Search Results (1,186)

Wireless sensor networks are composed of many nodes distributed in a region of interest to monitor different environments and physical variables. In many cases, access to nodes is not easy or feasible. As such, the system lifetime is a primary design parameter to consider in the design of these networks. In this regard, for some applications, it is preferable to extend the system lifetime by actively reducing the number of packet transmissions and, thus, the number of reports. The system administrator can be aware of such reporting reduction to distinguish this final phase from a malfunction of the system or even an attack. Given this, we explore different mathematical functions that drastically reduce the number of packet transmissions when the residual energy in the system is low but still allow for an adequate number of transmissions. Indeed, in previous works, where the negative exponential distribution is used, the system reaches the point of zero transmissions extremely fast. Hence, we propose different dampening functions with different decreasing rates that present oscillations to allow for packet transmissions even at the end of the system lifetime. We compare the system performance under these mathematical functions, which, to the best of our knowledge, have never been used before, to find the most adequate transmission scheme for packet transmissions and system lifetime. We develop an analytical model based on a discrete-time Markov chain to show that a moderately decreasing function provides the best results. We also develop a discrete event simulator to validate the analytical results. Full article

Soil organic pollution (such as heavy metals, PAHs, etc.) has caused serious environmental problems, which have resulted in unexpected effects on contaminated soil ecosystems. However, knowledge of the interactions between environmental PAHs and bacterial and fungal communities is still limited. In this study, soil samples from different PAH-contaminated areas including non-contaminated areas (NC), low-contaminated areas (LC), and high-contaminated areas (HC) were selected. Results of toxic equivalent quantity (TEQ) indicated that Benzo[a]pyrene (BaP) and Dibenzo[a,h]anthracene (DBahA) constituted the main TEQs of ∑16PAHs. Incremental lifetime cancer risk (ILCR) assessment revealed that the main pathway of exposure to soil PAHs was dermal contact in adults and children. Furthermore, adults faced a higher total cancer risk (including dermal contact, ingestion, and inhalation) from soil PAHs than children. The microbial community composition analysis demonstrated that soil PAHs could decrease the diversity of bacterial and fungal communities. The relative abundance of Acidobacteriota, Gemmatimonadota, Fimicutes, Bacteroidota, Ascomycota, and Basidiomycota exhibited varying degrees of changes under different concentrations of PAHs. Benzo[a]anthracene (BaA) and Chrysene (Chr) drove the bacterial community composition, while BaP and DBahA drove the fungal community compositions. Co-occurrence network analysis revealed the high contamination levels of PAHs that could change the relationships among different microorganisms and reduce the complexity and stability of fungal and bacterial networks. Overall, these findings provide comprehensive insight into the responses of bacterial and fungal communities to PAHs. Full article

In recent years, underwater environmental monitoring has primarily relied on monitoring systems based on underwater sensor networks (UWSNs). The underwater sensor node using a self-powered monitoring system has not been widely used because of the complicated design and high cost of its energy-harvesting device. Thus, the mobile monitoring nodes within UWSNs are typically powered by batteries with limited energy, and replacement on the seabed is challenging. As a result, optimizing the energy consumption of the mobile monitoring network is of significant importance. The clustering algorithm for UWSNs is acknowledged as a vital approach to balancing and reducing network energy consumption. Nevertheless, most existing clustering algorithms employ fixed schemes to balance the energy consumption among nodes, which are unable to dynamically adapt to changes in network topology and do not account for the complexities of the underwater channel environment, thus not aligning with the actual scenarios of marine environment monitoring. Consequently, this paper introduces an adaptive clustering algorithm for marine environment monitoring (MEMAC). The algorithm incorporates the multipath channel information of the underwater environment and the traffic weight between nodes into the probability model to calculate the probability of the node being elected as the cluster head (CH). The final calculated expected revenues are the user’s revenues after participating in the game under the influence of the multipath effect, and the revenues of all users jointly determine the performance of the clustering algorithm proposed in this paper. When the energy consumption of the CH node is too much and needs to be rotated, MEMAC, through a CH rotation mechanism and a comprehensive analysis of the overall remaining energy of the network, further optimizes the CH selection strategy while ensuring network stability. Simulation results indicate that the network lifetime of the proposed MEMAC method is extended by 58.9% and 19.17% compared to the two latest clustering algorithms, the Game Theory-Based Clustering Scheme (GTC) and the Centralized Control-Based Clustering Scheme (CCCS), respectively. This demonstrates that the algorithm can achieve efficient energy utilization and notably enhance network performance. Full article

Battery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network’s lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN’s lifetime. By categorizing existing strategies into node energy reduction, network energy balancing, and energy replenishment, this study assesses their effectiveness when implemented in LWSN applications, providing valuable insights to assist engineers during the design of green and energy-efficient LWSN monitoring systems. Full article

Women who receive substance use treatment have a particular classification of sensitivity to European drugs and drug use (according to the EMCDDA). The average level of women’s treatment is lower than men’s across Europe, while women’s abandonment is higher than men’s. The purpose of this study was to examine the factors associated with problems for women who use drugs, analyzing several psychological and social factors (gender, substance use, mental health, source of economic support, legal status, and abuse). Methodology: This was a quantitative study. Data on 2179 people receiving rehabilitation treatment were obtained from the EuropASI survey. The dependent variables in this study were (1) a patient’s known history of addiction and mental illness; (2) primary drug use; (3) drug use in their lifetime and the past month; (4) mood in their lifetime and the past month, physical condition, and sexual abuse history; (5) mental illness in their lifetime and the past month (including suicide attempts); (6) legal status in their lifetime and the past month; (7) and source of income in the past month and (8) and the number of patients. The factor of gender was taken as a dichotomous variable (male–female). The confidence interval used was 95%. Results: In summary, we found that women had consumed more alcohol, used more drugs, and suffered more from depression, anxiety, and suicidal ideation than men, both during their lifetimes and in the past month. In addition, women were more likely than men to be stigmatized for prostitution because their financial support came mainly from colleagues, friends, and family, as well as from prostitution. Finally, women had experienced more violence (emotional, physical, and sexual) during their lifetimes and in the last month than men. Conclusion: There appears to be a need to develop a comprehensive treatment network for addictive behaviors from a multifactorial perspective, including harm reduction, psychosocial support, and recovery programs; furthermore, targeting specific groups with special needs, such as women, especially those with mental health problems, women with alcoholism, and abused women, it also seems important to develop adaptive recovery programs within addictive behavior treatment networks. Full article

The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper presents an improved version of the LEACH protocol, termed MFG-LEACH, which incorporates the Mean Field Game (MFG) theory to optimize energy efficiency and network lifetime. The proposed MFG-LEACH protocol addresses the imbalances in energy consumption by modeling the interactions among nodes as a game, where each node optimizes its transmission energy based on the collective state of the network. We conducted extensive simulations to compare MFG-LEACH with Enhanced Zonal Stable Election Protocol (EZ-SEP), Energy-Aware Multi-Hop Routing (EAMR), and Balanced Residual Energy routing (BRE) protocols. The results demonstrate that MFG-LEACH significantly reduces energy consumption and increases the number of packets received across different node densities, thereby validating the effectiveness of our approach. Full article

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR). Full article

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission. Full article

The advancement to Urban 4.0 requires urban digitization and predictive maintenance of infrastructure to improve efficiency, durability, and quality of life. This study aims to integrate intelligent technologies for the predictive maintenance of photovoltaic panel systems, which serve as essential smart city renewable energy sources. In addition, we employ vision transformers (ViT), a deep learning architecture devoted to evolving image analysis, to detect anomalies in PV systems. The ViT model is pre-trained on ImageNet to exploit a comprehensive set of relevant visual features from the PV images and classify the input PV panel. Furthermore, the developed system was integrated into a web application that allows users to upload PV images, automatically detect anomalies, and provide detailed panel information, such as PV panel type, defect probability, and anomaly status. A comparative study using several convolutional neural network architectures (VGG, ResNet, and AlexNet) and the ViT transformer was conducted. Therefore, the adopted ViT model performs excellently in anomaly detection, where the ViT achieves an AUC of 0.96. Finally, the proposed approach excels at the prompt identification of potential defects detection, reducing maintenance costs, advancing equipment lifetime, and optimizing PV system implementation. Full article

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput. Full article

This study investigates the coevolution of coauthorship and citation networks and their influence on scientific metrics such as the h-index and journal impact factors. Using a preferential attachment mechanism, we developed a model that integrated these networks and validated it with data from the American Physical Society (APS). While the correlations between reference counts, paper lifetime, and team sizes with scientific impact metrics are well-known, our findings demonstrate how these relationships vary depending on specific model parameters. For instance, increasing reference counts or reducing paper lifetime significantly boosts both journal impact factors and h-indexes, while expanding team sizes without adding new authors can artificially inflate h-indexes. These results highlight potential vulnerabilities in commonly used metrics and emphasize the value of modeling and simulation for improving bibliometric evaluations. Full article

In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. Firstly, node places are grouped according to the firing time of node’s energy consumption transition to ensure that in each time slot, MV places only enable charging transitions for the node places with lower remaining lifetimes. Then, the FSOMCT (Firing Sequence Optimization of Mobile Charging Transition) problem is formulated under the constraints of MV places capacity, travelling arc weight, charging arc weight, and so on. The elite strategy and the Max–Min Ant Colony system are further introduced to improve the ant colony algorithm, while the improved FWA (fireworks algorithm) optimizes the path constructed by each ant. Finally, the optimal mobile charging transition firing sequence and charging times are obtained, ensuring that MVs have sufficient energy to return to the base station. Simulation results indicate that, compared with the periodic algorithm and the PE-FWA algorithm, the proposed method can improve charging benefit by approximately 48.7% and 26.3%, respectively. Full article

Around 13% of women will likely develop breast cancer during their lifetime. Advances in cancer metabolism research have identified a range of metabolic reprogramming events, such as altered glucose and amino acid uptake, increased reliance on glycolysis, and interactions with the tumor microenvironment (TME), all of which present new opportunities for targeted therapies. However, studying these metabolic networks is challenging in traditional 2D cell cultures, which often fail to replicate the three-dimensional architecture and dynamic interactions of real tumors. To address this, organoid models have emerged as powerful tools. Tumor organoids are 3D cultures, often derived from patient tissue, that more accurately mimic the structural and functional properties of actual tumor tissues in vivo, offering a more realistic model for investigating cancer metabolism. This review explores the unique metabolic adaptations of breast cancer and discusses how organoid models can provide deeper insights into these processes. We evaluate the most advanced tools for studying cancer metabolism in three-dimensional culture models, including optical metabolic imaging (OMI), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and recent advances in conventional techniques applied to 3D cultures. Finally, we explore the progress made in identifying and targeting potential therapeutic targets in breast cancer metabolism. Full article

Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network’s lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network. Full article

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs’ battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation. Full article