Relevant bibliographies by topics / Wireless sensor networks. Artificial intelligence

Contents

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

Academic literature on the topic 'Wireless sensor networks. Artificial intelligence'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Wireless sensor networks. Artificial intelligence"

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Kantarci, Burak, and Sema Oktug. "Special Issue: Wireless Sensor and Actuator Networks for Smart Cities." Journal of Sensor and Actuator Networks 7, no. 4 (2018): 49. http://dx.doi.org/10.3390/jsan7040049.

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Kumar, Shiu, Seong Min Jeon, and Seong Ro Lee. "Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks." Journal of Korea Information and Communications Society 39C, no. 9 (2014): 820–27. http://dx.doi.org/10.7840/kics.2014.39c.9.820.

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Ahmed, Mohammed Bakhtawar. "Wireless Sensor Networks: Techniques for Detecting Faults using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (2019): 1343–49. http://dx.doi.org/10.22214/ijraset.2019.4241.

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Kulkarni, Vaishali Raghavendra, and Veena Desai. "Sensor Localization in Wireless Sensor Networks Using Cultural Algorithm." International Journal of Swarm Intelligence Research 11, no. 4 (2020): 106–22. http://dx.doi.org/10.4018/ijsir.2020100105.

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Evolutionary computing-based cultural algorithm (CA) has been developed for anchor-assisted, range-based, multi-stage localization of sensor nodes of wireless sensor networks (WSNs). The results of CA-based localization have been compared with those of swarm intelligence-based algorithms, namely the artificial bee colony algorithm and the particle swarm optimization algorithm. The algorithms have been compared in terms of mean localization error and computing time. The simulation results show that the CA performs the localization in a more accurate manner and at a higher speed than the other two algorithms.
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Varun, Rajesh Kumar, Rakesh C. Gangwar, Omprakash Kaiwartya, and Geetika Aggarwal. "Energy-Efficient Routing Using Fuzzy Neural Network in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2021 (August 3, 2021): 1–13. http://dx.doi.org/10.1155/2021/5113591.

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In wireless sensor networks, energy is a precious resource that should be utilized wisely to improve its life. Uneven distribution of load over sensor devices is also the reason for the depletion of energy that can cause interruptions in network operations as well. For the next generation’s ubiquitous sensor networks, a single artificial intelligence methodology is not able to resolve the issue of energy and load. Therefore, this paper proposes an energy-efficient routing using a fuzzy neural network (ERFN) to minimize the energy consumption while fairly equalizing energy consumption among sensors thus as to prolong the lifetime of the WSN. The algorithm utilizes fuzzy logic and neural network concepts for the intelligent selection of cluster head (CH) that will precisely consume equal energy of the sensors. In this work, fuzzy rules, sets, and membership functions are developed to make decisions regarding next-hop selection based on the total residual energy, link quality, and forward progress towards the sink. The developed algorithm ERFN proofs its efficiency as compared to the state-of-the-art algorithms concerning the number of alive nodes, percentage of dead nodes, average energy decay, and standard deviation of residual energy.
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Marsh, David, Richard Tynan, Donal O’Kane, and Gregory M. P. O’Hare. "Autonomic wireless sensor networks." Engineering Applications of Artificial Intelligence 17, no. 7 (2004): 741–48. http://dx.doi.org/10.1016/j.engappai.2004.08.038.

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Bose, Pitchaimanickam, and Murugaboopathi Gurusamy. "Bacteria Foraging Algorithm Based Optimal Multi Sink Placement in Wireless Sensor Networks." Journal of Intelligent Systems 27, no. 4 (2018): 609–18. http://dx.doi.org/10.1515/jisys-2016-0271.

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Abstract Wireless Sensor Networks (WSN) are mainly utilized for time sensitive applications such as forest fire detection systems and health monitoring systems. Sensor nodes are operated on low power and limited computation process. It is essential to develop the solution for planning the topological area. Multiple sinks are located in the network and reduce the number of hops between the sensors and its sinks. We propose an efficient technique based on Bacteria Foraging Algorithm to identify the best optimal locations of sinks. The experimental results show that average end to end delay is minimized and average energy consumption of sensor nodes are reduced.
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Purohit, Rekha, and Prabhat Mathur. "Role of Wireless Sensor Networks in Communication with Artificial Intelligence System." International Journal of Wireless and Mobile Communication for Industrial Systems 3, no. 2 (2016): 35–40. http://dx.doi.org/10.21742/ijwmcis.2016.3.2.05.

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Capella, Juan V., Alberto Bonastre, and Rafael Ors. "Application of Wireless Sensor Networks and Artificial Intelligence in Citrus Industry." Advanced Science Letters 19, no. 12 (2013): 3639–43. http://dx.doi.org/10.1166/asl.2013.5238.

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Li, Tian, Peng Yuan Liu, and Yong Ke. "Battlefield Awareness Network Research Based on Intelligence Role Division and Wireless Sensor Network." Applied Mechanics and Materials 300-301 (February 2013): 580–84. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.580.

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This article mainly describes battlefield awareness network scheme based on distributed artificial intelligence theory and intelligence wireless sensor network technology. Critical technologies are discussed, such as role-divided wireless sensor group, mission decision based on intelligence cooperation and performance optimization for battlefield circumstance. The research takes on advanced theory significance and operable technical application foreground.
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Dissertations / Theses on the topic "Wireless sensor networks. Artificial intelligence"

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Qela, Blerim. "Adaptive Systems for Smart Buildings Utilizing Wireless Sensor Networks and Artificial Intelligence." Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20553.

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In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.
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Townsend, Larry. "Wireless Sensor Network Clustering with Machine Learning." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1042.

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Wireless sensor networks (WSNs) are useful in situations where a low-cost network needs to be set up quickly and no fixed network infrastructure exists. Typical applications are for military exercises and emergency rescue operations. Due to the nature of a wireless network, there is no fixed routing or intrusion detection and these tasks must be done by the individual network nodes. The nodes of a WSN are mobile devices and rely on battery power to function. Due the limited power resources available to the devices and the tasks each node must perform, methods to decrease the overall power consumption of WSN nodes are an active research area. This research investigated using genetic algorithms and graph algorithms to determine a clustering arrangement of wireless nodes that would reduce WSN power consumption and thereby prolong the lifetime of the network. The WSN nodes were partitioned into clusters and a node elected from each cluster to act as a cluster head. The cluster head managed routing tasks for the cluster, thereby reducing the overall WSN power usage. The clustering configuration was determined via genetic algorithm and graph algorithms. The fitness function for the genetic algorithm was based on the energy used by the nodes. It was found that the genetic algorithm was able to cluster the nodes in a near-optimal configuration for energy efficiency. Chromosome repair was also developed and implemented. Two different repair methods were found to be successful in producing near-optimal solutions and reducing the time to reach the solution versus a standard genetic algorithm. It was also found the repair methods were able to implement gateway nodes and energy balance to further reduce network energy consumption.
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Taylor, Christopher J. "Simultaneous Localization and Tracking in Wireless Ad-hoc Sensor Networks." [Cambridge, Mass.] : MIT Computer Sciece and Artificial Intelligence Laboratory, 2005. http://hdl.handle.net/1721.1/30549.

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In this thesis we present LaSLAT, a sensor network algorithm thatsimultaneously localizes sensors, calibrates sensing hardware, andtracks unconstrained moving targets using only range measurementsbetween the sensors and the target. LaSLAT is based on a Bayesian filter, which updates a probabilitydistribution over the quantities of interest as measurementsarrive. The algorithm is distributable, and requires only a constantamount of space with respect to the number of measurementsincorporated. LaSLAT is easy to adapt to new types of hardware and newphysical environments due to its use of intuitive probabilitydistributions: one adaptation demonstrated in this thesis uses amixture measurement model to detect and compensate for bad acousticrange measurements due to echoes.We also present results from a centralized Java implementation ofLaSLAT on both two- and three-dimensional sensor networks in whichranges are obtained using the Cricket ranging system. LaSLAT is ableto localize sensors to within several centimeters of their groundtruth positions while recovering a range measurement bias for eachsensor and the complete trajectory of the mobile.
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Delgado, Román María del Carmen. "Organisation-based co-ordination of wireless sensor networks." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/285080.

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Esta tesis presenta el Coalition Oriented Sensing Algorithm (COSA) como un mecanismo de auto-organización para redes de sensores inalámbricos (WSN). El objetivo del algoritmo es extender la vida útil de la red, al mismo tiempo que la funcionalidad básica de la misma – la monitorización fiel del entorno– también es garantizada. La evaluación del funcionamiento del algoritmo se apoya en una plataforma de simulación novedosa, RepastSNS. La implementación de COSA y la preparación de la plataforma para el desarrollo de los experimentos dan lugar a una estructura software reutilizable. Esta estructura favorece la implementación de futuras ampliaciones del algoritmo, así como su exportación a otros entornos. El uso de las WSNs se ha popularizado en los últimos años. Sus particulares características han favorecido la aplicación de las mismas a múltiples áreas. No obstante, la gestión energética de las WSNs sigue siendo objeto de estudio para los investigadores, que tratan de aliviar las fuertes restricciones que estas presentan en cuanto a disponibilidad de energía se refiere. En esta línea, se han propuesto diversas técnicas para conservación de la energía. La restricción energética es especialmente acusada cuando las WSNs se despliegan en entornos que no permiten la conexión de los nodos a la red ni la recarga de sus baterías. Este es el tipo de entorno considerado para la evaluación de COSA. El caso de uso estudiado considera una WSN desplegada a lo largo de un río navegable con el objetivo de monitorizar el estado del agua y detectar la presencia de polución en ella. La definición de COSA se inspira en el paradigma de los Sistemas Multiagente (MAS) mediante la identificación de los nodos de la WSN con agentes del MAS. COSA define un algoritmo para formación de coaliciones basado en diálogos por parejas de agentes (nodos). El algoritmo está completamente embebido en el comportamiento del agente. Los agentes que implementan COSA se comunican con sus vecinos para intercambiar información sobre su percepción del entorno y su estado. Como resultado de esta comunicación local, los agentes eligen su rol en la organización y establecen relaciones leader-follower. La definición de este tipo de relaciones se basa en dos funciones relacionales y un protocolo de negociación que establece las normas de coordinación. Los agentes se juntan en grupos para compensar la calidad de los datos recogidos y el consumo de energía asociado. Esta habilidad permite adaptar el consumo energético de la red a cambios en el entorno, al mismo tiempo que se satisfacen los objetivos de muestreo en cuanto a calidad de la información enviada al sink se refiere. Los resultados experimentales obtenidos apoyan las hipótesis preliminares en cuanto al comportamiento de COSA. A partir de estos resultados también se pone de manifiesto la relación existente entre la coordinación local y las ganancias obtenidas por el uso de COSA.<br>This thesis introduces the Coalition Oriented Sensing Algorithm (COSA) as a self-organisation mechanism for Wireless Sensor Networks (WSNs). This algorithm aims at extending the network lifetime at the same time that the primary goal of the network –faithfully monitoring the environment– is also guaranteed. The evaluation of the algorithm performance is based on a novel simulator, RepastSNS. The implementation of COSA and the development of its experimental setup define a reusable software structure to work over this simulation environment. It also favours the performance of future enhancements of the algorithm as well as its exportation. The use of WSNs has become widespread in the last years. The special characteristics of these networks have favoured their application to many different areas. One of the major concerns about WSNs refers to their energy management, as they are typically constraint in energy availability. This problem has gained the attention of researchers that try to improve this aspect of the WSNs by defining network energy conservation strategies. This constraint becomes especially acute when the network deployment environment does not allow for battery replenishment or node connection to the net. This is the case of the environment considered for COSA evaluation. The use case considered is a WSN deployed along a waterway in order to monitor the state of the water and detect the presence of pollutant sources. The definition of COSA is inspired by the Multiagent Systems (MAS) paradigm through the identification of nodes in a WSN with agents in a MAS. COSA defines a coalition formation algorithm based on peer-to-peer dialogues between neighbouring agents (nodes). The algorithm is completely embedded into the agent behaviour. Agents implementing COSA communicate with its neighbours to exchange information about their perception of the environment and their state. As a result of this local communication, agents select the role to play in the organisation and can then establish leader-follower relationships. The establishment of these peer-to-peer relationships is based on two relational functions and a negotiation protocol that lays down the norms of this co-ordination. Agents join in groups in order to trade off the accuracy of the sensed data and their energy consumption. As a consequence, COSA endows the network with self-organisation capacity. This ability is used to adapt energy consumption to changes in the environment and, at the same time, to fulfil sampling objectives in terms of the quality of the information reported to the sink. The results derived from experimentation support preliminary hypotheses about COSA good performance. They also provide insights on the relationship between local co-ordination and the gains obtained from COSA’s use.
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Yousefi, Zowj Afsoon. "A Genetic Programming Approach to Cost-Sensitive Control in Wireless Sensor Networks." ScholarWorks @ UVM, 2016. http://scholarworks.uvm.edu/graddis/493.

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In some wireless sensor network applications, multiple sensors can be used to measure the same variable, while differing in their sampling cost, for example in their power requirements. This raises the problem of automatically controlling heterogeneous sensor suites in wireless sensor network applications, in a manner that balances cost and accuracy of sensors. Genetic programming (GP) is applied to this problem, considering two basic approaches. First, a hierarchy of models is constructed, where increasing levels in the hierarchy use sensors of increasing cost. If a model that polls low cost sensors exhibits too much prediction uncertainty, the burden of prediction is automatically transferred to a higher level model using more expensive sensors. Second, models are trained with cost as an optimization objective, called non-hierarchical models, that use conditionals to automatically select sensors based on both cost and accuracy. These approaches are compared in a setting where the available budget for sampling is considered to remain constant, and in a setting where the system is sensitive to a fluctuating budget, for example available battery power. It is showed that in both settings, for increasingly challenging datasets, hierarchical models makes predictions with equivalent accuracy yet lower cost than non-hierarchical models.
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Li, Jiakai. "AI-WSN: Adaptive and Intelligent Wireless Sensor Networks." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341258416.

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Gao, Zhenning. "Parallel and Distributed Implementation of A Multilayer Perceptron Neural Network on A Wireless Sensor Network." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1383764269.

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Lu, Yapeng. "An integrated algorithm for distributed optimization in networked systems." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43224234.

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Shaon, Mohammad. "A computationally intelligent approach to the detection of wormhole attacks in wireless sensor networks." World Comp,14th International Conference on Wireless Networks, 2015, 2015. http://hdl.handle.net/1993/31981.

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This thesis proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN). The aim of the proposed research is to develop a detection scheme that can detect wormhole attacks (In-band, out of band, hidden wormhole attack, active wormhole attack) in both uniformly and non-uniformly distributed sensor networks. Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network. Most importantly, the probable location of the wormhole nodes can be tracked down by the proposed ANN-based detection scheme. We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate. The performance of the proposed model is also compared with other machine learning techniques (i.e. SVM and regularized nonlinear logistic regression (LR) based detection models) based detection schemes. The simulation results show that proposed ANN-based detection model outperforms the SVM and LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates.<br>February 2017
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Al-Olimat, Hussein S. "Optimizing Cloudlet Scheduling and Wireless Sensor Localization using Computational Intelligence Techniques." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1403922600.

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Books on the topic "Wireless sensor networks. Artificial intelligence"

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service), SpringerLink (Online, ed. Challenges and Opportunities of Connected k-Covered Wireless Sensor Networks: From Sensor Deployment to Data Gathering. Springer Berlin Heidelberg, 2009.

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Bar-Noy, Amotz. Algorithms for Sensor Systems: 8th International Symposium on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities, ALGOSENSORS 2012, Ljubljana, Slovenia, September 13-14, 2012. Revised Selected Papers. Springer Berlin Heidelberg, 2013.

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Abraham, Ajith, Rafael Falcon, and Mario Koeppen, eds. Computational Intelligence in Wireless Sensor Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47715-2.

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Rondeau, Thomas Warren. Artificial intelligence in wireless communications. Artech House, 2009.

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Wu xian chuan gan qi wang luo yu ren gong sheng ming. Guo fang gong ye chu ban she, 2008.

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Zhi neng wu xian chuan gan qi wang luo xi tong. 2nd ed. Ke xue chu ban she, 2013.

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Bestehorn, Markus. Querying Moving Objects Detected by Sensor Networks. Springer New York, 2013.

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Hara, Takahiro, Vladimir I. Zadorozhny, and Erik Buchmann. Wireless sensor network technologies for the information explosion era. Springer, 2010.

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Clark, James J. Data Fusion for Sensory Information Processing Systems. Springer US, 1990.

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International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (7th 2006 Las Vegas, Nev.). Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing: SNPD 2006 : including Seventh ACIS International Workshop on Self-Assembling Networks : SAWN 2006 : proceedings : 19-20 June, 2006, Las Vegas, Nevada. IEEE Computer Society, 2006.

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Book chapters on the topic "Wireless sensor networks. Artificial intelligence"

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Xu, Yubin, and Yun Ji. "A Clustering Algorithm of Wireless Sensor Networks Based on PSO." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23881-9_24.

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Rani, Ruby. "Distributed Query Processing Optimization in Wireless Sensor Network Using Artificial Immune System." In Computational Intelligence in Sensor Networks. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57277-1_1.

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Jiang, Yifei, and Haiyi Zhang. "Base Station Controlled Intelligent Clustering Routing in Wireless Sensor Networks." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21043-3_25.

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Shih, Hong-Chi, Jiun-Huei Ho, Bin-Yih Liao, and Jeng-Shyang Pan. "Hierarchical Gradient Diffusion Algorithm for Wireless Sensor Networks." In Recent Trends in Applied Artificial Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38577-3_49.

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del Rey, A. Martín, J. D. Hernández Guillén, and G. Rodríguez Sánchez. "Modeling Malware Propagation in Wireless Sensor Networks with Individual-Based Models." In Advances in Artificial Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44636-3_18.

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Farruggia, Alfonso, Giuseppe Lo Re, and Marco Ortolani. "Probabilistic Anomaly Detection for Wireless Sensor Networks." In AI*IA 2011: Artificial Intelligence Around Man and Beyond. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23954-0_44.

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Basile, Teresa M. A., Nicola Di Mauro, Stefano Ferilli, and Floriana Esposito. "Relational Temporal Data Mining for Wireless Sensor Networks." In AI*IA 2009: Emergent Perspectives in Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10291-2_42.

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Li, Xiaochen, Xizhong Lou, Ting Peng, Jia Xu, Qian Zhou, and Daorong Wu. "A New Balancing Type of Wireless Sensor Network Routing Algorithm." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33478-8_11.

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Dev, Jayashree, and Jibitesh Mishra. "Lifetime Enhancement of Wireless Sensor Network Using Artificial Intelligence Techniques." In Smart Sensor Networks Using AI for Industry 4.0. CRC Press, 2021. http://dx.doi.org/10.1201/9781003145028-3.

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Li, Wenli. "PSO Based Wireless Sensor Networks Coverage Optimization on DEMs." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25944-9_48.

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Conference papers on the topic "Wireless sensor networks. Artificial intelligence"

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Yuan, Lina, Huajun Chen, and Gong Jing. "Wireless RF Powered Sensor Networks." In 2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM). IEEE, 2020. http://dx.doi.org/10.1109/aiam50918.2020.00039.

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Guy, Chris. "Wireless sensor networks." In Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic technology, and Artificial Intelligence, edited by Jiancheng Fang and Zhongyu Wang. SPIE, 2006. http://dx.doi.org/10.1117/12.716964.

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Machado, Renita, Wensheng Zhang, and Guiling Wang. "Network Planning for Heterogeneous Wireless Sensor Networks in Environmental Survivability." In 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.115.

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Chatzimilioudis, Georgios, Alfredo Cuzzocrea, and Dimitrios Gunopulos. "Optimizing Query Routing Trees in Wireless Sensor Networks." In 2010 22nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2010. http://dx.doi.org/10.1109/ictai.2010.117.

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Aldhobaiban, Dema, Khaled Elleithy, and Laiali Almazaydeh. "Prevention of Wormhole Attacks in Wireless Sensor Networks." In 2014 2nd International Conference on Artificial Intelligence, Modelling & Simulation (AIMS). IEEE, 2014. http://dx.doi.org/10.1109/aims.2014.57.

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Al-Roubaiey, Anas, and Hamdi Al-Jamimi. "Online Power Tossim Simulator for Wireless Sensor Networks." In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2019. http://dx.doi.org/10.1109/ecai46879.2019.9042005.

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Halcu, Ionela, Grigore Stamatescu, and Valentin Sgarciu. "Enabling security on 6LoWPAN / IPv6 Wireless Sensor Networks." In 2015 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2015. http://dx.doi.org/10.1109/ecai.2015.7301201.

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Matlou, Omolemo Godwill, and Adnan M. Abu-Mahfouz. "Utilising artificial intelligence in software defined wireless sensor network." In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017. http://dx.doi.org/10.1109/iecon.2017.8217065.

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Saeed, Faisal, Seungmin Rho, Anand Paul, and Sangsoon Lim. "Artificial intelligence with wireless Sensor Network for Fire detection." In 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2020. http://dx.doi.org/10.1109/csci51800.2020.00136.

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Yong Li, Hairong Sun, and Yibin Huang. "A time synchronization mechanism for heterogeneous wireless sensor networks." In International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.0982.

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