Energy Consumption Of Sensor Devices Research Articles

Analysis of mobility support approaches for edge-based IoT systems using high data rate Bluetooth Low Energy 5

In remote monitoring edge-based IoT applications, high latency caused by the mobility of a sensor device can cause serious consequences such as inaccurate analysis and low quality of services. Therefore, it is required to have mobility support approaches that help reduce latency while maintaining a connection, high quality of service, and energy efficiency. However, the number of mobility support approaches for high data rate IoT applications using Bluetooth Low Energy (BLE) is limited and they have some disadvantages. For example. they have not been designed for edge-based applications where local computation occurs frequently. Many of them have not been implemented and tested in daily working environments with actual mobility cases. They have not comprehensively analyzed the mobility latency and energy consumption of sensor devices. Hence, this paper presents three possible mobility support approaches including passive and active handover mechanisms for edge-based IoT applications using high data rate BLE5. These approaches based on passive and active handover mechanisms are implemented and tested in an office environment. The results of latency and power consumption of a sensor device via many experiments are measured and analyzed. The results show that the presented mobility support approaches maintain the connection during mobility with a latency of around 900ms for many cases. The results also show that using BLE5’s LE 2M physical layer consumes less power than using LE 1M physical layer. Specifically, it can reduce energy consumption when sending or receiving larger data sizes at faster rates.

Independent Neighbour Set based Clustering Algorithm for Routing in Wireless Sensor Networks

Efficient energy consumption of sensor devices is a prominent problem in battery constrained, unattended wireless sensor networks (WSNs) and it directly influence on WSN lifetime. Designing energy-aware routing protocol can significantly reduce energy consumption in WSNs. Grouping of sensor nodes into clusters is a proficient routing technique for reducing the energy depletion of the nodes in the WSN. In this work, a novel multi-branch tree based clustering approach is proposed to extend the lifetime of the WSNs. A number of branches and selection of cluster heads are performed based on the independent neighbour set properties of nodes. For this, a maximum of five branches constructed by the algorithm and these branches are grown to different direction using the nature of INS. In this approach, inter-cluster communication distance is drastically minimized and evenly distributed the energy consumption of nodes over the network. The performance of the proposed algorithm is compared with the traditional existing methods in terms of cluster structure, nodes energy distribution and network lifetime through the extensive simulations.

Energy Consumption and Efficiency Issues in Human Activity Monitoring System

As people get interested in health issues, different types of human activity trackers or monitoring systems are emerging these days. Therefore, many researchers have been interested in this issue and have proposed various monitoring systems based on accelerometer sensors. However, few systems focus on energy consumption of sensor devices. In this paper, we focus on an application-level solution for saving energy consumption of a human daily activity monitoring system using a wireless wearable sensor. We propose an on-board data processing mechanism for monitoring daily activity in humans. This technique focuses on reducing the size of processed data and transmission rate to save the energy of the sensors. In addition, we develop an activity classification algorithm based on both an inclination angle and a standard deviation value. The proposed system is capable of monitoring most daily activities of the human body: standing, sitting, walking, lying, running, and so on. Furthermore, one of our key contributions is that all functionalities including data processing, activity classification, wireless communication, and storing classified activities were achieved in a single sensor node without compromising the accuracy of activity classification. Our experimental results show that the accuracy of our classification system is over 95 %.

Energy Efficient MAC Scheme for Wireless Sensor Networks with High-Dimensional Data Aggregate

This paper presents a novel and sustainable medium access control (MAC) scheme for wireless sensor network (WSN) systems that process high-dimensional aggregated data. Based on a preamble signal and buffer threshold analysis, it maximizes the energy efficiency of the wireless sensor devices which have limited energy resources. The proposed group management MAC (GM-MAC) approach not only sets the buffer threshold value of a sensor device to be reciprocal to the preamble signal but also sets a transmittable group value to each sensor device by using the preamble signal of the sink node. The primary difference between the previous and the proposed approach is that existing state-of-the-art schemes use duty cycle and sleep mode to save energy consumption of individual sensor devices, whereas the proposed scheme employs the group management MAC scheme for sensor devices to maximize the overall energy efficiency of the whole WSN systems by minimizing the energy consumption of sensor devices located near the sink node. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of active time of sensor devices, transmission delay, control overhead, and energy consumption. Therefore, the proposed scheme is suitable for sensor devices in a variety of wireless sensor networking environments with high-dimensional data aggregate.