Power Consumption Prediction Method for Train-Health Monitoring Wireless Sensor Networks

2018 ◽  
Vol 101 (6) ◽  
pp. 24-32 ◽  
Author(s):  
Tomoki Kawamura ◽  
Satoko Ryuo ◽  
Nagateru Iwasawa
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Health Monitoring ◽  
Prediction Method ◽  
Wireless Sensor ◽  
Power Consumption Prediction ◽  
Consumption Prediction

scholarly journals Self-Organization of Activity in Wireless Sensor Networks

2005 ◽  
Vol 1 (2) ◽  
pp. 245-252 ◽  
Author(s):  
P. Davis ◽  
A. Hasegawa ◽  
N. Kadowaki ◽  
S. Obana
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Wireless Sensors ◽  
Ad Hoc ◽  
Wireless Sensor ◽  
Self Organization ◽  
Sensor Systems ◽  
Low Resolution ◽  
Sensing Data

We propose a method for managing the spontaneous organization of sensor activity in ad hoc wireless sensor systems. The wireless sensors exchange messages to coordinate responses to requests for sensing data, and to control the fraction of sensors which are active. This method can be used to manage a variety of sensor activities. In particular, it can be used for reducing the power consumption by battery operated devices when only low resolution sensing is required, thus increasing their operation lifetimes.

scholarly journals Energy-Efficient Method for Wireless Sensor Networks Low-Power Radio Operation in Internet of Things

2020 ◽  
Author(s):  
Mehdi Amiri Nasab ◽  
Shahaboddin Shamshirband ◽  
Anthony Theodore Chronopoulos ◽  
Amir Mosavi ◽  
Narjes Nabipur
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Power Consumption ◽  
Low Power ◽  
Time Factors ◽  
Wireless Sensor ◽  
Common Source ◽  
Factors Affecting ◽  
Duty Cycles

The radio operation in wireless sensor networks (WSN) in the Internet of Things (IoT) applications are the most common source for power consumption. However, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. ContikiMAC is a low-power Radio Duty-Cycle protocol in Contiki OS used in WakeUp mode, which is a clear channel assessment (CCA) to check radio status periodically. The time spent to check the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in Radio Duty-Cycles and ContikiMAC. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Then, we propose lightweight CCA (LW-CCA) as an extension to ContikiMAC to reduce the percentage of Radio Duty-Cycles in false WakeUps and idle listenings by using dynamic received signal strength indicators (RSSI) status check time. The simulation results in the Cooja simulator show that LW-CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).

PECA

2014 ◽  
pp. 239-248
Author(s):  
Maytham Safar ◽  
Hasan Al-Hamadi ◽  
Dariush Ebrahimi
Keyword(s):  
Wireless Sensor Networks ◽  
Fault Tolerance ◽  
Sensor Networks ◽  
Power Consumption ◽  
Low Power ◽  
The Self ◽  
Wireless Sensor ◽  
Human Intervention ◽  
Electronic Circuits ◽  
Power Efficient

Wireless sensor networks (WSN) have emerged in many applications as a platform to collect data and monitor a specified area with minimal human intervention. The initial deployment of WSN sensors forms a network that consists of randomly distributed devices/nodes in a known space. Advancements have been made in low-power micro-electronic circuits, which have allowed WSN to be a feasible platform for many applications. However, there are two major concerns that govern the efficiency, availability, and functionality of the network—power consumption and fault tolerance. This paper introduces a new algorithm called Power Efficient Cluster Algorithm (PECA). The proposed algorithm reduces the power consumption required to setup the network. This is accomplished by effectively reducing the total number of radio transmission required in the network setup (deployment) phase. As a fault tolerance approach, the algorithm stores information about each node for easier recovery of the network should any node fail. The proposed algorithm is compared with the Self Organizing Sensor (SOS) algorithm; results show that PECA consumes significantly less power than SOS.

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