An Intelligent Forwarding Strategy in SDN-Enabled Named-Data IoV

Internet of Vehicles (IoV), a rapidly growing technology for efficient vehicular communication and it is shifting the trend of traditional Vehicular Ad Hoc Networking (VANET) towards itself. The centralized management of IoV endorses its uniqueness and suitability for the Intelligent Transportation System (ITS) safety applications. Named Data Networking (NDN) is an emerging internet paradigm that fulfills most of the expectations of IoV. Limitations of the current IP internet architecture are the main motivation behind NDN. Software-Defined Networking (SDN) is another emerging networking paradigm of technology that is highly capable of efficient management of overall networks and transforming complex networking architectures into simple and manageable ones. The combination of the SDN controller, NDN, and IoV can be revolutionary in the overall performance of the network. Broadcast storm, due to the broadcasting nature of NDN, is a critical issue in NDN based on IoV. High speed and rapidly changing topology of vehicles in IoV creates disconnected link problem and add unnecessary transmission delay. In order to cop-up with the above-discussed problems, we proposed an efficient SDN-enabled forwarding mechanism in NDN-based IoV, which supports the mobility of the vehicle and explores the cellular network for the low latency control messages. In IoV environment, the concept of Edge Controller (EC) to maintain and manage the in-time and real-time vehicular topology is being introduced. A mathematical estimation model is also proposed in our work that assists the centralized EC and SDN to find not only the shortest and best path but also the most reliable and durable path. The naming scheme and in-network caching property of the NDN nodes reduce the delay. We used ndnSIM and NS-3 for the simulation experiment along with SUMO for the environment generation. The results of NDSDoV illustrate significant performance in terms of availability with limited routing overhead, minimized delay, retransmissions, and increased packet satisfaction ratio. Besides, we explored the properties of EC that contribute mainly in path failure minimization in the network.     

An Efficient Greedy Traffic Aware Routing Scheme for Internet of Vehicles

A new paradigm of VANET has emerged in recent years: Internet of Vehicles (IoV). These networks are formed on the roads and streets between travellers who have relationships, interactions and common social interests. Users of these networks exchange information of common interest, for example, traffic jams and dangers on the way. They can also exchange files such as multimedia files. IoV is considered as part of the Internet of Things (IoT) where objects are vehicles, which can create a multitude of services dedicated to the intelligent transportation system. The interest is to permit to all connected vehicles to communicate with each other and/or with a central server, through other vehicles. Vehicle to Vehicle (V2V) communication is the main component, because the services encompassed in the IoV are based on the vehicles in question, such as transmitter, relay and receiver. This work is focusing on designing and developing a Quality of Service (QoS) routing scheme dedicated to IoV networks. Especially, we aim to improve the Greedy Traffic Aware Routing (GyTAR) protocol to support QoS in IoV networks. To evaluate the proposed approach in terms of QoS in the context of IoV networks, the performance metrics such as average end-to-end delay and packet delivery ratio are taken into consideration to analyse the network situation.     

SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

The content-centric networking (CCN) architecture allows access to the content through name, instead of the physical location where the content is stored, which makes it a more robust and flexible content-based architecture. Nevertheless, in CCN, the broadcast nature of vehicles on the Internet of Vehicles (IoV) results in latency and network congestion. The IoV-based content distribution is an emerging concept in which all the vehicles are connected via the internet. Due to the high mobility of vehicles, however, IoV applications have different network requirements that differ from those of many other networks, posing new challenges. Considering this, a novel strategy mediator framework is presented in this paper for managing the network resources efficiently. Software-defined network (SDN) controller is deployed for improving the routing flexibility and facilitating in the inter-interoperability of heterogeneous devices within the network. Due to the limited memory of edge devices, the delectable bloom filters are used for caching and storage. Finally, the proposed scheme is compared with the existing variants for validating its effectiveness.     

Game-Oriented Security Strategy Against Hotspot Attacks for Internet of Vehicles

With the rapid development of mobile communication technology, the application of internet of vehicles (IoV) services, such as for information services, driving safety, and traffic efficiency, is growing constantly. For businesses with low transmission delay, high data processing capacity and large storage capacity, by deploying edge computing in the IoV, data processing, encryption and decision-making can be completed at the local end, thus providing real-time and highly reliable communication capability. The roadside unit (RSU), as an important part of edge computing in the IoV, fulfils an important data forwarding function and provides an interactive communication channel for vehicles and server providers. Additional computing resources can be configured to accommodate the computing requirements of users. In this study, a virtual traffic defense strategy based on a differential game is proposed to solve the security problem of user-sensitive information leakage when an RSU is attacked. An incentive mechanism encourages service vehicles within the hot range to send virtual traffic to another RSU. By attracting the attention of attackers, it covers the target RSU and protects the system from attack. Simulation results show that the scheme provides the optimal strategy for intelligent vehicles to transmit virtual data, and ensures the maximization of users’ interests.     

Efficient Routing Protocol with Localization Based Priority & Congestion Control for UWSN

The nodes in the sensor network have a wide range of uses, particularly on under-sea links that are skilled for detecting, handling as well as management. The underwater wireless sensor networks support collecting pollution data, mine survey, oceanographic information collection, aided navigation, strategic surveillance, and collection of ocean samples using detectors that are submerged in water. Localization, congestion routing, and prioritizing the traffic is the major issue in an underwater sensor network. Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource. Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper. We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio, dispatching of packets, power, and distance among the nodes. This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic. The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet, delay of latency, and power utilization than the existing algorithms.     

Container-Based Internet of Vehicles Edge Application Migration Mechanism

Internet of Vehicles (IoV) applications integrating with edge computing will significantly drive the growth of IoV. However, the contradiction between the high-speed mobility of vehicles, the delay sensitivity of corresponding IoV applications and the limited coverage and resource capacity of distributed edge servers will pose challenges to the service continuity and stability of IoV applications. IoV application migration is a promising solution that can be supported by application containerization, a technology forseamless cross-edge-server application migration without user perception. Therefore, this paper proposes the container-based IoV edge application migration mechanism, consisting of three parts. The first is the migration trigger determination algorithm for cross-border migration and service degradation migration, respectively, based on trajectory prediction and traffic awareness to improve the determination accuracy. The second is the migration target decision calculation model for minimizing the average migration time and maximizing the average service time to reduce migration times and improve the stability and adaptability of migration decisions. The third is the migration decision algorithm based on the improved artificial bee colony algorithm to avoid local optimal migration decisions. Simulation results show that the proposed migration mechanism can reduce migration times, reduce average migration time, improve average service time and enhance the stability and adaptability of IoV application services.     

A non-linear traffic flow-based queuing model to estimate container terminal throughput with AGVs

Efficient handling of containers at a terminal can reduce the overall vessel sojourn times and minimise operational costs. The internal transport of containers in these terminals is performed by vehicles that share a common guide path. The throughput capacity of a terminal may increase by increasing the number of vehicles; however, simultaneously congestion may reduce the effective vehicle speed. We model this situation accurately using a traffic flow-based closed queuing network model. The vehicle internal transport is modelled using a load-dependent server that captures the interaction between the number of vehicles in a transport segment and the effective vehicle speed. Using a non-linear traffic flow model, we show that the throughput reductions due to vehicle congestion can be as large as 85%. Hence, the effect of vehicle congestion during internal transport cannot be ignored. The model can also be used to determine the appropriate number of vehicles required to achieve the required terminal throughput by explicitly considering the effect of vehicle congestion.     

Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

In recent years, with the continuous advancement of the intelligent process of the Internet of Vehicles (IoV), the problem of privacy leakage in IoV has become increasingly prominent. The research on the privacy protection of the IoV has become the focus of the society. This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms, proposes a privacy protection system structure based on untrusted data collection server, and designs a vehicle location acquisition algorithm based on a local differential privacy and game model. The algorithm first meshes the road network space. Then, the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model, thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service. On this basis, a statistical method is designed, which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions. Finally, this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai. The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements, and provide better privacy protection and service for the users of the IoV.     

Spatial econometrics models for congestion prediction with in-vehicle route guidance

The congestion dependence relationship among links using microsimulation is explored, based on data from a real road network. The work is motivated by recent innovations to improve the reliability of Dynamic Route Guidance (DRG) systems. The reliability of DRG systems can be significantly enhanced by adding a function to predict the congestion in the road network. The application of spatial econometrics modelling to congestion prediction is also explored, by using historical traffic message channel (TMC) data stored in the vehicle navigation unit. The nature of TMC data is in the form of a time series of geo-referenced congestion warning messages, which is generally collected from various traffic sources. The prediction of future congestion could be based on the previous year of TMC data. Synthetic TMC data generated by microscopic traffic simulation for the network of Coventry are used in this study. The feasibility of using spatial econometrics modelling techniques to predict congestion is explored. The results are presented at the end.     

Q-Learning Based Routing Protocol for Congestion Avoidance

The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state, Q-value, and reward function to set the desired path toward the destination. A new reward function that consists of a buffer occupancy, link reliability and hop count is considered. Moreover, look ahead algorithm is employed to update the Q-value with values within two hops simultaneously. This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account, accordingly. Finally, the simulation results presented approximately 20% higher packet delivery ratio and 15% shorter end-to-end delay, compared to those with the existing scheme by avoiding congestion adaptively.     

Optimal process planning for a combined punch-and-laser cutting machine using ant colony optimization

A machine that performs both punching and laser-cutting operations is referred as combined punch-and-laser machine. Such a machine has been in the market for about two decades. Although process-planning tools have been used on such a combined machine, the optimization of process planning dedicated to combined machines, based on our literature search results, has never been directly studied. This work addresses the process-planning problem for the combined punch-and-laser machine by integrating knowledge, quantitative analysis, and numerical optimization approaches. The proposed methodology helps making decisions on following issues: (i) which type of operation should be applied to each feature, and (ii) what is the optimal operation sequence (tool path) to achieve the maximum manufacturing efficiency. The ant colony optimization (ACO) algorithms are employed in searching the optimal tool path. Sensitivities of control parameters of ACO are also analysed. Through applications, the proposed method can significantly improve the operation efficiency for the combined punch-and-laser machine. The method can also be easily automated and integrated with the nesting and G-code generation processes. Some issues and possible future research topics have also been discussed.     

Robust end-to-end loss differentiation scheme for transport control protocol over wired/wireless networks

The authors propose a robust end-to-end loss differentiation scheme to identify the packet losses because of congestion for transport control protocol (TCP) connections over wired/wireless networks. The authors use the measured round trip time (RTT) values to determine whether the cause of packet loss is because of the congestion over wired path or regular bit errors over wireless paths. The classification should be as accurate as possible to achieve high throughput and maximum fairness for the TCP connections sharing the wired/wireless paths. The accuracies of previous schemes in the literature depends on varying network parameters such as RTT, buffer size, amount of cross traffic, wireless loss rate and congestion loss rate. The proposed scheme is robust in that the accuracy remains rather stable under varying network parameters. The basic idea behind the scheme is to set the threshold for the classification to be a function of the minimum RTT and the current sample RTT, so that it may automatically adapt itself to the current congestion level. When the congestion level of the path is estimated to be low, the threshold for a packet loss to be classified as a congestion loss is increased. This avoids unnecessary halving of the congestion window on packet loss because of the regular bit errors over the wireless path and hence improves the TCP throughput. When the congestion level of the path is estimated to be high, the threshold for a packet loss to be classified as the congestion loss not to miss any congestion loss is decreased and hence improves the TCP fairness. In ns 2 simulations, the proposed scheme correctly classifies the congestion losses under varying network parameters whereas the previous schemes show some dependency on subsets of parameters.     

A Novel Green IoT-Based Pay-As-You-Go Smart Parking System

The better management of resources and the potential improvement in traffic congestion via reducing the orbiting time for parking spaces is crucial in a smart city, particularly those with an uneven correlation between the increase in vehicles and infrastructure. This paper proposes and analyses a novel green IoT-based Pay-As-You-Go (PAYG) smart parking system by utilizing unused garage parking spaces. The article also presents an intelligent system that offers the most favorable prices to its users by matching private garages’ pricing portfolio with a garage’s current demand. Malta, the world’s fourth-most densely populated country, is considered as a case of a smart city for the implementation of the proposed approach. The results obtained confirm that apart from having a high potential system in such countries, the pricing generated correctly forecasts the demand for a particular garage at a specific time of the day and year. The proposed PAYG smart parking system can effectively contribute to the macro solution to traffic congestion by encouraging potential users to use the system’s services and reducing the orbiting time for parking.     

Nodal congestion price estimation in spot power market using artificial neural network

In the world wide increasing trend of restructured power system, open access in transmission system and competition in generation and distribution have introduced a frequently occurring problem of congestion. To establish a proficient price-based congestion management procedure, the nodal pricing strategy is found to be appropriate. From congestion management point of view, the optimal nodal prices are comprised of two basic components. First component is locational marginal price, that is marginal cost of generation to supply load and transmission losses both. Second component is nodal congestion price (NCP), that is the charges to maintain network security. Levenberg-Marquardt algorithm based neural network (LMANN) for estimating NCPs in spot power market by dividing the whole power system into various congestion zones is presented. Euclidian distance based clustering technique has been applied for feature selection before employing LMANN. The purpose of using artificial neural network (ANN) based approach for NCP estimation in spot power market is to exploit the tolerance for any missing or partially corrupted data to achieve tractability, robustness and very fast solution. The proposed ANN method also handles the congestion price volatility by taking continuously varying load and constrained transmission into account. The information provided by the proposed method regarding the formation of different congestion zones and the severity of congestion within a zone instructs both the market participants as well as independent system operator in making effective decisions. The proposed method has been examined for an RTS 24-bus system and is found to be quite promising.     

Load balancing in the provisioning of hose model virtual private networks with multi-path routing

Load balancing in the provisioning of virtual private network (VPN) service in the hose model is studied. Single-path routing and tree routing for the hose model tend to aggregate bandwidth reservations on a small number of links, thus leading to congestion problems in service provider networks. If the link capacity is depleted as a result of improper routing, all future non-VPN traffic will be blocked. We propose a novel multi-objective multi-path (MOMP) routing linear program with the maximum fraction of traffic on a path (MFTP) constraint to solve the problem. The MOMP routing algorithm is able to reduce the bandwidth reservation on the most loaded link by as much as 50%, thus effectively alleviating the potential congestion problems in service provider network. The MFTP constraint provides a guarantee of the availability of multiple paths for each VPN endpoint pair. Further reduction of the bandwidth reservation can be achieved depending on the MFTP value. This is highly significant.     

基于状态反馈的网络分布式拥塞管理方案

提出了一种新型的面向区分服务网络的分布式拥塞管理方案。其基本思想是利用拥塞状态反馈信息在边缘节点或主机上实施拥塞管理，该方案主要包括三个组成部分；拥塞状态控制分组，早期拥塞检测和流量控制算法，实验结果表明，与标准的区分服务网络相比，该方案能在TCP和UDP聚集之间公平地分配带宽并能显著地降低分组丢失率。     

Improve urban passenger transport management by rationally forecasting traffic congestion probability

A Bayesian network (BN) approach is proposed in this study to analyse the overall traffic congestion probability of an urban road network in consideration of the influence of applying various transport policies. The continually expanding urbanised region of Beijing has been chosen as the study area because of its rapid expansion and motorisation, which lead to the severe traffic congestion occurring nearly every day. It is demonstrated that the proposed BN approach is able to rationally predict the probability of the overall traffic congestion that will take place given a certain transport policy. It is also proven that increasing the number of buses providing convenient passenger transport service in the urbanised region of Beijing will most effectively reduce the probability of the traffic congestion in this area, especially when the newly constructed roads in the same region are put into use.     

Percolation-based health management of complex traffic systems

System health management, which aims to ensure the safe and efficient operation of systems by reducing uncertain risks and cascading failures during their lifetime, is proposed for complex transportation systems and other critical infrastructures, especially under the background of the New Infrastructure Projects launched in China. Previous studies proposed numerous approaches to evaluate or improve traffic reliability or efficiency. Nevertheless, most existing studies neglected the core failure mechanism (i.e., spatio–temporal propagation of traffic congestion). In this article, we review existing studies on traffic reliability management and propose a health management framework covering the entire traffic congestion lifetime, from emergence, evolution to dissipation, based on the study of core failure modes with percolation theory. Aiming to be “reliable, invulnerable, resilient, potential, and active”, our proposed traffic health management framework includes modeling, evaluation, diagnosis, and improvement. Our proposed framework may shed light on traffic management for megacities and urban agglomerations around the world. This new approach may offer innovative insights for systems science and engineering in future intelligent infrastructure management.     

Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks

Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN’s current and future challenges, there is a need to know how artificial intelligence (AI) optimization techniques can efficiently balance the load. This study aims to explore two artificial intelligence optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), used for load balancing in SDN. Further, we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing. Finally, we propose a conceptual framework for SDN futurology by evaluating node and link reliability, which can balance the load efficiently and improve QoS in SDN.     

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.