy distribution and weak network coverage. Additionally, they mostly failed to exploit other energy saving techniques such as sleep scheduling. This paper proposes a novel genetic algorithm (GA)-based approach to optimize sleep scheduling, routing, and clustering in HWSNs. The method comprises two phases, namely join sleep scheduling and tree construction, and clustering of normal nodes. Inspired by the concept of unequal clustering, the HWSN is split into some rings in the first phase, and the number of awake super nodes in each ring keeps the same. This approach addresses the challenges of balancing energy consumption and network lifetime. Furthermore, including network coverage and energy-related criteria in the proposed GA yields long-lasting network operation. Through rigorous simulations, we demonstrate that, on average, our algorithm reduces energy consumption and improves network coverage by 23% and 21.9%, respectively, and extends network lifetime by 501 rounds, compared to the state-of-the-art methods. Full article

Wireless Sensor Networks (WSNs) are vital to a wide range of applications, spanning from environmental monitoring to safety-critical systems. Ensuring dependable operation in these networks critically depends on selecting an optimal node scheduling algorithm; however, this remains a major challenge since no single approach performs best under all conditions. To address this issue, this paper proposes an AI-driven framework that evaluates scenario-specific functional requirements—such as coverage, connectivity, and network lifetime—to identify the optimal node scheduling algorithm from a pool that includes Hidden Markov Models (HMMs), BAT, Bird Flocking, Self-Organizing Maps (SOFMs), and Long Short-Term Memory (LSTM) networks. The framework was evaluated using a neural network trained on simulated data and tested across five real-world scenarios: healthcare monitoring, military operations, industrial IoT, forest fire detection, and disaster recovery. The results clearly demonstrate the effectiveness of the proposed framework in identifying the most suitable algorithm for each scenario. Notably, the LSTM algorithm frequently achieved near-optimal performance, excelling in critical objectives such as network lifetime, connectivity, and coverage. The framework also revealed the complementary strengths of other algorithms—HMM proved superior for maintaining connectivity, while Bird Flocking excelled in extending network lifetime. Consequently, this work validates that a scenario-aware selection strategy is essential for maximizing WSN dependability, as it leverages the unique advantages of diverse algorithms. Full article

Maximizing the lifetimes of Wireless Sensor Networks (WSNs) is a prominent area of research. The energy hole problem is a major cause of network shutdown, where nodes within the Sink coverage deplete their energy faster due to the high energy cost of forwarding data from distant nodes to the Sink. Several research works have proposed solutions to address this issue, including the use of a mobile Sink to balance energy consumption throughout the network. However, most Sink mobility models overlook the energy consumption caused by overhearing, which is a critical factor in WSNs. In this paper, we introduce Linear Programming (LP) and Cuckoo Search (CS) metaheuristic optimization-based solutions to maximize the lifetime of WSNs by determining the optimal Sink sojourn points and associated durations. The proposed approaches consider the energy consumption levels of both reception and transmission, in addition to accounting for overhearing as an additional source of energy consumption. This allows for a comparison between the LP and CS solutions in terms of their effectiveness. To further enhance our solution, we apply the Travel Salesman Problem (TSP) to find the shortest path between the Sink sojourn points. By incorporating the TSP, we can optimize the routing path for the mobile Sink, thereby minimizing energy consumption and maximizing network lifetime. Test results demonstrate that the LP solution provides more accurate Sink sojourn times and locations, while the CS solution is faster, particularly for large WSNs. Moreover, our findings indicate that overlooking overhearing leads to a 48% decrease in WSN lifetime, making it essential to consider this factor if one is to achieve realistic results. Full article

WSNs are an important component of the Internet of Things (IoT), and the research on their routing protocols has always been a hot topic in academia. However, in ARTFMRs’ collaborative operation along railway lines, there are common problems such as energy holes, high latency, and uneven energy consumption in LWSNs. To address these issues, this paper proposes a genetic algorithm-optimized energy-aware routing protocol (GAECRPQ). Firstly, a non-uniform deployment strategy of three-line isosceles triangles is constructed to enhance coverage and balance node distribution. Secondly, an energy–distance adaptive weighting mechanism based on a genetic algorithm is introduced for cluster head (CH) selection to reduce energy consumption in hotspots and extend the network lifetime. Finally, a task-aware TDMA dynamic time slot allocation method is proposed, which incorporates the real-time task status of ARTFMRs into communication scheduling to achieve priority transmission under latency constraints. The simulation results show, that compared with six unequal clustering protocols—EADUC, EAUCA, EBUC, EEUC, LEACH, and LEACH-C—the three-line isosceles triangle deployment has a wider coverage area, and the GAECRPQ protocol increases the network lifetime by 7.4%, the lifetime by 40%, and reduces the average latency by 55.77%, 53.07%, 47.61%, 39.87%, 52.08%, and 50.48%, respectively. This verifies that GAECRPQ has good performance in terms of network lifetime and energy utilization efficiency, providing a practical solution for the collaborative operation of ARTFMRs in railway maintenance scenarios. Full article

Multi-hop routing over low-power wide-area networks (LPWANs) has emerged as a promising technology for extending network coverage. However, existing protocols face high transmission disruption risks due to factors such as dynamic topology driven by stochastic events, dynamic link quality, and coverage holes induced by imbalanced energy consumption. To address this issue, we propose a failure risk-aware deep Q-network-based multi-hop routing (FRDR) protocol, aiming to reduce transmission disruption probability. First, we design a power regulation mechanism (PRM) that works in conjunction with pre-selection rules to optimize end-device node (EN) activations and candidate relay selection. Second, we introduce the concept of routing failure risk value (RFRV) to quantify the potential failure risk posed by each candidate next-hop EN, which correlates with its neighborhood state characteristics (i.e., the number of neighbors, the residual energy level, and link quality). Third, a deep Q-network (DQN)-based routing decision mechanism is proposed, where a multi-objective reward function incorporating RFRV, residual energy, distance to the gateway, and transmission hops is utilized to determine the optimal next-hop. Simulation results demonstrate that FRDR outperforms existing protocols in terms of packet delivery rate and network lifetime while maintaining comparable transmission delay. Full article

Wireless sensor networks (WSNs) play a pivotal role in monitoring and acting applications. However, suboptimal deployments and traffic imbalances lead to rapid network exhaustions. To address this, topology changes could be carried out by mobile robots. In this work, a software package to study different strategies and algorithms for the deployment, operation, and retrieval of mobile WSN is introduced. This package employs the globally known software ecosystem for robotics, ROS (Robot Operating System) 2, allowing to study the above-mentioned strategies and algorithms in simulation or in actual deployments. Two strategies concerning robot control are compared, the Social Potential Fields-only approach and an intelligent Agent layer. Each strategy is tested and optimized with different parameters. Results show that the Agents approach yields more consistent results and globally better metrics in terms of network lifetime and coverage. Full article

The Multi-Objective Optimization Problem (MOOP) in Wireless Sensor Networks (WSNs) is a challenging issue that requires balancing multiple conflicting objectives, such as maintaining coverage, connectivity, and network lifetime all together. These objectives are important for a functioning WSN safety-critical applications, whether in environmental monitoring, military surveillance, or smart cities. To address these challenges, we propose a novel bio-inspired Bird Flocking Node Scheduling algorithm, which takes inspiration from the natural flocking behavior of birds migrating over long distance to optimize sensor node activity in a distributed and energy-efficient manner. The proposed algorithm integrates the Lyapunov function to maintain connected coverage while optimizing energy efficiency, ensuring service availability and reliability. The effectiveness of the algorithm is evaluated through extensive simulations, namely MATLAB R2018b simulator coupled with a Pareto front, comparing its performance with our previously developed BAT node scheduling algorithm. The results demonstrate significant improvements across key performance metrics, specifically, enhancing network coverage by 8%, improving connectivity by 10%, and extending network lifetime by an impressive 80%. These findings highlight the potential of bio-inspired Bird Flocking optimization techniques in advancing WSN dependability, making them more sustainable and suitable for real-world WSN safety-critical systems. Full article

Wireless sensor networks (WSNs) are widely used in IoT, environmental monitoring, and industrial systems, but ensuring energy efficiency, extended network lifetime, and reliable communication under real-world constraints remains challenging. This work proposes a novel clustering framework that integrates kernel density estimation (KDE)-based adaptive node deployment, silhouette-optimized K-means clustering, Bayesian cluster head (CH) selection with Gaussian noise-based energy uncertainty modeling, energy-efficient coverage control, and carrier sense multiple access with collision avoidance-based data transmission. Unlike conventional approaches that rely on fixed clustering and uniform deployments, our framework supports terrain-aware node placement and dynamic CH selection based on residual energy and distance under imperfect sensing conditions. Simulation results demonstrate significant improvements in performance, including over 35% extension in network lifetime and higher coverage retention under energy constraints, compared to baseline methods such as LEACH and K-LEACH. While detailed metrics vary per run due to adaptive parameters and stochastic node behavior, these outcomes validate the scalability, robustness, and practical relevance of the proposed method for real-world WSN deployments. Full article

Wireless sensors deployed in large agricultural areas can monitor and collect data in real time, helping to achieve smart agriculture. But the complexity of the environment and the random deployment method seriously affect the coverage quality. The limited capacity of sensor batteries greatly limits the network lifetime. Therefore, how to extend the network lifetime while ensuring coverage quality is a highly challenging task. This paper proposes a node deployment optimization method to solve the problems of a poor coverage rate and a short network lifetime in hybrid self-powered sensor networks in obstacle environments. This method first optimizes the sensing direction of stationary nodes, expands the coverage range, and repairs coverage holes. Then, an improved bidirectional search A* algorithm is used to plan the obstacle avoidance moving path of mobile nodes, fill the remaining coverage holes, and improve the coverage quality of the network. Finally, a method based on an improved nutcracker optimizer algorithm is proposed to solve the optimal working sequence of nodes, schedule the “sleep or work” state of nodes, and extend the network lifetime. The simulation experiment verified the effectiveness of the proposed method, indicating that its performance in coverage quality, mobile energy consumption, and network lifetime is superior to other compared methods. Full article