Research on the Method of Implementing Named Data Network Interconnection Based on IP Network

The interaction between mobile Internet of Things (IoT) devices is based on a hybrid communication architecture. To improve packet delivery ratio, reduce end-to-end delay, and protect data privacy, designing an efficient data forwarding scheme is crucial in guaranteeing the quality of data transmission. Fog computing, a novel distributed computing framework, can decrease the amount of data transmission on the Internet and improve quality of services. In this paper, we propose a fog computing-assisted trustworthy forwarding (FCTF) scheme. To the best of our knowledge, this paper is the first study that investigates the role of fog infrastructure nodes (FINs) in forwarding scheme design. FCTF first selects the contact probability and the service degree as the basic trustworthy metrics between node pairs, and combines high-performance optimization algorithms to design a dynamic detection model of overlapping trustworthy communities (DOTCs). Then, we construct logical joint edge community structures on the basis of the results of DOTC and the distribution of FINs at each timestamp. Finally, based on the logical joint edge community, we define the forwarding utilities for FINs and mobile devices, respectively, and design self-adaptive forwarding rules. The experimental results prove that our FCTF scheme can enhance the delivery ratio, and decrease the latency and average hop-count more effectively, and has better routing quality compared with some popular forwarding models in mobile IoT applications.

A Responsibility-based Transport Control for Named Data Networking

Sensor networks comprise large number of sensor nodes densely located in an area for sensing purposes. A key consideration in sensor networks is that the sensor nodes have limited battery resources that determine the lifetime of the network. Motivated by the fact that geographical routing is localized, scalable and nearly-stateless, we propose three energy-aware geographical forwarding schemes. The aim is to improve the network lifetime by considering the residual energy of neighbours in deciding the next-hop while preserving the localized, scalable and nearly-stateless property of geographical routing. In view of a possible heavy traffic along the perimeter of the hole, which may cause nodes along this perimeter to die faster, a hole avoidance method is proposed to reduce the traffic along the hole to remove this possibility. Topology control algorithm is proposed to be executed at each node to remove undesirable geographical neighbours determined by a link cost. It removes geographical neighbours without causing unnecessary holes and still maintains the connectivity in the network. The main aim of removing undesirable geographical neighbours is to achieve the global objective of having a longer network lifetime. The effectiveness of the three proposed forwarding schemes and the topology control algorithm are verified through extensive simulation results

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%.

Internet technological trends indicate that the link transmission speed has been significantly improved in the last decade, while the network nodes (routers and switches) have become the common bottleneck of Internet. Based on this insight, this paper applies the newly developed Visualized IP-based Network Simulator (VINS) to study TCP/IP networks formed on fast speed links. VINS design complies with the related RFCs and BSD operating system in three ways: IP networking; socket hosting and interfacing; service on the node. Two sample scenarios are presented regarding IP networking and TCP end-to-end congestion control for bulk data delivery such as video files, and handling packet overflow and delay on intermediate nodes.

The growth of internet has shown the limitation TCP/IP architecture and has enforced the practitioners to invent a new architecture that are more capable to support large scale implementation of content based distribution. Today, Named Data Networking (NDN) has become a breakthrough alternative solution and a potential choice for next generation internet architecture. In this paper, we perform investigation to which extend existing forwarding strategies capable to support context information networking and increase service quality. We test this scenario to handle various types of user applications such as VoIP, multimedia streaming, and widely used application data delivery protocols, with specific requests (contexts), then find out which parts of the app prerequisites are consistent with the current strategy. We used NS3 with ndnSIM 2.0 to simulate and evaluate. This research mainly focused on the implementation of HTTP protocol alongside with the NDN. The experiment results show that Stochastic Adaptive Forwarding strategy provides excellent performance in different experiment scenarios.

In recent years, DoS attacks are becoming more serious in security of the Internet. They are rather easy to implement, but difficult to prevent. Therefore, tracing to the attackers and punishing them is considered to be an effective means against the attacks. Several tracing mechanisms have been proposed and analyzed for simplified models. This paper describes a simulation method for two network models, ICMP traceback (iTrace) and IP marking traceback (IP marking). This method modifies the OPNET to add the tracing codes. Also described is the results of simulation experiments, which indicate the effectiveness of the method.

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.

Named Data Network (NDN) is the future internet network that data-centric and adaptive to consumer requirement. Routing and forwarding systems on the NDN networks are different from IP networks due to the use of cache at each node on the network. The implementation of the Loop Free Inport-Dependent (LFID) routing protocol on NDN networks aims to eliminate loops on the network by eliminating the preferred routes or inefficient next hops. Forwarding strategies that can be implemented are Best Route, Access, Random, and Multicast. Therefore, machine learning technology is needed with various classification methods that can be implemented in machine learning so the output gives the recommendations that can be used to maximize the performance of the NDN network. The final result of this study recommends that the forwarding strategies of Best Route and Access provide good delay values, which in the range of 150 ms to 300 ms. Random forwarding strategy with a payload size> = 3072 kbps still provides a good delay value to the network, which in the 150 to 300 ms range. All forwarding strategies of Best Route, Access, Random, and Multicast provide delay values with a very good category of delay values, which is below 150 ms if the type of interest (data) that requested to the network is a popular interest. Keywords : Named Data Network , Routing, Forwarding, Machine Learning. DOI : http://doi.org/10.5281/zenodo.4320264

The Media Broadcast industry has evolved from Serial Digital Interface (SDI) based infrastructures to IP networks. While IP based video broadcast is well established in the data plane, the use of IP networks to transport media flows still poses challenges in terms of resource management and orchestration. SDN based orchestration architectures have emerged in the industry that use SDN to route the media flows of a broadcast service across the provider IP network. Several approaches to multimedia flow routing in IP based SDN networks have been proposed in the context of streaming applications over the Internet. These range from model based linear optimization solutions that have high complexity to simple shortest path based routing heuristics with either static or dynamic link costs. More recently model-free optimization methods such as Deep Reinforcement Learning (DRL) have been proposed for routing and Traffic Engineering of multimedia flows in SDN networks. The media broadcast scenario however has specific requirements, with services like Master Control Room operation and Live broadcasting of events, and it has been rarely addressed in the literature. In this work we propose a DRL based routing method for this scenario and compare it to static and dynamic link cost algorithms based on Dijkstra shortest paths. This is to our knowledge the first work to follow this approach in the context of Media Broadcast services in IP infrastructures. The algorithm is designed considering the specifications and capabilities of one of the leading SDN orchestrators in the market and considers the more common Service Level Agreement requirements in the industry. Three different DRL algorithms are implemented and compared and we evaluate them using a real service provider network topology. The results indicate that DRL based routing is applicable in real production scenarios and that it achieves considerable performance gains when compared to the static and dynamic link cost shortest path algorithms commonly used today.

Ex-ante impact of on-farm diversification and forward integration on agricultural value chain resilience: A system dynamics approach

An adaptive and multi-path greedy perimeter stateless routing protocol in flying ad hoc networks

The routing of vehicles or personnel in complex logistics systems is a task that needs to be solved in numerous applications, e.g., detailed models of transport networks or order picking areas. The number of relevant nodes in such networks can easily exceed 10,000 nodes. Often, a basic task is finding the shortest path from one node (start) to another (destination). Within the last years various simulation tools have been extended by respective algorithms. However, the execution time of the simulation model may significantly depend on the number of nodes in the network. We present algorithms from the literature and a comparison of three simulation tools with respect to execution time and model size for different scenarios. We further present an approach to work with so called sub-networks, i.e., the network is separated into areas, where finding the shortest path includes the task of starting at a node in one sub-network with a destination in another one.

Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, acoustic signals are used to transmit messages in an underwater environment. Their features of low speed and narrow bandwidth make media access control (MAC) protocols unsuitable for radio communications. Furthermore, a network consists of a large number of randomly deployed nodes, making it impossible to pre-define an optimized routing table or assign a central controller to coordinate the message propagation process. Thus, optimized routing should emerge via interaction among individual nodes in the network. To address these challenges, in this paper we propose a communication coordinator under the time division multiple access (TDMA) framework. Each node in the network is equipped with such a coordinator so that messages in the network can be sent following the shortest path in a self-organized way. The coordinator consists of a slot distributor and a forwarding guide. With the slot distributor, nodes in the sensor network occupy proper communication slots and the network finally converges to the state without communication collision. This is achieved with a set of ecological niche- and pheromone-inspired laws, which encourage nodes to occupy slots that can decrease the waiting time for a node to send a message packet while weakening the enthusiasm for a node to occupy the slots that it fails to occupy several times. With the forwarding guide, a node can send the message packet to the best successor node so that the message packet can be sent to the base station along the shortest path. It has been proven that the laws in the forwarding guide are equivalent to the Dijkstra Algorithm. Simulation experiment results indicate that with our coordinator, the network can converge to the state without collision using fewer coordination messages. In addition, the time needed to send a message to the destination is shorter than that of the classical Aloha protocol.

In this work we propose the routing standby model to save energy in an IP networks during low traffic hours. This model is based on the ability to put in standby mode all the IP processing functionalities of a network node. The main feature of this model is to save a great amount of energy, about the 50% of the total power consumption of a router, avoiding the drawback of a classical node standby, related to the removal of a node from the network. We also define a routing standby algorithm, based on the well-known Floyd-Warshall algorithm, able to detect a set of routing standby nodes so that traffic requirements are satisfied. The performance evaluation show that about the 50% of network routers can avoid IP operations when traffic decreases over 75% of peak value; in this way a remarkable energy saving can be obtained.