Energy efficieny in routing protocol and data collection approaches for WSN: A survey

Ali, Abdul; Parmanand,

doi:10.1109/ccaa.2015.7148437

Cited by 18 publications

(6 citation statements)

References 22 publications

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“…With the explosively increasing number of sensors and data, cloud computing is utilised on many traditional sensor networks [21][22][23]. As sensors are heterogeneous and dynamically changing which contribute to numerous different types of data and cost massive time to process, cloud computing cannot well solve them and the sensor networks require a more flexible and compatible architecture [24]. To deal with this issue, many researches are focusing on fog computing, which is localised and extends the distant cloud to the edge of network [25,26].…”

Section: Fog Computingmentioning

confidence: 99%

Software‐defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

Huang

Liu

et al. 2018

IET cyber-phys. syst.

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Section: Fog Computingmentioning

confidence: 99%

Software‐defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

Huang

Liu

et al. 2018

IET cyber-phys. syst.

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“…However, ZigBee is considered an ideal communication standard for WSNs because it is built on the power efficient IEEE 802.15.4 specification. Moreover, it has a multi-hop routing algorithm capable of adapting to link failures while minimizing communication power consumption [9]. The base station collects the sensed data alarm from sensors that evaluate and set the flood warning with the help of predefined data ranges.…”

Section: Flood Warning System Based On Wssnmentioning

confidence: 99%

A Wireless Smart Sensor Network for Flood Management Optimization

Alfarra¹,

Alsibai²

2017

IJETS

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Recent development in the sensing technologies and wireless sensor networks has encouraged many innovative applications in disaster management and forecasting. Flood is one of the most dangerous natural disasters that occurs frequently in south Asia. Therefore, water level monitoring and flood early prediction, play an important role in lives and properties saving. In this paper, an optimized Flood Warning System (FWS) is presented. The system is based on multi parameters Wireless Smart Sensor Network (WSSN) for early flood warning. WSSN performs pre-processing procedures at sensor level before sending the data to a base data analyzing station. This pre-processing step is to improve the reliability, data quality and transmission quality. For the purpose of validation, the proposed method is applied using two parameters: The water level (L) and streamflow of water (R) in rivers. The proposed system provides early flood detection by continuously measuring R and L in real-time. The collected data is to be used to predict flood time and place. Data is exchanged among sensors in real-time. Pre-analyzing is performed and reports are sent to the base station only if the analysis gave a high risk level. The main purpose is to cut down the data size. Analysis and simulation showed that the data size is improved significantly using this method. Calculations considers real cases on a part of Pahang River (Sungai Pahang).

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“…In [ 41 ], five energy-efficient data-gathering protocols for UAV-aided WSNs are discussed and compared qualitatively, mostly focusing on the main ideas and performance evaluation techniques. Focusing on the energy efficiency of WSNs, various data-collection approaches are discussed [ 42 , 43 ]. However, they did not consider mobile robot-based data collection.…”

Section: Introductionmentioning

confidence: 99%

Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study

Nazib

Moh

2021

Sensors

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Owing to automation trends, research on wireless sensor networks (WSNs) has become prevalent. In addition to static sinks, ground and aerial mobile sinks have become popular for data gathering because of the implementation of WSNs in hard-to-reach or infrastructure-less areas. Consequently, several data-gathering mechanisms in WSNs have been investigated, and the sink type plays a major role in energy consumption and other quality of service parameters, such as packet delivery ratio, delay, and throughput. However, the data-gathering schemes based on different sink types in WSNs have not been investigated previously. This paper reviews such data-gathering frameworks based on three different types of sinks (i.e., static, ground mobile, and aerial mobile sinks), analyzing the data-gathering frameworks both qualitatively and quantitatively. First, we examine the frameworks by discussing their working principles, advantages, and limitations, followed by a qualitative comparative study based on their main ideas, optimization criteria, and performance evaluation parameters. Next, we present a simulation-based quantitative comparison of three representative data-gathering schemes, one from each category. Simulation results are shown in terms of energy efficiency, number of dead nodes, number of exchanged control packets, and packet drop ratio. Finally, lessons learned from the investigation and recommendations made are summarized.

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Energy efficieny in routing protocol and data collection approaches for WSN: A survey

Cited by 18 publications

References 22 publications

Software‐defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

Software‐defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

A Wireless Smart Sensor Network for Flood Management Optimization

Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study

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