Load Balancing Algorithm on the Immense Scale of Internet of Things in SDN for Smart Cities

Babbar, Himanshi; Rani, Shalli; Gupta, Divya; Aljahdali, Hani Moaiteq; Singh, Aman; Al‐Turjman, Fadi

doi:10.3390/su13179587

Cited by 21 publications

(16 citation statements)

References 31 publications

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“…Centralized control coupled with a load estimation technique helps to balance and distribute the traffic across the network by analyzing and determining the predicted load and designing the network's flow routes. This load-balancing solution helps to conserve the network bandwidth, decrease energy consumption, and reduce the redundant data packets in the IoT network [14]. • Constrained Environment: Deploying IoT applications in a real-time environment requires excessive resources.…”

Section: Figure 7 -Sdiot Architecturementioning

confidence: 99%

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Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

Kamarudin,

Ameedeen,

Ab Razak

et al. 2023

ijcsm

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The Internet of Things (IoT) has transformed our interaction with the world by connecting devices, sensors, and systems to the Internet, enabling real-time monitoring, control, and automation in various applications such as smart cities, healthcare, transportation, homes, and grids. However, challenges related to latency, privacy, and bandwidth have arisen due to the massive influx of data generated by IoT devices and the limitations of traditional cloud-based architectures. Moreover, network management, interoperability, security, and scalability issues have emerged due to the rapid growth and heterogeneous nature of IoT devices. To overcome such problems, researchers proposed a new architecture called Software Defined Networking for Edge Computing in the Internet of Things (SDN-EC-IoT), which combines Edge Computing for the Internet of Things (EC-IoT) and Software Defined Internet of Things (SDIoT). Although researchers have studied EC-IoT and SDIoT as individual architectures, they have not yet addressed the combination of both, creating a significant gap in our understanding of SDN-EC-IoT. This paper aims to fill this gap by presenting a comprehensive review of how the SDN-EC-IoT paradigm can solve IoT challenges. To achieve this goal, this study conducted a literature review covering 74 articles published between 2019 and 2023. Finally, this paper identifies future research directions for SDN-EC-IoT, including the development of interoperability platforms, scalable architectures, low latency and Quality of Service (QoS) guarantees, efficient handling of big data, enhanced security and privacy, optimized energy consumption, resource-aware task offloading, and incorporation of machine learning.

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Section: Figure 7 -Sdiot Architecturementioning

confidence: 99%

“…On the other hand, SDN provides centralized control and an overview of the whole network [14]. Centralized control enables network resources to be optimized effectively and adjusted dynamically while ensuring interoperability across heterogeneous IoT networks.…”

Section: Introductionmentioning

confidence: 99%

Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

Kamarudin,

Ameedeen,

Ab Razak

et al. 2023

ijcsm

View full text Add to dashboard Cite

show abstract

“…Just as in the previous case only the IP address of the proxy server (and not that of the client) was known to the target server, a reverse proxy is able to hide the address of the target server from the client. The reverse proxy server in this case acts as a (public) gateway allowing access to the servers behind it, which can be in any location, either publicly accessible or on a private network [27].…”

Section: Basic Load Balancing Mechanismsmentioning

confidence: 99%

“…It was created in 1998 and since then it has been fully open source, released under the GNU GPL v2.0 license. The aim of the project from the beginning was to build a high-performance server for machines based on the Linux kernel, providing good scalability, availability, and quality of service [22,[27][28][29][30]. Technically, the software implements the load balancer in the transport layer of the ISO/OSI model; additionally, it operates in the kernel layer, not the user layer, which ensures a very high speed of switching processor commands.…”

Section: Lvsmentioning

confidence: 99%

Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service

2021

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As the number of users increased over the years, pioneering technologies and solutions in given areas ceased to be sufficient even in terms of performance. Therefore, there was a need for their development or even redesign and redefinition. One of the issues that undoubtedly has a huge impact on the current shape of the global network and the way information is processed in it is the issue of traffic balancing, especially the one in the server infrastructure related to the WWW service, providing users with the possibility of efficient and reliable web browsing. The paper presents a comparative analysis of selected open-source solutions used for traffic balancing in server infrastructures providing WWW service based on selected criteria. The designed architecture of the test environment and the test results of selected tools implementing the traffic-balancing functionality are presented. Methodologies, test plans, and comparison criteria are proposed. A comparative analysis of results based on specific criteria was performed. The balance between network traffic optimization and load balancing distribution among servers is crucial for the development of energy-efficient data processing centers.

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“…We will concentrate our investigation on the DREBIN [ 13 ] dataset, which is a machine-learning method that aspires static analysis to predict Android malware directly on the mobile device. This prediction of Android malware has a problem regarding classification, which indicates the class of an application [ 14 ]. The different classes comprised two categories: either the class is benign or malware application, which is signified by 0 or 1.…”

Section: Introductionmentioning

confidence: 99%

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

Babbar,

Rani,

Sah

et al. 2023

Sensors

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Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

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Load Balancing Algorithm on the Immense Scale of Internet of Things in SDN for Smart Cities

Cited by 21 publications

References 31 publications

Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

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