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1
Brooks, R. R., and Hemanth Siddulugari. "Adaptive Optimizations for Surveillance Sensor Network Longevity." International Journal of Distributed Sensor Networks 5, no. 2 (2009): 158–84. http://dx.doi.org/10.1080/15501320601062189.
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Sensor networks are typically wireless networks composed of resource-constrained battery powered devices. In this paper, we present a criterion for determining whether or not a surveillance sensor network is viable. We use this criterion to compare methods for extending the effective lifetime of the sensor network. The life extension methods we consider are local adaptations that reduce the energy drain on individual nodes. They are communications range management, node repositioning, and data agreement. Simulations of a surveillance scenario quantify the utility of these methods. Our results indicate that data agreement provides the most improvement in network longevity, and communications range management is also useful. Repositioning nodes to reduce the power needed for communications is dependent on the amount of attenuation experienced by the node's communications signal and the volume of traffic between nodes. When these factors are considered, node repositioning is an effective strategy for network life extension. Synergies between the energy conservation approaches are also explored.
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Mendenhall, Alexander, and George L. Sutphin. "INTERVENING IN THE LONGEVITY NETWORK." Innovation in Aging 3, Supplement_1 (2019): S616. http://dx.doi.org/10.1093/geroni/igz038.2293.
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Budovsky, Arie, Amir Abramovich, Raphael Cohen, Vered Chalifa-Caspi, and Vadim Fraifeld. "Longevity network: Construction and implications." Mechanisms of Ageing and Development 128, no. 1 (2007): 117–24. http://dx.doi.org/10.1016/j.mad.2006.11.018.
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Al-Khafaji, Baydaa-Jaffer. "New Technology for Advanced Results for Security Wireless Sensor Networks." Wasit Journal for Pure sciences 3, no. 1 (2024): 83–89. http://dx.doi.org/10.31185/wjps.286.
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Designing wireless sensor networks (WSNs), which are new technologies with the potential to perceive, analyze, communicate, and send data, is difficult since it impacts the network's performance and longevity. The goals of this study are to enhance network longevity, mitigate power usage, and safeguard transmitted data. An energy-saving procedure was suggested as a means to accomplish this objective. Every node in the network uses the same amount of electricity according to this protocol. The restricted resources of the nodes are also taken into account while recommending an algorithm. Additionally, in order to provide optimal outcomes for assessing the system's effectiveness. The suggested model and the network's performance were evaluated using the Quality of Service (Q o S) metrics of Packet Loss Ratio (PLR), Network Energy Consumption (NEC), and Network Throughput Ratio (NTR). , this method ensures that the data remains intact. Additionally, we came up with a method to save electricity. This protocol ensures that all nodes in the network use an equal amount of electricity
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Kohli, Priya. "An Improved Method Of Cluster Head Selection Using Machine Learning In Wsn." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1400–1408. http://dx.doi.org/10.17762/msea.v70i2.2332.
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Wireless Sensor Networks (WSNs) are comprised of numerous small, low-cost, and energy-limited sensor nodes that gather information from their immediate surroundings and relay it to a sink node. The utilisation of cluster-based routing protocols has been widely employed in Wireless Sensor Networks (WSNs) with the aim of enhancing network efficacy and extending network longevity. In the context of Wireless Sensor Networks (WSNs) that are cluster-based, the network is partitioned into clusters, with each cluster being assigned a Cluster Head (CH) responsible for data aggregation and forwarding to the sink node. The process of selecting Cluster Heads (CHs) is of utmost importance for optimising the performance of a network in terms of energy efficiency, network longevity, and communication overhead.
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Lithgow, Michael, Philip Garrison, Esther Han Beol Jang, and Nicolas Pacé. "Network Wisdom: The Role of Scaffolding in Expanding Communities of Practice and Technical Competencies in Community Networks." Canadian Journal of Communication 47, no. 2 (2022): 271–91. http://dx.doi.org/10.22230/cjc.2022v47n2a4235.
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‹ Volume 47 Issue 2, May 2022, pp. 271-291 › Articles Network Wisdom: The Role of Scaffolding in Expanding Communities of Practice and Technical Competencies in Community Networks Michael LithgowRelated informationAthabasca University Philip GarrisonRelated informationUniversity of Washington Esther Han Beol JangRelated informationUniversity of Washington Nicolas PacéRelated informationAlterMundi Michael Lithgow is Associate Professor at Athabasca University. Email: michael.lithgow@athabascau.ca. Philip Garrison is a PhD Candidate in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Email: philipmg@cs.washington.edu. Esther Han Beol Jang is a PhD candidate in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Email: infrared@cs.washington.edu. Nicolas Pacé is Community Networks Movement Builder at AlterMundi. Email: nicopace@altermundi.net Abstract Full Text References PDF EPUB Background: One of the key tensions to emerge from research on community owned and operated information and communications technology networks (“community networks”) is why some networks flourish while others fail. Analysis: These findings are based on interviews with 15 community network participants from four rural community networks in Córdoba, Argentina. Community network longevity is shaped by practices of scaffolding—knowledge sharing practices that expand what Étienne Wenger describes as “fields of negotiability” within communities of practice. Conclusion and implications: Network longevity was supported by scaffolding practices that decentralized technical capacities while encouraging deeper involvement among network participants. The network wisdom demonstrated in these cases appears to offer a promising strategy for community networks struggling to achieve longevity.
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Kiruba, M. Sweety, and K. P. Rajesh. "Advanced Hybrid Clustering and Routing Techniques for Enhanced Enegy Efficiency in Wireless Sensor Networks." Journal of Neonatal Surgery 14, no. 4S (2025): 534–40. https://doi.org/10.52783/jns.v14.1829.
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Wireless Sensor Networks (WSNs) are essential in various fields such as environmental monitoring, healthcare, and industrial automation. However, the constrained battery capacity of sensor nodes makes energy efficiency a critical challenge, requiring innovative solutions to extend network longevity. This paper introduces an innovative hybrid algorithm designed to improve efficiency and prolong the lifespan of the network. The proposed method combines advanced data organization and transmission strategies to optimize overall performance. Through extensive simulations and comparative analyses, the algorithm demonstrates significant improvements in network longevity, data accuracy, and reliability compared to traditional methods [1] [2].
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Jeon, Soobin. "Data Exchange in Cluster Structure for Longevity of IoT." Future Internet 12, no. 2 (2020): 32. http://dx.doi.org/10.3390/fi12020032.
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In the Internet of Things (IoT), the scope of wireless sensor nodes is extended to things deployed in a pervasive world. For various IoT service applications, things can gather and share their information with each other through self-decision-making. Therefore, we cannot apply the existing information aggregation methods of wireless sensor networks to the IoT environment, which aim to transmit the collected data to only a sink node or a central server. Moreover, since the existing methods involve all the sensor nodes in the process of data exchange, they can cause an increase in the network traffic, delay of data transmission, and amount of energy consumed by things. In this paper, we propose a clustering-property-based data exchange method for efficient energy consumption in IoT networks. First, the proposed method assigns properties to each thing according to the characteristics of the obtained data. Second, it constructs a cluster network considering the location of things and their energy consumption. Finally, the things in a cluster communicate with other things in a different cluster based on their properties. In the experiment, the proposed method exhibits a better performance than the existing method. Owing to the energy-saving effect, we demonstrate that the proposed method results in a more reliable network and improves the longevity of IoT networks.
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Ahmed, Ahmed, Rajesh Kumar Samala, Asha KS, Sorabh Sharma, Amit barve, and Deepak Minhas. "Machine Learning and Internet of Things Driven Energy Optimization in Wireless Sensor Networks through Crossbreed Clustering." Journal of Intelligent Systems and Internet of Things 13, no. 2 (2024): 52–59. http://dx.doi.org/10.54216/jisiot.130204.
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Key challenges in Wireless Sensor Networks (WSNs) include reduced dormancy, energy efficacy, reportage worries, and network lifetime. To solve the issues of energy efficiency and network longevity, more study of cluster-based WSNs is required. In order to address the challenges and constraints of WSNs, creative approaches are needed. WSNs use machine-learning techniques because of their unique characteristics. These characteristics include high communication costs, low energy reserves, high mobility, and frequent topological shifts. The current method picks cluster heads at random at the beginning of each cycle, not considering the remaining energy of these nodes. It is possible that the newly chosen CH nodes will have the lowest energy level in the network and will die off fast as a result. Energy is wasted while communicating over long distances between cluster heads and the BS, which occurs frequently in a big network due to Internet of things. This would mean that WSNs have a finite lifespan. Therefore, to increase the network's longevity and efficiency, we propose a machine-learning-based strategy called energy proficient crossbreed clustering methodology (ECCM). The experimental results reveal that the ECCM is superior to the LEACH approach, increasing residual energy by 35%, extending network lifetime by 37%, and increasing throughput by 15%.
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Li, Rui-Ding, Wen-Xuan Zheng, Qin-Ren Zhang, et al. "Longevity-Associated Core Gut Microbiota Mining and Effect of Mediated Probiotic Combinations on Aging Mice: Case Study of a Long-Lived Population in Guangxi, China." Nutrients 15, no. 7 (2023): 1609. http://dx.doi.org/10.3390/nu15071609.
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With an ageing population, healthy longevity is becoming an important scientific concern. The longevity phenomenon is closely related to the intestinal microflora and is highly complicated; it is challenging to identify and define the core gut microbiota associated with longevity. Therefore, in this study, 16S rRNA sequencing data were obtained from a total of 135 faecal samples collected as part of the latest sampling and pre-collection initiative in the Guangxi longevity area, and weighted gene co-expression network analysis (WGCNA) was used to find a mediumpurple3 network module significantly associated with the Guangxi longevity phenomenon. Five core genera, namely, Alistipes, Bacteroides, Blautia, Lachnospiraceae NK4A136 group, and Lactobacillus, were identified via network analysis and random forest (RF) in this module. Two potential probiotic strains, Lactobacillus fermentum and Bacteroides fragilis, were further isolated and screened from the above five core genera, and then combined and used as an intervention in naturally ageing mice. The results show a change in the key longevity gut microbiota in mice toward a healthy longevity state after the intervention. In addition, the results show that the probiotic combination effectively ameliorated anxiety and necrosis of hippocampal neuronal cells in senescent mice, improving their antioxidant capacity and reducing their inflammation levels. In conclusion, this longer-term study provides a new approach to the search for longevity hub microbiota. These results may also provide an important theoretical reference for the healthification of the intestinal microflora in the general population, and even the remodelling of the structure of the longevity-state intestinal microflora.
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Parmar, Khushboo. "A Review on Energy and Latency Efficient Routing Protocol Design for Wireless Sensor Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34750.
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Wireless sensor networks (WSNs) have been utilized by various industries, such as disaster management, the chemical and heavy industries, marine research, and space exploration. One of the primary challenges faced by wireless sensor networks is their limited network lifetime. Several techniques have been devised to extend the duration of network operation. This proposed method introduces a threshold-based algorithm to minimize the amount of data that needs to be transferred. In this approach, the selection of cluster heads and their sizes is not fixed. Instead, they are dynamically determined at the beginning of each iteration. The design of routing algorithms for wireless sensor networks faces three main challenges: lowering the rate at which the average node energy declines, minimizing the number of dead nodes, and eventually prolonging the network's lifetime. This paper provides a thorough examination of efficient routing protocols for wireless sensor networks with the goal of improving network longevity. Keywords: Efficient Routing, WSN Clustering, Network Latency, Network Lifetime.
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Teng, ChunYuen, and Lada Adamic. "Longevity in Second Life." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (2010): 343–46. http://dx.doi.org/10.1609/icwsm.v4i1.14071.
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User retention is important to the success of online social media, particularly in virtual world settings where users shape one another’s online experience. We study a rich set of variables, including social network and group membership, chatting, and transactions, in order to predict which users will stay and which ones will leave. We find that simple variables directly measuring the intensity and diversity of a user’s interaction with others are most predictive.
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Nivedhitha, V., A. Gopi Saminathan, and P. Thirumurugan. "Improving Network Longevity in Wireless Sensor Networks Using an Evolutionary Optimization Approach." Intelligent Automation & Soft Computing 28, no. 3 (2021): 603–16. http://dx.doi.org/10.32604/iasc.2021.016780.
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Alayu Melkamu, Yenework, Raguraman Purushothaman, Madugula Sujatha, et al. "Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network." Bulletin of Electrical Engineering and Informatics 14, no. 1 (2025): 733–41. http://dx.doi.org/10.11591/eei.v14i1.8863.
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Mobile ad hoc networks (MANETs) operate without fixed infrastructure, with mobile nodes acting as both hosts and routers. These networks face challenges due to node mobility and limited resources, causing frequent changes in topology and instability. Clustering is essential to manage this issue. Significant research has been devoted to optimal clustering algorithms to improve cluster-based routing protocols (CBRP), such as the weighted clustering algorithm (WCA), optimal stable clustering algorithm (OSCA), lowest ID (LID) clustering algorithm, and highest connectivity clustering (HCC) algorithm. However, these protocols suffer from high re-clustering frequency and do not adequately account for energy efficiency, leading to network instability and reduced longevity. This work aims to improve the CBRP to create a more stable and long-lasting network. During cluster head (CH) selection, nodes with high residual energy or degree centrality are chosen as CH and backup cluster head (BCH). This approach eliminates the need for re-clustering, as the BCH can seamlessly replace a failing CH, ensuring continuous cluster maintenance. The proposed modified cluster-based routing protocol (MCBRP) evaluated network simulator 2 (ns2) demonstrates that MCBRP is more energy-efficient, selecting optimal CH and balancing the load to enhance network stability and longevity.
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Mishra, Amitabh, Lucas S. Liberman, and Nagaraju Brahamanpally. "An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models." Sensors 24, no. 11 (2024): 3429. http://dx.doi.org/10.3390/s24113429.
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The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.
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Dogra, Roopali, Shalli Rani, Kavita, Jana Shafi, SeongKi Kim, and Muhammad Fazal Ijaz. "ESEERP: Enhanced Smart Energy Efficient Routing Protocol for Internet of Things in Wireless Sensor Nodes." Sensors 22, no. 16 (2022): 6109. http://dx.doi.org/10.3390/s22166109.
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Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet delivery ratio and network longevity, the proposed methodology is mathematically studied, and the results have been compared to identical current approaches such as a Genetic algorithm (GA), Ant Lion optimization (ALO) and Particle Swarm Optimization (PSO). The simulation shows that in the proposed approach for the longevity of the network, there are 3500 rounds; energy utilization achieves a maximum of 0.5 Joules; bandwidth transmits the data at the rate of 0.52 MBPS; the packet delivery ratio (PDR) is at the rate of 96% for 500 nodes, respectively.
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Shankaramma and Nagaraj G. S. "Survey on WSN Network Lifetime Through Leach Clustering Schemes." International Journal of Engineering and Advanced Technology 11, no. 3 (2022): 58–61. http://dx.doi.org/10.35940/ijeat.c3366.0211322.
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Sensor nodes deployed in wireless sensor networks (WSN) are resource constrained, battery operated and have a restricted energy resource. The significant number of wireless sensor network applications aim to extend the longevity of the network by using various strategies. Clustering algorithms were proven to be among the best efficient ways for increasing wireless sensor network reliability. In a wireless sensor network, clustering-based solutions control the network operations to manage the restricted energy in the optimal effective way to extend the network lifetime. Reviews in related subject could assist in gathering thorough and timely information about wireless sensor networks lifetime study through different clustering protocols. This research review study paper presents an extensive study of existing low energy adaptive clustering hierarchy (LEACH) homogeneous clustering procedures and analysis.
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Prasad, L. V. R. Chaitanya, Dr Yedukondalu Kamatham, and Dr Dhiraj Sunehra. "Congestion-aware Weighted Sum Routing for Enhanced Network Lifetime in Cognitive Radio Sensor Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 4 (2024): 325–34. https://doi.org/10.58346/jowua.2024.i4.021.
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Conventional Energy-Efficient routing in Cognitive Radio Sensor Networks (CRSNs) often compromises network lifespan. This research proposes a congestion-aware routing scheme that improves both energy efficiency and longevity. Unlike existing protocols, our approach uses a weighted sum of five parameters: distance, energy load, link quality, data flow rate, and neighbor density to guide packets along less congested, energy-efficient paths. The reduced Energy Consumption by 5.67 J, lowering End-to-End delay by 0.05-0.35 ms, decreased network overhead by 0.25-0.65% are attained and this will result in extending the operational Network Lifetime as demonstrated by Network Simulator 2 simulations.
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Jagan, G. C., and P. Jesu Jayarin. "Wireless Sensor Network Cluster Head Selection and Short Routing Using Energy Efficient ElectroStatic Discharge Algorithm." Journal of Engineering 2022 (February 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/8429285.
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Wireless sensor networks attract so much attention in current IoT-enabled industrial and domestic applications having either homogeneous or heterogeneous sensors deployed to acquire information of intent. WSNs are designed to operate using self-powered sensor nodes as their choice of application is geographic critical. Such nodes must support energy efficiency so that network longevity becomes high. Cluster head selection plays a crucial stage in a WSN architecture which mainly focuses on the minimization of network energy consumption. It groups sensor nodes in such a way that a sophisticated network cluster is formed to have enhanced life time besides a low power consumption. A popular clustering technique, known as LEACH and its variants, is found to be energy efficient compared to its counterparts. The authors propose a novel fully connected energy efficient clustering (FCEEC) mechanism using the electrostatic discharge algorithm to establish a fully connected network with shortest path routing from sensor nodes (SNs) to cluster head (CH) in a multihop environment. The proposed electrostatic discharge algorithm (ESDA) enhances network life time while attaining energy efficient full connectivity between sensor nodes. As a result of ESD, the dead node count is reduced significantly so that the network longevity is increased. In the end, simulation results exhibited improved performance metrics such as energy efficiency, dead node count, packet delivery, and network latency compared to certain conventional CH selection approach.
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J, Martin Sahayaraj, Sendhil Kumar Dr.N.C., Mukunthan Dr. P., Tamilarasan N, and Jaya Pratha S. "IEEHR: Improved Energy Efficient Honeycomb based Routing in MANET for Improving Network Performance and Longevity." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 7 (2022): 85–93. http://dx.doi.org/10.17762/ijritcc.v10i7.5575.
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In present scenario, efficient energy conservation has been the greatest focus in Mobile Adhoc Networks (MANETs). Typically, the energy consumption rate of dense networks is to be reduced by proper topological management. Honeycomb based model is an efficient parallel computing technique, which can manage the topological structures in a promising manner. Moreover, discovering optimal routes in MANET is the most significant task, to be considered with energy efficiency. With that motive, this paper presents a model called Improved Energy Efficient Honeycomb based Routing (IEEHR) in MANET. The model combines the Honeycomb based area coverage with Location-Aided Routing (LAR), thereby reducing the broadcasting range during the process of path finding. In addition to optimal routing, energy has to be effectively utilized in MANET, since the mobile nodes have energy constraints. When the energy is effectively consumed in a network, the network performance and the network longevity will be increased in respective manner. Here, more amount of energy is preserved during the sleeping state of the mobile nodes, which are further consumed during the process of optimal routing. The designed model has been implemented and analyzed with NS-2 Network Simulator based on the performance factors such as Energy Efficiency, Transmission Delay, Packet Delivery Ratio and Network Lifetime.
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Shankaramma and G. S. Nagaraj. "Survey on WSN Network Lifetime Through Leach Clustering Schemes." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 3 (2022): 58–61. https://doi.org/10.35940/ijeat.C3366.0211322.
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<strong>Abstract: </strong>Sensor nodes deployed in wireless sensor networks (WSN) are resource constrained, battery operated and have a restricted energy resource. The significant number of wireless sensor network applications aim to extend the longevity of the network by using various strategies. Clustering algorithms were proven to be among the best efficient ways for increasing wireless sensor network reliability. In a wireless sensor network, clustering-based solutions control the network operations to manage the restricted energy in the optimal effective way to extend the network lifetime. Reviews in related subject could assist in gathering thorough and timely information about wireless sensor networks lifetime study through different clustering protocols. This research review study paper presents an extensive study of existing low energy adaptive clustering hierarchy (LEACH) homogeneous clustering procedures and analysis.
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Narla, Mahendra, and S. Murugan. "Energy Efficient Power-Aware DSR-based Routing for Homogeneous and Heterogeneous WSNs." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23399–404. https://doi.org/10.48084/etasr.9787.
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The research on managing resources and bandwidth evaluation within ad-hoc networks has highlighted energy-efficient routing protocols as a critical mechanism for conserving energy and prolonging the network's operational lifespan. This study introduces the Energy-Efficient Power Aware DSR (EEPW-DSR) routing protocol, which enhances Cluster Head (CH) selection by incorporating a distance-aware approach and addresses energy consumption challenges. The proposed method optimizes CH selection based on proximity to the Base Station (BS) and the node energy levels, significantly reducing communication distance and balancing energy consumption. Conventional methods, such as Standard DSR and Multi-hop Routing (MR), were considered for comparative performance analysis with the EEPW-DSR. Simulation results demonstrate that the proposed method outperforms conventional methods in network lifetime, network stability, and period of active node operation. These improvements highlight the robustness of the proposed approach in ensuring network longevity in both homogeneous and heterogeneous Wireless Sensor Networks (WSNs).
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A., Dr Sathesh. "OPTIMIZED MULTI-OBJECTIVE ROUTING FOR WIRELESS COMMUNICATION WITH LOAD BALANCING." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (2019): 106–20. http://dx.doi.org/10.36548/jtcsst.2019.2.004.
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The rapid advancements in the wireless communication networks have increased the popularity of portable or mobile devices and the network framed with these mobile devices. These mobile networks framed using the volunteering portable devices are decentralized and have dynamic topologies experiencing sudden changes in the network structure. The main reason causing the topology changes are the limited energy availability of the device and their mobility. Improper trafficking of the tasks and improper selection of the portable devices causes maximum energy consumption resulting in the link failures and changes in the topology of the network. So the paper puts forward the hybridized optimization technique to handle the multi-objective problem faced by these decentralized networks. The proposed method is validated using the network simulator-2 to evince throughput, energy consumption and the network longevity achieved by the proposed method.
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Nageswararao, Malisetti, and Kumar Pamula Vinay. "The effectiveness of jaya optimization for energy aware cluster based routing in wireless sensor networks." i-manager’s Journal on Wireless Communication Networks 13, no. 1 (2024): 18. http://dx.doi.org/10.26634/jwcn.13.1.20965.
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The Internet of Things (IoT) has significantly impacted human life, enhancing quality of life and transforming various commercial sectors. The sensor nodes in the IoT are interconnected to facilitate the passage of data to the sink node over the network. Due to the constraints of battery power, energy in the nodes is preserved through the utilization of clustering techniques. Choosing a Cluster Head (CH) is crucial for prolonging the network's lifespan and increasing its throughput during the clustering process. Numerous optimization techniques have been developed to select the best Cluster Head (CH) to enhance energy efficiency in network nodes. Therefore, using incorrect CH selection methods leads to longer convergence times and faster depletion of sensor batteries. This research proposes a method that incorporates a CH selection strategy using the Jaya optimization method. The proposed methodology is evaluated against existing algorithms in terms of network longevity and energy efficiency. The simulation results indicate that the Jaya optimization algorithm-based CH selection scheme (Jaya-EEC) is much more effective in terms of network longevity compared to LEACH, LEACH-E, and PSO-C. Specifically, Jaya-EEC outperforms LEACH by 72%, LEACH-E by 64%, and PSO-C by 60%.
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Kavitha V and Panneer Arokiaraj S. "RPL-eSOA: Enhancing IoT network sustainability with RPL and enhanced sandpiper optimization algorithm." Scientific Temper 15, no. 03 (2024): 2634–43. http://dx.doi.org/10.58414/scientifictemper.2024.15.3.31.
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The internet of things (IoT) encompasses extensive networks of interconnected devices, playing a crucial role in various applications. However, managing these networks presents significant challenges, particularly in cluster head selection, which is critical for energy efficiency and sustainability. To eradicate these challenges, this paper combines the capability of routing protocol for low-power and lossy networks (RPL) with an enhanced sandpiper optimization algorithm (e-SOA) to dynamically optimize network configurations. This combination, termed RPL-eSOA, improves energy management and extends network longevity while maintaining robust communication pathways. Through simulation and comparative analysis, RPL-eSOA demonstrates superior performance in enhancing network lifetime and operational efficiency compared to traditional methods. It achieved a 100% packet delivery ratio (PDR) and significantly reduced latency to 475 ms.
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Abha Tiwari. "Optimizing Industrial Wireless Sensor Networks Using the Mean Shift Algorithm for Enhanced Efficiency and Longevity." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 525–31. https://doi.org/10.52783/jisem.v10i32s.5334.
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Optimizing sensor location and data aggregation in Industrial Wireless Sensor Networks (IWSNs) is essential to increasing network efficiency and prolonging the system's operational lifecycle. This study suggests using the Mean Shift method to improve sensor placement and data handling, hence enhancing the performance of IWSNs. We obtain notable gains in network longevity and energy efficiency by implementing the Mean Shift algorithm in a three-dimensional industrial setting. The approach's usefulness is demonstrated by experimental findings, which result in reduced energy consumption and more efficient data aggregation.
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Neha Gharat. "Machine Learning-Enhanced Hybrid Source Location Privacy Protocol for Improved Security and Network Longevity in IoT Networks." Journal of Information Systems Engineering and Management 10, no. 9s (2025): 512–25. https://doi.org/10.52783/jisem.v10i9s.1250.
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Efficient routing in IoT networks is critical for optimizing data transmission while addressing the inherent challenges of energy consumption, scalability, and resilience. Common routing techniques, such as flooding, tree-based, cluster-based, and geographic routing, each present unique advantages and limitations regarding energy efficiency, network congestion, and fault tolerance. In parallel, source location privacy protection (SLPP) has emerged as a pivotal concern in IoT applications like surveillance and environmental monitoring, where adversaries may exploit transmission patterns to identify sensitive source nodes. Despite advancements, current issues in SLP in IoT networks include balancing privacy with energy efficiency, ensuring scalability in dense network environments, and providing resilience against increasingly sophisticated adversarial models. Traditional SLP techniques, including phantom routing, random walks, and dummy packet generation, often impose trade-offs between privacy, energy consumption, and network longevity, limiting their practical application in large-scale IoT networks. Additionally, many existing protocols struggle with adapting to dynamic network topologies and fail to adequately address the challenges posed by hotspot formation, which can lead to uneven energy depletion and compromised privacy. Many SLP methods disrupt the Quality of Service (QoS) by introducing delays or reducing throughput, which can hinder the primary functions of IoT applications, particularly in time-sensitive scenarios. A new Hybrid Source Location Privacy (SLP) protocol that effectively integrates random walks, rumor routing, and Greedy Random Walks to obscure source node locations while optimizing energy consumption is implemented to overcome these limitations. The protocol employs a multi-layer grid framework, dynamic cluster head rotations, and phantom nodes to balance energy usage and reduce network hotspots. The new Hybrid SLP confuses adversaries by combining fake packet generation with adaptive routing strategies, enhancing privacy without compromising network performance. Simulations demonstrate that the Hybrid SLP protocol significantly outperforms existing techniques, achieving lower energy consumption, extended network lifetime, and robust privacy protections, making it ideal for privacy-sensitive IoT applications. The proposed Hybrid SLP protocol integrates a machine learning-based anomaly detection system to enhance its performance and security, highlighting the novelty of the proposed work. This novel combination of advanced routing strategies and machine learning strengthens network resilience against various threats.
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Baqer, Mohammed, and Luisella Balbis. "Sampling Sensor Nodes to Extend the Longevity of Wireless Sensor Networks." International Journal of Sensors, Wireless Communications and Control 9, no. 1 (2019): 53–63. http://dx.doi.org/10.2174/2210327908666180727125534.
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Background and Objective: Wireless Sensor Networks (WSN) are one of the most important elements in the Internet of Things (IoT) paradigm. It is envisaged that WSNs will seamlessly bridge the physical world with the Internet resulting in countless IoT applications in smart cities, wearable devices, smart grids, smart retails amongst others. It is necessary, however, to consider that sensing, processing and communicating large amounts of sensor data is an energy-demanding tasks. Recharging or replacing those battery-powered sensor nodes deployed in inaccessible locations is generally a tedious and time-consuming task. As a result, energy efficient approaches for WSN need to be devised in order to prolong the longevity of the network. Methods: In this paper, we present an approach that reduces energy consumption by controlling the sampling rate and the number of actively communicating nodes. The proposed approach applies compressive sensing to reduce the sampling rate and a statistical approach to decrease the sample size of sensor nodes. Results and Conclusion: The proposed approach is expected to significantly increase the lifetime of the network whilst maintaining the event detection accuracy.
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Gaigaliene, Asta, and Otilija Jurakovaite. "Post global financial crisis multiplex financial network structure longevity: Does it predict re-globalization? Empirical investigation for 234 countries." Equilibrium. Quarterly Journal of Economics and Economic Policy 19, no. 1 (2024): 23–57. http://dx.doi.org/10.24136/eq.3023.
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Research background: International capital flows show decreasing trend after each financial crisis as countries become unwilling to invest in risky counterparties. Recent discussions over structural changes within the global financial network suggest that its structure may be still undergoing some changes. Uncovered weaknesses of over-connectedness with unstable un-ally countries during global financial crisis were further highlighted by recent Covid-19 pandemic. However, previous research suggest that important structural changes happened after global financial crisis. Thus, such situation raises a question whether post global financial crisis multiplex network structure is long-term and is it exhibiting re-globalization pattern as suggested by re-globalization megatrend. Purpose of the article: The main objective of the paper is to assess post global financial crisis multiplex financial network structure longevity and to identify whether it exhibits re-globalization pattern. Methods: Multiplex financial network mapping, network topological analysis and structure longevity methods were employed to evaluate network connectedness, topological structural properties of multiplex financial network and its’ structure longevity. 5-layer multiplex financial network was mapped for different types of capital flows for each layer: net direct and portfolio investments in debt, equity and net banking assets for each year in the post global financial crisis period 2009–2020. Each network layer employed bilateral data from up to 234 countries of the world. Next step of research employed a set of network-level measures defining topological features of each network in the period of 2009–2020 in order to capture the trend of structural changes and evidence of re-globalization. The final stage analyzes multiplex financial network structure by layer and country longevity. Findings & value added: Analysis reveals that stock (positions) multiplex financial network aggregate connectedness, i.e., density and value, increased during post- global financial crisis period until Covid-19 pandemic period, thus, suggesting that globalization in terms of stock is continuing. Topological structural properties of multiplex financial network changed as number of strongly and weakly connected countries decreased and more countries became included in network. Longevity analysis of layer and country structure within multiplex financial network reveal that banking and debt investments are increasing since 2018, while equity part is decreasing. Developed countries, i.e., the United Kingdom and Germany, decrease in multiplex financial network structure, while developing, especially from Asia region (i.e., Hong Kong SAR, China and Singapore) increase. Such empirical results support the re-globalization megatrend in terms of investment type and counterparty structure as highlighted by most recent scientific discussion showing that it started early after global financial crisis. Research also uncovers some regional trends of Asia region increasing in investment network structure, while Europe decreasing. As developing countries attract more and more investment and further develop, global cost saving and, thus, returns from global investment into these countries may be decreasing, resulting in lower gain from globalization. Our analysis may facilitate investment strategy decisions by suggesting that even though gains from globalization might be decreasing, instead of localization, countries could follow ally investment strategy to manage the risk of over-dependency from unstable counterparty while preserving globalization wealth.
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Jain, Amit Kumar, Sushil Jain, and Garima Mathur. "Optimizing wireless sensor network routing with Q-learning: enhancing energy efficiency and network longevity." Engineering Research Express 6, no. 4 (2024): 045241. http://dx.doi.org/10.1088/2631-8695/ad9138.
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Abstract WSNs have gained importance in the recent years in the fields of industries, automobiles, military, agriculture and health care sectors, and others for data acquisition and monitoring. WSNs are made up of a large number of low-power nodes implemented with requisite sensing, computational, and wireless communication functionalities in relation to other nodes or a BS. LEACH is hierarchical routing protocol that adapts nodes into clusters, with an objective of achieving an equal distribution of energy load. However, the drawback of LEACH is that the selection of cluster head (CH) and the usage of single hop data transmission to the base station (BS) depends on the random probability, which is not efficient in large network. New additions involve the integration of a k-means clustering algorithm and reinforcement learning Q-learning for the selection of CHs and routing of data. For instance, Q-Learning improves the reliability and flexibility of a given network as it allows for acquisition of the best routes that involve multi-hope communication. An assessment of Q-Learning based routing is conducted on WSNs, based on energy depletion rate, node duration, and packet delivery ratio. Theoretical analysis shows that the Q-Learning based algorithm outperforms the traditional algorithms such as LEACH and k-means by adopting better energy utilization, reduced node mortality and high throughput. Overall, this study exposes the ability of Q-Learning algorithm in enhancing WSN life and efficiency in the modern world, and thus could be considered an optimal solution for changeability and limited resources in WSN networks.
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Buchnea, Emily. "Transatlantic Transformations: Visualizing Change Over Time in the Liverpool–New York Trade Network, 1763–1833." Enterprise & Society 15, no. 4 (2014): 687–721. http://dx.doi.org/10.1017/s1467222700016086.
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In the early modern Atlantic world, trade brought communities and commodities closer together and, as a result, many merchants became linked in expansive networks of exchange. The unstable nature of long-distance trade meant that these networks were continually being transformed. The Liverpool–New York trade network, in particular, underwent many changes between 1763 and 1833. The article aims to demonstrate how the use of network visualizations over three distinct phases (1760–1790, 1790–1815, and 1815–1833) can elucidate these changes and provide a different approach for studying the development of this trading community. These visualizations will serve to illustrate the extent to which this network was dynamic and further our understanding of how merchant networks sustained longevity and coped with risks in the ever-changing Atlantic world.
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Roukos, Dimitrios H. "Longevity with systems medicine? Epigenome, genome and environment interactions network." Epigenomics 4, no. 2 (2012): 119–23. http://dx.doi.org/10.2217/epi.12.1.
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Abdali Larki, Fawzia, Seyed Javad Mirabedini, and Ali Harounabadi. "Increased longevity of wireless Ad hoc network through fuzzy system." Decision Science Letters 3, no. 3 (2014): 445–56. http://dx.doi.org/10.5267/j.dsl.2014.1.001.
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Rodriguez, Karl A., Joseph M. Valentine, David A. Kramer, et al. "Determinants of rodent longevity in the chaperone-protein degradation network." Cell Stress and Chaperones 21, no. 3 (2016): 453–66. http://dx.doi.org/10.1007/s12192-016-0672-x.
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Mishra, Poorva, Sanjeev Kumar Gupta, and Laxmi Singh. "Energy conservation for environmental safety in wireless adhoc network." Environment Conservation Journal 26, no. 1 (2025): 172–80. https://doi.org/10.36953/ecj.30103146.
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In wireless ad hoc networks (WANs), saving energy is critical because the batteries can only hold so much, and the networks need to keep working. These networks, comprising mobile nodes that communicate without centralized infrastructure, frequently encounter issues concerning energy efficiency and environmental safety. This research examines the importance of energy conservation in WANs, highlighting its effects on extending network longevity and minimizing the environmental impact of communication systems. The present work analyze many energy-efficient solutions, including power-aware routing, energy-efficient communication protocols, and sleep/wakeup mechanisms. The study emphasize the significance of energy harvesting methods and low-power hardware in achieving sustainable network operations. Integrating energy saving with network design and operation enables wireless ad hoc networks to balance effective communication and environmental sustainability. This study seeks to advance the creation of eco-friendly communication technologies that promote environmental sustainability while addressing the increasing need for wireless access.
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Zhou, Zhen, Yuting Liu, Yushen Feng, et al. "Engineering longevity—design of a synthetic gene oscillator to slow cellular aging." Science 380, no. 6643 (2023): 376–81. http://dx.doi.org/10.1126/science.add7631.
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Synthetic biology enables the design of gene networks to confer specific biological functions, yet it remains a challenge to rationally engineer a biological trait as complex as longevity. A naturally occurring toggle switch underlies fate decisions toward either nucleolar or mitochondrial decline during the aging of yeast cells. We rewired this endogenous toggle to engineer an autonomous genetic clock that generates sustained oscillations between the nucleolar and mitochondrial aging processes in individual cells. These oscillations increased cellular life span through the delay of the commitment to aging that resulted from either the loss of chromatin silencing or the depletion of heme. Our results establish a connection between gene network architecture and cellular longevity that could lead to rationally designed gene circuits that slow aging.
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Levantesi, Susanna, Andrea Nigri, and Gabriella Piscopo. "Longevity risk management through Machine Learning: state of the art." Insurance Markets and Companies 11, no. 1 (2020): 11–20. http://dx.doi.org/10.21511/ins.11(1).2020.02.
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Longevity risk management is an area of the life insurance business where the use of Artificial Intelligence is still underdeveloped. The paper retraces the main results of the recent actuarial literature on the topic to draw attention to the potential of Machine Learning in predicting mortality and consequently improving the longevity risk quantification and management, with practical implication on the pricing of life products with long-term duration and lifelong guaranteed options embedded in pension contracts or health insurance products. The application of AI methodologies to mortality forecasts improves both fitting and forecasting of the models traditionally used. In particular, the paper presents the Classification and the Regression Tree framework and the Neural Network algorithm applied to mortality data. The literature results are discussed, focusing on the forecasting performance of the Machine Learning techniques concerning the classical model. Finally, a reflection on both the great potentials of using Machine Learning in longevity management and its drawbacks is offered.
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Pirredda, Michela, Iris Fañanás-Pueyo, Luis Oñate-Sánchez, and Sara Mira. "Seed Longevity and Ageing: A Review on Physiological and Genetic Factors with an Emphasis on Hormonal Regulation." Plants 13, no. 1 (2023): 41. http://dx.doi.org/10.3390/plants13010041.
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Upon storage, seeds inevitably age and lose their viability over time, which determines their longevity. Longevity correlates with successful seed germination and enhancing this trait is of fundamental importance for long-term seed storage (germplasm conservation) and crop improvement. Seed longevity is governed by a complex interplay between genetic factors and environmental conditions experienced during seed development and after-ripening that will shape seed physiology. Several factors have been associated with seed ageing such as oxidative stress responses, DNA repair enzymes, and composition of seed layers. Phytohormones, mainly abscisic acid, auxins, and gibberellins, have also emerged as prominent endogenous regulators of seed longevity, and their study has provided new regulators of longevity. Gaining a thorough understanding of how hormonal signalling genes and pathways are integrated with downstream mechanisms related to seed longevity is essential for formulating strategies aimed at preserving seed quality and viability. A relevant aspect related to research in seed longevity is the existence of significant differences between results depending on the seed equilibrium relative humidity conditions used to study seed ageing. Hence, this review delves into the genetic, environmental and experimental factors affecting seed ageing and longevity, with a particular focus on their hormonal regulation. We also provide gene network models underlying hormone signalling aimed to help visualize their integration into seed longevity and ageing. We believe that the format used to present the information bolsters its value as a resource to support seed longevity research for seed conservation and crop improvement.
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Bhimshetty, Sampoorna, and Agughasi Victor Ikechukwu. "Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 971. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp971-980.
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<div align="center"><span>The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining to battery longevity and energy efficiency, constraining the assurance of prolonged network lifetimes and expansive sensor coverage. Many existing solutions, although promising on paper, are intricate and often impractical for real-world implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored for wireless sensor networks (WSNs). This protocol harnesses RL to discern the optimal transmission route from the source to the sink node, factoring in the energy profile of each intermediary node. Training of the RL algorithm is facilitated through a reward function that includes energy outflow and data transmission efficacy. The model was compared against two prevalent routing protocols, LEACH and fuzzy C-means (FCM), for a comprehensive assessment. Simulation results highlight our protocol’s superiority with respect to the active node count, energy conservation, network longevity, and data delivery efficiency.</span></div>
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Bhimshetty, Sampoorna, and Agughasi Victor Ikechukwu. "Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 971–80. https://doi.org/10.11591/ijeecs.v33.i2.pp971-980.
Full textAbstract:
The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining to battery longevity and energy efficiency, constraining the assurance of prolonged network lifetimes and expansive sensor coverage. Many existing solutions, although promising on paper, are intricate and often impractical for real-world implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored for wireless sensor networks (WSNs). This protocol harnesses RL to discern the optimal transmission route from the source to the sink node, factoring in the energy profile of each intermediary node. Training of the RL algorithm is facilitated through a reward function that includes energy outflow and data transmission efficacy. The model was compared against two prevalent routing protocols, LEACH and fuzzy C-means (FCM), for a comprehensive assessment. Simulation results highlight our protocol’s superiority with respect to the active node count, energy conservation, network longevity, and data delivery efficiency.
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Kumar, S. Suren, and Dr S. Murugan. "Wireless Sensor Networks with Dynamic Advanced Node Selection for Longer Network Lifetime in Energy Hole Evolution." International Journal of Basic and Applied Sciences 14, no. 2 (2025): 125–31. https://doi.org/10.14419/z4f5hn69.
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Wireless sensor networks (WSNs) that collect data using battery-powered sensor nodes that sometimes detect their surroundings and send the samples they acquire to a sink node are evaluated using network lifespan, a critical performance metric. The network's performance deteriorates due to two main issues. One is the void hole that develops in a certain area due to the forwarder nodes not being available. The other is the existence of an energy hole brought on by an uneven load of data traffic on intermediary nodes. This research aims to identify the boundaries of an energy hole in a WSN that collects data and offers a mathematical framework to determine the network's lifetime from the beginning to the end. Theoretically, the traffic load, energy usage, and sensor node longevity are calculated throughout the entire network lifetime. In this paper, we present a scientific approach to determine the energy opening limit in an information-gathering WSN and measure the entire organization's lifetime from network introduction till it is completely crippled. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible.
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Balmorez, Timothy, Amy Sakazaki, and Shin Murakami. "Genetic Networks of Alzheimer’s Disease, Aging, and Longevity in Humans." International Journal of Molecular Sciences 24, no. 6 (2023): 5178. http://dx.doi.org/10.3390/ijms24065178.
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Human genomic analysis and genome-wide association studies (GWAS) have identified genes that are risk factors for early and late-onset Alzheimer’s disease (AD genes). Although the genetics of aging and longevity have been extensively studied, previous studies have focused on a specific set of genes that have been shown to contribute to or are a risk factor for AD. Thus, the connections among the genes involved in AD, aging, and longevity are not well understood. Here, we identified the genetic interaction networks (referred to as pathways) of aging and longevity within the context of AD by using a gene set enrichment analysis by Reactome that cross-references more than 100 bioinformatic databases to allow interpretation of the biological functions of gene sets through a wide variety of gene networks. We validated the pathways with a threshold of p-value < 1.00 × 10−5 using the databases to extract lists of 356 AD genes, 307 aging-related (AR) genes, and 357 longevity genes. There was a broad range of biological pathways involved in AR and longevity genes shared with AD genes. AR genes identified 261 pathways within the threshold of p < 1.00 × 10−5, of which 26 pathways (10% of AR gene pathways) were further identified by overlapping genes among AD and AR genes. The overlapped pathways included gene expression (p = 4.05 × 10−11) including ApoE, SOD2, TP53, and TGFB1 (p = 2.84 × 10−10); protein metabolism and SUMOylation, including E3 ligases and target proteins (p = 1.08 × 10−7); ERBB4 signal transduction (p = 2.69 × 10−6); the immune system, including IL-3 and IL-13 (p = 3.83 × 10−6); programmed cell death (p = 4.36 × 10−6); and platelet degranulation (p = 8.16 × 10−6), among others. Longevity genes identified 49 pathways within the threshold, of which 12 pathways (24% of longevity gene pathways) were further identified by overlapping genes among AD and longevity genes. They include the immune system, including IL-3 and IL-13 (p = 7.64 × 10−8), plasma lipoprotein assembly, remodeling and clearance (p < 4.02 × 10−6), and the metabolism of fat-soluble vitamins (p = 1.96 × 10−5). Thus, this study provides shared genetic hallmarks of aging, longevity, and AD backed up by statistical significance. We discuss the significant genes involved in these pathways, including TP53, FOXO, SUMOylation, IL4, IL6, APOE, and CEPT, and suggest that mapping the gene network pathways provide a useful basis for further medical research on AD and healthy aging.
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Hassan, Emad S., Marwa Madkour, Salah E. Soliman, et al. "Energy-Efficient Data Fusion in WSNs Using Mobility-Aware Compression and Adaptive Clustering." Technologies 12, no. 12 (2024): 248. http://dx.doi.org/10.3390/technologies12120248.
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To facilitate energy-efficient information dissemination from multiple sensors to the sink within Wireless Sensor Networks (WSNs), in-network data fusion is imperative. This paper presents a new WSN topology that incorporates the Mobility-Efficient Data Fusion (MEDF) algorithm, which integrates a data-compression protocol with an adaptive-clustering mechanism. The primary goals of this topology are, first, to determine a dynamic sequence of cluster heads (CHs) for each data transmission round, aiming to prolong network lifetime by implementing an adaptive-clustering mechanism resilient to network dynamics, where CH selection relies on residual energy and minimal communication distance; second, to enhance packet delivery ratio (PDR) through the application of a data-compression technique; and third, to mitigate the hot-spot issue, wherein sensor nodes nearest to the base station endure higher relay burdens, consequently influencing network longevity. To address this issue, mobility models provide a straightforward solution; specifically, a Random Positioning of Grid Mobility (RPGM) model is employed to alleviate the hot-spot problem. The simulation results show that the network topology incorporating the proposed MEDF algorithm effectively enhances network longevity, optimizes average energy consumption, and improves PDR. Compared to the Energy-Efficient Multiple Data Fusion (EEMDF) algorithm, the proposed algorithm demonstrates enhancements in PDR and energy efficiency, with gains of 5.2% and 7.7%, respectively. Additionally, it has the potential to extend network lifetime by 13.9%. However, the MEDF algorithm increases delay by 0.01% compared to EEMDF. The proposed algorithm is also evaluated against other algorithms, such as the tracking-anchor-based clustering method (TACM) and Energy-Efficient Dynamic Clustering (EEDC), the obtained results emphasize the MEDF algorithm’s ability to conserve energy more effectively than the other algorithms.
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Stubbings, Garrett, and Andrew Rutenberg. "Network topologies for maximal organismal health span and lifespan." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 2 (2023): 023124. http://dx.doi.org/10.1063/5.0105843.
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The population dynamics of human health and mortality can be jointly captured by complex network models using scale-free network topology. To validate and understand the choice of scale-free networks, we investigate which network topologies maximize either lifespan or health span. Using the Generic Network Model (GNM) of organismal aging, we find that both health span and lifespan are maximized with a “star” motif. Furthermore, these optimized topologies exhibit maximal lifespans that are not far above the maximal observed human lifespan. To approximate the complexity requirements of the underlying physiological function, we then constrain network entropies. Using non-parametric stochastic optimization of network structure, we find that disassortative scale-free networks exhibit the best of both lifespan and health span. Parametric optimization of scale-free networks behaves similarly. We further find that higher maximum connectivity and lower minimum connectivity networks enhance both maximal lifespans and health spans by allowing for more disassortative networks. Our results validate the scale-free network assumption of the GNM and indicate the importance of disassortativity in preserving health and longevity in the face of damage propagation during aging. Our results highlight the advantages provided by disassortative scale-free networks in biological organisms and subsystems.
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Bizouerne, Elise, Joseph Ly Vu, Benoît Ly Vu, et al. "Genetic Variability in Seed Longevity and Germination Traits in a Tomato MAGIC Population in Contrasting Environments." Plants 12, no. 20 (2023): 3632. http://dx.doi.org/10.3390/plants12203632.
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The stable production of high vigorous seeds is pivotal to crop yield. Also, a high longevity is essential to avoid progressive loss of seed vigour during storage. Both seed traits are strongly influenced by the environment during seed development. Here, we investigated the impact of heat stress (HS) during fruit ripening on tomato seed lifespan during storage at moderate relative humidity, speed (t50) and homogeneity of germination, using a MAGIC population that was produced under optimal and HS conditions. A plasticity index was used to assess the extent of the impact of HS for each trait. HS reduced the average longevity and germination homogeneity by 50% within the parents and MAGIC population. However, there was a high genetic variability in the seed response to heat stress. A total of 39 QTLs were identified, including six longevity QTLs for seeds from control (3) and HS (3) conditions, and six plasticity QTLs for longevity, with only one overlapping with a longevity QTL under HS. Four out of the six longevity QTL co-located with t50 QTL, revealing hotspots for seed quality traits. Twenty-one QTLs with intervals below 3 cM were analyzed using previous transcriptome and gene network data to propose candidate genes for seed vigour and longevity traits.
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LYUBUSHIN, Nikolai P., Elena N. LETYAGINA, Valentina I. PEROVA, and Nadezhda A. PEROVA. "Analysis of human capital development in Russia by means of physical culture and sports using neural network modeling." Economic Analysis: Theory and Practice 21, no. 11 (2022): 1982–2005. http://dx.doi.org/10.24891/ea.21.11.1982.
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Subject. The article investigates the human capital in the regions of the Russian Federation in the context of health promotion and active longevity of the population of the country. Objectives. The purpose is to examine the state of human capital driven by the development of physical culture and sports in Russian regions, using neural network modeling. Methods. Neural networks are used as a method of studying the multidimensional statistical data. The method of data clustering based on neural network modeling is not affiliated with model constraints. This method is aimed at self–training of neural networks, i.e. self-organizing Kohonen maps, and is a promising means of visual representation of multidimensional data space. Results. Using the neural network modeling, we placed 85 Russian regions in six cluster formations. We assessed the influence of each studied indicator on cluster construction. The paper demonstrates a strong difference in the number of regions of the Russian Federation in clusters, presents average values of the considered indicators in clusters for 2021. Conclusions. The study shows uneven development of human capital in Russian regions from the point of view of physical culture and sports, which predetermines different strategies for the development of the regional sports sphere. The positive trend in the number of sports facilities, personnel, and population engaged in physical culture and sports is facilitated by a systems approach based on program documents, which focuses on strengthening the health of citizens and their active longevity.
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Sivaraman, Himani. "A Distributed Algorithm for Topology Control in VANETs: Distributed Distant Node Graphical Structure (DDGST) Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (2020): 839–46. http://dx.doi.org/10.17762/turcomat.v11i1.13566.
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Vehicle ad hoc networks—a subset of MANETs—allow cars to directly connect. These networks are independent of previous infrastructure. VANETs promote traffic safety, efficiency, and ITS application development. Their link allows this. Vehicular Ad-hoc Networks (VANETs) have grown in popularity due to its ability to efficiently and reliably connect automobiles. Vehicular networks evolve rapidly, making network topology maintenance problematic. VANET topologies are managed decentralized here. The algorithm uses neighbor numbers and distances to calculate vehicle transmission range. Simulations indicate that the suggested method reduces network partitions and maximizes connection. Due to the great mobility of VANET vehicles, the network architecture changes quickly. As this network topology is continually changing, creating strong and effective routing protocols is difficult. Topology control can overcome this difficulty. We present a distributed topology control mechanism that keeps VANETs online, reduces control messages, and prevents collisions. Node centrality and network density are used to fine-tune each node's transmission power. Computer simulations compare the proposed approach to different topology control algorithms. The results show that the suggested method outperforms the alternatives in network connectivity, longevity, and control overhead.
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Painuly, Sakshi. "Optimized Traffic And Enhanced Lifetime Routing Algorithm In WSN." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1409–17. http://dx.doi.org/10.17762/msea.v70i2.2333.
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 Abstract:
 Wireless Sensor Networks (WSNs) have gained significant traction as a viable solution for a diverse range of applications that necessitate the monitoring and regulation of distant environments. Wireless sensor networks (WSNs) are frequently constrained by the energy resources of the individual sensor nodes, resulting in network failures and reduced operational lifetimes. In response to this challenge, we present a proposed optimised traffic and lifetime routing algorithm for Wireless Sensor Networks (WSNs). The algorithm has been specifically designed to minimise network traffic and ensure equitable energy consumption across all sensor nodes. The algorithm utilises a fusion of clustering and network coding methodologies to leverage spatial diversity and redundancy within the network. The performance of the proposed algorithm is assessed through simulation experiments and is juxtaposed with various contemporary routing algorithms. The findings indicate that the algorithm we employed exhibits superior performance compared to other algorithms with respect to network longevity, network flow, and energy usage. The algorithm under consideration exhibits potential for utilisation in diverse Wireless Sensor Network (WSN) applications that necessitate dependable and energy-efficient data transmission.
 
 
 
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Bhagat, Avinash, Manmohan Sharma, Ajay Shriram Kushwaha, Shilpa Sharma, and Hussien Sobahi Mohammed. "Nonlinear Energy Optimization in the Wireless Sensor Network through NN-LEACH." Mathematical Problems in Engineering 2023 (April 30, 2023): 1–9. http://dx.doi.org/10.1155/2023/5143620.
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Researchers have developed a range of methods and strategies to decrease wireless sensor network energy consumption. Mote clustering is one of the competent topological control approaches to boost the networks’ energy efficiency, scalability, and performance. Energy is dissipated during the cluster creation, cluster head selection, routing from the head and base stations of the cluster, and data aggregation—clustering and routing emphasis on the stability and the longevity of the network. This research work provides the optimization technique for the wireless sensor network to optimize the energy through NN-LEACH. The main goal is to extend network life and reduce power consumption by clustering and routing sensor nodes using the two-step NN-LEACH protocol, which is suggested. An additional goal might be to establish the appropriate course of action for the suggested approach for this network.
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Sun, Zeyu, Guisheng Liao, Cao Zeng, Lan Lan, and Guozeng Zhao. "GLBR: A novel global load balancing routing scheme based on intelligent computing in partially disconnected wireless sensor networks." International Journal of Distributed Sensor Networks 18, no. 4 (2022): 155013292210904. http://dx.doi.org/10.1177/15501329221090458.
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Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree.