Implementation of wireless MEMS sensor network for detection of gait events

Bora, Dhruba Jyoti; Kumar, Neelesh; Dutta, Ritushree

doi:10.1049/iet-wss.2018.5049

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…Continuous advancements in MEMS sensor technology provide the opportunity to integrate the low-power WSN with a wireless communication protocol for long-term, continuous, and real-time monitoring [76]. However, for terrestrial applications, the development of MEMS sensor technology, wireless communications, embedded systems, distributed processing, and wireless sensor applications have a major transition in the wireless sensor network for marine environmental monitoring.…”

Section: Role Of Mems Sensor Technology With Wireless Communication I...mentioning

confidence: 99%

Research advancements in ocean environmental monitoring systems using wireless sensor networks: a review

2023

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The ocean environment monitoring system is of great significance to the researchers because the ocean is the storehouse of natural resources. It is critical to comprehend and assess the ocean's environmental conditions. Several studies have been conducted over the last several decades that use sophisticated information and communication techniques to ensure the ocean ecosystem. Wireless sensor networks (WSNs) are a promising technology to monitor the ocean environment, which delivers significant benefits such as enhanced accuracy and real-time observations. The advancements in sensor technology such as micro electromechanical systems (MEMS), integrated systems, distributed processing, wireless communications, and wireless sensor applications have contributed to the development of WSNs. This paper describes the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also includes the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean.

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Section: Role Of Mems Sensor Technology With Wireless Communication I...mentioning

confidence: 99%

Research advancements in ocean environmental monitoring systems using wireless sensor networks: a review

2023

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“…In the research, each sensor node is developed from an arduino based microcontroller and Xbee wireless module based on Zigbee/IEEE 802.15.4 standards. The study found an increase in baud rate also increases throughput and decreases packet delay in line of sight (LOS) condition [17] [18]. Whereas in comparison of the performance of multihop communication networks and direct communication shows that the performance of multi-hop networks has decreased compared to direct transmission in terms of data throughput and packet delay [19]…”

Section: Introductionmentioning

confidence: 97%

Performance Analysis of Data Transmission on a Wireless Sensor Network Using the XBee Pro Series 2B RF Module

Desnanjaya¹,

Hartawan²,

Parwita³

et al. 2020

Indonesian J. Electron. Instrum. Syst.

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Wireless Sensor Network (WSN) is a wireless network technology that is capable of scanning and can be used to monitor environmental conditions, both indoors and outdoors. One of the devices used in data communication at WSN is XBee. XBee is a device in WSN that uses radio frequency as a data transmission path from one device to another. In sending data, Xbee can be affected by the distance and signal strength between devices. In this research, Xbee communication distance analysis is done outdoors and indoors so as to get data in the form of maximum distance vulnerable from Xbee. The results of this study indicate that the Xbee Pro Series 2B that is measured outside the room is capable of sending data up to a distance of 110 meters, while the Xbee Pro Series 2B device that is measured indoors is capable of sending data up to a distance of 20 meters.

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“…Several technologies, like digital electronics and MEMS and communication, 9 are more advanced, which have created the space for the development of different types of sensors with low cost and lightweight nodes for the WSNs. Advancements in the micro‐electro‐mechanical systems and wireless communications have eased the development of low‐power and low‐cost sensor nodes for monitoring the sensing environment with vital parameters like temperature, humidity, etc.…”

Section: Introductionmentioning

confidence: 99%

An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and prediction for wireless sensor networks

Loganathan¹,

Arumugam²,

Chinnababu³

2021

Concurrency and Computation

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Wireless sensor networks are used to track and regulate physical conditions like temperature and the environment's humidity. Wireless sensor networks with their advanced features are adopted for many real‐time applications. The limited capacity batteries usually power the sensor nodes. But the big challenge of limited battery capacity obstructs remote and inaccessible areas where their use is the most favorable. For extending the lifetime of the network, the battery should optimally utilize it for different operations. The requirement toward low complexity and low energy consumption motivate the wireless sensor networks' efficient clustering algorithm. The sensor nodes group into clusters; one sensor node is chosen as a cluster head, and communication to the sink node from the sensor nodes occurs through the cluster head (CH) node. In the proposed method, cluster heads are determined based on the sensor node's weighted metric. The sensor nodes are then self‐adaptive by making correct decisions in real‐time based on the sensed data, but detected information is often inaccurate due to some mechanical, wireless loss, and battery problems. The erroneous or irrelevant data should be overlooked to avoid unnecessary data transmission, which contributes to reducing the network's lifetime. In the neighborhood‐dependent self‐diagnosis fault detection technique, the faulty sensed data are filtered at the sensor node itself. In the data prediction algorithm, the filtered data are predicted at the cluster head. All the factors collectively contribute to enhance the network's lifetime. The lifetime improvement of the proposed approach is almost doubled compared with LEACH one time better than QLEACH and ECH, and 51% better than temporal approach.

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Implementation of wireless MEMS sensor network for detection of gait events

Cited by 12 publications

References 19 publications

Research advancements in ocean environmental monitoring systems using wireless sensor networks: a review

Research advancements in ocean environmental monitoring systems using wireless sensor networks: a review

Performance Analysis of Data Transmission on a Wireless Sensor Network Using the XBee Pro Series 2B RF Module

An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and prediction for wireless sensor networks

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