Algorithm for energy consumption minimisation in wireless sensor network

Shah, Indra Kumar; Maity, Tanmoy; Dohare, Yogendra Singh

doi:10.1049/iet-com.2019.0465

Cited by 34 publications

(18 citation statements)

References 39 publications

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“…In addition, we also consider that nodes may optimize their transmit power and adapt their duty-cycles according to groups, depending on the distance with respect to the gateway. Thus, different duty-cycles are spread along the network, which is different from the authors of [ 9 , 10 , 11 , 12 ] who considered the same duty-cycle for every node in the network. In addition, these results can be adapted to any particular MAC layer standard for a system implementation.…”

Section: Introductionmentioning

confidence: 87%

“…Energy efficiency in wireless sensor networks (WSNs) has been widely studied in the literature [6,7]. Notwithstanding, due to the constant growth of IoT networks, this subject has received renewed interest, especially considering the optimization of the sleep period of sensor nodes in a network-wide approach (e.g., [8][9][10][11][12]). For instance, to extend network lifetime without compromising performance, the authors of [8] proposed a cooperative scheme, where the nodes are equipped with only one ominidirectional antenna and the receiving circuitry of each relay is switched ON or OFF according to an ON-OFF probability.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [ 9 ] proposed a distance-based dynamic duty-cycle allocation algorithm in order to regulate the duty-cycle of the child nodes within a cluster. The nodes closest to the cluster head transmit with larger duty-cycles, since they require less transmit power, while the nodes farther away from the cluster head transmit with smaller duty-cycles.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we exploit the fact that wireless sensor nodes usually employ duty-cycles in order to save energy, so that their circuits switch constantly between active and sleeping states. However, unlike the authors of [ 9 , 10 , 11 , 12 ], we do not consider the duty-cycle of a particular MAC layer standard, such as IEEE 802.15.4 [ 10 , 11 ], WirelessHART [ 11 ] or IEEE 802.15.6 [ 12 ]. On the other hand, we consider cooperative and multiple antenna techniques at the sensor nodes, which considerably changes the optimization of the ON–OFF periods depending on the employed transmission scheme.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Energy Efficient Probabilistic Switching ON–OFF Operation in Multiantenna Cooperative Wireless Sensor Networks

Yabcznski

Brante

Souza

et al. 2021

Sensors

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The effective deployment of Internet of Things (IoT) applications such as smart cities, smart farming and smart transport systems must ensure the network robustness, scalability and longevity. Therefore, guaranteeing the successful delivery of information and extending the lifetime of the nodes that make up a wireless sensor network (WSN) are two essential aspects for IoT applications. This work evaluates the performance of a cooperative WSN by adopting two multiantenna schemes: antenna selection (AS) and beamforming transmission using the singular value decomposition (SVD) technique. In addition, cooperation is established according to an ON–OFF probability, so that the RF receiving circuits of the relays are activated in a probabilistic way, aiming at reducing the energy consumption of the sensors, extending their useful lifetime. Our main goal is to increase the amount of information effectively transmitted by the network, keeping an outage probability constraint. The results show that, when both techniques are used, there is a significant gain in the amount of information effectively transmitted by the network, with emphasis on the AS scheme at short transmission distances. By increasing the number of antennas, it was found that a lower ON–OFF probability is required, i.e., a trade-off is established between the nodes’ hardware complexity and their need for cooperation.

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Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Efficient Probabilistic Switching ON–OFF Operation in Multiantenna Cooperative Wireless Sensor Networks

Yabcznski

Brante

Souza

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…It consists of small sensors, control units, transceivers, and batteries. These sensors are deployed over the areas of inaccessibility [1]- [3]. In this type of network, nodes are responsible mainly for routing data to the base station.…”

Section: Introductionmentioning

confidence: 99%

A Modified Anchor Free Localization Technique for Wireless Sensor Network

Shah¹,

Dohare²,

Yadav³

2020

EAI Endorsed Transactions on Cloud Systems

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INTRODUCTION: A novel anchor-free localization technique for a single-hop wireless sensor network is proposed, which is based on internode distances only. The average of the internode distances is mainly used to localize the reference node in the proposed algorithm. Average localization error is used as the main performance metric. The simulation result shows that the proposed algorithm significantly reduces localization error as compared with conventional localization techniques with respect to node density variation and network size. OBJECTIVES: To find the location of the randomly deployed sensor nodes. METHODS: An anchor free localization method is used to find the location of the deployed sensor nodes. RESULTS: Location error is comparatively less in the proposed strategy. CONCLUSION: For single-hop wireless sensor networks using inters node distance, a novel anchor free localization technique is presented

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An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and prediction for wireless sensor networks

Loganathan¹,

Arumugam²,

Chinnababu³

2021

Concurrency and Computation

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Wireless sensor networks are used to track and regulate physical conditions like temperature and the environment's humidity. Wireless sensor networks with their advanced features are adopted for many real‐time applications. The limited capacity batteries usually power the sensor nodes. But the big challenge of limited battery capacity obstructs remote and inaccessible areas where their use is the most favorable. For extending the lifetime of the network, the battery should optimally utilize it for different operations. The requirement toward low complexity and low energy consumption motivate the wireless sensor networks' efficient clustering algorithm. The sensor nodes group into clusters; one sensor node is chosen as a cluster head, and communication to the sink node from the sensor nodes occurs through the cluster head (CH) node. In the proposed method, cluster heads are determined based on the sensor node's weighted metric. The sensor nodes are then self‐adaptive by making correct decisions in real‐time based on the sensed data, but detected information is often inaccurate due to some mechanical, wireless loss, and battery problems. The erroneous or irrelevant data should be overlooked to avoid unnecessary data transmission, which contributes to reducing the network's lifetime. In the neighborhood‐dependent self‐diagnosis fault detection technique, the faulty sensed data are filtered at the sensor node itself. In the data prediction algorithm, the filtered data are predicted at the cluster head. All the factors collectively contribute to enhance the network's lifetime. The lifetime improvement of the proposed approach is almost doubled compared with LEACH one time better than QLEACH and ECH, and 51% better than temporal approach.

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Algorithm for energy consumption minimisation in wireless sensor network

Cited by 34 publications

References 39 publications

Energy Efficient Probabilistic Switching ON–OFF Operation in Multiantenna Cooperative Wireless Sensor Networks

Energy Efficient Probabilistic Switching ON–OFF Operation in Multiantenna Cooperative Wireless Sensor Networks

A Modified Anchor Free Localization Technique for Wireless Sensor Network

An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and prediction for wireless sensor networks

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