Tracking of Wearable IoT Devices Through WAP Using Intelligent Rule-Based Location Aware Approach

Ramesh, L. Sai; Sundar, S. Shyam; Selvakumar, K.; Sabena, S.

doi:10.1142/s0219649221400050

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The survey work summarizes and discusses IoT devices which will be used as blockchain components, very important blockchain technologies and up thus far IoT Blockchain applications. IoT is gradually is becoming ready to use solutions in multitude of fields [9,10,11]. Dorri et al [12] gifted a light-weight illustration of a blockchain throughout a home automation setting.…”

Section: Literature Reviewmentioning

confidence: 99%

Secure and Privacy Based Home Patient Monitoring Internet of Things (HPMIoT)

Rini¹

2021

Advances in Parallel Computing Technologies and Applications

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The Internet of Things (IoT) refers to a network of physical objects that are embedded with sensors are able to collect and transfer information over a wireless network with human conciliation. In confined Home Patient Observation system light-weight wearable/embedded sensing element devices connected to record the patient health conditions. Adopting looming technologies like 5G and Mobile Edge Computing (MEC) a multi-staged journey builds ultra-low latency. The health care sector is drowning in information. An overflow of clinical records, patient history, complicated billing information, medical analysis and lot builds it very troublesome to manage such information at intervals the health care sector in a systematized approach. Escorted by IoT, since data mobility occurs, hackers gain access to clinical IoT device, acquire management and revamp data which oblige health partitioners take actions which will injury the health of their client. i) To overthrow security issues introduces Optimized blockchain with hyperledger fabric in edge-cloud computing is proposed. Since data stored in blockchain are immutable, the permission granted patients can only access their records using hash value. Thus, blockchains will provide an immutable audit trail of health facts. ii) Software Defined Network (SDN) deliver Quality of Service (QoS) steer further from network congestion.

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Section: Literature Reviewmentioning

confidence: 99%

Secure and Privacy Based Home Patient Monitoring Internet of Things (HPMIoT)

Rini¹

2021

Advances in Parallel Computing Technologies and Applications

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“…In this paper [9], the authors addressed the issue of misplaced wearable IoT devices by using their MAC address to track its location, this can be done through examining the access points in which the devices are currently connected. In this work [10], proposed a recommendation system for crop yield prediction using Support Vector Machine and Decision Tree algorithm.…”

Section: Related Workmentioning

confidence: 99%

Network Lifetime Analysis in IOT Environment in Healthcare Sectors Using Deep Learning Routing Approach

Janaki

2021

Advances in Parallel Computing Technologies and Applications

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The Internet of Things (IoT) provides an improved flexibility in data collection, network deployment and data transmission to the sink nodes. However, depending on the application, the IoT network tends to consume lot of power from the individual devices. Various conventional solutions are provided to reduce the consumption of energy but most methods focus on increasing the data acquisition speed, data transmission and routing capabilities. However, these methods tend to fall under the trade-off between these three factors. Hence, in order to maintain the trade-off between these constraints, a viable solution is developed in this paper. A deep learning-based routing is built considering the faster acquisition of data, faster data transmission and routing path estimation with increasing path estimation. The paper models a Deep belief Network (DBN) to route the data considering all these constraints. The experimental validation is conducted to check the network lifetime, energy consumption of IoT nodes. The results show that the DBN offers greater source of flexibility with increased data routing capabilities than other methods.

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“…Smarter development of the IoT objects or Things have brought the communications to be didactic [3]. When the data from the IoT components are organized and evaluated; it aids in the understanding of the environment and construction of composite systems to enhance the standard of living [4] suchlike smart grid, smart home [5], SC-(supervising parking spaces, traffic congestion, and managing street lighting smart environment), detection of forest fire, controlling CO2 emissions, smart industrial control-(quality of air and temperature are monitored), eHealth-(monitoring the health condition of patients in hospitals and elderly residences), detection of UV radiation, etc. [6,7].…”

Section: Introductionmentioning

confidence: 99%

Attack Detection on IoT Based Smart Cities using IDS Based MANFIS Classifier and Secure Data Transmission Using IRSA Encryption

Duraisamy

Subramaniam²

2021

Wireless Pers Commun

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Enhancing the quality of human's daily life in respects of comfort is the chief objective of smart environments (SE). The Internet of Things (IoT) is basically an increasing network of smart objects. It commences diverse services in human's routine life relying on its available and also dependable activities. The chief problems in any real-globe SE centered upon the IoT model are the security in addition to privacy. The security susceptibility in IoT-centered systems creates security risks that affect SE applications. So, an Intrusion Detection System (IDS) based Modified Adaptive Neuro-Fuzzy Inference System is proposed aimed at detecting the attacks on IoT Smart Cities (SM). The proposed method comprises '2' phases. They are training and testing. First, the IDS are trained by performing three processes: preprocessing, feature selection (FS) and classification. For training, the proposed technique utilizes the data from the NSL_KDD dataset. Then, IoT sensor values are tested employing the same steps of training. The result of testing comprises '2' models. They are the attacked data and non-attacked data. The non-attacked data is sent to the user securely with the help of Improved Rivest Shamir Adleman method. After that, the user receives and decrypts the data. Then, the decrypted data is forecasted for further analysis. The proposed techniques' experimental outcomes used in FS, classification, and also secure data transmission are contrasted with the existent methods. KeywordsAttack detection • Internet of Things (IoT) • Smart cities • Crow search optimization (CSO) • Chaotic mapping (CM) • CM based CSO (CM-CSO) • Adaptive neuro fuzzy inference system (ANFIS) • Modified ANFIS (MANFIS) • Intrusion detection system (IDS) • Rivest Shamir Adleman (RSA) encryption • Improved RSA (IRSA)

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Tracking of Wearable IoT Devices Through WAP Using Intelligent Rule-Based Location Aware Approach

Cited by 5 publications

References 15 publications

Secure and Privacy Based Home Patient Monitoring Internet of Things (HPMIoT)

Secure and Privacy Based Home Patient Monitoring Internet of Things (HPMIoT)

Network Lifetime Analysis in IOT Environment in Healthcare Sectors Using Deep Learning Routing Approach

Attack Detection on IoT Based Smart Cities using IDS Based MANFIS Classifier and Secure Data Transmission Using IRSA Encryption

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