Relevant bibliographies by topics / Best sensor node selection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Best sensor node selection'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Best sensor node selection"

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Ha, Duy Hung, Dac-Binh Ha, Van-Truong Truong, Van-Duc Phan, and Q. S. Vu5. "Performance enhancement of wireless sensor network by using non-orthogonal multiple access and sensor node selection schemes." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 886–94. https://doi.org/10.11591/ijeecs.v21.i2.pp886-894.

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In this paper, we investigate a relaying wireless sensor network (WSN) with the non-orthogonal multiple access (NOMA) and sensor node selection schemes over rayleigh fading. Precisely, the system consists of two sensor clusters, a sink node, and an amplify-and-forward (AF) relay. These sensors applying the NOMA and sensor node selection schemes transmit the sensing data from the sensor clusters via the relay to the sink. We derived the expressions of outage probability and throughput for two sensor nodes. We also provide numerical results to examine the behavior of the system. Finally, we verify the validity of our analysis by using the monte-carlo simulation.
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Rajkumar, Dhamodharan Udaya Suriya, Krishna Prasad Karani, Rajendran Sathiyaraj, and Pellakuri Vidyullatha. "Optimal shortest path selection using an evolutionary algorithm in wireless sensor networks." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024): 6743. http://dx.doi.org/10.11591/ijece.v14i6.pp6743-6752.

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A wireless sensor network comprises of distributed independent devices, called sensors that monitor the physical conditions of the environment for various applications, such as tracking and observing environmental changes. Sensors have the ability to detect information, process it, and forward it to neighboring sensor nodes. Wireless sensor networks are facing many issues in terms of scalability, which necessitates numerous nodes and network range. The route chosen between the source node and the destination node with the shortest distance determines how well the network performs. In this paper, evolutionary algorithm based shortest path selection provides high end accessibility of path nodes for data transmission among source and destination. It employs the best fitness function methodology, which involves the replication of input, mutation, crossover, and mutation methods, to produce efficient outcomes that align with the best fitness function, thereby determining the shortest path. This is a probabilistic technique that receives input from learning models and provides the best results. The execution results are presented well compared with earlier methodologies in terms of path cost, function values, throughput, packet delivery ratio, and computation time.
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Cao, Li, Yinggao Yue, and Yong Zhang. "A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization." Computational Intelligence and Neuroscience 2021 (September 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/9808449.

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In the clustering routing protocol, prolonging the lifetime of the sensor network depends to a large extent on the rationality of the cluster head node selection. The selection of cluster heads for heterogeneous wireless sensor networks (HWSNs) does not consider the remaining energy of the current nodes and the distribution of nodes, which leads to an imbalance of network energy consumption. A strategy for selecting cluster heads of HWSNs based on the improved sparrow search algorithm- (ISSA-) optimized self-organizing maps (SOM) is proposed. In the stage of cluster head selection, the proposed algorithm establishes a competitive neural network model at the base station and takes the nodes of the competing cluster heads as the input vector. Each input vector includes three elements: the remaining energy of the node, the distance from the node to the base station, and the number of neighbor nodes of the node. The best cluster head is selected through the adaptive learning of the improved competitive neural network. When selecting the cluster head node, comprehensively consider the remaining energy, the distance, and the number of times the node becomes a cluster head and optimize the cluster head node selection strategy to extend the network life cycle. Simulation experiments show that the new algorithm can reduce the energy consumption of the network more effectively than the basic competitive neural network and other algorithms, balance the energy consumption of the network, and further prolong the lifetime of the sensor network.
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Nisha, M., and S. Poongavanam. "Best Communication Node Election for well-organized Path in Flat Topology." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 2 (2017): 555. http://dx.doi.org/10.11591/ijeecs.v8.i2.pp555-556.

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<p>There has been an increasing attentiveness in the uses of sensor networks. Because sensors are normally controlled in on-board power supply, proficient supervision of the network is essential in improving the life of the sensor.<strong> </strong>The majority research protocols objective at offering link breakage reducing and mitigating from the same. Yet, selecting the well-organized communication do all the beneficial to the transmission process thus demonstrating better improvement in the network performance. In this article, we propose Best Communication Node Election for well-organized Path in Flat Topology The main goal of this<strong> </strong>work is to choose the best data transmission node in flat topology for improve the multi hop routing. This scheme, the best communication node selection based on Path Metric and this Path Metric is measured by the packet obtained rate, dropped rate, latency rate and node energy. This scheme provide guarantees quality of Service in the network.</p>
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Atul Pawar. "Energy-Efficient Cluster Formation in Wireless Sensor Networks." Journal of Information Systems Engineering and Management 10, no. 14s (2025): 595–603. https://doi.org/10.52783/jisem.v10i14s.2330.

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Sensor nodes that are wireless are extremely energy-constrained devices. Due to a number of sensor node limitations, including size and cost, their battery life is limited. Furthermore, the majority of Wireless Sensor Network (WSN) applications make it impossible to recharge or swap out sensor node batteries. Thus, one of the main challenges in wireless sensor networks is making the best use of node energy. An efficient way to maximize node energy utilization and extend the lifespan of an energy-constrained wireless sensor network is to cluster sensor nodes. In order to extend the lifespan of sensor networks, we present a location-based protocol for WSNs in this paper that supports energy-efficient clustering, cluster head selection/rotation, and data routing. With the fewest transmit-receive operations, the suggested clustering technique guarantees balanced size cluster formation within the sensing field. Even though the cluster head and sensor nodes in a cluster have different energy needs, the cluster head rotation protocol guarantees balanced node energy dissipation. In order to establish balanced energy consumption across the cluster's nodes and so extend the network's lifespan, the cluster head rotation protocol has been devised. Simulation findings show that by using effective clustering, cluster head selection/rotation, and data routing, the suggested protocol extends network lifetime.
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Spoorthi, K., Saha Snehanshu, and Mathur Archana. "Discrete Path Selection and Entropy Based Sensor Node Failure Detection in Wireless Sensor Networks." Cybernetics and Information Technologies 16, no. 3 (2016): 137–53. http://dx.doi.org/10.1515/cait-2016-0039.

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Abstract Exertion of wireless sensor networks has been increasing in recent years, and it imprints in almost all the technologies such as machine industry, medical, military and civil applications. Due to rapid growth in electronic fabrication technology, low cost, efficient, multifunctional and accurate sensors can be produced and thus engineers tend to incorporate many sensors in the area of deployment. As the number of sensors in the field increases, the probability of failure committed by these sensors also increases. Hence, efficient algorithms to detect and recover the failure of sensors are paramount. The current work concentrates mainly on mechanisms to detect sensor node failures on the basis of the delay incurred in propagation and also the energy associated with sensors in the field of deployment. The simulation shows that the algorithm plays in the best possible way to detect the failure in sensors. Finally, the Boolean sensing model is considered to calculate the network coverage of the wireless sensor network for various numbers of nodes in the network.
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Santos-Ruiz, Ildeberto, Francisco-Ronay López-Estrada, Vicenç Puig, Guillermo Valencia-Palomo, and Héctor-Ricardo Hernández. "Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory." Sensors 22, no. 2 (2022): 443. http://dx.doi.org/10.3390/s22020443.

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This paper presents a method for optimal pressure sensor placement in water distribution networks using information theory. The criterion for selecting the network nodes where to place the pressure sensors was that they provide the most useful information for locating leaks in the network. Considering that the node pressures measured by the sensors can be correlated (mutual information), a subset of sensor nodes in the network was chosen. The relevance of information was maximized, and information redundancy was minimized simultaneously. The selection of the nodes where to place the sensors was performed on datasets of pressure changes caused by multiple leak scenarios, which were synthetically generated by simulation using the EPANET software application. In order to select the optimal subset of nodes, the candidate nodes were ranked using a heuristic algorithm with quadratic computational cost, which made it time-efficient compared to other sensor placement algorithms. The sensor placement algorithm was implemented in MATLAB and tested on the Hanoi network. It was verified by exhaustive analysis that the selected nodes were the best combination to place the sensors and detect leaks.
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S, Kavya, and Praveen Kumar R. "Forward Node Selection by Evaluating Link Quality Using Fuzzy Logic in WBAN." International Journal of Electrical and Electronics Research 12, no. 2 (2024): 512–19. http://dx.doi.org/10.37391/ijeer.120224.

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WBAN technology plays a vital role in human life monitoring and maintaining health remotely without being hospitalized, particularly during pandemic situations. The miniature-sized and heterogeneous sensors involved in WBAN with limited resources face reliability as a key challenge that limits the growth of WBAN technology. Designing an efficient routing protocol helps to achieve reliable data transmission between sensor nodes in WBAN. The proposed Fuzzy logic-based Forward Node Selection chooses the best node to transmit the data by introducing fuzzy logic on routing parameters such as link quality, data rate, node’s residual energy and node-to-node distance. The key advantages of our proposed system are to extend the network lifetime and boost the packet delivery ratio. The efficiency of our proposed method is estimated by comparing the parameters of network lifetime and packet delivery ratio with DTS and EARP protocols.
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Susila, S. G., and J. Arputhavijayaselvi. "Multipart Layer Node Deployment and Computational Technique with Finest Cluster Head Selection for Network Lifetime Enhancement." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 6642–48. http://dx.doi.org/10.1166/jctn.2016.5609.

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The continual research and development in wireless sensor networks, power is most vital resource because each sensor node has limited battery power. Numerous clustering concept routing protocols have been developed to balance and enhance lifetime of the sensor nodes in wireless sensor networks. Available clustering routing protocols are select cluster heads periodically and they considered only how can select cluster heads energy-efficiently and the most excellent selection of cluster heads, without considering energy-efficient period of the cluster heads replacement. Herein paper, it is employed different formulae in homogeneous merged layer node deployment system, which has a threshold-based cluster head selection mechanism for clustering routing protocols of wireless sensor networks. The proposed routing protocol is minimizes the number of cluster head selection difficulty by using threshold of residual energy comparison. Reducing the amount of difficulty for cluster head selection procedure yields better life span of the whole sensor networks and it is compared with the available clustering routing protocols. In the proposed system of work, node scheduling or activation techniques are also integrated and the obtained simulation results illustrate that the best to the obtainable clustering protocols in wireless sensor networks (WSNs).
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Rao, Varun, Sandeep Nukala, Abirami G, Deepa R, and Revathi Venkataraman. "Huffman coding packet balancer based data compression techniques in Wireless Sensor Network." International Journal of Engineering & Technology 7, no. 2.24 (2018): 531. http://dx.doi.org/10.14419/ijet.v7i2.24.12152.

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In Wireless Sensor Networks, sensor devices perform sensing and communicating task over a network for data delivery from source to destination. Due to the heavy loaded information, during packet transmission, sensor node will drain off its energy frequently, thus led to packet loss. The novelty of the proposed work is mainly reducing the loss of packet and energy consumption during transmission. Thus, Huffman coding packet balancer select the best path between the intermediate nodes and are compared based on transmitting power, receiving and sensing power these measure the QOS in wireless sensor network. To satisfy the QOS of the node, compressed packet from source to destination is done by choosing the best intermediate node path. The advantages of the proposed work is minimum packet loss and minimize the end to end delay. Sparse recovery is used to reconstruct the path selection when there is high density of node.
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Dissertations / Theses on the topic "Best sensor node selection"

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Abu-Mahfouz, Adnan Mohammed. "Accurate and efficient localisation in wireless sensor networks using a best-reference selection." Thesis, University of Pretoria, 2011. http://hdl.handle.net/2263/28662.

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Many wireless sensor network (WSN) applications depend on knowing the position of nodes within the network if they are to function efficiently. Location information is used, for example, in item tracking, routing protocols and controlling node density. Configuring each node with its position manually is cumbersome, and not feasible in networks with mobile nodes or dynamic topologies. WSNs, therefore, rely on localisation algorithms for the sensor nodes to determine their own physical location. The basis of several localisation algorithms is the theory that the higher the number of reference nodes (called “references”) used, the greater the accuracy of the estimated position. However, this approach makes computation more complex and increases the likelihood that the location estimation may be inaccurate. Such inaccuracy in estimation could be due to including data from nodes with a large measurement error, or from nodes that intentionally aim to undermine the localisation process. This approach also has limited success in networks with sparse references, or where data cannot always be collected from many references (due for example to communication obstructions or bandwidth limitations). These situations require a method for achieving reliable and accurate localisation using a limited number of references. Designing a localisation algorithm that could estimate node position with high accuracy using a low number of references is not a trivial problem. As the number of references decreases, more statistical weight is attached to each reference’s location estimate. The overall localisation accuracy therefore greatly depends on the robustness of the selection method that is used to eliminate inaccurate references. Various localisation algorithms and their performance in WSNs were studied. Information-fusion theory was also investigated and a new technique, rooted in information-fusion theory, was proposed for defining the best criteria for the selection of references. The researcher chose selection criteria to identify only those references that would increase the overall localisation accuracy. Using these criteria also minimises the number of iterations needed to refine the accuracy of the estimated position. This reduces bandwidth requirements and the time required for a position estimation after any topology change (or even after initial network deployment). The resultant algorithm achieved two main goals simultaneously: accurate location discovery and information fusion. Moreover, the algorithm fulfils several secondary design objectives: self-organising nature, simplicity, robustness, localised processing and security. The proposed method was implemented and evaluated using a commercial network simulator. This evaluation of the proposed algorithm’s performance demonstrated that it is superior to other localisation algorithms evaluated; using fewer references, the algorithm performed better in terms of accuracy, robustness, security and energy efficiency. These results confirm that the proposed selection method and associated localisation algorithm allow for reliable and accurate location information to be gathered using a minimum number of references. This decreases the computational burden of gathering and analysing location data from the high number of references previously believed to be necessary.<br>Thesis (PhD(Eng))--University of Pretoria, 2011.<br>Electrical, Electronic and Computer Engineering<br>unrestricted
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Demigha, Oualid. "Energy Conservation for Collaborative Applications in Wireless Sensor Networks." Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0058/document.

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Les réseaux de capteurs sans fil est une technologie nouvelle dont les applications s'étendent sur plusieurs domaines: militaire, scientifique, médicale, industriel, etc. La collaboration entre les noeuds capteurs, caractérisés par des capacités minimales en termes de capture, de transmission, de traitement et d'énergie, est une nécessité pour réaliser des tâches aussi complexes que la collecte des données, le pistage des objets mobiles, la surveillance des zones sensibles, etc. La contrainte matérielle sur le développement des ressources énergétiques des noeuds capteurs est persistante. D'où la nécessité de l'optimisation logicielle dans les différentes couches de la pile protocolaire et du système d'exploitation des noeuds. Dans cette thèse, nous approchons le problème d'optimisation d'énergie pour les applications collaboratives via les méthodes de sélection des capteurs basées sur la prédiction et la corrélation des données issues du réseau lui-même. Nous élaborons plusieurs méthodes pour conserver les ressources énergétiques du réseau en utilisant la prédiction comme un moyen pour anticiper les actions des noeuds et leurs rôles afin de minimiser le nombre des noeuds impliqués dans la tâche en question. Nous prenons l'application de pistage d'objets mobiles comme un cas d'étude. Ceci, après avoir dresser un état de l'art des différentes méthodes et approches récentes utilisées dans ce contexte. Nous formalisons le problème à l'aide d'un programme linéaire à variables binaires dans le but de trouver une solution générale exacte. Nous modélisons ainsi le problème de minimisation de la consommation d'énergie des réseaux de capteurs sans fil, déployé pour des applications de collecte de données soumis à la contrainte de précision de données, appelé EMDP. Nous montrons que ce problème est NP-Complet. D'où la nécessité de solutions heuristiques. Comme solution approchée, nous proposons un algorithme de clustering dynamique, appelé CORAD, qui adapte la topologie du réseau à la dynamique des données capturées afin d'optimiser la consommation d'énergie en exploitant la corrélation qui pourrait exister entre les noeuds. Toutes ces méthodes ont été implémentées et testées via des simulations afin de montrer leur efficacité<br>Wireless Sensor Networks is an emerging technology enabled by the recent advances in Micro-Electro-Mechanical Systems, that led to design tiny wireless sensor nodes characterized by small capacities of sensing, data processing and communication. To accomplish complex tasks such as target tracking, data collection and zone surveillance, these nodes need to collaborate between each others to overcome the lack of battery capacity. Since the development of the batteries hardware is very slow, the optimization effort should be inevitably focused on the software layers of the protocol stack of the nodes and their operating systems. In this thesis, we investigated the energy problem in the context of collaborative applications and proposed an approach based on node selection using predictions and data correlations, to meet the application requirements in terms of energy-efficiency and quality of data. First, we surveyed almost all the recent approaches proposed in the literature that treat the problem of energy-efficiency of prediction-based target tracking schemes, in order to extract the relevant recommendations. Next, we proposed a dynamic clustering protocol based on an enhanced version of the Distributed Kalman Filter used as a prediction algorithm, to design an energy-efficient target tracking scheme. Our proposed scheme use these predictions to anticipate the actions of the nodes and their roles to minimize their number in the tasks. Based on our findings issued from the simulation data, we generalized our approach to any data collection scheme that uses a geographic-based clustering algorithm. We formulated the problem of energy minimization under data precision constraints using a binary integer linear program to find its exact solution in the general context. We validated the model and proved some of its fundamental properties. Finally and given the complexity of the problem, we proposed and evaluated a heuristic solution consisting of a correlation-based adaptive clustering algorithm for data collection. We showed that, by relaxing some constraints of the problem, our heuristic solution achieves an acceptable level of energy-efficiency while preserving the quality of data
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Oyeyele, Olawoye A. "A robust node selection strategy for lifetime extension in wireless sensor networks." 2004. http://etd.utk.edu/2004/OyeyeleOlawoye.pdf.

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Thesis (M.S.)--University of Tennessee, Knoxville, 2004.<br>Title from title page screen (viewed Sept. 27, 2004). Thesis advisor: Hairong Qi. Document formatted into pages (ix, 105 p. : ill. (some col.)). Vita. Includes bibliographical references (p. 89-97).
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Tun-YuChang and 張惇育. "The Adaptive Node-selection and Load Balancing Mechanisms in Wireless Sensor Networks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/82976626240268594170.

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博士<br>國立成功大學<br>資訊工程學系碩博士班<br>101<br>In many researches on load balancing in multi-sink WSN, sensors usually choose the nearest sink as destination for sending data. However, in WSN, events often occur in specific area. If all sensors in this area all follow the nearest-sink strategy, sensors around nearest sink called hotspot will exhaust energy early. We propose an adaptive learning scheme for load balancing scheme in multi-sink WSN. The agent in a centralized mobile anchor with directional antenna is introduced to adaptively partition the network into several zones according to the residual energy of hotspots around sink nodes. In addition, machine learning is applied to the mobile anchor to make it adaptable to any traffic pattern. Through interactions with the environment, the agent can discovery a near-optimal control policy for movement of mobile anchor. The policy can achieve minimization of residual energy’s variance among sinks, which prevent the early isolation of sink and prolong the network lifetime. This study also proposes a solar power-based adaptive node-selection protocol mechanism for a wireless sensor network to increase the monitor performance of wireless sensors. Using renewable energy, such as solar power, to improve the efficiency of sensors in wireless sensor networks has become a popular topic. Equipping the sensors with solar-powered equipment signifies that the sensors no longer have the limited battery life problem. This design can collect solar power to charge the sensor’s battery. To solve node-selection problem, an adaptive node-selection mechanism (ANSM) scheme is proposed. The algorithm builds the energy-aware Steiner tree between sensors and sink. This scheme selects the least active node to reduce the overlapping of the sensor coverage but ensure constant coverage of the target area in solar-powered wireless sensor networks. This approach also considers the solar power consuming rate and humidity to solve the solar power problem in various environments. While monitoring the stream environment, sensors are attached to the stream side to collect the sensed data and transmit the data back to the sink. The stream environment can be scaled in several similar environments. This type of geographic limitation not only exists in a stream environment, but also on streets, roads, and trails. This study presents an effective node-selection scheme to enhance the efficiency of saving power and coverage in stream environment of solar-powered WSNs. Analysis of the sensor deployment in the stream environment permits sensors to be classified into different segments, and then allows the selection of active nodes for building inter-stream connections, inter-segment connections, and intra-segment connections. Based on these connections, the number of active nodes and transmitted packets is minimized. Simulation results show that this scheme can significantly increase the energy efficient and maintain monitoring area in solar-powered WSNs.
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Ming-HsinCheng-Lo and 鄭駱明信. "An adaptive node-selection mechanism for environment monitoring in Solar-power wireless sensor networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/88438268153020151677.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>100<br>This thesis proposes a solar power based adaptive node-selection protocol mechanism of wireless sensor network to increase the monitor performance of wireless sensor in an easy way to deployment. There are already some researches that focus on how to deploy the sensor by accurate the distance between sensors to avoid the waste of the sensor coverage. However, most of the sensors deploy by using airplane to spread, so how to select some sensor work efficiently becomes the mainly problem. On the other side, there are also lots of node-selection or path-selection researches, but most of them didn’t consider about the energy problem, because it’s mainly using at base station instead of wireless sensors. Therefore, for solve the problem of electric power issue, we apply the solar-power equipment as the base of power supply, and also use the solar power as the selection of the nodes or paths. On this way, we can solve both electric problem and node-selection problem. Our adaptive node-selection mechanism includes three mainly parts, which is divide the target area into several grid to reduce the overlapping, the node-selection base on solar power and the path-selection to increase the coverage and sustainability. Also, we add the raining chance and humidity as the parameter to fix the solar power in different environment. Simulation result shows that we can adaptive select the nodes and monitor the target area at the same time.
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Lo, Iudice Francesco. "Driver and Sensor Node Selection Strategies Optimizing the Controllability Properties of Complex Dynamical Networks." Tesi di dottorato, 2016. http://www.fedoa.unina.it/10871/1/Main.pdf.

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In recent years, complex networks have attracted the attention of researchers throughout the fields of science due to their ubiquity in natural and artificial settings. While the spontaneous emergence of collective behavior has been thoroughly studied, and has inspired researchers in the design of control strategies able to reproduce it in artificial scenarios, our ability to arbitrarily affect the behavior of complex networks is still limited. To start filling this void, in the past five years, researchers have focused on the preliminary condition of selecting the nodes where input signals have to be injected so to ensure complete controllability of complex networks. Unfortunately, the scale of complex networks is such that more often than not too many input signals are required to arbitrarily modify the behavior of all the nodes of a network. Departing from the idea that achieving complete controllability of complex networks is a chimera, in this thesis, we present a comprehensive toolbox of input selection algorithms so to ensure controllability of the largest number of nodes of a network. Then, we complement this toolbox with algorithms for sensor placement so to also guarantee, when possible, observability of these nodes, thus allowing the implementation of feedback control strategies. Finally, an outlook on the topics that are currently being investigated by researchers working on controllability of complex networks is provided.
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Ahmed, Mohammed. "Collaborative beamforming for wireless sensor networks." Phd thesis, 2011. http://hdl.handle.net/10048/1952.

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Collaborative Beamforming (CB) has been introduced in Wireless Sensor Networks (WSNs) context as a long-distance and power-efficient communication scheme. One challenge for CB is the randomness of sensor node locations where different network realizations result in different CB beampatterns. First, we study the effect of sensor node spatial distribution on the CB beampattern. The characteristics of the CB beampattern are derived for circular Gaussian distributed sensor nodes and compared with the case of uniform distributed sensor nodes. It is shown that the mainlobe behavior of the CB beampattern is essentially deterministic. This suggests that the average beampattern characteristics are suitable for describing the mainlobe of a sample beampattern. However, the CB beampattern sidelobes are random and highly depends on the particular sensor node locations. Second, we introduce the multi-link CB and address the problem of random sidelobes where high level sidelobes can cause unacceptable interference to unintended Base Stations or Access Points (BSs/APs). Centralized sidelobe control techniques are impractical for distributed sensor nodes because of the associated communication overhead for each sensor node. Therefore, we propose a node selection scheme as an alternative to the centralized sidelobe control which aims at minimizing the interference at unintended BSs/APs. Our algorithm is based on the use of the inherent randomness of the channels and a low feedback that approves/rejects tested random node combinations. The performance of the proposed algorithm is analyzed in terms of the average number of trials and the achievable interference suppression and transmission rate. Finally, we study CB with power control aiming at prolonging the lifetime of a cluster of sensor nodes in the WSN. The energy available at different sensor nodes may not be the same since different sensor nodes may perform different tasks and not equally frequently. CB with power control can be used to balance the individual sensor nodes' lifetimes. Thus, we propose a distributed algorithm for CB with power control that is based on the Residual Energy Information (REI) at each sensor node while achieving the required average SNR at the BS/AP. The effectiveness of the proposed CB with power control is illustrated by simulations.<br>Communications
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Tsung-YingHsieh and 謝宗穎. "An effective node-selection scheme for data gathering in specific region of solar-powered Wireless Sensor Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/70673259959551055821.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>100<br>This thesis proposes a node selection scheduling on solar-powered Wireless Sensor Networks in order to increase energy and monitor coverage performance. The general problem of WSNs is power starvation, but most researches about WSNs issues focus on general environment, while deploying sensors on stream sides without effective energy control, the sensor would dead and lost sensed information causing by running out of energy. This thesis presents a node selection scheme to enhance the efficiency of power saving and coverage in solar-powered WNSs. Analysis the sensor deployment in the stream environment therefore sensors can be classified to different segments firstly, then select active nodes to build inter-stream connection, inter-segment connection and intra-segment connection. Based on these connections, the number of active nodes and transmitted packets would be minimized. This system would also apply best radius distance to sensors according to the stream wide. Simulation result shows that our scheme can significantly increase the throughput of power saving and monitoring area in Solar-Powered WSNs.
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Books on the topic "Best sensor node selection"

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Healy, Susan D. Adaptation and the Brain. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780199546756.001.0001.

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The rationale for this work is to make some sort of sense of the seeming myriad of adaptive explanations for why vertebrate brains vary in size. The role that natural selection has played in brain size has been addressed using the comparative method, which allows identification of evolutionary patterns across species. One starting assumption is that brain size is a useful proxy for intelligence and therefore that large-brained animals are more intelligent than smaller-brained animals. Five classes of selection pressure form the majority of explanations: ecology, technology, innovation, sex, and sociality. After chapters in which I describe the difficulties of measuring both brain size and intelligence (cognition), I address the evidence for each of the five factors in turn, reaching the conclusion that although ecology provides the best explanations for variation in the size of brain regions, none of the factors yet offers a robust and compelling explanation for variation in whole brain size. I end by providing the steps I consider necessary to reach such an explanation, steps that I suggest are feasible, if challenging.
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Book chapters on the topic "Best sensor node selection"

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Díez-González, Javier, Rubén Álvarez, Paula Verde, Rubén Ferrero-Guillén, Alberto Martínez-Gutiérrez, and Hilde Perez. "Optimal Node Distribution in Wireless Sensor Networks Considering Sensor Selection." In 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87869-6_49.

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Roy, Arijit, Sudip Misra, and Aditya Kotasthane. "QSens: QoS-Aware Sensor Node Selection in Sensor-Cloud Architecture." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4299-6_44.

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Kong, Lingping, Jeng-Shyang Pan, Shu-Chuan Chu, and John F. Roddick. "Relay Node Selection Strategy for Wireless Sensor Network." In Proceedings of the Fourth Euro-China Conference on Intelligent Data Analysis and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68527-4_25.

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Kim, ByungHee, and Tae HoCho. "Selective Sensor Node Selection Method for Making Suitable Cluster in Filtering-Based Sensor Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87442-3_79.

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Bhushan, Bharat, and G. Sahoo. "Secure Location-Based Aggregator Node Selection Scheme in Wireless Sensor Networks." In Proceedings of ICETIT 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30577-2_2.

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Roy, Monideepa, Pushpendu Kar, and Nandini Mukherjee. "A Jini Based Implementation for Best Leader Node Selection in MANETs." In Lecture Notes in Electrical Engineering. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6154-8_2.

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Singh, Surender, and Naveen Bilandi. "Selection of relay node using multi-criteria decision-making in wireless body area network." In Wireless Ad-hoc and Sensor Networks. CRC Press, 2024. http://dx.doi.org/10.1201/9781003528982-9.

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Tomic, Slavisa, Marko Beko, Rui Dinis, Goran Dimic, and Milan Tuba. "Distributed RSS-Based Localization in Wireless Sensor Networks with Node Selection Mechanism." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16766-4_22.

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M., Giri, Seethalakshmi R., and Jyothi S. "Optimal Active Node Selection, Neighborhood Discovery, and Reliability in Wireless Sensor Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7868-2_19.

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Karjee, Jyotirmoy, and H. S. Jamadagni. "Optimal Node Selection Using Estimated Data Accuracy Model in Wireless Sensor Networks." In Lecture Notes in Electrical Engineering. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3363-7_22.

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Conference papers on the topic "Best sensor node selection"

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Zhang, Zishi, Ye Yuan, Lei Zhu, Jing He, and Wei Yi. "Node Selection for Asynchronous Multi-Target Tracking in Heterogeneous Sensor Networks." In 2024 IEEE SENSORS. IEEE, 2024. https://doi.org/10.1109/sensors60989.2024.10785115.

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Wang, Wei, Jiaxin Wu, Rongfang Du, and Naiyu Cui. "Optimal Deployment of Wireless Sensor Networks Based on Reverse Node Selection." In 2024 IEEE 7th International Conference on Computer and Communication Engineering Technology (CCET). IEEE, 2024. https://doi.org/10.1109/ccet62233.2024.10838134.

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Liu, Chao, and Biao Jin. "Node selection method for underwater sensor networks based on deep reinforcement learning." In Sixteenth International Conference on Signal Processing Systems (ICSPS 2024), edited by Robert Minasian and Li Chai. SPIE, 2025. https://doi.org/10.1117/12.3061464.

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BARKA, Kamel, Lyamine GUEZOULI, and Assem REZKI. "UAV’s enhanced data collection for heterogeneous wireless sensor networks." In International Conference on Mechanical, Automotive and Mechatronics Engineering. ECER, 2023. http://dx.doi.org/10.53375/icmame.2023.253.

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In this article, we propose a protocol called DataGA-DRF (a protocol for Data collection using a Genetic Algorithm through Dynamic Reference Points) that collects data from Heterogeneous wireless sensor networks. This protocol is based on DGA (Destination selection according to Genetic Algorithm) to control the movement of the UAV (Unmanned aerial vehicle) between dynamic reference points that virtually represent the sensor node deployment. The dynamics of these points ensure an even distribution of energy consumption among the sensors and also improve network performance. To determine the best points, DataGA-DRF uses a classification algorithm such as K-Means.
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Zar Lwin Phyo and Aye Thida. "Best resource node selection using rough sets theory." In 2011 3rd International Conference on Computer Research and Development (ICCRD). IEEE, 2011. http://dx.doi.org/10.1109/iccrd.2011.5764174.

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Wnuk, Marian. "Antenna technology in energy recovery systems." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006392.

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Nowadays, we observe dynamic technological progress. Often, due to the multitude of responsibilities, we are not aware of the conveniences that modern technology gives us. Modern man cannot imagine life without the possibility of using radio communication systems, including mobile telephony. Systems for obtaining electricity from phenomena occurring in the natural or industrial human environment are known under two names: energy harvesting and energy scavering. Both of these names refer to the same phenomena and methods. Sometimes in the literature the use of these names depends on the nature of the energy being processed. The name energy scavering is used when the type of energy source and its efficiency are not known, while energy harvesting is used when the source of potential energy is well described and regular.The dynamic development of applications requiring autonomous energy sources favors the rapid development of EH technology. The main area of application of EH is wireless sensor networks (WSN), where the energy demand of a single autonomous node depends on the current operating mode. In the standby state, the demand for electricity usually does not exceed several μW, and during measurement it does not exceed 100 μW. The greatest demand occurs during information transmission and ranges from 0.1 to 1 mW. Such energy demand values clearly indicate the possibility of using EH generators as additional power sources for smartphones.Another area of application of EH technology are systems for recharging batteries used in larger measurement systems where there are other traditional ways of charging batteries.One of the fundamental problems that arise when analyzing the possibility of using electromagnetic radiation as a source of recovered energy is the issue related to the assessment of the field strength distribution in the area of operation of the designed system. Knowledge of this distribution allows the designer to assess the degree of land cover. Therefore, every designer faces a serious problem of selecting an appropriate propagation model that will best describe the reality created by the designed system.An important aspect when analyzing the possibility of using electromagnetic radiation as a source of recovered energy is the proper selection of antenna technologies that can be used for the above application. In this chapter of the study, microstrip antennas are proposed for the above purpose.The work presents a designed measurement antenna that can operate from 500 MHz to 7.6 GHz, the operating bandwidth is 7.1 GHz,
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Yim, R., N. B. Mehta, and A. F. Molisch. "Best Node Selection through Distributed Fast Variable Power Multiple Access." In 2008 IEEE International Conference on Communications. IEEE, 2008. http://dx.doi.org/10.1109/icc.2008.943.

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Jain Sudhir, Prathik, Ravindra Holalu Venkatadas, Naveen Prakash Goravi Vijaya Dev, and Ugrasen Gonchikar. "Estimation and Comparison of Acoustic Emission Parameters and Surface Roughness in Wire Cut Electric Discharge Machining of Stavax Material Using Multiple Regression Analysis and Group Method Data Handling Technique." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50596.

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Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. Selection of cutting parameters for obtaining higher cutting efficiency or accuracy in WEDM is still not fully solved, even with most up-to-date CNC WEDM machine. It is widely recognised that Acoustic Emission (AE) is gaining ground as a monitoring method for health diagnosis on rotating machinery. The advantage of AE monitoring over vibration monitoring is that the AE monitoring can detect the growth of subsurface cracks whereas the vibration monitoring can detect defects only when they appear on the surface. This study outlines the estimation of AE parameters viz., signal strength, absolute energy, RMS in the WEDM. Stavax (modified AISI 420) steel material was machined using different process parameters based on Taguchi’s L’16 standard orthogonal array. Among different process parameters voltage and flush rate were kept constant. Parameters such as pulse-on time, pulse-off time, current and bed speed was varied. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Simple functional relationships between the parameters were plotted to arrive at possible information on surface roughness and AE signals. But these simpler methods of analysis did not provide any information about the status of the work material. Thus, there is a requirement for more sophisticated methods that are capable of integrating information from the multiple sensors. Hence, methods like Multiple Regression Analysis (MRA) and Group Method of Data Handling (GMDH) have been applied for the estimation of surface roughness, AE signal strength, AE absolute energy and AE RMS. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into two sets: the training set and testing set. The training set is used to make the GMDH learn the process and the testing set will check the performance of GMDH. Different models can be obtained by varying the percentage of data in the training set and the best model can be selected from these, viz., 50%, 62.5% and 75%. The best model is selected from the said percentages of data. Number of variables selected at each layer is usually taken as a fixed number or a constantly increasing number. It is usually given as fractional increase in number of independent variables present in the previous level. Three different criterion functions, viz., Root Mean Square (Regularity) criterion, Unbiased criterion and Combined criterion were considered for the estimation. The choice of criterion for node selection is another important parameter for proper modeling. From the results it was observed that, AE parameters and estimated surface roughness values were correlates well with GMDH when compare to MRA.
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Gupta, Vidit, Kritika Kapoor, and Renuga Devi S. "Wireless Sensor node selection strategies for effective surveillance." In 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154840.

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Zhang, Hu, and Huiyan Zhang. "Node Selection Algorithm Optimized for Wireless Sensor Network." In First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008). IEEE, 2008. http://dx.doi.org/10.1109/wkdd.2008.134.

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Reports on the topic "Best sensor node selection"

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Holman, Allyson, William Gilbraith, and Holly Flynn. Applications of fuzzy logic and best-worst method for tritium sensor selection. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2406473.

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Paullin, Cheryl, Michael Ingerick, D. M. Trippe, and Laurie Wasko. Identifying Best Bet Entry-Level Selection Measures for US Air Force Remotely Piloted Aircraft (RPA) Pilot and Sensor Operator (SO) Occupations. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada554209.

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Zhao, George, Grang Mei, Bulent Ayhan, Chiman Kwan, and Venu Varma. DTRS57-04-C-10053 Wave Electromagnetic Acoustic Transducer for ILI of Pipelines. Pipeline Research Council International, Inc. (PRCI), 2005. http://dx.doi.org/10.55274/r0012049.

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In this project, Intelligent Automation, Incorporated (IAI) and Oak Ridge National Lab (ORNL) propose a novel and integrated approach to inspect the mechanical dents and metal loss in pipelines. It combines the state-of-the-art SH wave Electromagnetic Acoustic Transducer (EMAT) technique, through detailed numerical modeling, data collection instrumentation, and advanced signal processing and pattern classifications, to detect and characterize mechanical defects in the underground pipeline transportation infrastructures. The technique has four components: (1) thorough guided wave modal analysis, (2) recently developed three-dimensional (3-D) Boundary Element Method (BEM) for best operational condition selection and defect feature extraction, (3) ultrasonic Shear Horizontal (SH) waves EMAT sensor design and data collection, and (4) advanced signal processing algorithm like a nonlinear split-spectrum filter, Principal Component Analysis (PCA) and Discriminant Analysis (DA) for signal-to-noise-ratio enhancement, crack signature extraction, and pattern classification. This technology not only can effectively address the problems with the existing methods, i.e., to detect the mechanical dents and metal loss in the pipelines consistently and reliably but also it is able to determine the defect shape and size to a certain extent.
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Mizrach, Amos, Sydney L. Spahr, Ephraim Maltz, et al. Ultrasonic Body Condition Measurements for Computerized Dairy Management Systems. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568109.bard.

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The body condition (BC) score is recognized in the dairy industry as an essential tool for managing the energy reserves of the dairy cow, which is essential for sustaining optimal and efficient production over several lactations. The current use of BC scoring depends on the accuracy of subjective visual estimates, and this limits its kusefulness as a management aid in the dairy industry. A measuring tool that would frequently provide objective data on the cow's body reserves would be a major contribution to efficient dairy herd management. Ultrasonic sensors have the potential to be developed into an efficient BC measuring device, and the experimental use of such sensors for subcutaneous fat thickness (SDFT) estimates, as an indication for BC in beef cattle, supports this assumption. The purposes of this project were: 1. To compare visual BC scoring and ultrasonic fat thickness with on-line automated body weight (BW) measurements as monitors of nutritional adequacy of dairy cows at various stages of lactation. 2. To determine the effects of variation in digestive fill in early and late lactation on the accuracy of body weight measurements in lactating cows. 3. To modify an existing ultrasonic system and develop a specialized, low-cost sensor for repeatable determination of body condition scores by users with minimal training and skill. 4. To develop a standard for the assignment of body condition scores based on ultrasonic measurements of subdermal fat thickness. The procedure to execute these objectives involved: 1. Frequent measurement of BW, milk yield (MY), BC (visually scored) and subdermal fat thickness ultrasonically measured of dairy cows, and data analysis on average and individual basis. 2. Testing and selection of an appropriate special-purpose sensor, finding an optimum body location for working an ultrasonic measurement, prcessing the signals obtained, and correlating the resulting measurements with performance responses in lactating cows. Linking the ultrasonic signals to BC scores, and developing a BC scoring data acquisition system are the first steps towards fulfilling the necessary requirements for incorporating this device into an existing dairy herd management system, in order to provide the industry with a powerful managment tool. From the results obtained we could conclude that: 1. BC does not correlate with BW changes during all stages of lactation, although in general terms it does. These results were confirmed by individual cow BW and BC data obtained during the course of lactation, that were supported by individual objective ultrasonic measurement of SDFT. 2. BW changes reflect energy metabolism reliably ony after peak milk yield; early in lactation, a decrease in BW expresses mobilization of body reserves only qualitatively, and not quantitatively. 3. Gastrointestinal content increases throughout the whole period during which dry matter intake (DMI) increases. The drastic increase very early in lactation prevents the use of BW changes as a basis for quantitative estimatio of energy meatabolism; at this stage of lactation, konly a BC score or any other direct measurements willl provide a quantitative estimate of energy metabolism. 4. Ultrasonic measurements of subdermal fat thickness can be used to quantify changes that correlate with the actual condition of the cow, as assessed by performance and the traditional way of scoring. 5. To find the best site on the cow's body at which to obtain responses to BC and its changes in the course of lactation, additional sites have to be examined. From the present study, it seems that the sites between ribs 12 and 13 have the potential for this purpose. 6. The use of templates made it easier to repeat measurements at a desired site and spot. However, the convenient easy-to-handle way to standardize the measurement, described in this study, koffers scope for improvement. 7. The RF peak values of the A-mode are better indicators of the location of fat layer borders than image analysis, from the point of view of future commercial development. 8. The distances between the RF peaks of the A-mode can be automatically measured by suitable software, for future commercial development. 9. Proper analysis of daily body weight and milk yield data can provide the necessary information on body condition changes during lactation, until a direct BC measurement device is developed. 10. In any case, at least one visual BC assessment has to be done, preferably immediately after calving, for calibration purposes.
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