A novel multi-path traffic control mechanism in named data networking

Named Data Networking (NDN) proposes a contentcentric rather than a host-centric approach to data retrieval. Data packets with unique and immutable names are retrieved from a content store (CS) using Interest packets. The current NDN architecture relies on forwarding strategies that are dependent upon on-path caching and is therefore inefficient. This approach reduces data transfer efficiency by ignoring the cached content available on the adjacent off-path routers in the network. In this paper, we propose a novel distributed multipath (D-MP) forwarding strategy and enhancements to the NDN Interest forwarding pipeline. Furthermore, we develop a centralized SDNenabled control for the multipath forwarding strategy (S-MP) that distributes Interests efficiently by using the global knowledge of the NDN network states. We perform extensive evaluations of our proposed methods on an at-scale WAN testbed spanning six geographically separated sites. Our solutions outperform the existing NDN forwarding strategies by a significant margin. We show that the D-MP strategy results in performance gains ranging between 10.4x to 12.5x over the default NDN implementation without in-network caching, and gains of 12.2x to 18.4x with in-network caching. In addition, for the S-MP case, we demonstrate a performance improvement of 10.6x to 12.6x, and 12.9x to 18.5x, for with- and without in-network caching respectively.

A Responsibility-based Transport Control for Named Data Networking

At this time forwarding strategy became the center of attention of researchers in area named data networking (NDN). This is because the forwarding strategy is responsible for managing every packet of interest received by the NDN router, then the characteristics of forwarding strategies will have a significant impact on NDN performance. Therefore this study aims to perform analysis using a large approach in forwarding strategy that is broadcast approach. It is expected that the results of this study can be utilized by researchers in the field of NDN to determine the direction of research. Parameter of study in this research is delay in observation time interval. A forwarding strategy using a broadcast approach is simulated in order to understand the NDN delay characteristics within the observation interval. The simulation of the research was conducted on the NDNSim simulator with several scenarios. From the simulation results obtained that cumulatively delay broadcast forwarding strategy in multi producer scenario there is no difference between fulldelay and lastdelay. However, in the single producer scenario there is a difference between fulldelay and lastdelay this can be influenced by the large number of consumers who send interest to the producer and also based on the flooding method of the interest package in order to find the nearest NDN router containing the expected content as soon as possible, So that it can overload the network performance and effects overhead.

A priority scheduling algorithm with double threshold and a traffic control mechanism with hysteresis effect are proposed. The double threshold priority scheduling is studied based on the minimum laxity threshold (MLT) and the queue length threshold (QLT). The hysteresis effect traffic control is specified by the hysteresis effect QLT and traffic rate control. According to the simulation results, it can be shown that the proposed dynamic priority scheduling has a better processing performance than the existing MLT and QLT algorithm. The results also demonstrate that the QLT with hysteresis effect and traffic rate control with the hysteresis effect enhance the performance in comparison with those of single threshold.

An optimization model and traffic light control scheme for heterogeneous traffic systems

Traffic congestion on major roads consequent upon existing bottlenecks at intersections is a major problem in Akure Metropolis. To change this trend, this research was carried out in order to design effective traffic control mechanisms at the “troubled spots” in the metropolis. Odole intersection was identified as one of the critical intersections during a reconnaissance survey and as such, selected for study. Data on geometric features were collected using Oedometer and Google Earth software. Peak and off-peak traffic volume data were collected between 7:30 and 8:30 am and between 12.00 noon and 1.00 pm respectively every other day using Cine camera. Furthermore, discharge headway and delay data were collected using stop watch. The geometric and traffic data collected were analysed using Microsoft Excel. An appraisal of Odole Intersection indicated that the major contributors to traffic are Motorcycles (70.88%) and Passenger Cars (28.72%). Other modes of transportation account for about 0.4% of vehicles traversing the intersection. The critical traffic volume at the intersection was over 4000 veh/hr and the average delay was 22 seconds. An Average delay of 22 seconds at the intersection was an indication that the operating level of service was C (i.e. fairly stable traffic condition with average delays attributable to traffic control by personnel). By juxtaposing the results of geometric and traffic data analyses with the pros and cons of various traffic control mechanisms, traffic control by signalisation was selected and designed to suit Odole intersection. In tandem with the results of this research, appropriate measures have been recommended to ameliorate traffic and commuting problems in the metropolis.

In Named Data Networking (NDN), content can be retrieved from multiple sources through multiple path, which makes the traditional end-to-end congestion control mechanisms unsuitable for NDN. A lot of works have been done to achieve the congestion control by limiting the transmission rate of interest packets, but most of them overlooked the intelligent forwarding feature in NDN. In this paper, we propose a ratebased, multipath-aware congestion control algorithm MNRCP, which is inspired by Rate Control Protocol (RCP). MNRCP calculates the transmission rate for every flow and feedbacks it to the consumer to control the sending rate of interest packets. In order to fully exploit the features of multiple data sources, in-network caching and multipath forwarding and adapt to the practical scenarios that interest packets requesting the same data can arrive from different interfaces, we propose a novel multipath forwarding strategy and a multipath feedback mechanism to achieve fairness and network utilization. In addition, simulation results of our algorithm in ndnSIM2.3 show that compared with other congestion control mechanisms, MNRCP can achieve higher total throughput while ensuring fairness. According to the available bandwidth of the path, MNRCP also can achieve the optimal traffic split ratio, and thus performs better in load balancing.

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in intelligent transportation system (ITS) recently has received increasing attention. This study devised an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this goal we have worked on a model, which has three levels of control. Every intersection is controlled by its own traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the traffic pattern of each intersection in any particular day of the week and hour of the day. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. The proposed architecture can achieve dynamic traffic signal control. We have also developed a NetLogo-based traffic simulator to serve as the agents' world. Our approach is tested with traffic control of a large connected junction and the result obtained is promising; The average delay time can be reduced by 21.76% compared to the conventional fixed sequence traffic signal and 14.77% compared to the vehicle actuated traffic signal control strategy.

ABSTRACTSoftware‐defined networking (SDN), a cornerstone of future‐generation networks, is adopted in Named Data Networks (NDN) for large‐scale deployment. The forwarding strategies proposed for SDN‐based NDN primarily use the centralized controller to optimize Interest forwarding and Data delivery. The nodes direct the Interests to the controller to discover the content source(s) and suppress the suboptimal responses. To support such content discovery and delivery, the controller experiences frequent path calculation and trades excessive control messages to install the paths in the nodes due to rapid cache admission and replacement. Besides, the typical NDN forwarding solutions are not viable to realize or need considerable modifications in SDN‐based NDN. To that end, the proposed strategy optimizes Interest forwarding and Data delivery using a Single‐State Q‐learning‐based technique, namely, SDN‐Q. In SDN‐Q, each content source learns to suppress the suboptimal responses, with the learning task offloaded to the controller. The controller communicates the learning decision to the nodes. Each node only retains the action (decision) to entertain an incoming Interest. Once an Interest hits, the source either replies with the Data or remains silent and sends the Interest's information (meta‐data) to the controller for the learning task. Thus, SDN‐Q enables the NDN nodes to remain light‐loaded. Each node can instantly answer an Interest request without redirecting it to the controller. Additionally, we optimize Interest forwarding using a hop‐based scoped‐flooding approach. The proof‐of‐concept implementation in software (simulation) reveals that the proposed system outperforms the competing strategies by reducing the traffic load, latency, and control messages in SDN‐based NDN (at most by 40%, 7%, and four (4) times respectively), without negotiating packet delivery ratio.

Named data networking (NDN) has emerged as a future networking architecture having the potential to replace the Internet. In order to do so, the NDN needs to cope with inherent problems of the Internet, such as attacks that cause information leakage from an enterprise. Since NDN has not yet been deployed on a large scale, it is currently unknown how such attacks can occur, let alone what countermeasures can be taken against them. In this paper, we first show that information leakage in NDN can be caused by malware inside an enterprise, which uses steganography to produce malicious interest names encoding confidential information. We investigate such attacks by utilizing a content name dataset based on uniform resource locators (URLs) collected by a Web crawler. Our main contribution is a name filter based on anomaly detection that takes the dataset as input and classifies a name in the Interest as legitimate or not. Our evaluation shows that the malware can exploit the path part in the URL-based NDN name to create malicious names, thus, information leakage in the NDN cannot be prevented completely. However, we show for the first time that our filter can dramatically choke the leakage throughput causing the malware to be 137 times less efficient at leaking information. This finding opens up an interesting avenue of research that could result in a safer future networking architecture.

Named Data Network (NDN) is a next-generation network architecture that shifts the traditional data communications paradigm Unlike conventional networks that rely on IP addresses, NDN delivers content based on data names rather than specific locations. In NDN, consumers express their requests by sending interest packets containing content names. These names are then propagated through the network nodes, which forward them to the appropriate destinations. The forwarding strategy in an NDN network plays a crucial role in ensuring efficient data delivery. This strategy includes a set of rules that determine the next hop for each interest packet. These rules are designed to optimize the forwarding process, minimizing delays and improving network efficiency. However, if the forwarding strategy is implemented without accurately identifying the appropriate face (i.e., the network interface) to forward interests toward the producer or the nearest cache node, it can lead to significant delays and packet drops. This, in turn, negatively impacts Quality of Service (QoS) parameters and the overall performance of the NDN network. This study applies supervised learning to classify consumer-requested interests to overcome this issue. This technique leverages several related variables to accurately classify these interests. The outcomes of the conducted research demonstrated that raw data from the mini-NDN output can be processed and transformed into a usable dataset. This data is then utilized to train a classification model with supervised learning. In a scenario with 9 NDN nodes and varying numbers of interests, distributed both uniformly and according to Zipf's law, the Random Forest model performs effectively, achieving an accuracy rate of 86.2% with an error rate of 14.8%.

Named Data Networking (NDN), an emerging communication model in the content centric networks, has recently presented a solid framework for the future Internet. The NDN exploits content name instead of host name (IP address) and content caching. These features make NDN particularly efficient in networks with intermittent connectivity and dynamic topologies such as mobile ad hoc networks (MANETs). Due to the structure of NDN nodes, design of forwarding strategies has a vital impact on the network performance. In this paper, we propose a new forwarding strategy: redundancy elimination forwarding (REF). In REF, the NDN node structure and the operation of the data structures are modified. These modifications improve network performance in terms of throughput, overhead and resource requirements. REF strategy is simulated in different scenarios based on ndnSIM and the results demonstrate that it provides robust performance for various environments.

Boosting named data networking for data dissemination in urban VANET scenarios

In this paper the integrated traffic control and traffic assignment problem is studied. This problem can be considered as a non-cooperative game in which the traffic authority, that controls the traffic signals, and the road users are the players, who use their own strategy and seek their own optimum. The game theoretical formulation leads to several different control strategies in which users’ reactions to traffic control decisions are taken into account. Users’ reactions can be the choice of route, departure time or even mode, but here only route choice is considered. In this paper some of these control strategies for traffic signal control are described: Webster control, Smith’s P , Anticipatory Control and System Optimum Control. The first two control strategies are well known and described in the literature. The anticipatory control strategy can be formulated as a bi-level optimisation problem and this problem is solved using genetic algorithms. Also the system optimum solution can be found using genetic algorithms, simultaneously optimising route flows and green times. In the paper the assignment method, together with the traffic model, is formulated. In the assignment several user classes, each with its own perception of route costs, are defined and taken into account. For several simple example networks the traffic control methods are tested and it is shown that taking route choice into account is beneficial to the network performance. Further research will focus on the improvement of the traffic model used in the control optimisation, realistic networks and the problem of departure time choice.

This paper investigates the issue of multi-hop forwarding in Vehicular Ad hoc Networks (VANETs), which is suffering from rapidly changing topology, short-lived and intermittent connectivity. Designed to replace TCP/IP using named data, Named Data Networking (NDN) has natural advantages to greatly overcome those challenges because of its multi-source and in-network caching characteristics. However, in the wireless environment the Interest and Data, proposed in NDN, are usually flooded because of the absence of Forwarding Information Base (FIB). To tackle this problem, we propose HVNDN, a hybrid forwarding strategy through NDN. More specifically, HVNDN introduces opportunistic and probabilistic forwarding strategy for location-dependent and location-independent information, respectively, which takes full advantage of geographic information and the characteristics of named data in VANETs. To make Vehicle-to-Vehicle (V2V) communication more effective and reliable, we design elaborate Interest and Data packets formats, retransmission and acknowledgement mechanism in HVNDN. Simulation results from NS-3 demonstrate that the proposed forwarding strategy outperforms the state-of-art scheme with less end-to-end delay and overhead.