Reducing the Energy Budget in WSN Using Time Series Models

Engmann, Felicia; Katsriku, Ferdinand Apietu; Abdulai, Jamal-Deen; Adu-Manu, Kofi Sarpong

doi:10.1155/2020/8893064

Cited by 15 publications

(9 citation statements)

References 39 publications

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“…Data acquisition and reduction approaches have been used individually or simultaneously to mitigate energy consumption. The acquisition schemes measure data samples that are correlated spatially or temporally [35]. Temporally because subsequent data samples may not differ much from each other while data from neighboring sensor nodes may not differ much in spatial-correlation.…”

Section: Mobility Modelsmentioning

confidence: 99%

Applications of Prediction Approaches in Wireless Sensor Networks

Engmann¹,

Adu-Manu²,

Abdulai³

et al. 2021

Wireless Sensor Networks - Design, Deployment and Applications

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Wireless Sensor Networks (WSNs) collect data and continuously monitor ambient data such as temperature, humidity and light. The continuous data transmission of energy constrained sensor nodes is a challenge to the lifetime and performance of WSNs. The type of deployment environment is also and the network topology also contributes to the depletion of nodes which threatens the lifetime and the also the performance of the network. To overcome these challenges, a number of approaches have been proposed and implemented. Of these approaches are routing, clustering, prediction, and duty cycling. Prediction approaches may be used to schedule the sleep periods of nodes to improve the lifetime. The chapter discusses WSN deployment environment, energy conservation techniques, mobility in WSN, prediction approaches and their applications in scheduling the sleep/wake-up periods of sensor nodes.

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Section: Mobility Modelsmentioning

confidence: 99%

Applications of Prediction Approaches in Wireless Sensor Networks

Engmann¹,

Adu-Manu²,

Abdulai³

et al. 2021

Wireless Sensor Networks - Design, Deployment and Applications

Self Cite

View full text Add to dashboard Cite

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“…The network flow is a specific flow solution that is closely related to linear programming [20]. This paper presents a new idea that edges have been defined as Voronoi edges and the flow as the moving target passed by this edge.…”

Section: Parameter Calculationmentioning

confidence: 99%

Multiagent Minimum Risk Path Intrusion Strategy with Computational Geometry

Sun

Yan

2021

Wireless Communications and Mobile Computing

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In wireless sensor networks (WSNs), inefficient coverage does affect the quality of service (QoS), which the minimum exposure path (MEP) is traditionally used to handle. But intelligent mobile devices are generally of limited computation capability, local storage, and energy. Present methods cannot meet the demand of multiple target intrusion, lacking the consideration of energy consumption. Based on the Voronoi diagram in computational geometry, this paper proposed an invasion strategy of minimum risk path (MRP) to such a question. MRP is the path considered both the exposure of the moving target and energy consumption. Federated learning is introduced to figure out how to find the MRP, expressed as C t i , t j = f E , e . The value of C t i , t j can measure the success of an invasion. At the time when a single smart mobile device invades, horizontal federated learning is taken to partition the path feature, and a single target feature federated (SPF) algorithm is for calculating the MRP. Moreover, for multi smart mobile device invasion, it has imported the time variable. Vertical federated learning can partition the feature of multipath data, and the multi-target feature federated (MFF) algorithm is for solving the multipath MRP dynamically. The experimental results show that the SPF and MFF have the dominant advantage over traditional computational performance and time. It primarily applies the complex conditions of a massive amount of sensor nodes.

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“…Water parameters that are monitored could produce either linear or nonlinear data. Linear data could be predicted using time series models, while nonlinear data could be predicted using neural networks or support vector machines [40]. Water quality data are usually a combination of both linear and nonlinear.…”

Section: Future Directionsmentioning

confidence: 99%

Smart River Monitoring Using Wireless Sensor Networks

Adu-Manu

Katsriku

Abdulai

et al. 2020

Wireless Communications and Mobile Computing

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Water quality monitoring (WQM) systems seek to ensure high data precision, data accuracy, timely reporting, easy accessibility of data, and completeness. The conventional monitoring systems are inadequate when used to detect contaminants/pollutants in real time and cannot meet the stringent requirements of high precision for WQM systems. In this work, we employed the different types of wireless sensor nodes to monitor the water quality in real time. Our approach used an energy-efficient data transmission schedule and harvested energy using solar panels to prolong the node lifetime. The study took place at the Weija intake in the Greater Accra Region of Ghana. The Weija dam intake serves as a significant water source to the Weija treatment plant which supplies treated water to the people of Greater Accra and parts of Central regions of Ghana. Smart water sensors and smart water ion sensor devices from Libelium were deployed at the intake to measure physical and chemical parameters. The sensed data obtained at the central repository revealed a pH value of 7. Conductivity levels rose from 196 S/cm to 225 S/cm. Calcium levels rose to about 3.5 mg/L and dropped to about 0.16 mg/L. The temperature of the river was mainly around 35°C to 36°C. We observed fluoride levels between 1.24 mg/L and 1.9 mg/L. The oxygen content rose from the negative DO to reach 8 mg/L. These results showed a significant effect on plant and aquatic life.

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Reducing the Energy Budget in WSN Using Time Series Models

Cited by 15 publications

References 39 publications

Applications of Prediction Approaches in Wireless Sensor Networks

Applications of Prediction Approaches in Wireless Sensor Networks

Multiagent Minimum Risk Path Intrusion Strategy with Computational Geometry

Smart River Monitoring Using Wireless Sensor Networks

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