An Energy Efficient Method for Secure and Reliable Data Transmission in Wireless Body Area Networks Using RelAODV

Raja, Kanaga Suba; Kiruthika, Usha

doi:10.1007/s11277-015-2577-x

Cited by 38 publications

(30 citation statements)

References 17 publications

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“…Usage of Raspberry Pi kits inside the classroom setup is still in the testing stage, and the same has been discussed in my previous article [15]. It can be summarized as follows: Smart conference rooms using bluetooth and Kinetics [13], Bio sensors with database configured to Raspberry Pi along with Moodle on Wi-Fi to record attendances [14], smart projectors that upload / downloads PPTs without any laptops in the classes, and store them in separate servers logging into their unique credentials [13], Navigate through PPTs using gestures [16], Google voice assistant and Raspberry Pi to develop effective classroom [17], Lecture projection systems along with NETIO application, controlled via Wired or Wireless connections [18], or mobile applications [19], to control and sharing of displays with remote desktops [20], to handle android phones for an intranet infrastructures [21], Using Virtual Network Computing protocols to promote access in projections [22], Real-time attendance tracking systems and Recording systems with RFID, Knowledge Base System (KBS) and Twilio cloud platform [23], Smart dustbins can be used inside college classrooms [24], ELAB an IoT platform and Xively to promote smart learning environment [25], Paradigm called "Magic Mirror: to function as an interactive display to give updates or information through voice commands [26], Smart systems to secure hostels though RFID and MATLAB using Face and thumb recognition [27], To conduct online exam assessments via Wi-Fi and PHP [28], voice based controls can be used inside the college or classrooms so as to create effective classroom or college management [29], security system with PIR sensor and night vision modules to explore visual captures [30], To check the classroom temperature occupancy etc [31], To automate blood bank services for a college through direct link between donor and recipient via GSM, Raspberry Pi and SMS modules at emergency situations [32], Digital Notice boards with Mobile alerts through Wi-Fi to send notices using texts [33], To monitor environment inside the college environment in terms of temperature, air, moisture levels etc with ThingSpeak on Zigbee communications and send those results via an app [34], Garbage monitoring with Zigbee and GSM on GUI platform [35], To manage electrical appliances with Flask, SQLite3 insi...…”

Section: Smart Classrooms Using Raspberry Pimentioning

confidence: 99%

Enabling Intelligence through Deep Learning using IoT in a Classroom Environment based on a multimodal approach

M¹

2021

TURCOMAT

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Smart Classrooms are becoming very popular nowadays. The boom of recent technologies such as the Internet of Things, thanks to those technologies that are tremendously equipping every corner of a diverse set of fields. Every educational institution has set some benchmark on adopting these technologies in their daily lives. But due to some constraints and setbacks, these IoT technological embodiments in the educational sector is still in the premature stage. The major success of any technological evolution is based on its full-fledged implementation to fit the society in the broader concern. The breakthrough in recent years by Deep Learning principles as it outperforms traditional machine learning models to solve any tasks especially, Computer Vision and Natural language processing problems. A fusion of Computer Vision and Natural Language Processing as a new astonishing field that have shown its existence in the recent years. Using such mixtures with the IoT platforms is a challenging task and and has not reached the eyes of many researchers across the globe. Many researchers of the past have shown interest in designing an intelligent classroom on a different context. Hence to fill this gap, we have proposed an approach or a conceptual model through which Deep Learning architectures fused in the IoT systems results in an Intelligent Classroom via such hybrid systems. Apart from this, we have also discussed the major challenges, limitations as well as opportunities that can arise with Deep Learning-based IoT Solutions. In this paper, we have summarized the available applications of these technologies to suit our solution. Thus, this paper can be taken as a kickstart for our research to have a glimpse of the available papers for the success of our proposed approach.

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Section: Smart Classrooms Using Raspberry Pimentioning

confidence: 99%

Enabling Intelligence through Deep Learning using IoT in a Classroom Environment based on a multimodal approach

M¹

2021

TURCOMAT

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“…There are numerous challenges in WBAN, but the most important are: Quality of service (QoS): The WBAN must ensure the Quality of Service to its users in the form of Reliability, Data Availability, Fault Tolerance, energy Consumption, Synchronization, etc. (10)(11)(12)(13)(14).…”

Section: Challenges In Wbanmentioning

confidence: 99%

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

Bilandi¹,

Verma²,

Dhir³

2020

EAI Endorsed Transactions on Pervasive Health and Technology

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INTRODUCTION: In today's world, Wireless Body Area Networks (WBAN) has emanated as a budding field within the healthcare scenario for monitoring, detecting, predicting, and diagnosing various fatal diseases such as cancer, cardiovascular, diabetes and other infectious diseases such as tuberclosis, pneumonia and ebola. It is an assemblage of bio-sensors which could be put on or embedded in a patient's body to surveil the functionality of various body organs. OBJECTIVES: The purpose of that proposed protocol is to select the relay node and record the various physiological parameters like body temperature, blood pressure, rate of blood sugar of human being. Energy consumption is a major hurdle in WBAN as replacing as well as recharging the nodes pinioned to human body may bring about discomfort to the patient. Therefore, in this work, the emphasis is given to improve the performance of WBAN i.e. network lifetime, greater mobility, stability and energy efficiency. METHODS: To find the shortest energy-efficient route and avoid any kind of relay node failure, a novel routing scheme called Enhanced Opportunistic Protocol using Relay nodes (E-OPR) is proposed for data routing. CONCLUSION: Finally, an enhanced opportunistic protocol using relay node in WBAN has been proposed for cost effective real time health monitoring system. The proposed solution seems to give better results as compare single-hop, multi-hop routing protocols in terms of throughput, network lifetime, energy consumption and path loss.

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“…Energy-efficient data transmission was enabled by reliable ad hoc on-demand distance vector (RelAODV) routing protocol. 27 Here, sensor nodes were allowed to follow two different modes such as relay mode and direct mode. On-sensor processing was exploited before data transmission to perform data classification.…”

Section: Problem Definitionmentioning

confidence: 99%

Designing insistence-aware medium access control protocol and energy conscious routing in quality-of-service-guaranteed wireless body area network

Abdu

Bayat

Uçan

2019

International Journal of Distributed Sensor Networks

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Wireless body area network is a type of wireless sensor network that enables efficient healthcare system. To minimize frequent sensor replacement due to resource restrictions, it is necessary to improve energy efficiency in wireless body area network. This article deals with energy efficiency and quality-of-service improvement together in novel wireless body area network architecture. A novel wireless body area network architecture is designed with dual sink nodes in order to minimize delay and energy consumption. A novel insistence-aware medium access control protocol which is aware of criticality of sensed data is presented in the proposed wireless body area network. Prior knowledge-based weighted routing algorithm is responsible to select optimal route for data transmission. In prior knowledge-based weighted routing, weight value is computed by considering significant metrics such as residual energy, link stability, distance, and delay in order to improve energy efficiency and quality of service in the network. Energy consumption is further minimized by incorporating graph-based sleep scheduling algorithm. In graph-based sleep scheduling, criticality of sensor node is also considered as major metric. In coordinator, split and map-based neural network classifier is involved to perform packet classification. After classification, packets are assigned to corresponding sink node according to packet type. Then, throughput and delay metrics are improved by frame aggregation process which is involved in sink node. Extensive simulation in OMNeT + + shows better performance in network lifetime, throughput, residual energy, dropped packets, and delay.

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An Energy Efficient Method for Secure and Reliable Data Transmission in Wireless Body Area Networks Using RelAODV

Cited by 38 publications

References 17 publications

Enabling Intelligence through Deep Learning using IoT in a Classroom Environment based on a multimodal approach

Enabling Intelligence through Deep Learning using IoT in a Classroom Environment based on a multimodal approach

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

Designing insistence-aware medium access control protocol and energy conscious routing in quality-of-service-guaranteed wireless body area network

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