The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization’s profit as well as users’ satisfaction. A customer’s request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.

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A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques

Platooning strategy is an important part of autonomous driving technology. Due to the limited resource of autonomous vehicles in platoons, mobile edge computing (MEC) is usually used to assist vehicles in platoons to obtain useful information, increasing its safety. Specifically, vehicles usually adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit large amount of data to the base station (BS) through vehicle-to-infrastructure (V2I) communications, where the useful information can be extracted by the edge server connected to the BS and then sent back to the vehicles to make correct decisions in time. However, vehicles may be moving on different lanes with different velocities, which incurs the unfair access due to the characteristics of platoons, i.e., vehicles on different lanes transmit different amount of data to the BS when they pass through the coverage of the BS, which also results in the different amount of useful information received by various vehicles. Moreover, age of information (AoI) is an important performance metric to measure the freshness of the data. Large average age of data implies not receiving the useful information in time. It is necessary to design an access scheme to jointly optimize the fairness and data freshness. In this paper, we formulate a joint optimization problem in the MEC-assisted V2I networks and present a multi-objective optimization scheme to solve the problem through adjusting the minimum contention window under the IEEE 802.11 DCF mode according to the velocities of vehicles. The effectiveness of the scheme has been demonstrated by simulation.

Velocity-adaptive Access Scheme for MEC-assisted Platooning Networks: Access Fairness Via Data Freshness

With the development of energy harvesting technologies and smart grid, the future trend of radio access networks will present a multi‐source power supply. In this article, joint renewable energy cooperation and resource allocation scheme of the fog radio access networks (F‐RANs) with hybrid power supplies (including both the conventional grid and renewable energy sources) is studied. In this article, our objective is to maximize the average throughput of F‐RAN architecture with hybrid energy sources while satisfying the constraints of signal to noise ratio (SNR), available bandwidth, and energy harvesting. To solve this problem, the dynamic power allocation scheme in the network is studied by using Q‐learning and Deep Q Network respectively. Simulation results show that the proposed two algorithms have low complexity and can improve the average throughput of the whole network compared with other traditional algorithms.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/eng2.12497

Resource allocation of fog radio access network based on deep reinforcement learning

The purpose of this study is to provide an overview of distributed denial of service (DDoS) attack detection in intelligent systems. In recent times, due to the endemic COVID‐19, the use of intelligent systems has increased. However, these systems are easily affected by DDoS attacks. A DDoS attack is a reliable tool for cyber‐attackers because there is no efficient method which can detect or filter it properly. In this context, we analyze different types of DDoS attacks and defense techniques for intelligent systems. For the analysis, we used Scopus databases to collect relevant papers in English between 2014 and 2022. This study makes an important contribution to the field of DDoS attack detection for intelligent systems, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of various perspectives, definitions, and trends in the field.

A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques

Vehicular ad hoc networks (VANETs), an application of the Internet of Things (IoT) providing a better intelligent transportation system (ITS), has received substantial attention from both industry and academia. Heterogeneous vehicular communications in VANETs occur when both vehicles and the infrastructure use different cryptographic technologies to exchange safety messages. However, the safety messages between vehicles and the infrastructure are communicated wirelessly; therefore, security issues are a serious concern in this domain. The existing schemes secure the transmission of safety messages with respect to confidentiality, authentication, and nonrepudiation. However, they are inappropriate with respect to efficiency. They cause computational overhead on the receiver and bandwidth overhead in the communications. To cope with this, we propose an elliptic curve cryptosystem-based hybrid signcryption (ECCHSC) protocol that satisfies the security requirements (i.e., message confidentiality, message’s source authentication, message integrity, nonrepudiation, and identity–anonymity) for heterogeneous vehicle-to-infrastructure (V2I) communications in a single logical step. This protocol allows secure transmission of a safety message from a vehicle using identity-based cryptography (IDC) to a roadside unit (RSU) using public-key infrastructure (PKI). In addition, the ECCHSC protocol enables the RSU to receive multiple ciphertexts, aggregate them, and de-signcrypt them simultaneously through the batch de-signcryption method, which further improves the performance. The ECCHSC protocol has indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) and existential unforgeability against adaptive chosen message attacks (EUF-CMAs) in the random oracle model (ROM). The performance analysis of our protocol demonstrates a significant reduction in computational overhead and in communication/storage overhead as compared to the state-of-the-art schemes. 

ECCHSC: Computationally and Bandwidth Efficient ECC-Based Hybrid Signcryption Protocol for Secure Heterogeneous Vehicle-to-Infrastructure Communications

This paper introduces and describes a novel architecture scenario based on Cloud Computing and counts on the innovative model of Federated Learning. The proposed model is named Integrated Federated...

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E PsannisKonstantinos

Papers

InFeMo: Flexible Big Data Management Through a Federated Cloud System