Energy consumption in wireless sensor networks for network coding structure and ARQ protocol

Alaoui, Nabih; Cances, Jean-Pierre; Meghdadi, Vahid

doi:10.1109/eitech.2015.7162963

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Energy consumption of the sensor nodes in WSNs is primarily due to three processes: sensing, computation, and communication [22]. Communication by the nodes consumes the most energy as compared to the other processes.…”

Section: Energy Consumption Modelmentioning

confidence: 99%

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Regulating the Packet Transmission Cost of Source Location Privacy Routing Schemes in Event Monitoring Wireless Networks

Mutalemwa

Shin

2019

IEEE Access

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Sensor nodes in wireless sensor networks (WSNs) are usually battery-operated and resourceconstrained. The sensor nodes are often deployed in remote areas where the batteries cannot easily be recharged or replaced. Consequently, power becomes a limited resource in WSNs. Thus, energy consumption of the sensor nodes is among parameters of paramount importance. Subsequently, source location privacy (SLP) routing schemes must be energy-efficient and overall cost-effective. Angle-based routing schemes can cost-effectively protect the SLP. The goal of this study is to propose a new path node offset angle routing algorithm to improve the packet transmission cost of two existing SLP routing schemes. The proposed algorithm considers path node offset angles, arbitrary factors, and contrived regions to compute relatively short but greatly randomized routing paths. The routes offer a reduced number of packet forwarding events in the near-sink region and eventually diminish the packet transmission cost. Performance analysis results verify that the proposed path node offset angle routing algorithm effectively improves the packet transmission cost of the schemes and guarantees strong SLP protection throughout the WSN domain. Furthermore, the routing algorithm is capable of alleviating the energy-hole problem. INDEX TERMSSource location privacy, wireless sensor network, offset angle, safety period, energy consumption, packet transmission cost. LILIAN C. MUTALEMWA received the B.Eng. degree in telecommunications engineering from the

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Section: Energy Consumption Modelmentioning

confidence: 99%

“…3 and in the energy consumption model equations ( 1) and (2), as adopted from [3] and [4]. The model is also assumed in [2], [5], [7], [12], [22]. Transmitting a packet to a greater distance d or a larger packet containing a large number of bits causes higher energy consumption.…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Regulating the Packet Transmission Cost of Source Location Privacy Routing Schemes in Event Monitoring Wireless Networks

Mutalemwa

Shin

2019

IEEE Access

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“…The energy consumption in a WSN is due to sensing energy (sensors), computing energy (data processing) and communication energy (short radio-frequency circuit that performs data transmission and reception) [3]. The WSNs rely on batteries to feed power to the sensor nodes and gateway.…”

Section: Introductionmentioning

confidence: 99%

Predicting Renewable Energy Resources using Machine Learning for Wireless Sensor Networks

Bhatti

Khan

Hussain

et al. 2022

2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

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Wireless Sensor Network (WSN) nodes rely on batteries that are hazardous and need constant replacement. Therefore, we propose WSNs with solar energy harvesters that scavenge energy from the Sun. The key issue with these harvesters is that solar energy is intermittent. Consequently, we propose machine learning (ML) algorithms that enable WSN nodes to accurately predict the amount of solar irradiance, so that the node can intelligently manage its own energy. Our ML models were based on historical weather datasets from California (USA) and Delhi (India) for the period between 2010 to 2020. In addition, we performed data pre-processing, followed by feature engineering, identification of outliers and grid search to determine the most optimized ML model. In comparison with the linear regression model, the support vector regression (SVR) model showed accurate forecasting of solar irradiance. Moreover, it was also found that the models with time duration of 1 year and 1 month has much better forecasting results than 10 years and 1 week, with both root square mean error (RMSE) and mean absolute error (MAE) less than 7% for

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“…A wireless sensor network (WSN) contain thousands of assembled sensors configured into a cooperative network [1,2]. Sensors nodes together monitor physical phenomena, such as temperature, humidity, sound, motion, heat, vibrations, or pressure.…”

Section: Introductionmentioning

confidence: 99%

Multi-path and Multi-hop Energy Efficient Routing in Wireless Sensor Network

Kumbhalkar¹,

Gangele²

2019

IJCA

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Wireless sensor networks have strict constrains in size, weight and energy. The conflict of long lifetime and strict energy constrain brings a natural integration of energy harvesting into wireless sensor networks. A modified low energy adaptive clustering hierarchy (LEACH) protocol has been proposed to increase the network life time. Modified LEACH is divided into two main phases at the first setup phase and at the second steady state phase. Setup phase has been modified such that cluster head (CH) selection will be selected only from advanced nodes. Results showed significant improvement in WSN overall lifetime. Simulations show number of dead nodes vs rounds. Comparison between pure LEACH and modified one showed significant improvement in lifetime, also comparison between modified LEACH and multi-path multi-hop energy efficient routing protocol shows significant improvement in performance.

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Energy consumption in wireless sensor networks for network coding structure and ARQ protocol

Cited by 4 publications

References 5 publications

Regulating the Packet Transmission Cost of Source Location Privacy Routing Schemes in Event Monitoring Wireless Networks

Regulating the Packet Transmission Cost of Source Location Privacy Routing Schemes in Event Monitoring Wireless Networks

Predicting Renewable Energy Resources using Machine Learning for Wireless Sensor Networks

Multi-path and Multi-hop Energy Efficient Routing in Wireless Sensor Network

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