Evolution of wireless sensor network design from technology centric to user centric: An architectural perspective

Singh, Akhilendra Pratap; Luhach, Ashish Kr.; Gao, Xiao‐Zhi; Kumar, Sandeep; Roy, Diptendu Sinha

doi:10.1177/1550147720949138

Cited by 43 publications

(22 citation statements)

References 96 publications

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“…Such systems can be implemented at offices, homes, and experiment labs, making them less scalable and expensive. Non-wearable systems also compromise users’ privacy [ 35 , 36 ]. Therefore, it is not optimal to use such systems for most real-life applications.…”

Section: Fall Detection and Prevention Systemsmentioning

confidence: 99%

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Usmani

Saboor

Haris

et al. 2021

Sensors

117

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Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

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Section: Fall Detection and Prevention Systemsmentioning

confidence: 99%

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Usmani

Saboor

Haris

et al. 2021

Sensors

117

View full text Add to dashboard Cite

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“…A systematic analysis was presented by Singh et al [21] to capture the emergence of construction methods in sensor network applications. The benefits and drawbacks of various epochs in development were examined in their study to identify the potential research topics in the wireless sensor unifying framework.…”

Section: Related Workmentioning

confidence: 99%

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

Kanthavel

Dhaya

Khalaf

et al. 2022

Sensors

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Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.

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“…The researchers develop a set of recommendations for all countries to recover (WHO, 2019;WHO, 2020c;WHO, 2020d;Kujawski et al, 2020) coronavirus disease with its challenges CDC, 2021a;Keir, 2020;Lu et al, 2020;Mackenzie and Smith, 2020;Mahase, 2020;McKay et al, 202;Wadman, 2017). Cyber criminals are providing false medical information, criminal's cloud network and social media infodemic are running phishing scams (Butun et al, 2014;Alotaibi, 2019;Babamiri et al, 2019;Crawford and Smith, 2021;Bram et al, 2015;Butun et al, 2015;Cheng et al, 2020;Cinelli et al, 2020;Dhaka Tribune, 2020;Dias and Cunha, 2018;Galvao, 2020;Gao et al, 2016;Garzon et al, 2019;Irwin, 2020;Islam et al, 2020;Kassal et al, 2018;Khan and Pathan, 2018;Kim et al, 2011;Kiyuka et al, 2018;Kumar and Reddy, 2020;Labrique et al, 2013;Lorincz et al, 2004;Meneghello et al, 2018;Mitchel et al, 2013;Mitchell and Kan, 2019;Roozenbeek et al, 2020;Ross et al, 2014;Morol, 2021;Singh et al, 2020;Shohraby et al, 2007;…”

Section: Challengesmentioning

confidence: 99%

Myths about Coronavirus: A Research Defense

Miah¹,

Hasan²,

Hannan³

et al. 2022

GJHS

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The discovery of coronavirus is a state-of-the-art scientific study, so there is no doubt in the whole world that it is an innovation. But cybercriminals are working to create suspicion so that people are confused and afraid of the coronavirus. Cybercriminals are committing crimes from the global cloud network. They are carrying out extrajudicial killings around the world by abusing wireless sensor technology based on the victim’s active open eye, voice, adjacent sensor device and a specific GPS location, while spreading lies about the coronavirus pandemic in the media. New variants of the coronavirus are being created by cybercriminals and over time cyber killer groups are spreading to target-based regions of the universe, putting the world in a serious crisis threatening everyone’s survival. Surveys show that the death toll is rising and no one can say when the coronavirus will end and everyone will regain their right to life. The study indicates that lawmakers, health workers and technologists are blindfolded and foolishly looking for vaccines as a way to escape the coronavirus in the open air while others are dying helplessly. For coronavirus pandemic management and disease-free living for all, their sensor technological knowledge is essential, but such knowledge is insufficient. This is because cybercriminals have hypnotized each of their brains to their specific GPS location, depriving them of the right to decide for exposure in the media. The results of a unique study - coronavirus survival and its origin - published research is very difficult to reach relevant legislators and stakeholders. Research published on the publisher’s website has been bounced by cybercriminals for political malpractice and lack of proper sensor security. The study illustrates the interrelationship among the political agenda, mysterious lockdowns and digital assassination through global wireless clouding. Today or the day after tomorrow, the importance of this research should be made known to all, otherwise no one will be saved from the digital death of cyber criminals. Lying about coronavirus is nothing more than propaganda of GPS sensor abusers. To avoid this propaganda, everyone in the world must be aware and the legal system of each country must be properly implemented to suppress cyber criminals. This advanced study is an absolute witness to the building of a peaceful world for all generations, which encourages all to be vigilant and create a conscious circle to deal with this pandemic while maintaining equality and neutrality.

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Evolution of wireless sensor network design from technology centric to user centric: An architectural perspective

Cited by 43 publications

References 96 publications

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

Myths about Coronavirus: A Research Defense

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