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Relevant bibliographies by topics / Wireless sensor networks energy-efficiency

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Academic literature on the topic 'Wireless sensor networks energy-efficiency'

Author: Grafiati

Published: 4 June 2025

Last updated: 8 July 2025

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Journal articles on the topic "Wireless sensor networks energy-efficiency"

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Juwaied, Abdulla, Lidia Jackowska-Strumillo, and Artur Sierszeń. "Enhancing Clustering Efficiency in Heterogeneous Wireless Sensor Network Protocols Using the K-Nearest Neighbours Algorithm." Sensors 25, no. 4 (2025): 1029. https://doi.org/10.3390/s25041029.

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

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Chen, Bowen. "Wireless Communication Chip Designs: analysis of the Wireless Integrated Network Sensors." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 580–87. http://dx.doi.org/10.54097/hset.v70i.13989.

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With the development of wireless technology, wireless integrated network sensor is a new form of sensor network. It enables highly efficient data acquisition and transmission by connecting the sensor nodes wirelessly. The purpose of this study is to investigate the basic principles and techniques of wireless integrated network sensors, analyze their application fields, and conduct experimental studies to verify their performance. This study first introduces the basic principles of wireless integrated network sensors, including wireless communication, sensor nodes, and network topology. Then, related technologies, including energy management, routing protocols and network security, are studied to improve the performance and stability of wireless integrated network sensors. Wireless integrated network sensors have wide application prospects in environmental monitoring, intelligent transportation and agriculture. Meanwhile, the energy utilization efficiency and network stability of the sensor network can be improved by adopting the new energy management mechanism and routing protocol. This study reveals the potential and value in practical applications through the exploration and research of wireless integrated network sensors. In future studies, the energy management and routing mechanisms of sensor networks can be further optimized to improve their performance and reliability. In addition, more application scenarios suitable for wireless integrated network sensors can be explored to provide solutions for practical problems.

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HUANG, GUANGYAN, XIAOWEI LI, JING HE, and XIN LI. "DATA MINING VIA MINIMAL SPANNING TREE CLUSTERING FOR PROLONGING LIFETIME OF WIRELESS SENSOR NETWORKS." International Journal of Information Technology & Decision Making 06, no. 02 (2007): 235–51. http://dx.doi.org/10.1142/s0219622007002538.

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Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively.

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Samara, Ghassan, Mohammad Hassan, and Yahya Zayed. "An Intelligent Vice Cluster Head Election Protocol in WSN." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (2021): 202–22. http://dx.doi.org/10.15849/ijasca.211128.14.

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Wireless sensor networks (WSNs) has a practical ability to link a set of sensors to build a wireless network that can be accessed remotely; this technology has become increasingly popular in recent years. Wi-Fi-enabled sensor networks (WSNs) are used to gather information from the environment in which the network operates. Many obstacles prevent wireless sensor networks from being used in a wide range of fields. This includes maintaining network stability and extending network life. In a wireless network, sensors are the most essential component. Sensors are powered by a battery that has a finite amount of power. The battery is prone to power loss, and the sensor is therefore rendered inoperative as a result. In addition, the growing number of sensor nodes off-site affects the network's stability. The transmission and reception of information between the sensors and the base consumes the most energy in the sensor. An Intelligent Vice Cluster Head Selection Protocol is proposed in this study (IVC LEACH). In order to achieve the best performance with the least amount of energy consumption, the proposed hierarchical protocol relies on a fuzzy logic algorithm using four parameters to calculate the value of each node in the network and divides them into three hierarchical levels based on their value. This improves network efficiency and reliability while extending network life by 50 percent more than the original Low Energy Adaptive Clustering Hierarchy protocol. Keywords: Wireless Sensor Networks, Sensors, Communication Protocol, Fuzzy logic, Leach protocol.

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Idan, Zainab S., and Ahmed Al-Fatlawi. "Energy Efficient Clustering Using Improved Particle Swarm Optimization in Wireless Sensor Networks." BIO Web of Conferences 97 (2024): 00106. http://dx.doi.org/10.1051/bioconf/20249700106.

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The Wireless Sensor Network (WSN) includes many low-cost nodes that have the capacity to perceive, operate, and communicate wirelessly. WSN can spread the information to all around through a cooperative node approach. It also has many advantages in terms of both cost and cooperative intelligence. In a wireless sensor network, nodes have limited energy resources, so their life cycle is considered as one of the main concerns about wireless sensor networks. Energy efficiency grouping and routing are two well-known issues in optimization that have been widely studied in order to increase the lifetime of wireless sensor networks. In this paper, an improved particle swarm optimization (IPSO) clustering algorithm for energy efficiency network management is introduced in order to find a route for creating optimal clusters. To evaluate the efficiency of the proposed clustering algorithm, this algorithm is simulated and compared with the particle swarm optimization(PSO) algorithm based on parameters such as network energy, number of live nodes and network life.

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Chugh, Amit, and Supriya Panda. "Energy Efficient Techniques in Wireless Sensor Networks." Recent Patents on Engineering 13, no. 1 (2019): 13–19. http://dx.doi.org/10.2174/1872212112666180731114046.

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Background: Wireless Sensor Network (WSN) is a collection of tiny electromechanical nodes termed as sensors. Sensors are equipped with sensing unit, which is designed for application specific. When deployed either by planned or unplanned after deployment, sensor’s energy starts depleting due to various roles like sensing, communication and aggregation. Method: WSN is challenged with limited battery power. The aim is to enhance energy efficiency that leads to a prolonged lifetime of networks. Results: We have reviewed the patents related to energy efficiency in wireless sensor networks. This Paper presents the study of various energy efficient techniques, which can enhance the lifetime of sensor networks; it covers basics of WSN, their design, Classification, Communication in WSN and a survey of different techniques for effective utilization of sensor’s energy. Conclusion: Paper has emphasized on energy efficient clustering technique along with feature wise summary of existing clustering protocols.

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Voulkidis, Artemis C., Markos P. Anastasopoulos, and Panayotis G. Cottis. "Energy efficiency in wireless sensor networks." ACM Transactions on Sensor Networks 9, no. 4 (2013): 1–27. http://dx.doi.org/10.1145/2489253.2489260.

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Paliwal, Rakesh, and Irfan Khan. "Design and Analysis of Soft Computing Based Improved Routing Protocol in WSN for Energy Efficiency and Lifetime Enhancement." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 3 (2022): 12–24. http://dx.doi.org/10.17762/ijritcc.v10i3.5521.

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Mobile wireless sensor networks have been developed as a result of recent advancements in wireless technologies. Sensors in the network are low-cost and have a short battery life, in addition to their mobility. They are more applicable in terms of the essential properties of these networks. These networks have a variety of uses, including search and rescue operations, health and environmental monitoring, and intelligent traffic management systems, among others. According to the application requirements, mobile wireless sensor nodes are energy limited equipment, so energy conservation is one of the most significant considerations in the design of these networks. Aside from the issues posed by sensor node mobility, we should also consider routing and dynamic clustering. According to studies, cluster models with configurable parameters have a substantial impact on reducing energy usage and extending the network's lifetime. As a result, the primary goal of this study is to describe and select a smart method for clustering in mobile wireless sensor networks utilizing evolutionary algorithms in order to extend the network's lifetime and ensure packet delivery accuracy. For grouping sensor nodes in this work, the Genetic Algorithm is applied initially, followed by Bacterial Conjugation. The simulation's results show a significant increase in clustering speed acceleration. The speed of the nodes is taken into account in the suggested approach for calibrating mobile wireless sensor nodes.

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Hung, Li-Ling. "Charging Protocol for Partially Rechargeable Mobile Sensor Networks." Sensors 23, no. 7 (2023): 3438. http://dx.doi.org/10.3390/s23073438.

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Wireless sensor networks (WSNs) have wide applicability in services used in daily life. However, for such networks, limited energy is a critical issue. The efficiency of a deployed sensor network may be subject to energy supply. Wireless rechargeable sensor networks have recently been proposed and discussed. Most related studies have involved applying static rechargeable sensors to an entire rechargeable environment or having mobile chargers patrol the environment to charge sensors within it. For partially rechargeable environments, improving the recharge efficiency and extending the lifetime of WSNs are considerable challenges. Scientists have devoted attention to energy transmission technologies and mobile sensor network (MSN) applications. In this paper, we propose a flexible charging protocol in which energy can be transmitted from certain energy supply regions to other regions in an MSN. Mobile rechargeable sensors are deployed to monitor the environment. To share energy in a certain region, the sensors move to replenish their energy and transmit energy to sensors outside the energy supply region. The efficiency of the proposed protocol is also discussed in the context of various situations. The evaluation results suggest that the flexible protocol is more efficient than other charging protocols in several situations.

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M. Rajalakshmi. "Survey on Enhancing Energy Efficiency in Wireless Sensor Networks Based on Rapid Data Collection." Communications on Applied Nonlinear Analysis 31, no. 3s (2024): 74–81. http://dx.doi.org/10.52783/cana.v31.732.

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Wireless Sensor Networks (WSNs) are widely used in various applications such as environmental monitoring, healthcare, and business automation. These networks rely on the integration of sensors to collect data quickly. However, operating WSNs efficiently, especially in scenarios where quick and reliable data acquisition is crucial, faces significant challenges due to the limited power resources of sensor nodes. This survey paper provides a detailed review of current methodologies and techniques focused on improving energy efficiency in WSNs for fast data collection. It begins by discussing the basic concepts of wireless sensor networks, highlighting the important role sensors play in gathering accurate information. Afterwards, the paper provides a summary of the issues surrounding power usage, underscoring the importance of creative solutions to prolong the network's longevity. An extensive section of the study delves into various energy-efficient protocols and algorithms aimed at improving data collection in Wireless Sensor Networks (WSNs). The paper classifies these approaches according to their strategies for forming clusters, communication protocols, and optimization algorithms.

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Dissertations / Theses on the topic "Wireless sensor networks energy-efficiency"

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Prasad, Pratap Simha. "Energy efficiency in wireless sensor networks." Auburn, Ala., 2007. http://repo.lib.auburn.edu/2007%20Spring%20Theses/PRASAD_PRATAP_30.pdf.

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Rault, Tifenn. "Energy-efficiency in wireless sensor networks." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2228/document.

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Dans cette thèse, nous avons proposé des solutions originales et performantes pour l’économie d’énergie dans les réseaux de capteurs sans fil (RCSF). Ces contributions s'organisent autour de deux grands axes : les réseaux de capteurs génériques et les réseaux de capteurs sans fil dédiés aux applications santé. Dans un premier temps, nous avons réalisé un état-de-l’art des mécanismes d'économie d’énergie pour les RCSF. Nous avons ensuite proposé deux solutions originales : la première optimise le déplacement d’une station de base, ainsi que la façon dont les données sont stockées dans les capteurs et routées vers le puit mobile ; la seconde optimise le déploiement de chargeurs mobiles, qui une fois dans le réseau permettent de satisfaire la demande en énergie des nœuds via la transmission d’énergie sans fil sur plusieurs sauts. Dans un second temps, nous nous sommes intéressés plus particulièrement aux applications des RCSF pour la supervision de patients à distance. Nous avons introduit une nouvelle classification des techniques économes en énergie adaptées à la spécificité de ces applications santé. Nous avons ensuite proposé une nouvelle architecture pour la supervision de patient à distance à l’aide de capteurs sans fil qui permet de prolonger la durée de vie des capteurs et de la station de base. Cette solution prend en compte l’environnement du patient et l’hétérogénéité des appareils. Nos résultats montrent que la durée de vie des réseaux de capteurs sans fil peut être étendue en utilisant les différentes stratégies proposées. L’efficacité de ces approches a été confirmée à l’aide de nombreuses expérimentations numériques et simulations<br>In this thesis, we propose new strategies for energy conservation in wireless sensor networks, so that the operational time of these networks can be extended. The work can be divided into two main focus area, namely general wireless sensor networks, and healthcareoriented wearable sensor networks. In the first part of this thesis we provide a comprehensive survey of the existing energy-efficient mechanisms. Then, we propose two new solutions: the first one optimizes the displacement of a mobile base station as well as buffer usage and data routing at sensor nodes; the second one optimizes the deployment of wireless chargers in the network to satisfy the energy demand of the sensors. The second part of this thesis is dedicated to healthcare application where wearable sensors are used to remotely supervise a patient. We begin with a state-of-the-art of the energy-efficient techniques existing in the literature. We then introduce a new energy-efficient architecture that allows to optimize the lifetime of both the sensor and the base station. This is a context-aware solution that takes into consideration heterogeneous devices. Our results show that the lifetime of the sensor networks can be extended using the proposed strategies. All the results obtained are supported by numerical experiments and extensive simulations

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Zhu, Junhua. "Energy efficiency and reliability in wireless sensor networks /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20ZHU.

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Busse, Marcel. "Algorithms for energy efficiency in wireless sensor networks." [S.l. : s.n.], 2007. http://madoc.bib.uni-mannheim.de/madoc/volltexte/2008/1809/.

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Cao, Hui. "Stabilization in wireless sensor networks." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1211079872.

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Vergara, Gallego Maria Isabel. "Smarter Radios for Energy efficiency in Wireless Sensor Networks." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM020/document.

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Les contraintes présentes dans les réseaux de capteurs impliquent l'introduction de techniques d'optimisation à différents niveaux de conception : du matériel au logiciel et dans la pile de communication. En effet, le déploiement des réseaux de capteurs, à faible consommation énergétique, exige une conception conjointe du matériel et du logiciel adaptée à l'application visée. Étant donné la nature évènementielle et multitâche des applications dans les réseaux de capteurs, nous pourrions penser à rajouter différentes unités de traitement qui coopèrent pour gérer les évènements et les tâches de manière optimale. Ainsi, la complexité des tâches accomplies par le processeur principal peut être réduite, ce qui contribue à atteindre l'efficacité énergétique. Dans cette thèse nous étudions un ensemble de protocoles qui facilitent l'implémentation des smart radios. L'idée principale des smart radios est l'introduction de l'intelligence dans la puce radio de manière à ce qu'elle soit capable de prendre des décisions ainsi que d'exécuter plusieurs tâches de manière autonome et sans l'intervention du processeur principal. Cette dernière sera responsable du bootstrap du réseau et, après qu'un état stable est atteint, le processeur peut rester inactif la plupart du temps, alors que la puce radio continue à fournir un ensemble de services. Le protocole proposé est appelé Wake on Idle et il fournit la maintenance de voisinage intégrée avec une méthode d'accès au canal. Ces services sont basés sur des transmissions analogiques qui sont codées dans le temps. De cette manière, dès que le réseau entre dans l'état stable (c.à.d. la topologie est formée et les noeuds sont associés et synchronisés), le traitement numérique de trames n'est pas nécessaire. Puisque Wake on Idle est basé sur des informations de bas niveau, il peut être facilement intégré dans la puce radio et fonctionner comme un coprocesseur qui fournit des services de haut niveau au processeur principal, comme la maintenance du voisinage et l'accès au canal. Grâce à une analyse théorique et une implémentation préliminaire, nous démontrons la faisabilité du protocole et nous montrons plusieurs caractéristiques intéressantes qui aident à atteindre l'efficacité énergétique et de bonnes performances. Ensuite, nous exploitons la signalisation analogique afin d'optimiser le duty-cycle des protocoles d'accès au canal existants. Nous proposons également un mécanisme appelé Sleep on Idle qui est basé sur l'échange de signaux analogiques ou busy tones. Sleep on Idle peut être intégré dans la radio et il peut décider quand le processeur doit être réveillé. Enfin, nous avons intégré le mécanisme de notification dans le standard IEEE802.15.4 et nous avons évalué ce mécanisme par des simulations et expérimentations. Les résultats montrent un gain important en termes de consommation en énergie et de réactivité du réseau<br>The constraints of Wireless Sensor Networks scenarios require the introduction of optimization techniques at different design levels: from the hardware to the software and communication protocol stack. In fact, the design of energy efficient WSNs involves an appropriate hardware/software co-design oriented to the concerned application. Given the event driven and multitasking nature of WSNs applications, one could think of adding different processing units that cooperate to manage events and tasks in an optimal way. Then, the complexity of tasks performed by the main processing unit can be reduced and energy efficiency can be achieved. In this PhD thesis we study protocols that leverage the implementation of smart radios. The idea of smart radios is introducing intelligence into the radio chip; in this way, it will be able to take decisions and perform several tasks in an autonomous way and without any intervention of the main processing unit. The processing unit will be in charge of bootstrapping the network and, after a stable state is reached, it can remain inactive most of the time while the radio chip provides a given set of services. The proposed protocol is called Wake on Idle and it provides integrated neighborhood maintenance and low duty-cycle medium access control. These services are provided based on analog transmissions that are time encoded; then, as soon as the network enters the stable state (i.e. the topology is formed and nodes are associated and synchronized) digital processing of frames is not needed. Since it relies on low-level information, Wake on Idle can be easily implemented on hardware and integrated into the radio chip; then, it works as a coprocessor that provides high-level services (i.e. neighborhood maintenance and medium access) to the main processing unit. Through theoretical analysis and a preliminary implementation we demonstrate the feasibility of the protocol and we show several interesting characteristics that help achieving energy efficiency and good performance. Then, we further exploit analog signaling to optimize duty cycle of existing medium access control protocols. We propose a mechanism called Sleep on Idle and it is based on the exchange of analog busy tones. Sleep on Idle can also be integrated into the smart radio to take decisions about whether the main processing unit has to be woken up. We apply the decision mechanism to the slotted ieee802.15.4 standard and validate it through simulations and experimentations. The results show an important gain in terms of energy consumption and network reactivity

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Ageev, Anton. "Time Synchronization and Energy Efficiency in Wireless Sensor Networks." Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/367826.

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Time synchronization is of primary importance for the operation of wireless sensor networks (WSN): time measurements, coordinated actions and event ordering require common time on WSN nodes. Due to intrinsic energy limitations of wireless networks there is a need for new energy-efficient time synchronization solutions, different from the ones that have been developed for wired networks. In this work we investigated the trade-offs between time synchronization accuracy and energy saving in WSN. On the basis of that study we developed a power-efficient adaptive time synchronization strategy, that achieves a target synchronization accuracy at the expense of a negligible overhead. Also, we studied the energy benefits of periodic time synchronization in WSN employing synchronous wakeup schemes, and developed an algorithm that finds the optimal synchronization period to save energy. The proposed research improves state-of-the-art by exploring new ways to save energy while assuring high flexibility and reliable operation of WSN.

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Ageev, Anton. "Time Synchronization and Energy Efficiency in Wireless Sensor Networks." Doctoral thesis, University of Trento, 2010. http://eprints-phd.biblio.unitn.it/260/1/Ageev_PhD_Thesis.pdf.

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Time synchronization is of primary importance for the operation of wireless sensor networks (WSN): time measurements, coordinated actions and event ordering require common time on WSN nodes. Due to intrinsic energy limitations of wireless networks there is a need for new energy-efficient time synchronization solutions, different from the ones that have been developed for wired networks. In this work we investigated the trade-offs between time synchronization accuracy and energy saving in WSN. On the basis of that study we developed a power-efficient adaptive time synchronization strategy, that achieves a target synchronization accuracy at the expense of a negligible overhead. Also, we studied the energy benefits of periodic time synchronization in WSN employing synchronous wakeup schemes, and developed an algorithm that finds the optimal synchronization period to save energy. The proposed research improves state-of-the-art by exploring new ways to save energy while assuring high flexibility and reliable operation of WSN.

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Brownfield, Michael I. "Energy-efficient Wireless Sensor Network MAC Protocol." Diss., This resource online, 2006. http://scholar.lib.vt.edu/theses/available/etd-04102006-170423/.

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Barceló, Lladó Joan Enric. "Communications in Wireless Sensor Networks: Compression, Energy Efficiency and Secrecy." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/97359.

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Les xarxes de sensors sense fils (WSNs) han esdevingut un dels sistemes de comunicació amb més projecció d'aquesta dècada. Abasten una àmplia varietat d’aplicacions tals com la monitorització del medi ambient, la predicció de desastres naturals, en medicina, en transport, posicionament en interiors, i tasques militars. Els nodes que composen la xarxa, són típicament de baix cost, cosa que atorga una sèrie de limitacions en termes d’energia, velocitat de càlcul i d’ample de banda. Amb els avenços de les comunicacions sense fils i la creixent demanda de noves i més complexes aplicacions, les WSNs s’han d’optimitzar per tal de minimitzar aquestes limitacions. Aquesta tesi proposa un conjunt de tècniques que proporcionen a una WSN les següents característiques: 1. Implementació distribuïda sense necessitat de senyalització entre nodes sensors. 2. Comunicacions energèticament eficients. 3. Poca complexitat als nodes sensors. 4. Empra pocs recursos (temps, ample de banda, etc.) 5. Presenta un error quadràtic mig baix en reconstrucció al receptor. 6. Comunicacions secretes a capa física. Primer, s’estudia la transmissió seqüencial de mostreig reduït. Aquesta tècnica permet la disminució del nombre de transmissions i, per tant, reduir la despesa energètica associada a la comunicació a la xarxa. En particular, s’estudia el rendiment dels codificadors determinístics, probabilístics i condicionals de mostreig reduït per senyals autoregressius. S’obtenen expressions tancades de l’error quadràtic mig pel cas de mostreig reduït determinístic i probabilístic, mentre que pel cas condicional es deriven aproximacions ajustades. A continuació, s’analitza la compressió de la informació per WSNs grans. Pel cas on els paràmetres de correlació del senyal són desconeguts a priori, es proposen dos estimadors millorats: i) un per la predicció emprant el filtre de Wiener i ii) un per l’error quadràtic mig obtingut. Ambdós estimadors s’empren pels dos passos claus de l’algorisme de codificació distribuïda de canal. Aquests estimadors milloren notablement el rendiment de l’algorisme en comparació amb els estimadors de mostres clàssics, especialment quan la dimensió del vector d’observacions és comparable en magnitud amb el nombre de mostres usades a la fase d’entrenament de l’algorisme. Posteriorment, es proposa un esquema de comunicació distribuïda i energèticament eficient anomenat Amplify-and-Forward Compressed Sensing. Aquest esquema es basa en la tècnica de sensat comprimit i aprofita la correlació existent al senyal rebut per tal de reduir tant el nombre de recursos emprats com les despeses energètiques del sistema. Específicament, el sistema es dissenya seguint una funció de cost que controla el compromís existent entre error quadràtic i consum energètic de la xarxa. Per aconseguir aquest disseny, es deriva un model simple que aproxima el rendiment de l’esquema proposat en termes d’error quadràtic mig. A més, es contribueix a la teoria de sensat comprimit amb una nova i més ajustada relació entre el mínim nombre de mesures necessàries donades unes determinades propietats del senyal. Finalment, s’estudia l’esquema proposat Amplify-and-Forward Compressed Sensing des d’un punt de vista de secretisme a capa física. Es demostra que aquest esquema assoleix secretisme perfecte sota la presència d’un o d’un grup reduït d’espies, mentre que per un nombre més gran, és possible deteriorar notablement les seves capacitats d’espionatge gràcies a una tècnica proposta especialment dissenyada per introduir un extra d’incertesa solament a l’estimació dels espies.<br>Wireless Sensor Networks (WSNs) have emerged as one of the most promising wireless communication systems in the last decade. They can be used in a wide variety of applications such as environmental monitoring, natural disaster prediction, healthcare, transportation, indoor positioning, and military tasks. The cost and the complexity of the nodes within a WSN are typically low, which results in constraints such as energy limitation, low computational speed, and reduced communication bandwidth. With the advances in wireless communications and the growing demand of new and more complex applications, WSNs must be optimized in order to overcome their intrinsic limitations in terms of complexity and power. In this dissertation, and according to these constraints, we propose a set of techniques that provide to a WSN the following interesting features: 1. Distributed operation without the need of signaling among sensing nodes. 2. Energy-efficient communications. 3. Low complexity at the sensing nodes. 4. Low resource (i.e., bandwidth, time, etc.) utilization. 5. Low distortion level at the receiver. 6. Secret communications at the physical layer. First, we study the zero-delay downsampling transmission. This technique allows the system to reduce the number of transmissions and hence decrease the total energy spent. In particular, we study the performance of deterministic, probabilistic and conditional downsampling encoding-decoding pairs for the case of the autoregressive signal model. We obtain closed form expressions for the quadratic error of the deterministic and probabilistic encoder-decoders, while accurate approximations are derived for the quadratic error of the conditional downsampling schemes. Second, we analyze data compression applied to large WSNs. For the realistic case where the correlation parameters are not known a priori, we obtain two enhanced correlation estimators: i) one for the linear Wiener filter vector and ii) one for the achieved mean square error. Both estimators are employed in the two key steps of the distributed source coding algorithm. These estimators notably improve the performance of the algorithm in comparison to the application of classical sample estimators, specially when the dimension of the observation vector is comparable in magnitude to the number of samples used in the training phase. Then, we propose a distributed and energy-efficient communication scheme named Amplify-and-Forward Compressed Sensing. This scheme is based on compressed sensing and exploits the correlation present in the signal in order to reduce both the resource utilization and the energy consumption. More specifically, the system is designed according to a cost function that controls the trade-off between the quadratic error in the reconstruction and the energy consumption of the network. In order to aid the system design, a simple model that accurately approximates the performance of the proposed scheme in terms of the quadratic error has been derived. Furthermore, we contribute to the compressed sensing theory with a tighter relationship between the minimum number of measurements that are required for a given network dimension and the sparsity level of the transmitted signal. Finally, the proposed Amplify-and-Forward Compressed Sensing scheme is also studied in terms of secrecy and wiretap distortion at the physical layer. It is shown that the proposed scheme is perfectly secret in the presence of one or even a small group of eavesdroppers whereas for a larger eavesdropping set, it is still possible to notably deteriorate its espionage capabilities thanks to a proposed technique specifically designed to introduce extra uncertainty only in the channel estimation of the eavesdroppers.

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Books on the topic "Wireless sensor networks energy-efficiency"

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Ragab, Khaled, Noor Zaman, and Azween Bin Abdullah. Wireless sensor networks and energy efficiency: Protocols, routing, and management. Information Science Reference, 2012.

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Ragab, Khaled, Noor Zaman, and Azween Bin Abdullah. Wireless sensor networks and energy efficiency: Protocols, routing, and management. Information Science Reference, 2012.

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Roundy, Shad, Paul Kenneth Wright, and Jan M. Rabaey. Energy Scavenging for Wireless Sensor Networks. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4615-0485-6.

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Zaman, Noor, Vasaki Ponnusamy, Tang Jung Low, and Anang Hudaya Muhamad Amin. Biologically-inspired energy harvesting through wireless sensor technologies. Information Science Reference, 2016.

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Zhang, Rongrong, and Jihong Yu. Energy-Efficient Algorithms and Protocols for Wireless Body Sensor Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-28580-7.

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Roundy, Shad. Energy Scavenging for Wireless Sensor Networks: With Special Focus on Vibrations. Springer US, 2004.

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Roundy, Shad. Energy scavenging for wireless sensor networks: With special focus on vibrations. Kluwer Academic Publishers, 2004.

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Sharma, Vibhu. SRAM Design for Wireless Sensor Networks: Energy Efficient and Variability Resilient Techniques. Springer New York, 2013.

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Mauri, Kuorilehto, ed. Ultra-low energy wireless sensor networks in practice: Theory, realization and deployment. John Wiley & Sons, 2007.

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Jumira, Oswald, and Sherali Zeadally. Energy Efficiency in Wireless Networks. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118579954.

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Book chapters on the topic "Wireless sensor networks energy-efficiency"

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Jumira, Oswald, and Sherali Zeadally. "Energy Harvesting in Wireless Sensor Networks." In Energy Efficiency in Wireless Networks. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579954.ch4.

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Mehta, Neetu, and Arvind Kumar. "Enhanced Energy Efficiency in Wireless Sensor Networks." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9113-3_20.

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Delicato, Flávia C., and Paulo F. Pires. "Network-Wide Strategies for Energy Efficiency in Wireless Sensor Networks." In Energy-Efficient Distributed Computing Systems. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118342015.ch25.

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Havinga, Paul, Sandro Etalle, Holger Karl, et al. "EYES – Energy Efficient Sensor Networks." In Personal Wireless Communications. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39867-7_20.

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Chai, Senchun, Zhaoyang Wang, Baihai Zhang, Lingguo Cui, and Runqi Chai. "Energy Balanced Routing Protocols for Wireless Sensor Networks." In Wireless Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5757-6_2.

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Hakim, Gad, and Robin Braun. "Wireless Sensor Network Routing for Energy Efficiency." In Advances in Systems Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92604-5_30.

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Mini, Raquel A. F., and Antonio A. F. Loureiro. "Energy in Wireless Sensor Networks." In Middleware for Network Eccentric and Mobile Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89707-1_1.

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Bourhnane, Safae, Mohamed Riduan Abid, Khalid Zine-Dine, Najib Elkamoun, and Driss Benhaddou. "Energy Efficient Wireless Sensor Networks -." In Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90639-9_67.

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Huang, Liang. "Energy Harvesting Sensor Node Scheduling in Wireless Sensor Networks." In Encyclopedia of Wireless Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_260.

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Huang, Liang. "Energy Harvesting Sensor Node Scheduling in Wireless Sensor Networks." In Encyclopedia of Wireless Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32903-1_260-1.

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Conference papers on the topic "Wireless sensor networks energy-efficiency"

1

Kumar, Sumit, Hitesh Mohapatra, and Asish Kumar Dalai. "Enhancing Energy Efficiency in Wireless Sensor Networks via Clustering Approach." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870799.

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Raskar, Sandeep, Gulshan Dhasmana, M. Lakshminarayana, Harshal Patil, Bhagya Shree S, and Natrayan L. "Enhancing Energy Efficiency in Wireless Sensor Networks using Deep Learning." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894539.

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Chyad, Shaymaa A., and Ibrahim Ahmed Saleh. "Improving Energy Efficiency Wireless Sensor Networks Clustering Based on SVM." In 2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2025. https://doi.org/10.1109/ssd64182.2025.10989818.

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Amit and Geeta Hanji. "Energy Efficiency Techniques in Wireless Sensor Network for Sensor Nodes." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862433.

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Hilmani, Adil, Yassine Sabri, Abderrahim Maizate, Siham Aouad, Mohammed Koundi, and Fouad Ayoub. "Energy Efficiency Optimization of Wireless Sensor Networks with K-Means Integration." In 2024 7th International Conference on Advanced Communication Technologies and Networking (CommNet). IEEE, 2024. https://doi.org/10.1109/commnet63022.2024.10793341.

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Theja, A. S. Sai Puneeth, S. Swarnalatha, and B. Shoban Babu. "Metaheuristic Approaches for Energy Efficiency and Network Lifetime Optimization in Wireless Sensor Networks." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10960790.

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Pund, Ishwari V., Nidhi Mishra, and Nisha A. Auti. "IoT-Based Sensor Networks: Evaluating Various Clustering Methods for Energy Efficiency in Wireless Sensor Networks (WSNs)." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862032.

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Wilkins, Ross. "Session details: Energy Efficiency Wireless Sensor Networks." In SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems. ACM, 2015. http://dx.doi.org/10.1145/3260465.

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Panful, Benjamin, and Xie Zhibin. "A Study of Energy Efficiency in Wireless Sensor Networks." In 28th iSTEAMS Multidisciplinary Research Conference AIUWA The Gambia. Society for Multidisciplinary and Advanced Research Techniques - Creative Research Publishers, 2021. http://dx.doi.org/10.22624/aims/isteams-2021/v28n3p9.

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Abstract:

The wireless sensor networks (WSNs) field is one of the emerging and fast-growing fields in the scientific world. This has brought about the development of low-cost, and multifunctional sensor nodes for easy and secure dissemination of information. Nonetheless, the problem of sensor nodes running out of energy quickly has been an issue. Many energyefficient routing algorithms have been proposed to solve this problem and preserve the longevity of the network. Most of the recent papers have shown so many protocols mainly designed to minimize energy consumption in the network but there are some limitations with regards to the area of operation and the optimization decisions. This paper proposes an Energy Efficiency Hierarchical Routing Technique (EEHRT) which is based on LEACH protocol to extend the lifespan of a WSN. In this work, the proposed Energy Efficient Hierarchical Routing Technique selects cluster heads based on the prediction of transmission energy through the shortest distance to the base station. Our approach rotates the role of Cluster Heads (CHs), and optimizes the CH selection by the prediction of energy transmission in every round and aggregating data before transmission to the BS. The important features which include member nodes formation and rotation, cluster head selection and rotation, and cluster optimization of our proposed hierarchical routing technique in transmitting data to the base station are analyzed and emphasized. Keywords: Wireless Sensor Network, Base Station, Member Node, Clustering, LEACH, Hierarchy Proceedings Reference Format Benjamin Panful & Xie Zhibin (2021): Rural Women’s Access to Land and its Implications for Empowerment in Nigeria: The Case of Ilorin East Local Government Area, Kwara State, Nigeria. Proceedings of the 28th iSTEAMS Intertertiary Multidisciplinary Conference. American International University West Africa, The Gambia. Series 28, Vol 3 October 2021. Pp 105-120 www.isteams.net/gambia2021. DOI - https://doi.org/

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Zhang Fan and Li Wenfeng. "Energy efficiency testbed for wireless sensor networks." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642014.

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Reports on the topic "Wireless sensor networks energy-efficiency"

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Yang, Yuanyuan. Energy Efficiency Issues in Wireless Sensors Networks. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada499610.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada434605.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada419061.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada423016.

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Mahdavi, Rod, and William Tschudi. Wireless Sensor Network for Improving the Energy Efficiency of Data Centers. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1171531.

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Ravindran, Vijay, and Chockalingam Vannila. An Energy-efficient Clustering Protocol for IoT Wireless Sensor Networks Based on Cluster Supervisor Management. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.12.12.

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Hardy, J. E. Wireless Sensors and Networks for Advanced Energy Management. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/885995.

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Carlos H. Rentel. Low-Cost, Robust, Threat-aware Wireless Sensor Network for Assuring the Nation's Energy Infrastructure. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/920622.

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Carols H. Rentel. Low-Cost, Robust, Threat-Aware Wireless Sensor Network for Assuring the Nation's Energy Infrastructure. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/920623.

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Carlos H. Rentel and Peter J. Marshall. Low-Cost, Robust, Threat-Aware Wireless Sensor Network for Assuring the Nation's Energy Infrastructure. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/924028.

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