M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities

Rehman, Amjad; Haseeb, Khalid; Kolivand, Hoshang

doi:10.1016/j.eti.2021.101802

Cited by 21 publications

(8 citation statements)

References 26 publications

(18 reference statements)

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“…Furthermore, the devised movable sink and data transmission or collection techniques curtail transmission distance between the sink and CH would diminish the hotspot problems. Rehman et al [15] intend to devise a method of security measures utilizing the Green IoT including Cloud Integrated Data Management (M-SMDM) for Smart City. Initially, it would form energyefficient and long-run connectivity by utilizing distributing load factors and self-balancing trees homogeneously in a green transmission mechanism.…”

Section: Related Workmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network's efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

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Section: Related Workmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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“…IoT is one of the most promising technologies of the current era and interacts with sensors for observing the physical world [ 19 , 20 , 21 ]. These technologies have expanded into the real-time environment and support the applications to govern their operations.…”

Section: Related Workmentioning

confidence: 99%

Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors

Haseeb

Rehman

Bahaj

et al. 2022

Sensors

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Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users’ devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations.

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“…In the embedded system, IoT technologies are broadly used to integrate wireless objects for information collection, processing, and facilitating the physical world [21][22][23]. The data gathering and transmission depend on various parameters including computing power, storage space, and energy utilization.…”

Section: Related Workmentioning

confidence: 99%

Optimized Embedded Healthcare Industry Model with Lightweight Computing Using Wireless Body Area Network

Saba

Haseeb

Bahaj

et al. 2022

Wireless Communications and Mobile Computing

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Wireless technology is offering numerous growth to develop communication systems. The Internet of Things (IoT) is combined with the sensing ecosystem to transfer and process the physical environment. Recently, IoT devices have collaborated with wireless devices to improve embedded medical applications. Many solutions are proposed to decrease the power consumption of the sensing ecosystem and support the health industry. However, optimizing the transformation of collected data with lightweight power consumption is still a burning research issue. Moreover, uncontrolled network devices and healthcare professionals are remotely accessed by such embedded systems. Thus, securing sensitive information is also a significant factor for mobile communications. Therefore, this research presents an optimized embedded healthcare industry model with lightweight computing using a wireless body area network (WBAN), aiming to lessen the control overheads and improve the power consumption in mobile e-health services. To begin, it employs an optimal learning algorithm to lower the management costs of embedded systems in order to transform and administer the electronic health record (EHR) more efficiently. Second, with the help of trustworthy gateways, it delivers a safe EHR algorithm as well as lightweight computing resources for embedded systems. The proposed model is tested with a variety of experiments and demonstrates its significant improvement over state-of-the-art techniques.

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M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities

Cited by 21 publications

References 26 publications

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors

Optimized Embedded Healthcare Industry Model with Lightweight Computing Using Wireless Body Area Network

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