A Kalman Filter Process for Energy Optimization in WSNs

Dbibih, Imane; Zytoune, Ouadoudi; Rziza, Mohammed; Aboutajdine, Driss

doi:10.24138/jcomss.v15i1.561

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Researchers usually consider scheduling nodes by sleep and wake periods as a successful way used in wireless sensor networks to save energy. Iala et al [25] present a solution for reducing the power consumption of WSN thanks to a process that wakes nodes up only if they should transmit or receive data packets. We suggest that to decrease energy dissipate causes and to help nodes to operate properly for long duration of time, medium access strategy must truck network parameters for every superframe to distribute slots equitably.…”

Section: Improvement Of Wsn Mac Protocolsmentioning

confidence: 99%

Elastic hybrid MAC protocol for wireless sensor networks

Bhar

Bouazzi

2021

IJECE

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The future is moving towards offering multiples services based on the same technology. Then, billions of sensors will be needed to satisfy the diversity of these services. Such considerable amount of connected devices must insure efficient data transmission for diverse applications. Wireless sensor network (WSN) represents the most preferred technology for the majority of applications. Researches in medium access control (MAC) mechanism have been of significant impact to the application growth because the MAC layer plays a major role in resource allocation in WSNs. We propose to enhance a MAC protocol of WSN to overcome traffic changes constraints. To achieve focused goal, we use elastic hybrid MAC scheme. The main interest of the developed MAC protocol is to design a medium access scheme that respect different quality of services (QoS) parameters needed by various established traffic. Simulation results show good improvement in measured parameters compared to typical protocol.

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Section: Improvement Of Wsn Mac Protocolsmentioning

confidence: 99%

Elastic hybrid MAC protocol for wireless sensor networks

Bhar

Bouazzi

2021

IJECE

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“…On the other hand, many studies addressed the WSN problems and proposing solutions that aim at reducing the energy dissipation and the end-to-end latency due to idle listening problem. These solutions [23,24] are based on the duty-cycle scheme principle to extend the network lifetime by optimizing the transceiver energy consumption without influencing negatively the latency. The authors of [25] proposed an algorithm called Energy based Collision Avoidance (ECA-MAC) that aims to control the access to the medium according to nodes energy level and to reduce the collision by the use of several Contention Windows.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Algorithm for End-to-End Latency Optimization Over IEEE 802.15.4 Wireless Network for IoT Applications

Dbibih¹

2021

IJIES

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The continuous evolution of new wireless technologies has given birth to new application areas for the Internet of Things (IoT) such as Smart Cities, Smart Home, Intelligent Transport Systems, Wireless Sensor Networks, etc. IoT is a network of networks that enables, via small wireless electronic devices called "sensor", to identify and communicate with physical objects. The main IoT objective is to measure and exchange data between physical and virtual worlds. However, the use of these sensors in applications characterized by the coexistence of different traffic loads in the same network could lead to power consumption and latency problems. In this article, we present a new efficient algorithm called (BMPriority-based CSMA/CA) based on message priority and battery energy level of sensor to manage access to the transmission channel. More precisely, our algorithm uses a weighting function to calculate the contention window during which a sensor node must wait before starting its data transmission. Simulation results show that our proposed protocol outperforms S-MAC, IEEE 802.15.4, and ECA-MAC protocols in terms of optimizing Packet Delivery Ratio (PDR), end-to-end latency, and throughput.

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“…In [16] Kalman filter process for optimization of energy consumption in Wireless Sensor Networks (WSNs) was proposed to minimize the consumption of energy thereby extend network lifespan. The results obtained showed that the technique gave better performance with high energy efficiency without any negative effect on the latency.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Power Consumption Utilization in a Cloud Computing Data Centre using Kalman Filter parameters with Genetic Algorithm

Afolabi

Adebisi

Adoghe

2023

IJEEI

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Data Centre (DC) has become a critical computing infrastructure that is essential to modern society by providing services such as cloud computing, Internet of Things (IoT) and big data. However, the cost of maintaining DC continues to rise as the demand for information technology services increase and this situation is further exacerbated in a country like Nigeria where there is highly unstable power supply from the national grid. The optimization of energy consumption in cloud computing DC using Genetic Algorithm (GA) to minimize the consumption of energy thereby extending network lifespan was one of the techniques used for optimization of power consumption. But the optimization was carried out with the assumption that all the parts of the modular server that are not carrying traffic is on idle mode and not completely off which consumes extra power compare to when it is completely off. Therefore, this work proposed optimization of power consumption utilization in a cloud computing DC using Kalman Filter (KF) with GA. Historical consumption trend and network traffic is analyzed to reduce the amount spent on power with assumption that servers in the DC operate as modular units which can be powered separately as required, in contrast to keeping entire servers always powered. Data from five different servers were collected from MTN Abuja DC in Nigeria. The servers were named BSC 13, BSC 14, BSC 15, RNC 05 and RNC 06. These consist of data recorded for two year -5th January to 30th December 2019 as well as 5th January to 31st December 2020. The GA optimizer is used to obtain the best possible values for the Kalman Filter (KF) parameters. Then, the KF model is used to predict the future power consumption value on hourly basis for each day of the week. The proposed model gives low power consumption with accurate prediction when compared with the existing models.

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A Kalman Filter Process for Energy Optimization in WSNs

Cited by 7 publications

References 26 publications

Elastic hybrid MAC protocol for wireless sensor networks

Elastic hybrid MAC protocol for wireless sensor networks

An Efficient Algorithm for End-to-End Latency Optimization Over IEEE 802.15.4 Wireless Network for IoT Applications

Prediction of Power Consumption Utilization in a Cloud Computing Data Centre using Kalman Filter parameters with Genetic Algorithm

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