Implicit Cooperative Caching based on Information Popularity for Vehicular Networks

Efficient content distribution is a critical challenge in vehicular networks (VANETs). This is due to the characteristics of vehicular networks, such as high mobility, dynamic topologies, short-lived links and intermittent connectivity between vehicles. Recently, information-centric networking (ICN) has been proposed to VANET scenarios for improving content delivery of infotainmentapplications. However, ICN in VANETs suffers from the Interest transmission broadcast problem, which results in a waste of resources and diminishes the performance of VANETs' applications. In this paper, we propose the link stability-based Interest forwarding for content request (LISIC) protocol, in order to tackle the Interest broadcast storm problem during a content search in information-centric VANETs. The proposed protocol controls Interesttransmission by prioritizing neighboring vehicles with more stable links with the current sender. Simulation results show that the proposed protocol improves the content delivery rate by 40% while decreases the Interest packet transmissions by 26%, in scenario of a low number of content producers in the network.

Content caching in vehicular networks is a promising technology to dramatically reduce the request-response time and transmission delay. The existing caching policies often suffer from high computation and communication overhead and fail to well capture the dynamics of the vehicular networks and content popularity. In this paper, we propose a novel Cooperative Caching algorithm for vehicular networks with multi-level federated Reinforcement Learning (named CoCaRL) to dynamically determine which contents should be replaced and where the content requests should be served. In CoCaRL, Deep Reinforcement Learning (DRL) is employed to optimize the cooperative caching policy between RoadSide Units (RSUs) of vehicular networks, while a federated learning framework applies to reduce the computation and communication overhead in a decentralized way. To speed-up the convergence rate, we also develop a two-level aggregation mechanism for federated learning, where the low-level aggregation is performed at the RSUs and the high-level aggregation is executed at a Global Aggregator (GA). Through extensive simulation experiments, we demonstrate that our algorithm can: 1) achieve a higher hit rate than four baseline algorithms, 2) converge faster than original federated reinforcement learning without multi-level aggregation, and 3) perform good adaptability to different cache capacities and content quantities.

On efficient neighbor sensing in vehicular networks

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.

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.

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.

Nowadays, Internet is mostly used to access content and users are also interested in content, instead of location. Information-Centric Networking (ICN) has drawn substantial consideration over past few years because its appearance introduced a new potential Internet architecture (deploying extensive cache structure to distribute and deliver information). Though ICN has undoubtedly become a promising Future Internet architecture to solve current host-centric Internet problems, conventional ICN design still has its own drawbacks. These include caching, routing, etc., particularly mobility issue and energy-efficiency problem due to additional energy consumption for Content Routers’ caching capability. In order to address the energy flaw and enhance the mobility performance of ICNArchitecture, in this article, we have designed, evaluated and proposed a wireless Information Centric Networking (ICN) based access model within the context of Intelligent Transport System (ITS) for ICN standardization procedure. We take into consideration the fact that number of mobile devices increases rapidly day by day as well as the public transportation (e.g. train, bus, etc) becomes more popular nowadays. Besides, mobile users may ask for different content interests via Internet during the period of time when they are on public transport vehicle. We then propose a wireless communication topology in ICN for the ITS by utilizing the smart scheduling algorithm with proactive-caching based strategy to deal with possible and practical situations of commuters (mobile content users). In our scheme, the dedicated content provider proactively sends appropriate segments of requested content (based on chunk-level structure of content) to expected proximity Aggregation content nodes located at neighbor stations (future attachment points) on the moving path of public transportation vehicle. This working scheme substantially enhances the mobile user’s quality of experience and network performance in ICN. Moreover, the proposed ICN based system adapts the network nodes to their optimized network utilization values according to content popularity by conducting cross-layer optimization. This mechanism provides mobile users more reliable and effective Green Information and Communication Systems (ICS) when they travel with public transportation. Therefore, the proposal integrates Green networking into ICN by combining the benefits of innovated rate-adaptivity and proactive-caching based schemes for achieving highly scalable, reliable and energy-efficient network performance in future transportation Information-centric communication system with data-awareness. In addition, we identify that there are currently various ICN-based models and emphasize the need of an official ICN international standard for wireless communication in general and transportation system in particular. Following, by evaluating the proposal, we show that the proposed cross-layer wireless content access model in ICN for ITS is a promising and feasible contribution for the standardization process of ICN.

As devices for the Internet of Things (IoT) are typically battery-powered, energy efficiency is a major challenge for IoT networks. In this paper, we leverage the in-network caching of Information-Centric Networking (ICN) to propose a novel cooperative caching scheme, based on the IoT data lifetime and user request rate, to improve the energy efficiency of IoT networks. By caching IoT data at different nodes (such as content routers, base stations, etc.), IoT devices can stay in sleep mode for a larger portion of time and therefore reduce the overall energy consumption. With the help of an auto- configuration mechanism, the proposed IoT data Lifetime-based Cooperative Caching (LCC) scheme can dynamically adapt to the change of request rate. Extensive evaluations were performed and the simulation results show that LCC outperforms existing schemes in terms of total energy consumption reduction (up to 40%) and the reduction in the average number of hops traversed along the path (up to 20%), which is also directly related to the response time. Keywords- Internet of Things (IoT), Cooperative Caching, Information-Centric Network (ICN).

In today's digital era, multimedia streaming is one of the major sources of internet traffic and it is also expected to grow significantly in upcoming years. Even though traditional Vehicular Ad Hoc Networks (VANET) provide solutions for multimedia content delivery through best effort, the Quality of Experience (QoE) of these techniques is crucially damaged by dynamic network changes, fluctuations in bandwidth and congestion in the network due to lack of global view. However, all these limitations can be substantially reduced by the introduction of Software Defined Network (SDN) architecture. Hence this hybrid network called Software Defined Vehicular Network (SDVN) introduces a global view, flexibility, scalability and smartness in multimedia streaming. The proposed work uses a POX controller with an OpenFlow Spanning Tree topology discovery unit-based SDN framework for the real-world vehicular network. This modified POX was compared with the traditional vehicular network and a normal POX for QoE parameters like Multi Scale Structural Similarity, Three Component Structural Similarity, Blurring, Delta, Mean sum of absolute difference and Noise estimation. For testing, a Standard Definition (SD) video was streamed between vehicular nodes under dense and real-world traffic scenarios. The QoE Metrics of Modified POX improved between 1 % to 42%. Hence the proposed Modified POX presents an enhanced end-to-end QoE for multimedia streaming service.

In vehicular wireless networks, mobile nodes move at a high speed. Thus, the network topology changes frequently. When the traffic is heavy, the collision probability increases dramatically. Network coding is one of the efficient packet transmission schemes to improve throughput and reduce the number of packet transmission. In this paper, we propose an Opportunistic Delayed Transmission (ODT) scheme. This scheme is a trade-off between delay and coding opportunity where the intermediate node may wait for a certain period to maximize the coding opportunity. We use NS-2 network simulator to evaluate the performance of the proposed scheme. The simulation results show that our proposed scheme improves not only the coding ratio but also the packet delivery ratio.

Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed spectrum for high bandwidth inter-vehicular messaging, driver-assist functions, and passenger entertainment services. Recent rulings that mandate the use of spectrum databases introduce additional challenges in this highly mobile environment, where the CR enabled vehicles must update their spectrum data frequently and complete the data transfers with roadside base stations. As the rules allow local spectrum sensing only under the assurance of high accuracy, there is an associated tradeoff in obtaining assuredly correct spectrum updates from the database at a finite cost, compared to locally obtained sensing results that may have a finite error probability. This paper aims to answer the question of when to undertake local spectrum sensing and when to rely on database updates through a novel method of exploiting the correlation between 2G spectrum bands and TV whitespace. We describe experimental studies that validate our approach and quantify the cost savings made possible by intermittent database queries.

In vehicular ad hoc networks (VANETs), the nodes are vehicles and these vehicles communicate with one another on the road. The vehicular nodes on the road move continuously and it introduces dynamic changes in the topology of the network. When the node density in the vehicular network is high, then it is still more challenging as these situations introduce different challenges for network scalability and hurdles the process of determining an optimal route in the vehicular network. At this juncture, clustering protocols are often utilized for resolving the issue of network scalability and the factors that hinders process of identifying optimal routes in vehicular network. In this article, Border Collie optimization algorithm‐based node clustering technique (BCOA‐NCT) is proposed for achieving optimal cluster head selection that minimizes network overhead in uncertain node density situations. This BCOA‐NCT is proposed for facilitating equal concentration on both exploitation and exploration during the process of clustering. It utilized the strategy of eyeing for preventing the algorithm from being stuck into local optima during the cluster head selection process. The simulation experiments of the proposed BCOA‐NCT and the benchmarked schemes confirmed better throughput of 6.19%, minimized end‐to‐end delay of 7.24%, reduced mean energy consumptions of 6.89%, and reduction in the number of beacon messages utilized by 8.26% under the impact of different density of vehicular node. The outcome of the results indicated that the proposed BCOA‐NCT on par with the baseline approaches is capable enough in handling the issue of scalability and better estimation of optimal routes under the increase of node density in the vehicular network.

Public transportation is vital in everyday life. As a consequence, the increasing need for intelligent surveillance systems over public transportation provides promising areas for wireless vehicular communication research for in-tunnel applications. This paper particularly emphasizes on tunnel implementation regarding to a prototype surveillance system on railway environments based on autonomous, smart monitoring devices that capture data only upon the occurrence of an incident, potentially dangerous for the passengers (like an explosion blast or the triggering of the fire detector). In this paper an attempt has been made to evaluate the performance of wireless vehicular communication inside a challenging tunnel environment after an emergency event. Due to the fact that extreme tunnel conditions (i.e. blind spots, intersymbol interference, NLOS) can cause major wireless network propagation failure, we develop a wireless hybrid network architecture consisted of IEEE 802.11b/g/e and IEEE 802.16e protocols. Furthermore this network was implemented in order to transmit the captured data from the monitoring devices of the train coaches to the Base Stations that are placed towards the metro stations. Finally, we use the OMNeT++ simulator tool for creating a simulation environment of the vehicular wireless network in order to compare the results with the experimental measurements.

In the scenario of vehicular networks, due to connectivity transitory and throughput degradation caused by high vehicle mobility, traditional privacy-preserving methods such as k-anonymity are not sufficient. As an effort toward this issue, we propose a framework which enhances the privacy of LBS by actively caching in the wireless vehicular network scenario. The scheme is designed under the dedicated short-range communication (DSRC) standard, with three original contributions. First, a point of interest (POI) query probability model for in-vehicle users is defined, considering the spatial relationship between the current location and the queried location.