Log in

Relevant bibliographies by topics / Wireless sensor networks. Artificial intelligence / Journal articles

To see the other types of publications on this topic, follow the link: Wireless sensor networks. Artificial intelligence.

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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Wireless sensor networks. Artificial intelligence.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

Arivubraka, P., and V. R. S. Dhulipala. "Sentry Based Intruder Detection Technique for Wireless Sensor Networks." Journal of Artificial Intelligence 6, no. 2 (2013): 175–80. http://dx.doi.org/10.3923/jai.2013.175.180.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Komila, M., and A. Jayalakshmi. "Intelligence Based Algorithm for Wireless Sensor Networks using Artificial Neural Networks and Fuzzy Logic." International Journal of Communication and Networking System 6, no. 1 (2016): 20–27. http://dx.doi.org/10.20894/ijcnes.103.006.001.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Wang, Yangzi, and Yi Zhao. "Coverage Optimization Model of Wireless Sensor Networks Based on Artificial Intelligence Algorithm." Journal of Computational and Theoretical Nanoscience 13, no. 11 (2016): 7752–56. http://dx.doi.org/10.1166/jctn.2016.5773.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Alrajeh, Nabil Ali, and J. Lloret. "Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 9, no. 10 (2013): 351047. http://dx.doi.org/10.1155/2013/351047.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Zong Chen, Dr Joy Iong, and Kong-Long Lai. "Machine Learning based Energy Management at Internet of Things Network Nodes." Journal of Trends in Computer Science and Smart Technology 2, no. 3 (2020): 127–33. http://dx.doi.org/10.36548/jtcsst.2020.3.001.

Full text

Abstract:

The Internet of Things networks comprising wireless sensors and controllers or IoT gateways offers extremely high functionalities. However, not much attention is paid towards energy optimization of these nodes and enabling lossless networks. The wireless sensor networks and its applications has industrialized and scaled up gradually with the development of artificial intelligence and popularization of machine learning. The uneven network node energy consumption and local optimum is reached by the algorithm protocol due to the high energy consumption issues relating to the routing strategy. The smart ant colony optimization algorithm is used for obtaining an energy balanced routing at required regions. A neighbor selection strategy is proposed by combining the wireless sensor network nodes and the energy factors based on the smart ant colony optimization algorithm. The termination conditions for the algorithm as well as adaptive perturbation strategy are established for improving the convergence speed as well as ant searchability. This enables obtaining the find the global optimal solution. The performance, network life cycle, energy distribution, node equilibrium, network delay and network energy consumption are improved using the proposed routing planning methodology. There has been around 10% energy saving compared to the existing state-of-the-art algorithms.

APA, Harvard, Vancouver, ISO, and other styles

16

ZHANG, Yaqiong, Hui ZHANG, and Jiyan LIN. "Multi-hop and clustering routing algorithm in wireless sensor networks." Revue d'intelligence artificielle 32, s1 (2018): 91–102. http://dx.doi.org/10.3166/ria.32.s1.91-102.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Liu, Jia, Mingchu Li, Yuanfang Chen, Sardar M. N. Islam, and Noel Crespi. "Variational Channel Estimation with Tempering: An Artificial Intelligence Algorithm for Wireless Intelligent Networks." Sensors 20, no. 20 (2020): 5939. http://dx.doi.org/10.3390/s20205939.

Full text

Abstract:

With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs.

APA, Harvard, Vancouver, ISO, and other styles

18

Arroyo, Patricia, Jesús Lozano, and José Suárez. "Evolution of Wireless Sensor Network for Air Quality Measurements." Electronics 7, no. 12 (2018): 342. http://dx.doi.org/10.3390/electronics7120342.

Full text

Abstract:

This study addresses the development of a wireless gas sensor network with low cost, small size, and low consumption nodes for environmental applications and air quality detection. Throughout the article, the evolution of the design and development of the system is presented, describing four designed prototypes. The final proposed prototype node has the capacity to connect up to four metal oxide (MOX) gas sensors, and has high autonomy thanks to the use of solar panels, as well as having an indirect sampling system and a small size. ZigBee protocol is used to transmit data wirelessly to a self-developed data cloud. The discrimination capacity of the device was checked with the volatile organic compounds benzene, toluene, ethylbenzene, and xylene (BTEX). An improvement of the system was achieved to obtain optimal success rates in the classification stage with the final prototype. Data processing was carried out using techniques of pattern recognition and artificial intelligence, such as radial basis networks and principal component analysis (PCA).

APA, Harvard, Vancouver, ISO, and other styles

19

Sandeli, Mohamed, and Souham Meshoul. "Computational Intelligence Approaches for Energy Optimization in Wireless Sensor Networks." International Journal of Computational Intelligence and Applications 16, no. 03 (2017): 1750020. http://dx.doi.org/10.1142/s1469026817500201.

Full text

Abstract:

Wireless sensor networking is a promising technology that can lead to automatic, intelligent, easier and more secure systems. A wireless sensor network (WSN) consists of small battery powered devices with limited energy resources. One of the major challenges in WSN lies in the energy constraint and computation resources available at the sensor nodes. One way to achieve energy efficiency would be through the use of a clustering technique. In this paper, we propose computational intelligence (CI) approaches to deal with the problem of sensor nodes clustering in a WSN with the ultimate goal to reduce energy expenditures and thus to extend the lifetime of the network. The main motivation is that CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures, and scenario changes. The main features of the proposed work span over two aspects. First, four metaheuristics have been adapted to deal with the tackled problem namely genetic algorithms, evolution strategies, particle swarm optimization and artificial bees colony. In this context, a suitable solution representation scheme has been developed and accordingly the algorithms operators, and overall dynamics have been defined. Second, the four developed algorithms are combined with an exhaustive search that is triggered whenever the number of alive sensor nodes drops below a given threshold. The performance of the proposed algorithms has been assessed and compared to the most known state of the art clustering based method namely low energy adaptive clustering hierarchy algorithm (LEACH). The obtained results show that the proposed approaches optimize in an efficient manner the lifetime of WSNs. They also show that the proposed algorithms compete and even outperform LEACH algorithm.

APA, Harvard, Vancouver, ISO, and other styles

20

Derakhshan, Farnaz, and Shamim Yousefi. "A review on the applications of multiagent systems in wireless sensor networks." International Journal of Distributed Sensor Networks 15, no. 5 (2019): 155014771985076. http://dx.doi.org/10.1177/1550147719850767.

Full text

Abstract:

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.

APA, Harvard, Vancouver, ISO, and other styles

21

Frohlich, Antonio Augusto, and Lucas Francisco Wanner. "Operating System Support for Wireless Sensor Networks." Journal of Computer Science 4, no. 4 (2008): 272–81. http://dx.doi.org/10.3844/jcssp.2008.272.281.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Singh, Rajvir, C. Rama Krishna, Rajnish Sharma, and Renu Vig. "Energy efficient fixed-cluster architecture for wireless sensor networks." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 8727–40. http://dx.doi.org/10.3233/jifs-192177.

Full text

Abstract:

Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.

APA, Harvard, Vancouver, ISO, and other styles

23

He, Yongqiang, and Mingming Yang. "Research on cross-layer design and optimization algorithm of network robot 5G multimedia sensor network." International Journal of Advanced Robotic Systems 16, no. 4 (2019): 172988141986701. http://dx.doi.org/10.1177/1729881419867016.

Full text

Abstract:

Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance requirements of the layers in the protocol stack into objective functions and constraints in mathematical optimization problems. In this article, the cross-layer optimization problem of wireless Mesh networks using multi-radio interface multi-channel technology is studied. The optimization problem is modelled based on the network utility maximization method, and the corresponding algorithm is proposed. Based on the random network utility maximization method, the cross-layer optimization model of network robot 5G multimedia sensor network is established. Aiming at the time-varying randomness of random data flow and wireless propagation environment in network robot 5G multimedia sensor network, a model of joint congestion control and power control based on chance constrained programming is proposed, and its genetic algorithm is used to verify it. Reforming research will help speed up the practical pace of the field, with certain theoretical forward-looking and practical value.

APA, Harvard, Vancouver, ISO, and other styles

24

Alawad, Hamad, and Sakdirat Kaewunruen. "Wireless Sensor Networks: Toward Smarter Railway Stations." Infrastructures 3, no. 3 (2018): 24. http://dx.doi.org/10.3390/infrastructures3030024.

Full text

Abstract:

Railway industry plays a critical role in transportation and transit systems attributed to the ever-growing demand for catering to both freight and passengers. However, owing to many challenges faced by railway stations such as harsh environments, traffic flow, safety and security risks, new and adaptive systems employing new technology are recommended. In this review, several wireless sensor networks (WSNs) applications are proposed for use in railway station systems, including advanced WSNs, which will enhance security, safety, and decision-making processes to achieve more cost-effective management in railway stations, as well as the development of integrated systems. The size, efficiency, and cost of WSNs are influential factors that attract the railway industry to adopt these devices. This paper presents a review of WSNs that have been designed for uses in monitoring and securing railway stations. This article will first briefly focus on the presence of different WSN applications in diverse applications. In addition, it is important to note that exploitation of the state-of-the-art tools and techniques such as WSNs to gain an enormous amount of data from a railway station is a new and novel concept requiring the development of artificial intelligence methods, such machine learning, which will be vital for the future of the railway industry.

APA, Harvard, Vancouver, ISO, and other styles

25

Mittal, Nitin, Urvinder Singh, and Balwinder Singh Sohi. "An energy-aware cluster-based stable protocol for wireless sensor networks." Neural Computing and Applications 31, no. 11 (2018): 7269–86. http://dx.doi.org/10.1007/s00521-018-3542-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Albu Salih, Alaa Taima, and Sayeed Amen Hosseini Seno. "An Energy Efficient Data Collection Using Multiple UAVs in Wireless Sensor Network: A Survey Study." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 26, no. 8 (2018): 130–36. http://dx.doi.org/10.29196/jubpas.v26i8.1678.

Full text

Abstract:

Today, with scientific and technological advances in robotics, artificial intelligence, control and computers, land, air, and sea vehicles, they have been considered. Unmanned aerial vehicles (UAVs) have also significantly improved and are very useful for many important applications in the business, urban and military environment. One of the important uses of UAVs in Wireless Sensor Networks (WSNs) is that devices with low energy and may not be able to communicate in large areas. Nevertheless, a UAV can provide a tool for collecting the data of WSN from one device and transmitting it to another device. This article focuses on the field of research on practical applications of UAVs as mobile collectors for wireless sensor networks. First, the investigations of the proposed UAV were studied and compared their weaknesses with each other. Then, the technical challenges of the applications of UAVs in the wireless sensor network were explored.

APA, Harvard, Vancouver, ISO, and other styles

27

Gil, Paulo, Hugo Martins, and Fábio Januário. "Outliers detection methods in wireless sensor networks." Artificial Intelligence Review 52, no. 4 (2018): 2411–36. http://dx.doi.org/10.1007/s10462-018-9618-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Zahedi, Zeynab Molay, Reza Akbari, Mohammad Shokouhifar, Farshad Safaei, and Ali Jalali. "Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks." Expert Systems with Applications 55 (August 2016): 313–28. http://dx.doi.org/10.1016/j.eswa.2016.02.016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

SHEN, Y., and M. WANG. "Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network." Expert Systems with Applications 34, no. 2 (2008): 900–907. http://dx.doi.org/10.1016/j.eswa.2006.10.024.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Rohini, R., Adamu Murtala Zungeru, S. Ravi, and Dasari Narasimha Rao. "Inter Cluster Group Management in Wireless Sensor Networks." Journal of Computer Science 14, no. 4 (2018): 491–98. http://dx.doi.org/10.3844/jcssp.2018.491.498.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Maksimović, Mirjana, Vladimir Vujović, and Vladimir Milošević. "Fuzzy logic and Wireless Sensor Networks – A survey." Journal of Intelligent & Fuzzy Systems 27, no. 2 (2014): 877–90. http://dx.doi.org/10.3233/ifs-131046.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Chandramat, S., U. Anand, T. Ganesh, S. Sriraman, and D. Velmurugan. "Energy Aware Optimal Routing for Wireless Sensor Networks." Journal of Computer Science 3, no. 11 (2007): 836–40. http://dx.doi.org/10.3844/jcssp.2007.836.840.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Y. "Wireless Sensor Networks Fault Identification Using Data Association." Journal of Computer Science 8, no. 9 (2012): 1501–5. http://dx.doi.org/10.3844/jcssp.2012.1501.1505.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Lalwani, Praveen, Sagnik Das, Haider Banka, and Chiranjeev Kumar. "CRHS: clustering and routing in wireless sensor networks using harmony search algorithm." Neural Computing and Applications 30, no. 2 (2016): 639–59. http://dx.doi.org/10.1007/s00521-016-2662-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Ding, Shuxin, Chen Chen, Jie Chen, and Bin Xin. "An Improved Particle Swarm Optimization Deployment for Wireless Sensor Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 2 (2014): 107–12. http://dx.doi.org/10.20965/jaciii.2014.p0107.

Full text

Abstract:

This paper addresses the issues associated with deployment of sensors, which are critical in wireless sensor networks. This paper provides an improved particle swarm optimization (PSO) algorithm by changing the basic form of PSO and introducing disturbance (d-PSO). By comparing with other PSO-based algorithms, simulation results show that the d-PSO algorithm provides a good-coverage solution with a satisfying coverage rate in a short time. This feature is especially useful for the rapid deployment of sensors.

APA, Harvard, Vancouver, ISO, and other styles

36

Shivappa, Nagesha, and Sunilkumar S. Manvi. "ANFIS-Based Resource Mapping for Query Processing in Wireless Multimedia Sensor Networks." Journal of Intelligent Systems 26, no. 3 (2017): 505–22. http://dx.doi.org/10.1515/jisys-2015-0114.

Full text

Abstract:

AbstractWireless multimedia sensor networks (WMSNs) are usually resource constrained, and where the sensor nodes have limited bandwidth, energy, processing power, and memory. Hence, resource mapping is required in a WMSN, which is based on user linguistic quality of service (QoS) requirements and available resources to offer better communication services. This paper proposes an adaptive neuro fuzzy inference system (ANFIS)-based resource mapping for video communications in WMSNs. Each sensor node is equipped with ANFIS, which employs three inputs (user QoS request, available node energy, and available node bandwidth) to predict the quality of the video output in terms of varying number of frames/second with either fixed or varying resolution. The sensor nodes periodically measure the available node energy and also the bandwidth. The spatial query processing in the proposed resource mapping works as follows. (i) The sink node receives the user query for some event. (ii) The sink node sends the query through an intermediate sensor node(s) and cluster head(s) in the path to an event node. A cluster head-based tree routing algorithm is used for routing. (iii) The query passes through ANFIS of intermediate sensor nodes and cluster heads, where each node predicts the quality of the video output. (iv) The event node chooses the minimum quality among all cluster heads and intermediate nodes in the path and transmits the video output. The work is simulated in different network scenarios to test the performance in terms of predicted frames/second and frame format. To the best of our knowledge, the proposed resource mapping is the first work in the area of sensor networks. The trained ANFIS predicts the output video quality in terms of number of frames/second (or H.264 video format) accurately for the given input.

APA, Harvard, Vancouver, ISO, and other styles

37

Agudo, J. Enrique, Juan F. Valenzuela-Valdés, Francisco Luna, Rafael M. Luque-Baena, and Pablo Padilla. "Analysis of beamforming for improving the energy efficiency in wireless sensor networks with metaheuristics." Progress in Artificial Intelligence 5, no. 3 (2016): 199–206. http://dx.doi.org/10.1007/s13748-016-0087-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Ganesan, Deepak, Alberto Cerpa, Wei Ye, Yan Yu, Jerry Zhao, and Deborah Estrin. "Networking issues in wireless sensor networks." Journal of Parallel and Distributed Computing 64, no. 7 (2004): 799–814. http://dx.doi.org/10.1016/j.jpdc.2004.03.016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Khedr, Ahmed M., Walid Osamy, and Dharma P. Agrawal. "Perimeter discovery in wireless sensor networks." Journal of Parallel and Distributed Computing 69, no. 11 (2009): 922–29. http://dx.doi.org/10.1016/j.jpdc.2009.08.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Khalifeh, Ala’, Husam Abid, and Khalid A. Darabkh. "Optimal Cluster Head Positioning Algorithm for Wireless Sensor Networks." Sensors 20, no. 13 (2020): 3719. http://dx.doi.org/10.3390/s20133719.

Full text

Abstract:

Wireless sensor networks (WSNs) are increasingly gaining popularity, especially with the advent of many artificial intelligence (AI) driven applications and expert systems. Such applications require specific relevant sensors’ data to be stored, processed, analyzed, and input to the expert systems. Obviously, sensor nodes (SNs) have limited energy and computation capabilities and are normally deployed remotely over an area of interest (AoI). Therefore, proposing efficient protocols for sensing and sending data is paramount to WSNs operation. Nodes’ clustering is a widely used technique in WSNs, where the sensor nodes are grouped into clusters. Each cluster has a cluster head (CH) that is used to gather captured data of sensor nodes and forward it to a remote sink node for further processing and decision-making. In this paper, an optimization algorithm for adjusting the CH location with respect to the nodes within the cluster is proposed. This algorithm aims at finding the optimal CH location that minimizes the total sum of the nodes’ path-loss incurred within the intra-cluster communication links between the sensor nodes and the CH. Once the optimal CH is identified, the CH moves to the optimal location. This suggestion of CH re-positioning is frequently repeated for new geometric position. Excitingly, the algorithm is extended to consider the inter-cluster communication between CH nodes belonging to different clusters and distributed over a spiral trajectory. These CH nodes form a multi-hop communication link that convey the captured data of the clusters’ nodes to the sink destination node. The performance of the proposed CH positioning algorithm for the single and multi-clusters has been evaluated and compared with other related studies. The results showed the effectiveness of the proposed CH positioning algorithm.

APA, Harvard, Vancouver, ISO, and other styles

41

Ding, Wei, S. S. Iyengar, Rajgopal Kannan, and William Rummler. "Energy equivalence routing in wireless sensor networks." Microprocessors and Microsystems 28, no. 8 (2004): 467–75. http://dx.doi.org/10.1016/j.micpro.2004.05.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Al-Karaki, Jamal N., and Ghada A. Al-Mashaqbeh. "Energy-centric routing in wireless sensor networks." Microprocessors and Microsystems 31, no. 4 (2007): 252–62. http://dx.doi.org/10.1016/j.micpro.2007.02.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Shaikh, Riaz Ahmed, Sungyoung Lee, and Aiiad Albeshri. "Security Completeness Problem in Wireless Sensor Networks." Intelligent Automation & Soft Computing 21, no. 2 (2014): 235–50. http://dx.doi.org/10.1080/10798587.2014.970345.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Gupta, Meeta, and Adwitiya Sinha. "Particle Swarm Optimization-Based Data Aggregation in Wireless Sensor Network." International Journal of Swarm Intelligence Research 12, no. 1 (2021): 1–16. http://dx.doi.org/10.4018/ijsir.2021010101.

Full text

Abstract:

Wireless sensor networks have battery-operated sensor nodes, which need to be conserved to have prolonged network lifetime. The amount of power consumed for routing sensed data from the sensor node to the sink node is large. Thus, in order to optimize the energy usage in sensor network efficient data aggregation techniques are needed. Particle swarm optimization (PSO) is a speculative and evolutionary computing technique based on swarm intelligence for solving optimization problems in sensor network such as nodes deployment, node scheduling, data clustering, and aggregation. The paper proposes a PSO-based sensor network aggregation protocol (PSO-SNAP) with K-means to provide initial centroid. The PSO has been used to find the optimal aggregated value having minimum quantization error. The output of the K-means algorithm is used as an initial centroid in PSO. Apart from K-means, K-medoid and simple average has also been used to provide initial seed to the PSO algorithm and results of all three approaches are compared.

APA, Harvard, Vancouver, ISO, and other styles

45

Kumar, Rajeev, and Dilip Kumar. "Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks." Journal of Sensors 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/5836913.

Full text

Abstract:

Currently, wireless sensor networks (WSNs) are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC) algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO) is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i) selection of optimal number of subregions and further subregion parts, (ii) cluster head selection using ABC algorithm, and (iii) efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS). The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.

APA, Harvard, Vancouver, ISO, and other styles

46

Lei, Jianjun, Haiyang Bi, Ying Xia, Jun Huang, and Haeyoung Bae. "An in-network data cleaning approach for wireless sensor networks." Intelligent Automation & Soft Computing 22, no. 4 (2016): 599–604. http://dx.doi.org/10.1080/10798587.2016.1152769.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Kumar, Hradesh, and Pradeep Kumar Singh. "Comparison and Analysis on Artificial Intelligence Based Data Aggregation Techniques in Wireless Sensor Networks." Procedia Computer Science 132 (2018): 498–506. http://dx.doi.org/10.1016/j.procs.2018.05.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Agrawal, Deepika, Sudhakar Pandey, Punit Gupta, and Mayank Kumar Goyal. "Optimization of cluster heads through harmony search algorithm in wireless sensor networks." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8587–97. http://dx.doi.org/10.3233/jifs-189175.

Full text

Abstract:

Wireless Sensor Networks is a complex network with millions of small-scale sensor nodes, working together to detect certain physical phenomena. Sensor nodes are operated by battery therefore the major concern is energy efficiency. Clustering is an effective technique to decrease the energy depletion in the network. However, choosing the optimum Cluster Heads is an NP-Hard problem. This paper proposes an unequal clustering technique that selects probationary Cluster Heads through fuzzy logic and the optimization of this probationary Cluster Heads is done through Harmony Search Algorithm (HSA). The proposed algorithm exhibits the dynamic capability of fuzzy logic and high search efficiency of HSA that extends the network lifespan. The findings are simulated against traditional clustering protocols and compared. The findings obtained show that the protocol proposed is performing superior in terms of network lifespan prolongation and other metrics.

APA, Harvard, Vancouver, ISO, and other styles

49

Apiletti, Daniele, Elena Baralis, and Tania Cerquitelli. "Energy-saving models for wireless sensor networks." Knowledge and Information Systems 28, no. 3 (2010): 615–44. http://dx.doi.org/10.1007/s10115-010-0328-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

B., Tamilarasi, and UmaRani R. "WNBLI - A Multifarious Liability Examination for Wireless Sensor Networks." Journal of Computer Science 14, no. 6 (2018): 764–76. http://dx.doi.org/10.3844/jcssp.2018.764.776.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!