Optimizing Energy Consumption for Cloud Internet of Things

Ahmed, Zeinab E.; Hasan, Mohammad Kamrul; Saeed, Rashid A.; Hassan, Rosilah; Islam, Shayla; Mokhtar, Rania A.; Khan, Sheroz

doi:10.3389/fphy.2020.00358

Cited by 53 publications

(21 citation statements)

References 24 publications

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“…The proposed method goes to its highest value, which is 50, as compared to fuzzy-IoT, two fuzzy, and FDMS; for this specific value, the vehicle speed and velocity are the same as other clusters' vehicles. The vehicles leave the cluster if the vehicle moves faster or slower velocity than the other vehicles [78][79][80][81][82][83]. The possibilities of vehicle to reside inside the cluster are more when the value of Fuzzy-IoT is increased.…”

Section: Performance Resultsmentioning

confidence: 99%

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

et al. 2021

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Many algorithms use clustering to improve vehicular ad hoc network performance. The expected points of many of these approaches support multiple rounds of data to the roadside unit and constantly include clustering in every round of single-hop data transmission towards the road side unit; however, the clustering in every round maximizes the number of control messages and there could be the possibility of collision and decreases in network energy. Multi-hop transmission prolongs the cluster head node’s lifetime and boosts the network’s efficiency. Accordingly, this article proposes a new fuzzy-clustering-based routing algorithm to benefit from multi-hop transmission clustering simultaneously. This research has analyzed the limitation of clustering in each round, different algorithms were used to perform the clustering, and multi-hop routing was used to transfer the data of every cluster to the road side unit. The fuzzy logic was used to choose the head node of each cluster. Three parameters, (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. The results of this research were compared to various other algorithms in relation to parameters like dead node in every round, first node expire, half node expire, last node expire, and the network lifetime. The simulation results show that the proposed approach outperforms other methods. On the other hand, the vehicular ad hoc network (VANET) environment is vulnerable at the time of data transmission. The NS-2 software tool was used to simulate and evaluate the proposed fuzzy logic opportunistic routing’s performance results concerning end-to-end delay, packet delivery, and network throughput. We compare to the existing protocols, such as fuzzy Internet of Things (IoT), two fuzzy, and Fuzzy-Based Driver Monitoring System (FDMS). The performance comparison also emphasizes an effective utilization of the resources. Simulations on the highway environment show that the suggested protocol has an improved Quality of Service (QoS) efficiency compared to the above published methods in the literature.

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Section: Performance Resultsmentioning

confidence: 99%

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

et al. 2021

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“…e sensor in intelligent IoV enhances vehicle and traffic systems' safety, while harmonized traffic data transfer in the IoV system network enhances vehicular system efficiency. However, the amount of energy consumption, required capacity, green buffer-awareness, and message exchange through IoVs may compromise severe data transfer risk [12]. AI based on selfdriven vehicles encourages several types of applications with many benefits of intelligence.…”

Section: Artificial Intelligence Methods For Iov Multimediamentioning

confidence: 99%

“…e AI edge-based IoV typically has high spatial locality for road conditions, map information, and temporal locality for traffic conditions. On the contrary, with big sensing data, intelligent vehicles are facing tremendous computation burdens [12,15].…”

Section: Intelligent Iov Edge-based Algorithmmentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

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Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

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“…is section examines earlier work in data encryption in terms of power consumption, processing speed, data type, throughput, avalanche effect, and packet size. is section reviews the literature in cryptography algorithms [18][19][20][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Internet of ings (IoT) has made it imperative to have several devices on a network.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications

Hasan

Shafiq²,

Islam

et al. 2021

Complexity

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As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.

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Optimizing Energy Consumption for Cloud Internet of Things

Cited by 53 publications

References 24 publications

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications

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