Cost-Effective Cluster-Based Energy Efficient Routing for Green Wireless Sensor Network

Verma, Sandeep; Sood, Neetu; Sharma, Ajay K.

doi:10.2174/2666255813666200312145504

Cited by 24 publications

(19 citation statements)

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“…Hence, the fate of the cluster-based routing in terms of the network lifetime is in the selection of CH. 20 In our work, we use two data collecting sinks (DCS) opposite to each other so that multihop communication can be avoided and the energy hole problem 21 can be mitigated.…”

Section: Overview and Backgroundmentioning

confidence: 99%

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

Menon¹,

Verma

Kaur

et al. 2021

Big Data

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An exponential progression in the miniaturization of communicating devices has proliferated the generation of a large volume of data termed as ''big data.'' The technological advancements in the micro-electro/mechanical system has made it possible to design the low-cost, low-power consuming artificial intelligence (AI)-based wireless sensor nodes to gather the big data belonging to various attributes from their surroundings. These nodes help in the early detection and prediction for the occurrence of landslides, which are among the catastrophic hazards. A profusion of research has focused on exploiting the potential of sensors for continuous monitoring and detecting the landslides at the earliest. However, the limited energy resources of sensor nodes give rise to the huge challenge for the network longevity pertaining to landslide detection. To address this concern, in this article, we propose an optimized routing and big data gathering system for landslide detection using (AI)-based wireless sensor network (WSN) (ORLAW). Since we propose a distributed routing mechanism, AI has a major role to play in the intelligent detection of landslides that too without the intervention of an external entity. We use the Dynamic Salp Swarm Algorithm for the cluster head selection in ORLAW. Two data collecting sinks are deployed on the opposite sides of the network, which is assumed to be a mountainous area. It is discerned from the simulation examination that ORLAW elongates the reliability period by 23.9% compared with the recently proposed clusterbased intelligent routing protocol, and also outperforms many others in the perspective of energy efficient management of big data.

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Section: Overview and Backgroundmentioning

confidence: 99%

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

Menon¹,

Verma

Kaur

et al. 2021

Big Data

Self Cite

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“…Recently Artificial Intelligence (AI) technology has give us immense opportunity to improve the performance of various healthcare applications in a efficient and well defined manner [24]- [26]. Integrating AI with IoT has become a promising solution to many issues in the healthcare domain, particularly wearable device networks and Body Area Networks (BAN) [27]- [30]. In this paper we propose an assistive model paving the evolution of smart cities using AI-IoT enabled smart exoskeleton for rehabilitation in connected communities.…”

Section: Introductionmentioning

confidence: 99%

AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities

et al. 2021

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In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities.

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“…To avoid the multiple transmissions from the sensors to the sink, the cluster‐based topology is adopted. In every cluster, the cluster head (CH) plays a significant role in gathering data from the cluster members 6 . The selection of CH is done based on various essential parameters, namely, residual energy, distance, and so on.…”

Section: Introductionmentioning

confidence: 99%

Intelligent‐Routing Algorithm for wireless body area networks (I‐RAW)

Sharma

Mishra²,

Tripathi

2021

Int J Communication

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Summary The progressive strides in the exploration of sensing technology and the potential use of electrical devices have rendered a promising technology, termed as Wireless Body Area Networks (WBANs). This technology has rendered competency to reform healthcare and making available ubiquitous health care support to remotely located patients. However, the sensor nodes which are embedded on or underneath the body of the patients to measure multifaceted physiological parameters are suffered from the limited battery constraints. In this paper, to address this issue, we propose Intelligent‐Routing Algorithm for WBANs (I‐RAW) to elongate life period of these sensor nodes. The two sinks are located on the front and back side of the patient's body that collects data from the sensor nodes which form clusters and forward the data through the cluster head (CH). We apply Tunicate Swarm Algorithm (TSA) for selection of CH which considers the essential parameters namely, residual energy, network's average energy, distance of node from the sink, path loss model, and energy consumption rate. The use of two sinks in WBAN mitigate hot‐spot problem in the network by avoiding the multi‐hop communication. The simulation results show that I‐RAW improves stability period and network operational period by 37.7% and 42.7%, respectively, as compared to Dual Sink approach using Clustering in Body area network (DSCB) protocol, respectively. Further, I‐RAW also shows supreme performance for various performance metrics as compared to other state‐of‐the‐art protocols.

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Cost-Effective Cluster-Based Energy Efficient Routing for Green Wireless Sensor Network

Cited by 24 publications

References 0 publications

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities

Intelligent‐Routing Algorithm for wireless body area networks (I‐RAW)

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