Design and implementation of low power consumption wireless sensor node

This work proposes an implementation of wireless sensor node characterized by its very low power consumption. The node comprises three main components: Xbee module, low power PIC microcontroller and digital sensor. The node can be set to sense then transmit data via one of two transmission methods: periodic and by interruptions. To evaluate the power consumption; currents in the node is measured during the different transmission stages for both methods. As a result, a significant reduction in the power consumption is shown particularly in sleep mode compared to conventional transmission methods. The characteristic of low power consumption makes the proposed node practically ecologic. It can also be fed with the extrem low power supplied by an energy harvesting system

A Survey of Energy Harvesting Sources for IoT Device

Environmental Energy is an alternative energy for wireless devices. A Survey of Energy Harvesting Sources for IoT Device is proposed. This paper identifies the sources of energy harvesting, methods and power density of each technique. Many reassert have carried to extract energy from environment. The IoT and M2M are connected through internet or local area network and these devices come with batteries. The maintenance and charging of batteries becomes tedious due to thousands of device are connected. The concept of Energy harvesting gives the solution for powering IoT, M2M, Wireless nodes etc. The process of extracting energy from the surrounding environment is termed as energy harvesting and derived from windmill and water wheel, thermal, mechanical, solar

Self-Powered 6LoWPAN Sensor Node for Green IoT Edge Devices

Copyright © 2020 The Authors. In this paper, a simulation model and practical testbed for green Internet of Things (IoT) edge devices are proposed based on solar harvester with constant voltage-maximum power point tracking (CV-MPPT) technique. Billions of connected edge devices represent the essential part of the IoT through the IP-enabled sensor networks based on IPv6 over Low power Wireless Personal Area Network (6LoWPAN). In traditional IoT edge devices, the stored energy in the non-rechargeable battery determines the node lifetime while it is being depleted with time. Therefore, purchasing billions of such batteries is costly and must be disposed of efficiently. This paper is aimed at simulating and implementing a new class of green IoT edge devices that can report data wirelessly and powered perpetually using clean energy. The developed edge device utilizes solar energy harvesting mechanism through photovoltaic (PV) module, this approach will avoid periodical battery replacement and hence, the energy supplied to the sensor mode is not limited anymore. The implemented testbed is based on open-source hardware and software platforms while the simulation environment is based on MATLAB/SIMULINK 2019a. The effects of temperature and solar irradiance on the performance of the developed approach are examined in order to confirm the leverage of the proposed methodology scheme. The lifetime of the developed green IoT device is predicted based on the device's activities, current consumption, and energy storage capacity. The obtained results showed that the battery lifetime is extended by 38-49% when the edge device runs on an independent power source

Intelligent Power Aware Algorithms for Traffic Sensors

The Internet of Things (IoT) is reshaping our world. Soon our world will be based on smart technologies. According to IHS Markit forecasts, the number of connected devices will grow from 15.4 billion in 2015 to 30.7 billion in 2020. Forrester Research predicts that fleet management and the transportation sectors lead others in IoT growth. This may come as no surprise, since the infrastructure (roadways, bridges, airports, etc.) is a prime candidate for sensor integration, providing real-time measurements to support intelligent decisions. The energy cost required to support the anticipated enormous number of predicted deployed devices is unknown. Currently, experts estimate that 2 to 4% of worldwide carbon emissions can be attributed to power consumption in the information and communication industry [1]. This thesis presents several algorithms to optimize power consumption of an intelligent vehicle counter and classifier sensor (iVCCS) based on an event-driven methodology wherein a control block orchestrates the work of various components and subsystems. Data buffering and triggered vehicle detection techniques were developed to reduce duty cycle of corresponding components (e.g., microSD card, magnetometer, and processor execution). A sleep mode is also incorporated and activated by an artificial intelligence-enabled, reinforcement learning algorithm that utilizes the field environment to select proper processor mode (e.g., run or sleep) relative to traffic flow conditions. Sensor life was extended from 48 hours to more than 200 days when leveraging 2300 mAh battery along with algorithms and techniques introduced in this thesis