Energy Savings in Applications for Wireless Sensor Networks Time Critical Requirements

Hung, Tran Cong; Trung, Ho Huu

doi:10.5121/ijcnc.2016.8403

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, for IoT WSN which is a mobile application, a high computational power consumption means a drawback for the IoT-WSN node. Researchers have realized that this mobile IoT suffers from small and limited energy [236]. This small energy capacity has limited IoT-WSN node capability to operate on mobile and for a long time [237].…”

Section: B Computational Intelligence With Optimizing Low Power Consu...mentioning

confidence: 99%

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Hakim

Septiyana

Suwarno

2022

Journal of Robotics and Control

View full text Add to dashboard Cite

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

show abstract

Section: B Computational Intelligence With Optimizing Low Power Consu...mentioning

confidence: 99%

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Hakim

Septiyana

Suwarno

2022

Journal of Robotics and Control

View full text Add to dashboard Cite

show abstract

“…The conditions were chosen to make the result accurate, comparable, and repeatable. The calculation of the energy consumption is based on LEACH algorithms and was calculated using the implementation of [38] with reference to [39]. Equations ( 6) to ( 9) represent the energy consumption, where E is the energy, Si is the sensor number, p is the packet size, d is the distance, Etx is the energy consumed for sending packets, Emp is the amplification coefficient of energy transfer, Efs is energy loss in space, Erx is the energy consumed for receiving a packet, Eda is data aggregation energy, and do is the distance to the base where Emp or Efs depends on do, For sending a packet,…”

Section: A Simulation Setupmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Al-Naser

Al-Junaid

Almesaeed

2022

View full text Add to dashboard Cite

“…However even though WSN has many advantages, it also has a disadvantage. A lot of WSN node can only store a small energy [4]. This happen because WSN node store power and using it to power up WSN node operation from battery [5].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking In Microcontroller Development Board Power Consumption For Low Power Iot Wsn Application

Hakim

Hajar²,

Firdausi³

et al. 2022

JTE

View full text Add to dashboard Cite

One of the advantages of Wireless Sensor Network would be its ability to reduce cost of communication system using node to node communication. However Wireless Sensor Network also had a disadvantage which is has limited energy which is include this as low power application. This small energy capacity has limit WSN node capability to operate for a long time. In this paper, we compare power consumption for 3 popular microcontroller development platforms that use for fast development and prototyping Wireless Sensor Network node. The power consumption was including active mode (using most energy) and deep sleep mode (using least energy) operation. From benchmarking we can see that lolin ESP32 as a microcontroller development platform has the most efficient in power consumption which is only 40 mA in active and 0.05 in deep sleep mode, compare with arduino pro mini 8 mA in active and 0.3 mA in deep sleep mode, and wemos D1 mini 74 mA in active and 0.13 mA in deep sleep mode. This low power consumption in deep sleep mode has resulting in longer operational time which is almost 48 Month for lolin ESP32

show abstract

Energy Savings in Applications for Wireless Sensor Networks Time Critical Requirements

Abstract: ABSTRACT

Cited by 3 publications

References 10 publications

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

International Journal of Electronics and Telecommunications

Benchmarking In Microcontroller Development Board Power Consumption For Low Power Iot Wsn Application

Contact Info

Product

Resources

About