Wireless Sensor Network: A Survey

Srivastava, Saumya; Singh, Manpreet; Gupta, Satyam

doi:10.1109/icace.2018.8687059

Cited by 36 publications

(14 citation statements)

References 16 publications

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“…The minimum required for GM(1,1) is 4 which reduces the energy spent on transmission. A sensitivity analysis performed with GM(1,1) of varying lengths of data beginning at 4 to 10 training data set showed better performance of GM (1,1). Two approaches were used to improve the performance of GM(1,1) to provide near accurate predictions for shorter periods in a continuous stream of data.…”

Section: Discussionmentioning

confidence: 99%

“…Wireless sensor network (WSN) is the backbone of ubiquitous computing applications such as military surveillance, disaster recoveries, environmental and structural monitoring, health and security monitoring and control, wildlife monitoring and precision agriculture, and habitat monitoring. Sensor nodes which are the basic components in a WSN may sense the environment, aggregate and compute the data collected, and usually transmit the data from source nodes to a sink node called a base station [1].…”

Section: Introductionmentioning

confidence: 99%

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Reducing the Energy Budget in WSN Using Time Series Models

Engmann

Katsriku

Abdulai

et al. 2020

Wireless Communications and Mobile Computing

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Energy conservation is critical in the design of wireless sensor networks since it determines its lifetime. Reducing the frequency of transmission is one way of reducing the cost, but it must not tamper with the reliability of the data received at the sink. In this paper, duty cycling and data-driven approaches have been used together to influence the prediction approach used in reducing data transmission. While duty cycling ensures nodes that are inactive for longer periods to save energy, the data-driven approach ensures features of the data that are used in predicting the data that the network needs during such inactive periods. Using the grey series model, a modified rolling GM(1,1) is proposed to improve the prediction accuracy of the model. Simulations suggest a 150% energy savings while not compromising on the reliability of the data received.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reducing the Energy Budget in WSN Using Time Series Models

Engmann

Katsriku

Abdulai

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Wireless sensor networks have limited resources (Srivastava et al, 2018, Oct. 3-4) and therefore need to be designed efficiently to facilitate their deployment and configuration. Furthermore, due to a large number of sensor nodes, the self-configuration of the network is a technological challenge (Kobo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Autoconfiguration with Global Addresses Using IEEE 802.15.4 Standard in Multi-hop Networks

Acosta

Espinosa³

2021

Enfoque UTE

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Wireless sensor networks continue to attract a lot of attention from academia and industry promoting large-scale deployments in applications related to the Internet of Things (IoT). Unfortunately, a network containing a large number of sensor nodes also leads to difficulty in the configuring process and assignment of identifiers to nodes. Various approaches have been proposed to solve problems of auto-configuration in Wireless sensor networks, however, still, there are some issues remaining related to automatic assign identifiers A cluster-based hierarchical global address allocation scheme is proposed for a wireless sensor network. The proposal uses the IEEE 802.15.4 protocol and aims to reduce the latency of the identifier assignments and reduce the network level processes to be performed at the node. The address allocation process assigns each node a unique global address, which allows the node to have end-to-end connectivity without network-level involvement. The scenario of adding new nodes to the network or nodes that leave it is contemplated. Finally, the proposed scheme is evaluated experimentally, verifying the correct operation of the algorithm proposed in the implemented prototype.

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“…To meet this requirement, indoor localization systems based on wireless sensor networks (WSNs) have been developed [1][2][3]. The WSNs are a kind of self-organizing network composed of many nodes with wireless transceivers, microprocessors, and sensors [4][5][6]. The received signal strength indication (RSSI) is the easiest to measure by a wireless transceiver, so it is often used to estimate distances and locations between nodes [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

Fang

Chen

2020

Sensors

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The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks based on received signal strength has been extensively used in indoor positioning. However, wireless signals have serious multipath effects in indoor environments. In order to reduce the adverse influence of multipath effects on distance estimation between nodes, a multi-channel ranging localization algorithm based on signal diversity is herein proposed. In real indoor environments, the parameters used for multi-channel localization algorithms are generally unknown or time-varying. In order to increase the positioning accuracy of the multi-channel location algorithm in a multipath environment, we propose an optimal multi-channel trilateration positioning algorithm (OMCT) by establishing a novel multi-objective evolutionary model. The presented algorithm utilizes a three-edge constraint to prevent the traditional multi-channel localization algorithm falling into local optima. The results of a large number of practical experiments and numerical simulations show that no matter how the channel number and multipath number change, the positioning error of our presented algorithm is always smaller compared with that of the state-of-the-art algorithm.

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Wireless Sensor Network: A Survey

Cited by 36 publications

References 16 publications

Reducing the Energy Budget in WSN Using Time Series Models

Reducing the Energy Budget in WSN Using Time Series Models

Autoconfiguration with Global Addresses Using IEEE 802.15.4 Standard in Multi-hop Networks

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

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