Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city

Singh, Shailendra Pratap; Viriyasitavat, Wattana; Juneja, Sapna; Alshahrani, Hani; Shaikh, Asadullah; Dhiman, Gaurav; Singh, Aman; Kaur, Amandeep

doi:10.1016/j.phycom.2022.101893

Cited by 18 publications

(3 citation statements)

References 35 publications

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“…In the construction of smart cities, information and communication technologies are used to improve the living standards and management of citizens and governments [ 13 , 14 ]. The Internet of Things (IoT) using sensors is widely used in smart cities [ 15 ]. In particular, the coverage-related problem [ 16 , 17 ] is a fundamental topic in WSNs to measure the monitoring quality of a sensor network deployed in a given region.…”

Section: Related Workmentioning

confidence: 99%

AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City

Shao,

Wang,

et al. 2023

Sensors

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Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead.

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Section: Related Workmentioning

confidence: 99%

AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City

Shao,

Wang,

et al. 2023

Sensors

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“…This paper mainly focuses that efficiency of IDS, IPS and penetration system [11][12][13][14] depends on the machine learning algorithm used in these systems. The introduction of this paper outlines the importance of machine learning algorithms [15][16][17][18][19][20][21] in cybersecurity measures. Section 2, discusses the about of various types of machine learning algorithms which are used in cybersecurity context.…”

Section: Introductionmentioning

confidence: 99%

“…It provides the ability to learn to produce the desired prediction without involving a rigorous amount of programming. It is divided into supervised learning, unsupervised learning and semi-supervised learning[21]. In the former type of machine learning, the learning function maps input to output.…”

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confidence: 99%

Harnessing Machine Learning for Effective Cyber security Classifiers

Jena,

Shankar,

Singhdeo

2023

AJRCoS

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Machine learning has emerged as a transformative force, innovating diverse industries through its capacity to infuse meaningful insights from large datasets. It plays a pivotal role in powering data analysis, discover pattern matching, identifying hidden or evolving risks in securing systems. The ability of categorizing and behavior analysis is central to its efficacy in cybersecurity. This paper highlights the importance of machine learning in landscape of cyber threats. In this paper, we have identified few machine learning algorithms to categorize huge dataset. The complexities of identifying hidden risks increases by many folds, when the input data is voluminous. Evaluating and contemplating the underlying meaning of data is time-consuming and can be missed easily. We compared different types of machine learning algorithms. Each machine learning algorithm has its strength and weakness. It is found that, the TressJ48 algorithm is proficient in classifying the large dataset, better than Naive Bayes and Decision Stump algorithms. The efficient classifier helps to generate insight, which can be further used to make decisions in terms of cybersecurity.

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Multi-strategy chimp optimization algorithm for global optimization and minimum spanning tree

Du,

Zhou,

Luo

et al. 2023

Soft Comput

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Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city

Cited by 18 publications

References 35 publications

AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City

AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City

Harnessing Machine Learning for Effective Cyber security Classifiers

Multi-strategy chimp optimization algorithm for global optimization and minimum spanning tree

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