Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP’s profit under varying cloud environment conditions

Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time

A reliable route repairing scheme for internet of vehicles

Recently, Internet of Vehicles (IoVs) has been recognised as a key solution for vehicular communications. Connected vehicles and infrastructure' roadside units have been shaping the underlying architecture of IoVs technology, where the conventional routing protocols cannot facilitate reliable and efficient communication for dynamic IoVs topologies. Hence, this technology is highly susceptible to frequent network fragmentations, thus exposing communication channels to regular failure problems. This paper, thus, introduces a novel routing repair strategy, referred as Reliable Route Repairing Strategy (RRRS) to tackle routing failure problems. Repairing the operation of channel communications is prioritised according to a stability degree of the connected vehicles. The RRRS features are combined with the traditional AOMDV protocol, and a comparison study has been conducted to compare the AOMDV, the RRRS-AOMDV and the HM-AOMDV protocols. The simulation results demonstrate that the RRRS-AOMDV achieves better performance, about 30% to 45% in terms of packets overhead and latency

A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

Prediction of the future location of vehicles and other mobile targets is instrumental in intelligent transportation system applications. In fact, networking schemes and protocols based on machine learning can benefit from the results of such accurate trajectory predictions. This is because routing decisions always need to be made for the future scenario due to the inevitable latency caused by processing and propagation of the routing request and response. Thus, to predict the highprecision trajectory beyond the state-of-the-art, we propose a Generative Adversarial Network-based VEhicle trajEctory Prediction method, GAN-VEEP, for urban roads. The proposed method consists of three components, 1) vehicle coordinate transformation for data set preparation, 2) neural network prediction model trained by GAN, and 3) vehicle turning model to adjust the prediction process. The vehicle coordinate transformation model is introduced to deal with the complex spatial dependence in the urban road topology. Then, the neural network prediction model learns from the behavior of vehicle drivers. Finally, the vehicle turning model can refine the driving path based on the driver’s psychology. Compared with its counterparts, the experimental results show that GAN-VEEP exhibits higher effectiveness in terms of the Average Accuracy, Mean Absolute Error, and Root Mean Squared Error

Routing Schemes in Software-Defined Vehicular Networks: Design, Open Issues and Challenges

Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations of current vehicular networking. However, existing vehicular routing schemes are not equipped to handle communication in SDVNs. In addition, routing schemes in SDVNs, in general, has been lightly addressed in the literature. To fill this gap, this article explores the potential of SDVNs from the aspect of routing and studies the design principles of routing schemes in SDVNs and classifies the current routing solutions based on different criteria. SDVN routing schemes are then compared through comprehensive analysis, and key open issues and opportunities for future research directions are discussed

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller

Vehicular Computation Offloading for Industrial Mobile Edge Computing

Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This paper considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile edge computing (MEC), including unmanned aerial vehicles (UAVs), and the fixed-position MEC servers, to optimize the system cost including execution time, energy consumption, and the ID rental price. Moreover, to increase the access probability of IV by the UAVs, the geographical area is divided into small partitions and schedule the UAVs regarding the regional IV density dynamically. A minimum incremental task allocation (MITA) algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time. Experimental results show the proposed solution can significantly reduce the system cost

A Survey of Limitations and Enhancements of the IPv6 Routing Protocol for Low-power and Lossy Networks: A Focus on Core Operations

Driven by the special requirements of the Low-power and Lossy Networks (LLNs), the IPv6 Routing Protocol for LLNs (RPL) was standardized by the IETF some six years ago to tackle the routing issue in such networks. Since its introduction, however, numerous studies have pointed out that, in its current form, RPL suffers from issues that limit its efficiency and domain of applicability. Thus, several solutions have been proposed in the literature in an attempt to overcome these identified limitations. In this survey, we aim mainly to provide a comprehensive review of these research proposals assessing whether such proposals have succeeded in overcoming the standard reported limitations related to its core operations. Although some of RPL’s weaknesses have been addressed successfully, the study found that the proposed solutions remain deficient in overcoming several others. Hence, the study investigates where such proposals still fall short, the challenges and pitfalls to avoid, thus would help researchers formulate a clear foundation for the development of further successful extensions in future allowing the protocol to be applied more widely

A Novel Chaotic Permutation-Substitution Image Encryption Scheme Based on Logistic Map and Random Substitution

Privacy is a serious concern related to sharing videos or images among people over the Internet. As a method to preserve images’ privacy, chaos-based image encryption algorithms have been used widely to fulfil such a requirement. However, these algorithms suffer from a low key-space, significant computational overhead, and a lag in resistance against differential attacks. This paper presents a novel chaos-based image encryption method based on permutation and substitution using a single Substitution Box (S-Box) to address issues in contemporary image encryption algorithms. The proposed encryption technique’s efficiency is validated through extensive experiments as compared to the state-of-the-art encryption algorithms using different measures and benchmarks. Precisely, the collected results demonstrate that the proposed technique is more resilient against well-known statistical attacks and performs well under plaintext attacks. Indeed, the proposed scheme exhibits very high sensitivity concerning the plaintext attack. A minor change in the encryption key or the plain text would result in a completely different encrypted image

Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications

Wireless communication using existing coding models poses several challenges for RF signals due tomultipath scattering, rapid fluctuations in signal strength and path loss effect. Unlike existing works, thisstudy presents a novel coding technique based on Analogue Network Coding (ANC) in conjunction withSpace Time Block Coding (STBC), termed as Space Time Analogue Network Coding (STANC). STANCachieves the transmitting diversity (virtual MIMO) and supports big data networks under low transmittingpower conditions. Furthermore, this study evaluates the impact of relay location on smart devices networkperformance in increasing interfering and scattering environments. The performance of STANC is analyzedfor Internet of Things (IoT) applications in terms of Symbol Error Rate (SER) and the outage probabilitythat are calculated using analytical derivation of expression for Moment Generating Function (MGF).In addition, the ergodic capacity is analyzed using mean and second moment. These expressions enableeffective evaluation of the performance and capacity under different relay location scenario. Differentfading models are used to evaluate the effect of multipath scattering and strong signal reflection. Undersuch unfavourable environments, the performance of STANC outperforms the conventional methods suchas physical layer network coding (PNC) and ANC adopted for two way transmission