Forwarding Decisions Research Articles

Learning automata based routing and content delivery for vehicular named data networking

In Vehicular Named Data Networking (VNDN), vehicle mobility leads to frequent reverse-path interruptions and stale forwarding information, which further results in content delivery failures and disenables aggregation. Taking into account these limitations, we propose a learning automata based routing and content delivery solution for VNDN. The novelties of the solution are threefold: (1) Learning automata is leveraged to make forwarding decisions and deliver vehicular contents; (2) Various metrics including connection durations, unsatisfied requests and data response delays are utilized to compute the selection probability of each vehicle in order to select the best vehicle for content delivery; and (3) Aggregation and in-network caching are achieved through stable reverse paths. The typical application of the proposal is that vehicles rapidly access nearby road conditions to maneuver safe driving. The proposal is evaluated quantitatively. Compared with the existing solutions, the proposal reduces the content delivery latency and overheads by nearly 32.54% and 38.3%, respectively.

Who are the best voluntary disseminators on social networking sites? An exploration based on panel data from twitter

PurposeUsers' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.Design/methodology/approachVia Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.FindingsPreference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.Originality/valueThis work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

MultiTASC++: A continuously adaptive scheduler for edge-based multi-device cascade inference

Cascade systems, consisting of a lightweight model processing all samples and a heavier, high accuracy model refining challenging samples, have become a widely-adopted distributed inference approach to achieving high accuracy and maintaining a low computational burden for mobile and IoT devices. As intelligent indoor environments, like smart homes, continue to expand, a new scenario emerges, the multi-device cascade. In this setting, multiple diverse devices simultaneously utilize a shared heavy model hosted on a server, often situated within or close to the consumer environment. This work introduces MultiTASC++, a continuously adaptive multi-tenancy-aware scheduler that dynamically controls the forwarding decision functions of devices to optimize system throughput while maintaining high accuracy and low latency. Through extensive experimentation in diverse device environments and with varying server-side models, we demonstrate the scheduler's efficacy in consistently maintaining a targeted satisfaction rate while providing the highest available accuracy across different device tiers and workloads of up to 100 devices. This demonstrates its scalability and efficiency in addressing the unique challenges of collaborative DNN inference in dynamic and diverse IoT environments.

Maximizing SDN resilience to node‐targeted attacks through joint optimization of the primary and backup controllers placements

AbstractIn software defined networks (SDN) packet data switches are configured by a limited number of SDN controllers, which respond to queries for packet forwarding decisions from the switches. To enable optimal control of switches in real time the placement of controllers at network nodes must guarantee that the controller‐to‐controller and switch‐to‐controller communications delays are bounded. Apart from the primary controllers that control the switches in the nominal state, separate backup controllers can be introduced that take over when the primary controllers are unavailable, and whose delay bounds are relaxed. In this paper, we present optimization models to jointly optimize the placement of primary and backup controllers in long‐distance SDN networks, aimed at maximizing the network's resilience to node‐targeted attacks. Applying the models to two well‐known network topologies and running a broad numerical study we show that, when compared with the standard approach of using only primary controllers, the use of backup controllers provides significant resilience gains, in particular in case of tight delay bounds.

IoT data dissemination scheme for reducing delay in multi-broker environments

With the rapid increase of Internet of Things (IoT) devices, an efficient communication protocol is needed to effectively manage the exchange and generation of data from these devices. Message Queuing Telemetry Transport (MQTT) is widely adopted in IoT systems due to its lightweight design and the support for topic-based publish–subscribe architectures. In MQTT, multiple IoT devices are connected to a server, referred to as a broker, to facilitate efficient data transmission and reduce network overhead. However, the traditional single-broker system is unable to efficiently support the massive number of IoT devices. Although a multi-broker system is proposed as a solution, the use of the flooding method for updating topics has generated a significant amount of inter-broker traffic, causing severe congestion in the brokers. As a result, this will lead to long topic update delay. To resolve this issue, in this paper, we propose a topic update dissemination scheme by considering the congestion in brokers. We formulate the topic update dissemination problem using the Lyapunov function, to minimize the topic update delay in the multi-broker system. To solve the problem, we propose a heuristic algorithm for the update dissemination forwarding decision. The simulation results show that the proposed algorithm can effectively reduce the topic update delay compared to other existing algorithms.

Mistill: Distilling Distributed Network Protocols From Examples

Traffic Engineering (TE) mechanisms in data center networks make distributed forwarding decisions based on the global network state. Thus, new TE mechanisms require the design and implementation of effective information exchange and efficient decentralized algorithms to compute forwarding decisions, which is challenging and time-intensive. To automate and simplify this process, we propose Mistill. Mistill distills the forwarding behavior of TE policies from exemplary forwarding decisions into a Neural Network. Mistill learns (i) how to encode local state into update messages, (ii) which network devices must exchange updates, and (iii) how to map the exchanged updates into forwarding decisions. We demonstrate the abilities of Mistill by learning three TE policies, verifying their performance in simulations on synthetic and realworld traffic patterns, and by showing that the learned policies generalize to unseen traffic patterns. We implement Mistill as a proof-of-concept and show that Mistill reacts on average within 1.3 ms to changes in the network.

Selective wakeup for parallel forward engines under variable load towards energy conservation in IP routers

SummaryRecent studies have shown that a large amount of energy consumption in the communication medium occurs at the data processing plane. The data plane executes the forwarding decisions in the routers and switches. Through initial studies, the line card was identified as the most granular component of the router and later forward engine, a sub component of line card has been recognized as the most power intensive component of routers. Fundamental techniques like link rate adaption, sleep and wake‐up have been imposed to reduce the energy at line cards and forward engines. On the other hand, router's architectures undergo drastic changes like empowering data plane with parallel processing due to the rise of gigabit routers. In this paper, to increase the degree of local autonomy and better energy savings, we have adopted the parallel processing design and imposed a variable amount of traffic on different line cards. The existing parallel forward engines perform load balancing in synchronous manner. In our design, asynchronous behavior is imposed such that every line card could independently decide to wake up or turn off their forward engine based on the traffic parameters. We have done simulations and witnessed increased energy savings (average sleeping time) without compromising the completion of lookup process.

MASK: Practical Source and Path Verification Based on Multi-AS-Key

The source and path verification in Path-Aware Networking considers the two critical issues: (1) end hosts could verify that the network follows their forwarding decisions, and (2) both on-path routers and destination host could authenticate the source of packets and filter the malicious traffic. Unfortunately, the state-of-the-art mechanisms require heavy communication overhead in the network and computation overhead in the router; moreover, it is difficult to meet the dynamic requirements of the end host. We propose a user-driven mechanism, source and path verification based on Multi-AS-Key (MASK). MASK decreases the communication overhead by a short additional packet header and reduces the computation overhead by separating the control and data plane in terms of the cryptographic operation. Furthermore, it utilizes the stateful user to instruct the stateless routers to process the packet with a user-driven policy, thus satisfying the user’s requirements such as detecting the packet drop and replay attack. With the plausible design, the communication overhead for realistic path lengths is 1/2 to 1/10 compared with the state-of-the-art mechanisms. We implement MASK in the BMv2 environment and commodity Barefoot Tofino programmable switch, testify that MASK introduces significantly less overhead than the state-of-the-art mechanisms, and demonstrate that MASK could achieve the verification in the programmable switch at line rate.

Regular routine aware routing in opportunistic mobile social networks

AbstractDue to intermittent connectivity, routing protocols in opportunistic mobile social networks (OMSN) forward messages through opportunistic contacts. Previous opportunistic routing protocols focus on the past contact history of nodes to make a forwarding decision. It was found that predicting temporally evolving contacts and communities according to different times of the day would improve the accuracy of the prediction. In this paper, a routing paradigm called RRoutine is proposed that allows message holders to exchange multiple copies of the message based on the predicted number of communities. This distribution continues until the remaining copies of the message become one. Each message holder applies either inter or intra‐community forwarding and selects a set of potential forwarders based on the prediction of future encounters in the community and an expected remaining duration between two nodes (ERIMD). The estimation of ERIMD takes into account the sequence of daily activities, the time scale of current activities, and the return time to regular meetings of the nodes. Also, unlike traditional routing, where a single forwarder is selected, a set of potential candidates is selected and prioritized according to their contact prediction, with the most likely candidate receiving the highest priority. Simulation results obtained from synthetic and real mobility data show that the proposed paper improves the performance of the system in terms of message delivery rate, throughput, and latency.

MHSEER: A Meta-Heuristic Secure and Energy-Efficient Routing Protocol for Wireless Sensor Network-Based Industrial IoT

Several industries use wireless sensor networks (WSN) for various tasks such as monitoring, data transmission, and data gathering. They find applications in the industrial internet of things (IIoT). WSNs are utilized to track and monitor changes in the environment. Since they include multiple small sensor nodes (SN), they are severely constrained, so resource management geared toward energy efficiency is crucial in this kind of network. Minimizing the power to interpret, transmit, and store data between various sensors poses important challenges. Experts have considered various ways to address these issues that unavoidably affect the network’s performance: reducing energy usage while maintaining system throughput remains the primary research issue. Another important concern relates to network security. Specifically, intrusion detection and avoidance are major concerns. In this work, we introduce the meta-heuristic-based secure and energy-efficient routing (MHSEER) protocol for WSN-IIoT. The protocol learns the forwarding decisions using the number of hops, connection integrity characteristics, and accumulated remaining energy. To make the method more secure, the protocol also employs counter-encryption mode (CEM) to encrypt the data. A meta-heuristics study designed to achieve reliable learning is used in the suggested protocol. The protocol consists of two stages. The first stage uses a heuristics method to improve the option for dependable data routing. Security based on a computationally simple and random CEM is accomplished in the second stage. The proposed MHSEER protocol has been compared to the secure trust routing protocol for low power (Sectrust-RPL), heuristic-based energy-efficient routing (HBEER), secure and energy-aware heuristic-based routing (SEHR), and secure energy-aware meta-heuristic routing (SEAMHR) in terms of packet drop ratio, throughput, network delay, energy usage, and faulty pathways. The proposed protocol increases throughput to 95.81% and decreases the packet drop ratio, packet delay, energy consumption, and faulty pathways to 5.12%, 0.10 ms, 0.0102 mJ, and 6.51%, respectively.

Dynamic SDN Controller Placement based on Deep Reinforcement Learning

Software-defined Networking (SDN) is a revolutionary network architecture whose benefits stem partly from separating the data plane and control plane. In this scheme, the control functionalities are relocated to a logically centralized SDN controller which makes efficient and globally optimal forwarding decisions for network devices. Despite the fact that network virtualization technologies enable elastic capacity engineering and seamless fault recovery of the SDN controller, an optimal controller placement strategy that can adapt to changes in networks is an important but underexplored research topic. This paper proposes a novel deep reinforcement learning-based model that dynamically and strategically adjusts the location of the controller to minimize the OpenFlow latency in a virtualized environment. The experimental results demonstrate that the proposed strategy out performs both a random strategy and a generic strategy. Furthermore, this paper provides detailed instructions on how to implement the proposed model in realworld software-defined networks.

The value of time and temperature history information for the distribution of perishables

We model a supply chain that transports a perishable product from product origin to a destination market via a waypoint. The operational decision of interest is the transportation mode choice from the waypoint to the destination market, dependent on available information, including time and temperature history via RFID and sensors. We use analytical modeling to derive optimal transportation policies and generate generalizable, managerial insights. We then apply the analytical model in a numerical case study investigating the transportation of vine-ripened tomatoes from the Netherlands to the United States. Our analytical and numerical studies result in a number of interesting findings. First, the quality of sensor measurements may or may not impact the optimal policy and the decision maker can be guided accordingly. Second, better information may enable more profitable transport decisions, but doing so can have a negative impact on product quality at the destination. Third, we show that more stringent quality requirements by retailers may drive salvaging produce at the waypoint and thereby negatively impact service levels, despite penalties. Fourth, we identify the factors that drive the value of information under multiple information scenarios and establish both the direction and magnitude of their effects. Finally, both the analytical and numerical findings indicate that the information value is robust under measurement error. Thus, even if measurements are not perfect, RFID and sensor technology enabled information can be used to dynamically adjust forwarding decisions for perishable products, which can yield significant improvements to operational performance.

Defense Against Software-Defined Network Topology Poisoning Attacks

Software-Defined Network (SDN) represents a new network paradigm. Unlike conventional networks, SDNs separate control planes and data planes. The function of a data plane is enabled using switches, whereas that of a control plane is facilitated by a controller. The controller learns network topologies and makes traffic forwarding decisions. However, some serious vulnerabilities are gradually exposed in the topology management services of current SDN controller designs. These vulnerabilities mainly exist in host tracking and link discovery services. Attackers can exploit these weak points to poison the network topology information in SDN controllers. In this study, a novel solution is proposed to defend against topology poisoning attacks. By analyzing the existing topology attack principles and threat models, this work constructs legal conditions for host migration to detect host hijacking attacks. The checking of the Link Layer Discovery Protocol (LLDP) source and integrity is designed to defend against link fabrication attacks. A relay-type link fabrication attack detection method based on entropy is also designed. Results show that the proposed solution can effectively detect existing topological attacks and provide complete and comprehensive topological security protection.

Homomorphic encryption based privacy-aware intelligent forwarding mechanism for NDN-VANET

Machine learning has been widely used for intelligent forwarding strategy in Vehicular Ad-Hoc Networks (VANET). However, machine learning has serious security and privacy issues. BRFD is a smart Receiver Forwarding Decision solution based on Bayesian theory for Named Data Vehicular Ad-Hoc Networks (NDN-VANET). In BRFD, every vehicle that received an interest packet is required to make a forwarding decision according to the collected network status information. And then decides whether it will forward the received interest packet or not. Therefore, the privacy information of a vehicle can be revealed to other vehicles during information exchange of the network status. In this paper, a Privacy-Aware intelligent forwarding solution PABRFD is proposed by integrating Homomorphic Encryption (HE) into the improved BRFD. In PABRFD, a secure Bayesian classifier is used to resolve the security and privacy issues of information exchanged among vehicle nodes. We informally prove that this new scheme can satisfy security requirements and we implement our solution based on HE standard libraries CKKS and BFV. The experimental results show that PABRFD can satisfy our expected performance requirements.

Resource-aware routing in opportunistic networks: existing protocols and open research issues

Opportunistic networks provide an effective communication in challenging scenarios such as interplanetary communication, post-disaster situation, etc. As the nodes have limited resources, thus designing an efficient routing protocol in opportunistic networks always remains a challenging task. Several review papers presenting features and applications of opportunistic networks, mobility models, challenges, taxonomies of routing, and incentive schemes exist, but a comprehensive review on resource-aware routing appears to be missing. Therefore, we propose a novel taxonomy for categorising existing resource-aware routing protocols proposed in this field. These are mainly categorised into energy-aware, buffer aware, bandwidth aware, and hybrid routing that considers the energy of nodes, buffer space, bandwidth, and multiple parameters respectively to make forwarding or routing decision. Further, we discuss various existing routing protocols with their strengths and weaknesses, leading to open research issues. This survey aims to help future researchers devise a novel routing protocol for opportunistic networks.

Data Center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

According to Cisco’s Internet Report 2020 white paper, there will be 29.3 billion connected devices worldwide by 2023, up from 18.4 billion in 2018. 5G connections will generate nearly three times more traffic than 4G connections. While bringing a boom to the network, it also presents unprecedented challenges in terms of flow forwarding decisions. The path assignment mechanism used in traditional traffic scheduling methods tends to cause local network congestion caused by the concentration of elephant flows, resulting in unbalanced network load and degraded quality of service. Using the centralized control of software-defined networks, this study proposes a data center traffic scheduling strategy for minimization congestion and quality of service guaranteeing (MCQG). The ideal transmission path is selected for data flows while considering the network congestion rate and quality of service. Different traffic scheduling strategies are used according to the characteristics of different service types in data centers. Reroute scheduling for elephant flows that tend to cause local congestion. The path evaluation function is formed by the maximum link utilization on the path, the number of elephant flows and the time delay, and the fast merit-seeking capability of the sparrow search algorithm is used to find the path with the lowest actual link overhead as the rerouting path for the elephant flows. It is used to reduce the possibility of local network congestion occurrence. Equal cost multi-path (ECMP) protocols with faster response time are used to schedule mouse flows with shorter duration. Used to guarantee the quality of service of the network. To achieve isolated transmission of various types of data streams. The experimental results show that the proposed strategy has higher throughput, better network load balancing, and better robustness compared to ECMP under different traffic models. In addition, because it can fully utilize the resources in the network, MCQG also outperforms another traffic scheduling strategy that does rerouting for elephant flows (namely Hedera). Compared with ECMP and Hedera, MCQG improves average throughput by 11.73% and 4.29%, and normalized total throughput by 6.74% and 2.64%, respectively; MCQG improves link utilization by 23.25% and 15.07%; in addition, the average round-trip delay and packet loss rate fluctuate significantly less than the two compared strategies.

Making the Most of Sporadic Feedback: Low-Complexity Application Layer Coding for Data Recovery in the Internet of Things

We propose application-layer coding schemes to recover lost data in delay-sensitive uplink (sensor-to-gateway) communications in the Internet of Things. Built on an approach that combines retransmissions and forward erasure correction, the proposed schemes' salient features include low computational complexity and the ability to exploit sporadic receiver feedback for efficient data recovery. Reduced complexity is achieved by keeping the number of coded transmissions as low as possible and by devising a mechanism to compute the optimal degree of a coded packet in O(1). Our major contributions are: (a) An enhancement to an existing scheme called windowed coding, whose complexity is greatly reduced and data recovery performance is improved by our proposed approach. (b) A technique that combines elements of windowed coding with a new feedback structure to further reduce the coding complexity and improve data recovery. (c) A coded forwarding scheme in which a relay node provides further resilience against packet loss by overhearing source-to-destination communications and making forwarding decisions based on overheard information.

Toward pragmatic modeling of privacy information propagation in online social networks

The growing popularity of online social networks (OSNs) in recent years has generated a lot of concern on personal privacy. One approach of protecting privacy in OSNs is to intervene in the flow of privacy information, making the study of the dynamics of privacy information propagation necessary for the design of effective privacy protection mechanisms. Although previous work on information propagation has produced some models, these models are not adequate for privacy information since they do not reflect the main characteristics of privacy information. In this paper, we propose a model for privacy information propagation. We first analyze the structural characteristics of privacy information and then design the model by incorporating these characteristics. A unique feature of the model is that it infers the privacy attitudes of the information recipients to the privacy concerning subject implicated in the privacy information to determine the forwarding decisions of the recipients. Thus, by mapping the heterogeneous tendency of information forwarding by the recipients to a limited number of privacy attitudes, the model can predict the decisions on forwarding privacy information and thus describe the macroscopic process of privacy information propagation. Results of the experiment based on real OSN datasets show that the proposed model can be used to learn both the scope and the trend of privacy information propagation in OSNs, demonstrating the importance of the privacy attitudes of recipients on privacy information propagation. The properties of the model are also studied through experiment to examine the impact of various factors on privacy information propagation in OSNs.

Hybrid routing for safety data with intermittent V2I connectivity

V2I communications complement V2V safety applications by dealing with emergency situations such as crash scenarios that the V2V program cannot handle or that could be addressed more effectively. However, depending on the deployed infrastructure, we can distinguish between three situations regarding allowed communications: infrastructure-based, infrastructure-less and intermittent infrastructure. We will focus in vehicular networks with an intermittent connectivity, where the unavailability of direct communication with RSUs poses huge problems, especially when vehicles have to send safety-related messages to the infrastructure. In fact, a scattered vehicular infrastructure leads to non-homogeneous coverage, especially in highway scenarios extending over long distances; it is very expensive to set up an infrastructure across the whole road. Our objective is to address the challenges related to safety information forwarding towards the RSU in such networks by enabling vehicles to make an optimal forwarding decision based on contextual information relative to the infrastructure and the neighbourhood. In this work, we present HyRSIC (Hybrid Routing for Safety data with Intermittent V2I Connectivity), a routing approach, combining geographical and topological routing, proposed to deal with data forwarding in intermittent infrastructure-based networks. We conducted an in-depth evaluation from three different perspectives: scalability, reliability and efficiency. The results show that HyRSIC is able to scale quickly and adapt promptly to density and speed variations. Compared to GPSR and its variant ILTS+GPSR, HyRSIC offers the best packet delivery ratio for sparse networks, 12-27% higher, and the lowest End-to-End delivery delay, 25-50% less.

On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information

In this paper, we study the potential of using the metric of Age of Information (AoI) for enhancing delay-tolerant routing protocols. The latter have been proposed for alleviating the impact of long roundtrip time in networks operating in harsh environments, e.g., in distributed applications deployed in a desert/sparsely populated area without infrastructure, a space network, etc. Delay-tolerant routing protocols can prevent excessive packet timer expiration, but do not provide any packet delivery time guarantee. Thus, they are unsuitable for time-sensitive applications that are more intensely desired nowadays in the next generation networking applications. By incorporating AoI into the operation of delay-tolerant routing protocols, we aim at devising routing protocols that can cope with both long propagation times and challenges related to time-sensitivity in packet delivery. More specifically, in this work, we modify the operation of a well-known delay-tolerant routing protocol, namely FRESH, to make AoI-based packet forwarding decisions, aiming at achieving specific delay guarantees regarding the end-to-end delivery time. We investigate the advantages and disadvantages of such an approach compared to the traditional FRESH protocol. This work serves as a cornerstone for successfully demonstrating the potential of exploiting AoI in delay-tolerant routing and its applications.