Conserving energy in WSN through clustering and power control

Sadouq, Zouhair A.; Mabrouk, Marouane El; Essaaidi, Mohamed

doi:10.1109/cist.2014.7016654

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…Most importantly, subject to the nature of each node that abides the IEEE 802.15.4 standard protocol, the computation and the communication capability of each node in WSN is doomed to be limited [7], hence, if the pressure of computation and communication imposed on each node increases even a little bit, the robustness and the reliability of the whole WSN will be dramatically weaken. Thus, coming up with a method that can defend against intrusion without adding pressure on each node is quite necessary.…”

Section: Network Securitymentioning

confidence: 99%

The Enhancement of GSD Algorithm with Data Preprocessing Technique for WSN

Luo

Wang

et al. 2015

Proceedings of the Second Workshop on Mobile Sensing, Computing and Communication

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As the application scope of wireless sensor network (WSN) grows at an amazing rate, the security issue of WSN is becoming increasingly important. Considering a distinct way to address the security problem rather than using the two most common methods which are data encryption and authentication, in this paper, we enhance an off-the-shelf data clustering based method called GSD by focusing on preprocessing RSSI and LQI data of WSN with sliding window and statistic methods for intrusion detection. Our results show that by preprocessing the RSSI and LQI data with the proposed Moving Average Variance method, GSD algorithm performs better in intrusion detection for WSNs.

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Section: Network Securitymentioning

confidence: 99%

The Enhancement of GSD Algorithm with Data Preprocessing Technique for WSN

Luo

Wang

et al. 2015

Proceedings of the Second Workshop on Mobile Sensing, Computing and Communication

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“…The goal of the scheduler is to group a set of nodes into a cluster and select better clusters heads in an adaptive fashion with the help of genetic. WSN is used by many civil organizations in order to monitor the physical activity of end-users and used in domains like health, agriculture, habitat monitoring, routing of traffic, military and security applications [53]. The sensed data is transmitted by sensors to gateways and then towards the controlling station in a multi-hop fashion, thus leading to energy consumption and reduction in lifetime ratio.…”

Section: F Transmission and Time Schedulingmentioning

confidence: 99%

Reckoning Network Lifetime Ratio for Wireless Sensor Network

Saqhib¹,

S²

2019

IJITEE

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Advanced Technologies such as Internet of Things, Machine Networking give rise to the deployment of autonomous Wireless Sensor Nodes. They are used for various domains namely battlefield monitoring, enemy detection and monitoring the environment change. These Wireless Sensor Nodes have the properties of low cost and high battery life. NL (Network Lifetime) is an important phase of Wireless Sensor Network (WSNs), in which the nodes can maintain sensing for a more amount of time. NL can be improved by use of multiple techniques namely Opportunistic Transmission, Scheduling of Timed Data Packets, Clustering of Nodes, Energy Harvesting and Connectivity. This paper provides the energy consumption computation, life time ratio definition and the overview of NL improvement techniques. The paper also presents brief review of the Destination based and Source based routing algorithm

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“…As shown in Figure 2, star topology organizes all the peripheral nodes around the sink also called central hub. The sink is logically (and or physically) at the center of the network via direct link [21]. The sink can be either the base station itself or a gateway node that is in direct communication with the base station.…”

Section: Wireless Sensor Node Energy Consumptionmentioning

confidence: 99%

Autonomous Wireless Sensors Network Based on Piezoelectric Energy Harvesting

Mouapi¹,

Hakem²,

Delisle³

2016

OJAPr

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Wireless sensor networks (WSNs) offer an attractive solution to many environmental, security and process monitoring. However, their lifetime remains very limited by battery capacity. Through the use of piezoelectric energy harvesting techniques, ambient vibration can be captured and converted into usable electricity to create selfpowering WSN which is not limited by finite battery energy. This paper investigates analytically and experimentally the performance of a WSN powered by a Piezoelectric Energy Harvesting System (PEHS) and a material block-level modeling considering most key energy consumption of a wireless sensor node in a star topology network is proposed. By using real hardware parameters of existing components, the proposed model is used to evaluate the energetic budget of the node. The sensor node performance is evaluated regarding transmit packet size, duty cycle and the number of nodes that can be deployed. From the spectral properties of the available vibration inside two moving vehicles (automobile and train), the maximal recoverable power for each type of vehicle is estimated. Using a PEHS based on a cantilever beam optimized for low-frequency applications, 6 mW power is recovered in the case of the train while a 12.5 mW power is reached in the case of the automobile. It is observed that the sink may not operate with the recovered energy. However, the sensor node can sense and transmit data with a maximum size of 105.5 kbits when the duty cycle is 4 × 10 −15. It is also achieved that the node is most effective when the measured physical phenomena vary slowly, such as the variations in temperature due to thermal inertia. Considering an optimized PEHS based on non-linear processing, it is shown that the sink can operate for 190% improvement of the recovered power.

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Conserving energy in WSN through clustering and power control

Cited by 12 publications

References 21 publications

The Enhancement of GSD Algorithm with Data Preprocessing Technique for WSN

The Enhancement of GSD Algorithm with Data Preprocessing Technique for WSN

Reckoning Network Lifetime Ratio for Wireless Sensor Network

Autonomous Wireless Sensors Network Based on Piezoelectric Energy Harvesting

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