Adaptive Forwarding Research Articles

Learning an adaptive forwarding strategy for mobile wireless networks: resource usage vs. latency

Learning an adaptive forwarding strategy for mobile wireless networks: resource usage vs. latency

Distributed Adaptive Forwarding Finite-Time Output Consensus of High-Order Multiagent Systems via Immersion and Invariance-Based Approximator.

A finite-time output consensus control problem is investigated in this article for an uncertain nonlinear high-order multiagent systems (MASs). For this class of MASs, the order of individual follower is reduced gradually by implementing the immersion and invariance (I&I) control theory repeatedly, and a requirement of solving partial differential equations (PDEs) in I&I control theory is obviated. Furthermore, an I&I-based radial basis function neural network (RBFNN) approximator is developed, where an extra cross term is added in the approximation mechanism, and the form of an update law for weights is transformed into a proportional and integral one. This I&I-based RBFNN approximator does not rely on a cancellation of the perturbation term, and these uncertainties are reconstructed by the I&I manifold adaptively, which is for improvement of approximation behaviors of traditional RBFNNs. On this basis, a distributed adaptive forwarding finite-time output consensus control strategy is proposed by combining a sign function, and the convergence time of the MAS can be adjusted with appropriate finite-time parameters. Finally, two illustrative examples verify the effectiveness of the theoretical claims.

Adaptive Forwarding and Routing of Named Data Networking A Survey

In recent years, Named Data Networking (NDN) has been one of the future internet network architectures by proposing it as a substitute for existing IP networks. NDN assigns addresses/names to data or content, whereas IP assigns addresses to devices. NDN router has a Content Store (CS) component to store requested packets and reducing the time for duplicate content requests. As network architecture, NDN uses several algorithms and strategies in the process. This paper will specifically discuss the forwarding and routing algorithm. Forwarding mechanisms play a significant role in packet delivery on the NDN system. Even though routing was not the main component of the NDN system, the routing mechanism and Routing Information Base remain important, considering Forwarding Information Base was generated after Routing Information Base was generated. Routing has significant control, whereas forwarding can give finer control over the delivery path. Considering the previous statement, the combination of routing and forwarding strategy must be considered to optimize the performance of the packet delivery system on NDN. In addition to getting information about the entire network, centralized routing, and adaptive networking are needed to distribute all network traffic fairly. The challenges and opportunities can also be a reference and a guideline for future Adaptive NDN research.

An RSU-Assisted Hybrid Emergency Message Broadcasting Protocol for VANETs

In vehicular ad hoc networks (VANETs), emergency messages broadcasting has been considered as one of the important parts for safety related applications. The efficiency and reliability of the emergency message broadcasting are severely affected by the network structure, message redundancy, channel contention, etc. To address this problem, in this paper, we propose an RSUs-assisted hybrid emergency message broadcasting (RA-HEMB) protocol for two-way grid roads in urban VANETs. In doing so, the broadcasting target region is first generated based on the instantaneous traffic status and types of the emergency messages. To balance the deliver latency and the reliability, an adaptive forwarding nodes selection scheme based on position is proposed to dynamically determine the forwarding area according to different vehicle densities and vehicle-to-vehicle (V2V) communication ranges. Then, based on the forwarding nodes selection scheme and roadside unit (RSU) deployment, an RA-HEMB protocol is established by properly utilizing both the wired links via RSUs and the wireless links to broadcast the emergency message in hybrid vehicular networks. Simulation results show that the proposed RA-HEMB can greatly shorten the broadcasting time needed to notice the nodes within the target region performance compared to the benchmark protocols.

LEAF: Let’s Efficiently Make Adaptive Forwarding in Payment Channel Networks

Blockchain technology is widely applicable to modern payment systems but has inherent throughput limitations. Off-chain networks are proposed to solve scalability issues, which allows parties to efficiently perform micropayments without committing all of the payments to the blockchain. Off-chain payments with security and privacy protection requirements use the smart contract to ensure security and reduce the risk of sensitive information leakage. Although off-chain payments avoid expensive on-chain operations, it raises many concerns, such as the capacity limitation of payment channels and highly dynamic channel status, lowering the throughput of payment channel networks (PCNs). In our work, we explore the path overlap issues in PCNs and propose a decentralized payment routing scheme to improve the network throughput and reduce the redundant traffic overhead of PCNs, thereby guaranteeing efficient payments. The simulation results indicate that the proposed routing algorithm can achieve higher throughput than other routing schemes while guaranteeing short payment times.

Optimization Simulation of Balanced Distribution of Multimedia Network Modular Teaching Resources

The balanced distribution of multimedia wireless network modular teaching resources is improved by optimizing and adjusting the balanced distribution of multimedia wireless network modular teaching resources. Based on block grid balanced scheduling and resource spectrum fusion, a balanced distribution method of multimedia wireless network modular teaching resources is proposed. A blockchain Internet of Things accesses control network architecture system for the balanced allocation of multimedia wireless network modular teaching resources established using the priority business scheduling method. Modular resource allocation of multimedia wireless networks and adaptive forwarding are adjusted to control the data packets. The bandwidth guarantee mechanism is used for the equitable allocation of multimedia wireless network modular teaching resources information flow. Multi-service hierarchical management of multimedia wireless network modular teaching resources spectrum is controlled by block grid balanced scheduling and resource spectrum fusion. The high-energy physical computing system structure is used to allocate the resource spectrum balance of the modular teaching resources of the multimedia wireless networks, ensuring the bandwidth requirements of various services. The classified management and balanced allocation of the multimedia wireless networks’ teaching storage, system, and teaching spectrum resources are made possible. The results of the tests show that this method effectively reduces the delay in the allocation of modular multimedia wireless network teaching resources, improves the utilization rate of modular multimedia wireless network teaching resources, and significantly improves their reading and writing performance.

Provides an Approach based on Adaptive Forwarding and Label Switching to Improve the Speed of Packet Forwarding in NDN Networks

Provides an Approach based on Adaptive Forwarding and Label Switching to Improve the Speed of Packet Forwarding in NDN Networks

Provides an Approach based on Adaptive Forwarding and Label Switching to Improve the Speed of Packet Forwarding in NDN Networks

Named Data Networking (NDN) is a recently designed Internet architecture that benefits data names instead of locations and creates essential changes in the abstraction of network services from "delivering packets to specific destinations” to "retrieving data with special names" makes. This fundamental change creates new opportunities and intellectual challenges in all areas, especially network routing and communication, communication security, and privacy. The focus of this dissertation is on the forwarding aircraft feature introduced by NDN. Communication in NDN is done by exchanging interest and data packets. Consumers send interest packets to request data, routers send them based on the data prefix name, and manufacturers respond with data packets exactly how interest is received. During this process, routers maintain information about the status of suspended interests. As regards NDN is still in its early stage, none of these strong efficiency features have done systematically designed, measured, or tracked in data retrieval and multi-path detection. This paper tries to improve transport performance by combining label switching with adaptive transport methods. For this purpose, in addition to RTT, another timer called T is used, an RTT factor for interface rating. And uses the BDP to choose the best interface to respond to interest. During this process, response time, throughput, and data recovery time are improved by 12%, 10%, and 5%, respectively.

On the Prefix Granularity Problem in NDN Adaptive Forwarding

One unique architectural benefit of Named Data Networking (NDN) is adaptive forwarding, i.e., the forwarding plane is able to observe past data retrieval performance and use it to adjust forwarding decisions for future Interests. To be effective, adaptive forwarding assumes that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interest Routing Locality</i> is related to Interests’ common name prefix, meaning that Interests sharing the same prefix are likely to follow a similar forwarding path within a short period of time. Since Interests can have multiple common prefixes with different lengths, the real challenge is determining which prefix length should be used in adaptive forwarding to record path performance measurements - we refer to this as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prefix Granularity Problem</i> . The longer the common prefix is, the better the Interest Routing Locality, and the larger the forwarding table. Given the limited FIB size, route names are designed to be considerably shorter than Interest names. Existing adaptive forwarding designs use route names to record path performance measurements, which looses forwarding adaptability as it promises in the event of partial network failures. In this work, we propose to dynamically aggregate and de-aggregate name prefixes in the forwarding table in order to use the prefixes that are the most appropriate given current network situation. In addition, to reduce the overhead of adaptive forwarding, we propose mechanisms to minimize the use of the longest prefix matching in Data packet processing. Simulations demonstrate that the proposed techniques can result in better forwarding decisions in the event of partial network failures with significantly reduced overhead.

Moving Target Defense Based on Adaptive Forwarding Path Migration for Securing the SCADA Network

Static characteristics of supervisory control and data acquisition (SCADA) system are often exploited to perform malicious activities on smart grids. Most of the time, the success of cyberattacks begins with the profiling of the target system and follows by the analysis of the limited resources. To alleviate the asymmetry between attack and defense, network-based moving target defense (MTD) techniques have been applied in the network system to defend against cyberattacks by constructing a dynamic attack surface to the adversary. In this paper, we propose a novel MTD technique based on adaptive forwarding path migration (AFPM) that focuses on improving the defense capability and optimizing the network performance of path mutation. Considering the transient problems present in path mutation caused by the dynamic switching of the forwarding path, we formalize the mutation constraints based on the satisfiability modulo theory (SMT) to select the mutation path. Considering the limited defense capability of path mutation owing to the traditional mutation selection mechanism, we design the mutation path generation algorithm based on the network security capacity matrix to obtain an optimal combination of mutation path and mutation period. Finally, we compare and analyze various cyber defense techniques used in the SCADA network and demonstrate experimentally that our MTD technique can prevent more than 92% of passive monitoring under specified conditions while ensuring the quality of service (QoS) to be almost the same as the static network.

LAFS: a learning-based adaptive forwarding strategy for NDN-based IoT networks.

Named Data Networking (NDN) is a data-driven networking model that proposes to fetch data using names instead of source addresses. This new architecture is considered attractive for the Internet of Things (IoT) due to its salient features, such as naming, caching, and stateful forwarding, which allow it to support the major requirements of IoT environments natively. Nevertheless, some NDN mechanisms, such as forwarding, need to be optimized to accommodate the constraints of IoT devices and networks. This paper presents LAFS, a Learning-based Adaptive Forwarding Strategy for NDN-based IoT networks. LAFS enhances network performances while alleviating the use of its resources. The proposed strategy is based on a learning process that provides the necessary knowledge allowing network nodes to collaborate smartly and offer a lightweight and adaptive forwarding scheme, best suited for IoT environments. LAFS is implemented in ndnSIM and compared with state-of-the-art NDN forwarding schemes. As the obtained results demonstrate, LAFS outperforms the benchmarked solutions in terms of content retrieval time, request satisfactory rate, and energy consumption.

Stochastic Adaptive Forwarding Strategy Based on Deep Reinforcement Learning for Secure Mobile Video Communications in NDN

Named Data Networking (NDN) can effectively deal with the rapid development of mobile video services. For NDN, selecting a suitable forwarding interface according to the current network status can improve the efficiency of mobile video communication and can also avoid attacks to improve communication security. For this reason, we propose a stochastic adaptive forwarding strategy based on deep reinforcement learning (SAF-DRL) for secure mobile video communications in NDN. For each available forwarding interface, we introduce the twin delayed deep deterministic policy gradient algorithm to obtain a more robust forwarding strategy. Moreover, we conduct various numerical experiments to validate the performance of SAF-DRL. Compared with BR, RFA, SAF, and AFSndn forwarding strategies, the results show that SAF-DRL can reduce the delivery time and the average number of lost packets to improve the performance of NDN.

Intelligent Forwarding Strategy for Congestion Control Using Q-Learning and LSTM in Named Data Networking

Named data networking (NDN) is a future network architecture that replaces IP-oriented communication with content-oriented communication and has new features such as cache, multiple paths, and multiple sources. Services such as video streaming, to which NDN can be applied in the future, can cause congestion if data is concentrated on one of the nodes during high demand. To solve this problem, sending rate control methods such as TCP congestion control have been proposed, but they do not adequately reflect the characteristics of NDN. Therefore, we use reinforcement learning and deep learning to propose a congestion control method that takes advantage of multipath features. The intelligent forwarding strategy for congestion control using Q-learning and long short-term memory in NDN proposed in this paper is divided into two phases. The first phase uses an LSTM model to train a pending interest table (PIT) entry rate that can be used as an indicator to detect congestion by knowing the amount of data returned. In the second phase, it is forwarded to an alternative path that is not congestive via Q-learning based on the PIT entry rate predicted by the trained LSTM model. The simulation results show that the proposed method increases the data reception rate by 6.5% and 19.5% and decreases the packet drop rate by 7.3% and 17.2% compared to an adaptive SRTT-based forwarding strategy (ASF) and BestRoute.

PFECC: a precise feedback-based explicit congestion control algorithm in named data networking

Named data networking (NDN), as a future Internet architecture that is a promising alternative to TCP/IPnetworks, has the new features of connectionless, in-network cache, and hop-by-hop forwarding, which makes thecongestion control algorithms of traditional TCP/IP networks unable to be directly applied to NDN. In addition, sincethe optimal size of the sending window cannot be determined, the existing window-based congestion control algorithmsgenerally use the AIMD-like window adjustment algorithm, which cannot achieve the optimal throughput. In thispaper, we propose a precise feedback-based, multipath-aware congestion control algorithm PFECC, which is inspiredby Accel-Brake Control algorithm. PFECC considers the influence of Interest flows, uses a fair queuing algorithm atthe intermediate node to calculate the target rate of each flow, and gives accurate feedback on each dequeued datapacket. The consumer changes the size of the sending window according to the feedback to quickly converge to thetarget bandwidth. To fully exploit the hop-by-hop adaptive forwarding feature of NDN, each downstream node timelysenses the congestion trend of the upstream link to split the forwarding rate of Interest to avoid congestion. Simulationresults show that PFECC can effectively reduce transmission delay, treat each flow fairly, and converge to the bestthroughput faster.

Adaptive Forwarding With Probabilistic Delay Guarantee in Low-Duty-Cycle WSNs

Despite many existing research on data forwarding in low-duty-cycle wireless sensor networks (WSNs), relatively little work has been done on energy-efficient data forwarding with probabilistic delay bounds. Probabilistic delay guarantees ( i.e. , delay bounded data delivery with reliability constraints) are of increasing importance for many delay-constrained applications, since deterministic delay bounds are prohibitively expensive to guarantee in WSNs. However, radio duty-cycling and unreliable wireless links pose challenges for achieving the probabilistic delay guarantee in WSNs. In this paper, we propose EEAF, a novel energy-efficient adaptive forwarding technique tailored for low-duty-cycle WSNs with unreliable wireless links. We show the existence of path diversity in low-duty-cycle WSNs, where delay-optimal routing and energy-optimal routing are likely following different paths. The key idea of EEAF is to exploit the intrinsic path diversity to provide probabilistic delay guarantees while minimizing transmission cost. In EEAF, an early arriving packet will be adaptively switched to the energy-optimal path for energy conservation. Delay quantiles are derived at each node in a distributed manner and are used as the guidelines in the adaptive forwarding decision making. Extensive testbed experiment and large-scale simulation show that EEAF effectively reduces the transmission cost by 12%~25% with probabilistic delay guarantees under various network settings. In addition, we extend the EEAF technique with data aggregation for event-based traffic scenarios. Evaluation using publicly available WSN event traffic traces yields very encouraging results with up to 40% energy saving in probabilistic delay bounded data delivery.

AFSndn: A novel adaptive forwarding strategy in named data networking based on Q-learning

Named Data Networking (NDN) is a new network architecture, which employs a new content-centric communication model to replace the traditional host-centric communication model. In TCP/IP network, data packets are forwarded by routers according to routing table established previously. While in NDN, routing nodes can dynamically make forwarding decisions based on network status. By considering this forwarding feature, we proposed a novel adaptive forwarding strategy in Named Data Networking (AFSndn) based on Q-learning to minimize the delivery time. AFSndn is divided into two phases—Exploration phase and Exploitation phase. The Exploration phase aims to collect information, while the Exploitation phase aims to dynamically forward interest packets. Simulation experiment results show that AFSndn has better performance compared to others famous algorithms.

Low-power clustering scheduling algorithm for wireless sensor nodes in the internet of things

Aiming at the problems of high energy consumption and long packet forwarding time existing in the existing wireless sensor node scheduling algorithm, a low-power scheduling algorithm for wireless sensor nodes in the internet of things based on clustering power consumption balanced scheduling is proposed. In this algorithm, a distributed balancing control model is used for optimised deployment design of wireless sensor nodes and design of node transmission route of the internet of things; the full-network power consumption equalisation model is adopted for path planning of wireless sensor nodes; based on an adaptive route forwarding protocol, clustering balancing control of wireless sensor nodes is performed; the minimum power consumption load is taken as the constraint cost to perform optimised design of clustering forwarding scheduling algorithm for wireless sensor nodes in the internet of things. Simulation results show that in clustering scheduling of wireless sensor nodes in the internet of things, this method costs relatively low power consumption, provides good energy equalisation of the full network, controls the network energy efficiency well, and provides high success forwarding rate of clustering scheduling of sensor nodes and good overall network performance, it has high application advantages.

Grid based Bio-Inspired Energy Efficient Trusted Hybrid Routing Protocol for MANET

Device to device (D2D) data communication in cellular wireless network can be done either directly from source equipment (SE) to destination equipment (DE) or through relay equipment (RE). This type of network mainly known as Wireless ad hoc networks. Mobile ad-hoc networks (MANETs) have constraints such as such as limited power, route failure, bandwidth allocation, and computational complexity. The major impact on the performance of network depends on routing strategy. Routing strategy for matching the multi constraints of network is so far challenge in conventional network. MANETs suffers from dynamic movement of node, power, dynamic routing and storage complexity. Dynamic movement force the computation of re-routing, which act as extra computational burden on the system. In this paper, we propose the Grid Cluster based Multi-Objective Genetic Algorithm for Energy Efficient Trusted Network (gCMOGAEETN) algorithm for finding optimal routes from a given SE to a given DE. Our simulation results show that gCMOGAEETN algorithms are efficient in solving these routing problems and are capable of finding the optimal solutions at lower complexity than the ’brute-force’ exhaustive search, when the number of user equipment (UEs) is higher than or equal to 50. The analytical and simulation result shows that proposed method exhibit significantly higher performance than optimal adaptive forwarding strategy (OAFS), sub-optimal adaptive forwarding strategy (SAFS), memory enhanced genetic algorithm (MEGA), Elitism-based Immigrants Genetic algorithm (EIGA), dynamic load-balanced clustering problem (DLBCP) and Genetic Algorithm Based Optimization of Clustering (GABOC)..

Performance Evaluation of an Adaptive Forwarding Strategy in Named Data Networking

Performance Evaluation of an Adaptive Forwarding Strategy in Named Data Networking

Design and Verification of an NDN-Based Safety-Critical Application: A Case Study With Smart Healthcare

Internet of Things (IoT) is an emerging networking paradigm where smart devices generate, aggregate, and seamlessly exchange data over the predominantly wireless medium. The Internet, so far, has played a significant role in connecting the world, but still, IoT-based solutions are suffering from two primary challenges: 1) how to secure the sensors data and 2) how to provide efficient local and global communication among various heterogeneous devices. Recently, named data networking (NDN), a future Internet paradigm is proposed to improve and simplify such IoT communication issues. NDN allowed users to fetch data by names irrespective of the actual hosting entity connected through a host-specific IP address. NDN well suits the content-centric pattern of machine-to-machine (M2M) communications predominantly used in IoT. In this paper, we leverage the basic feats of NDN architecture for designing and verification of an NDN-based smart health IoT (NHealthIoT) system. NHealthIoT uses pure-NDN-based M2M communication for capturing and transmission of raw sensor data to the home server which can detect emergency healthcare events using Hidden Markov Model. Emergency events are notified to the cloud server using a novel context-aware adaptive forwarding (Cdf) strategy. Post emergency notifications, and user health information is periodically pulled by the cloud server and by other interested parties using NDN-based publish/subscribe paradigm. The cloud server carries out long-term decision making using probabilistic modeling for detecting the possibility of chronic diseases at the early stage. We extend the workflows intuitive formal approach model for verifying the correctness of NHealthIoT during the emergency. We evaluate the cdf strategy using ndnSIM. Moreover, to validate and to show the usability of NHealthIoT, we develop a proof-of-concept prototype testbed and evaluate it extensively. We also identify some research challenges of the NDN-IoT for researchers.