Design and implementation of low power consumption wireless sensor node

Djadour, Imededdin; Aidel, Salih; Medkour, Hicham

doi:10.12928/telkomnika.v17i6.12047

Cited by 8 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The protocol requires each node to maintain information about its neighbours and uses geographic forwarding to find the paths. In addition, SPEED [22] strives to ensure a certain speed for each packet in the network so that each application can estimate the end-to-end delay for the packets by dividing the distance to the sink by the speed of the packet before making the admission decision. Moreover, SPEED can provide congestion avoidance when the network is congested.…”

Section: Speedmentioning

confidence: 99%

Approach to minimizing consumption of energy in wireless sensor networks

Noureddine¹,

Benahmed²,

Mohammed³

2020

IJECE

View full text Add to dashboard Cite

The Wireless Sensor Networks (WSN) technology has benefited from a central position in the research space of future emerging networks by its diversity of applications fields and also by its optimization techniques of its various constraints, more essentially, the minimization of nodal energy consumption to increase the global network lifetime. To answer this saving energy problem, several solutions have been proposed at the protocol stack level of the WSN. In this paper, after presenting a state of the art of this technology and its conservation energy techniques at the protocol stack level, we were interested in the network layer to propose a routing solution based on a localization aspect that allows the creation of a virtual grid on the coverage area and introduces it to the two most well-known energy efficiency hierarchical routing protocols, LEACH and PEGASIS. This allowed us to minimize the energy consumption and to select the clusters heads in a deterministic way unlike LEACH which is done in a probabilistic way and also to minimize the latency in PEGASIS, by decomposing its chain into several independent chains. The simulation results, under "MATLABR2015b", have shown the efficiency of our approach in terms of overall residual energy and network lifetime.

show abstract

Section: Speedmentioning

confidence: 99%

Approach to minimizing consumption of energy in wireless sensor networks

Noureddine¹,

Benahmed²,

Mohammed³

2020

IJECE

View full text Add to dashboard Cite

show abstract

“…The association between the PMV-index and the predicted percentage of dissatisfaction (PPD) was determined through steady state experiments as shown in Figure 2. This figure shows an equivalent quantity of complaints on the 'warm' side as on the 'cold' side, the curve is identical about the PMV of (0) [16][17][18][19][20][21][22][23][24][25].…”

Section: Fanger's Thermal Comfort Approachmentioning

confidence: 99%

Optimization of energy consumption and thermal comfort for intelligent building management system using genetic algorithm

Mohammed¹,

Awad²,

Radhi³

2020

IJEECS

View full text Add to dashboard Cite

This paper presents a design, simulation and performance evaluation of an optimized model for the Heating, Ventilation and Air-Conditioning (HVAC) systems using intelligent control algorithm. Fanger’s comfort method and genetic algorithms were used to obtain the optimal and initial values. The heat transmission coefficient between internal and external environments were determined depending on several inputs and factors acquired via supervisory control and data acquisition (SCADA) system sensors. The main feature of the real-time model is the prediction of the internal buildings environment, in order to control HVAC system for indoor environment and to utilize the optimum power consumed depending on optimized air temperature value. The predicted air temperature value and Predictive Mean Vote (PMV) value was applied using intelligent algorithm to obtain an optimal comfort level of the air temperature. The optimized air temperature value can be used in HVAC system controller to ensure that the temperature of indoor can reach a specific value after a known period of time. The use of genetic algorithm (GE) ensures that the used power is well below its peak value and maintains the comfort of the user’s environment.

show abstract

“…All the sensors in the traditional WSN system forward the gathered data into a common static sink, and it can become a problem in the long term [10]. Specifically, with restricted energy supply to network nodes, it causes WSNs to deal with critical functional restrictions [11,12]. Deploying a static sink would shorten the lifetime of the sink's neighbors due to the gradual depletion of energy nodes and cause them to die earlier than other nodes [13,14].…”

Section: Introductionmentioning

confidence: 99%

Lightweight route adjustment strategy for mobile sink wireless sensor networks

Ghazi

Mahdi

Abdulaziz

2021

IJEECS

View full text Add to dashboard Cite

<span>As a result of numerous applications and low installation costs, wireless sensor networks (WSNs) have expanded excessively. The main concern in the WSN environment is to lower energy consumption amidst nodes while preserving an acceptable level of service quality. Using multi-mobile sinks to reduce the nodes' energy consumption have been considered as an efficient strategy. In such networks, the dynamic network topology created by the sinks mobility makes it a challenging task to deliver the data to the sinks. Thus, in order to provide efficient data dissemination, the sensor nodes will have to readjust the routes to the current position of the mobile sinks. The route re-adjustment process could result in a significant maximization in the communication cost, which boosts the total energy depletion. This paper proposes a lightweight routes re-adjustment strategy for mobile sink wireless sensor networks (LRAS-MS) aimed at minimizing communication cost and energy consumption by reducing route re-adjustment in a cluster-based WSN environment. The simulation results show a significant reduction in communication costs and extending the network lifetime while maintaining comparable low data delivery delay. </span>

show abstract

Design and implementation of low power consumption wireless sensor node

Cited by 8 publications

References 23 publications

Approach to minimizing consumption of energy in wireless sensor networks

Approach to minimizing consumption of energy in wireless sensor networks

Optimization of energy consumption and thermal comfort for intelligent building management system using genetic algorithm

Lightweight route adjustment strategy for mobile sink wireless sensor networks

Contact Info

Product

Resources

About