LISIC: A Link Stability-Based Protocol for Vehicular Information-Centric Networks

In recent years, the Internet of Things (IoT) has become a standard facet of modern communications, and information-centric networks have been pointed as an alternative to bypass the restrictions imposed by the traditional Internet protocol networks regarding the mobility of its network elements. However, the improvements imposed by this new paradigm fall short in large scale mobile wireless distributed environments inherent to IoT, due to high node mobility, dynamic topologies and intermittent connectivity. To tackle these issues, we present a named data network (NDN)-based publish–subscribe mechanism with support for both Consumer and Producer mobility. This approach handles the Producer mobility by combining the Data packets with infrastructure specific information, fixing the broken paths between the Producer and the Consumer; and the Consumer mobility by monitoring and anticipating mobile node trajectories while compelling the infrastructure to adjust to new paths. Simulation results, assuming a smart city use case and using real traces of vehicular mobility, have shown that the proposed solution far surpasses the native NDN workflow and traditional publish–subscribe solutions. With respect to the Producer mobility, the proposed solution delivers 79% of Data packets against 14% with the Native implementation, when using 25 mobile Producers; regarding the Consumer mobility, results have shown that our solution achieves almost the same Consumer satisfaction ratio as previous implementations but reducing substantially the network overhead related with the transmission of Interest packets.

VC2-MAC: A two-cycle cooperative MAC protocol in vehicular networks

Information-centric networking (ICN) technology is becoming a popular research topic in vehicular networks due to the connectionless and lightweight characteristics of this networking paradigm. Caching plays an essential role in information-centric networks, but current caching techniques for ICN are not ideal for the dynamic and wireless vehicular networks. This paper presents a caching approach for ICN-based vehicular networks that takes into account both the dynamicity of vehicular networks and the popularity of the information being distributed. We introduce an interval metric for selective caching. With this metric and estimates of information popularity and vehicle density, cooperative caching can be realized without exchanging cache management information among the vehicular nodes. Simulation results show that the proposed approach can increase the storage space utilization and has low data response time for vehicular networks.

Vehicular networks are experiencing rapid growth and evolution under the increasing demand of vehicular traffic management and ubiquitous network connectivity. In particular, the amount of information to be downloaded from the roadside-deployed gateways is dramatically increasing. Infected by high mobility, intermittent connectivity, and unreliability of the wireless channel, it is challenging to satisfy the need for massive data transmission in vehicular networks. In this paper, we propose a novel protocol called vehicular cooperative media access control (VC-MAC), which utilizes the concept of cooperative communication tailored for vehicular networks, particularly for gateway-downloading scenarios. VC-MAC leverages the broadcast nature of the wireless medium to maximize the system throughput. Spatial diversity and user diversity are exploited by concurrent cooperative relaying to overcome the unreliability of the wireless channel in vehicular networks. We theoretically analyze the selection of an optimal relay set using a weighted independent set (WIS) model and then design a backoff mechanism to select the optimal relays in a distributed manner. We have carried out extensive simulations to demonstrate that VC-MAC effectively enhances cooperative information downloading and significantly increases the system throughput compared with existing strategies.

The Internet Protocol (IP) architecture could not fully satisfy Vehicular Ad-hoc Networks (VANETs) needed efficiency due to their dynamic topology and high mobility. This paper presents a technique to update the software of Electronic Control Units (ECUs) in vehicles using Information Centric Network (ICN) architecture. The proposed technique replaces Flashing Over The Air (FOTA) using IP with FOTA using ICN. The importance of FOTA is illustrated as well as the impact of applying the ICN architecture on VANETs. Through our experiments, we compare between the known FOTA over IP and the newly introduced FOTA technique over ICN.KeywordsFlashing Over The Air (FOTA)Information Centric Networking (ICN)ndnSIMVehicle software updateVehicular Ad-hoc Networks (VANETs).

The increasing demands of several emergent services brought new communication problems to vehicular networks (VNs). It is predicted that the transmission system assimilated with unmanned aerial vehicles (UAVs) fulfills the requirement of next-generation vehicular network. Because of its higher flexible mobility, the UAV-aided vehicular network brings transformative and far-reaching benefits with extremely high data rates; considerably improved security and reliability; massive and hyper-fast wireless access; much greener, smarter, and longer 3D communications coverage. The clustering technique in UAV-aided VN is a difficult process because of the limited energy of UAVs, higher mobility, unstable links, and dynamic topology. Therefore, this study introduced an Enhanced Artificial Gorilla Troops Optimizer–based Clustering Protocol for a UAV-Assisted Intelligent Vehicular Network (EAGTOC-UIVN). The goal of the EAGTOC-UIVN technique lies in the clustering of the nodes in UAV-based VN to achieve maximum lifetime and energy efficiency. In the presented EAGTOC-UIVN technique, the EAGTO algorithm was primarily designed by the use of the circle chaotic mapping technique. Moreover, the EAGTOC-UIVN technique computes a fitness function with the inclusion of multiple parameters. To depict the improved performance of the EAGTOC-UIVN technique, a widespread simulation analysis was performed. The comparison study demonstrated the enhancements of the EAGTOC-UIVN technique over other recent approaches.

With the advancement in network infrastructure and the growth of IoT, more and more vehicular systems are getting connected. Architectures like Information Centric Networks (ICN) are being explored for vehicular communication to achieve robust content distribution in highly mobile, dynamic, and error prone domains. Consumer mobility is implicitly supported in ICN while producer mobility is an important challenge. In this paper, we design a mechanism to support producer mobility using the spatial locality of moving producers and reverse paths of data. Then, we model the consumer (requesting) application as a GI/M/c/N queue which is used to estimate system parameters like content delivery time distribution. We perform simulations using real world traces to evaluate the accuracy and working of our model.

Information Centric Networking (ICN) is a new and promising network concept which is founded upon the idea that most users in the internet are interested in accessing digital objects, irrespectively of their locations. Digital objects in ICN have unique names that are used in order to route data content from the source node to the destination node. During the content delivery from sources to destinations, the data contents are cached to intermediate nodes in order to achieve efficient and reliable distribution of the data content among the network infrastructure (in-network caching). In this research project we focus on the efficiency of ICN for delivering Big Data with Persistent Identifiers. We proposed a Mapping Architecture for resolving PIDs to ICN names and we evaluate the efficiency of in-network caching when delivering Big Data objects. Our results showed that in-network caching can offer significant performance benefits when the cache size of the network elements that perform in-network caching is bigger than the Big Data object size.

The repetition of content requests happens frequently in the vehicular networks, and it is increasing depending on the vehicular density in a certain area. On the other hand, information-centric networking (ICN) is being used in the vehicular networks to fulfill the faster content communication requirements, reduce latency, and enhance the network capacity. Although, the ICN-based vehicular networks have many benefits, it has several drawbacks, e.g., interest packet flooding, inefficient content caching, and so on. Moreover, the network scalability is related to the drawbacks of the existing ICN-based vehicular networks. It is also important that the current Internet architecture is considered as a scale-free network. Therefore, the solution of the existing drawbacks can be solved using the concept of the scale-free ICN network. In this paper, we propose a cluster-based mechanism for vehicular networks in the scale-free ICN core networks. We also simulate the various scenarios of a scale-free network and show the comparative analysis of different scenarios in terms of the total number of clusters vs the number of nodes in a cluster. Our simulation result ensures the solution to the interest flooding problem and the efficiency of the content caching mechanism.

Vehicular networks aim to provide a communication framework for moving vehicles, road infrastructure and pedestrians. Such kind of networks is the entrance to a new era of services that will make areas such as security and safety, information, transactions, entertainment and sustainability (green transportation) more efficient than they are today, especially in the upcoming era of autonomous vehicles and self-driving cars. However, the severe nature of vehicular environments makes efficient inter-vehicular communication very difficult to achieve. Vehicular Delay-Tolerant Networks (VDTN), as these networks are called, have very sparse, intermittent connections, and the lack of a fixed topology gives rise to one of the main challenges that they face: packet routing. A range of routing algorithms has been proposed in recent years to optimize communication in vehicular networks, and significant progress has been made in the matter but the quest for the optimal performance is still ongoing. In this paper, we explore a deep learning approach to the routing problem in VDTN, proposing a routing architecture and algorithm based on Deep Neural Networks that helps routers make packet forwarding decisions based on the current conditions of its surroundings. In order to assess the performance of the proposed architecture, simulations were run showing important gains in terms of network overhead and hop count with respect to popular routers, while maintaining acceptable packet delivery rates and average delivery delays.

Products related with Intelligent Transportation Systems (ITS) are becoming a reality on our roads. All car manufacturers are starting to include Internet access in their vehicles and to integrate smartphones directly from the dashboard, but more and more services will be introduced in the near future. Connectivity through will become a cornerstone of every new proposal, and offering an adequate quality of service is obviously desirable. However, a lot of work is needed for vehicular networks to offer performances similar to those of the wired networks. Vehicular networks can be characterized by two main features: high variability due to mobility levels that can reach up to 250 kilometers per hour, and heterogeneity, being that various competing versions from different vendors have and will be released. Therefore, to make the deployment of efficient services possible, an extensive study must be carried out and adequate tools must be proposed and developed. This PhD thesis addresses the service deployment problem in these networks at three different levels: (i) the physical and link layer, showing an exhaustive analysis of the physical channel and models; (ii) the network layer, proposing a forwarding protocol for IP packets; and (iii) the transport layer, where protocols are proposed to improve data delivery. First of all, the two main wireless technologies used in vehicular networks where studied and modeled, namely the 802.11 family of standards, particularly 802.11p, and the cellular networks focusing on LTE. Since 802.11p is a quite mature standard, we defined (i) a propagation and attenuation model capable of replicating the transmission range and the fading behavior of real 802.11p devices, both in line-of-sight conditions and when obstructed by small obstacles, and (ii) a visibility model able to deal with large obstacles, such as buildings and houses, in a realistic manner. Additionally, we proposed a model based on high-level performance indicators (bandwidth and delay) for LTE, which makes application validation and evaluation easier. At the network layer, a hybrid protocol called AVE is proposed for packet forwarding by switching among a set of standard routing strategies. Depending on the specific scenario, AVE selects one out of four different routing solutions: a) two-hop direct delivery, b) Dynamic MANET On-demand (DYMO), c) greedy georouting, and d) store-carry-and-forward technique, to dynamically adapt its behavior to the specific situation. At the transport layer, we proposed a content delivery protocol for reliable and bidirectional unicast communication in lossy links that improves content delivery in situations where the wireless network is the bottleneck. It has been designed, validated, optimized, and its performance has been analyzed in terms of throughput and resource efficiency. Finally, at system level, we propose an edge-assisted computing model that allows reducing the response latency of several queries by placing a computing unit at the network edge. This way, traffic traversal through the Internet is avoided when not needed. This scheme could be used in both 802.11p and cellular networks, and in this thesis we decided to focus on its evaluation using LTE networks. The platform presented in this thesis combines all the individual efforts to create a single efficient platform. This new environment could be used by any provider to improve the quality of the user experience obtainable through the proposed vehicular network-based services.

Mobile multimedia streaming is an open topic in vehicular environment. Due to the high intermittent links, it has become a critical challenge to deliver high quality video streaming in vehicular networks. In this paper, we reform the Information Centric Networking (ICN) concept for multimedia delivery in urban vehicular networks. By leveraging the ICN perspective, we highlight that vehicular peers can obtain multimedia chunks via the vehicle-to-cloud (V2C) approach to improve the delivery quality. Based on this, we propose a lightweight multipath selection strategy to guide the network system to adaptively adjust the forwarding means. Extensive simulations show that the proposed solution can optimize the utilization of network paths, lighten network loads as well as avoid wasting resources.

Named Function Networking (NFN) offers to compute and deliver results of computations in the context of Information Centric Networking (ICN). While ICN offers data delivery without specifying the location where these data are stored, NFN offers the production of results without specifying where the actual computation is executed. In NFN, computation workflows are encoded in (ICN style) Interest Messages using the lambda calculus and based on these workflows, the network will distribute computations and find locations. Depending on the use case of the actual network, the decision where to execute a compuation can be different: A resolution strategy running on each node decides if a computation should be forwarded, split into subcomputations or executed locally. This work focuses on the design of resolution strategies for selected scenarios and the online derivation of execution based on network status and history. Starting with a simple resolution strategy suitable for data centers, we focus on improving load distribution within the data center or even between multiple data centers. We have designed resolution strategies that consider the size of input data and the load on nodes, leading to priced plans from which one can select the ones with the least costs. Moreover, we use these plans to create templates: Templates can be used to create a resolution strategy by simulating the using the planning system, tailored to the specific use case at hand. Finally we designed a resolution strategy for edge computing which is able to handle mobile scenarios typical for vehicular networking. This “mobile edge computing resolution strategy” handles the problem of frequent handovers to a sequence of road-side units without creating additional overhead for the non-mobile use case. All these resolution strategies were evaluated using a simulation system and were compared to the state of the art behavior of data center environments and/or cloud configurations. In the case of the vehicular networking strategy, we enhanced existing road-side units and implemented our NFN-based system and plan derivation such that we were able to run and validate our solution in real world tests for mobile edge computing.

Hybrid routing for safety data with intermittent V2I connectivity

Efficient content distribution is one of the emerging applications in vehicular networks. To provide scalable content distribution in vehicular networks, Chord Peer-to-Peer (P2P) overlay could be applied. Most P2P protocols, including Chord, are designed for wired-line network, and might perform poorly in mobile networks. Mobile Chord (MChord) is proposed to enhance the P2P performance over Vehicular Ad hoc Network (VANET). In addition, cross-layer design to improve MChord performance in VANET is also investigated. Extensive NS-2 simulations with vehicular mobility traces are conducted to evaluate the P2P overlay performance in VANET. MChord and its cross-layer design outperforms the original Chord in various aspects, including application layer forwarding steps, query response ratio, correct query response ratio, and application delay.