Network Traversal Research Articles

The Maximum Attainable Flow and Minimal Cost Problem in a Network

This paper, presents an efficient algorithm that solves such a large class of optimization problems. Ford-Fulkerson determines the maximum flow in a network by iteratively augmenting flow paths until no further improvement is possible. On the other hand, Dijkstra's algorithm excels in finding the shortest path in a weighted graph, making it suitable for minimizing costs in network traversal. However, this paper simultaneously optimizes both objectives (flow and cost) dependently in unique iterations by considering all constraints and objectives holistically. The aim of this work is to develop efficient algorithms that can handle complex optimization problems in transportation, network design, and other fields, ultimately improving resource utilization and minimizing costs as its crucial for enhancing decision-making processes, improving efficiency in resource utilization, and achieving cost savings in diverse applications ranging from transportation networks to production planning. This paper deals about formulating the linear programming for the optimizations problems and finding the maximum amount of flow that can be sent from a source node to a sink node while minimizing the total cost of sending that flow by using simplex method (Two phase method). Through computational experiments and case studies, everybody demonstrate the effectiveness and efficiency of the proposed approach in solving real-world network flow problems. Our method yields efficient algorithms with in smallest numbers of iterations and time that enable the optimal allocation of resources within networks, achieving both maximum flow and minimum cost simultaneously.

A trustworthy security model for IIoT attacks on industrial robots

The security of industrial Internet of Things (IIOT) has recently attracted significant attention. As typical IIoT systems, industrial robots are suffering from lots of threats involving control, communication, and computing, which are difficult to detect IIoT attacks accurately in real-time due to resource constraints. How to efficiently and accurately identify IoT attacks on industrial robots is challenging. To address this, we propose a trustworthy security model (TSM) with a fusion design that integrates an improved deep Q-network (IDQN) and a control model, thus accelerating the model training process by reducing the network traversal space and improving detection accuracy by establishing a prejudgment mechanism. We initially provide a detailed overview of existing methods for robot security and derive a robot control model consisting of kinematics and kinetic. Then, a 17-labeled dataset named iRobot security dataset is established to train the TSM. Moreover, we established a robot physical platform to evaluate the performance of TSM, and five cyber security indicators are employed to quantify the performance. Experimental results show that TSM detects attacks at an average rate of 98.7 % and a 0.01 s latency, which demonstrates the excellent performance in detection accuracy and detection efficiency, and provides an idea for solving these issues.

TNT: A Modular Approach to Traversing Physically Heterogeneous NOCs at Bare-wire Latency

The ideal latency for on-chip network traversal would be the delay incurred from wire traversal alone. Unfortunately, in a realistic modular network, the latency for a packet to traverse the network is significantly higher than this wire delay. The main limiter to achieving lower latency is the modular quantization of network traversal into hops. Beyond this, the physical heterogeneity in real-world systems further complicate the ability to reach ideal wire-only delay. In this work, we propose TNT or Transparent Network Traversal . TNT targets ideal network latency by attempting source to destination network traversal as a single multi-cycle ‘long-hop’, bypassing the quantization effects of intermediate routers via transparent data/information flow. TNT is built in a modular tile-scalable manner via a novel control path performing neighbor-to-neighbor interactions but enabling end-to-end transparent flit traversal. Further, TNT’s fine grained on-the-fly delay tracking allows it to cope with physical NOC heterogeneity across the chip. Analysis on Ligra graph workloads shows that TNT can reduce NOC latency by as much as 43% compared to the state of the art and allows efficiency gains up to 38%. Further, it can achieve more than 3x the benefits of the best/closest alternative research proposal, SMART [ 43 ].

Graph-Based Interdependent Cyber-Physical Risk Analysis of Power Distribution Networks

This paper proposes a graph-based model for interdependent cyber-physical risk analysis of power distribution networks. The proposed methodology evaluates device criticality in the interdependent cyber and physical networks that comprise modern power distribution networks. A network traversal algorithm is developed to identify latent interdependent support and dependency structures as network subgraphs. Identifying these subgraphs provides a context-aware approach to model feasible cyber-attack vectors, define metrics for device criticality, and measure overall network resilience to cyber-attacks. The proposed methodology is demonstrated in a cyber-attack detection study. The results indicate that the proposed context-aware criticality metrics, based on the dependency subgraph framework, better identify the risks of cascading dependencies in power distribution networks. Further, several characteristics of the power distribution network such as interdependencies, device vulnerabilities, and attack target criticalities detailed in the proposed methodology have significant impacts on the performance of attack detection strategies.

A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information. Primarily, the proposed approach minimizes the network size and eliminates undesirable connections. For that, the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer. Therefore, the proposed approach discards the nodes having a rank (α) lesser than 0.5 to reduce the network complexity. α is the variable value represents the rank of each node that varies between 0 to 1. Node with the higher value of α gets the higher priority and vice versa. The threshold value α = 0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems. Finally, the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information. The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks. Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network. Moreover, the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network. Furthermore, the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.

Hub-collision avoidance and leaf-node options algorithm for fractal dimension and renormalization of complex networks.

The box-covering method plays a fundamental role in the fractal property recognition and renormalization analysis of complex networks. This study proposes the hub-collision avoidance and leaf-node options (HALO) algorithm. In the box sampling process, a forward sampling rule (for avoiding hub collisions) and a reverse sampling rule (for preferentially selecting leaf nodes) are determined for bidirectional network traversal to reduce the randomness of sampling. In the box selection process, the larger necessary boxes are preferentially selected to join the solution by continuously removing small boxes. The compact-box-burning (CBB) algorithm, the maximum-excluded-mass-burning (MEMB) algorithm, the overlapping-box-covering (OBCA) algorithm, and the algorithm for combining small-box-removal strategy and maximum box sampling with a sampling density of 30 (SM30) are compared with HALO in experiments. Results on nine real networks show that HALO achieves the highest performance score and obtains 11.40%, 7.67%, 2.18%, and 8.19% fewer boxes than the compared algorithms, respectively. The algorithm determinism is significantly improved. The fractal dimensions estimated by covering four standard networks are more accurate. Moreover, different from MEMB or OBCA, HALO is not affected by the tightness of the hubs and exhibits a stable performance in different networks. Finally, the time complexities of HALO and the compared algorithms are all O(N2), which is reasonable and acceptable.

Egocentric Network Exploration for Immersive Analytics

AbstractTo exploit the potential of immersive network analytics for engaging and effective exploration, we promote the metaphor of “Egocentrism”, where data depiction and interaction are adapted to the perspective of the user within a 3D network. Egocentrism has the potential to overcome some of the inherent downsides of virtual environments, e.g., visual clutter and cyber‐sickness. To investigate the effect of this metaphor on immersive network exploration, we designed and evaluated interfaces of varying degrees of Egocentrism. In a user study, we evaluated the effect of these interfaces on visual search tasks, efficiency of network traversal, spatial orientation, as well as cyber‐sickness. Results show that a simple Egocentric interface considerably improves visual search efficiency and navigation performance, yet does not decrease spatial orientation or increase cyber‐sickness. An occlusion‐free Ego‐Bubble view of the neighborhood only marginally improves the user's performance. We tie our findings together in an open online tool for Egocentric network exploration, providing actionable insights on the benefits of the Egocentric network exploration metaphor.

Propagation path optimization of product attribute design changes based on Petri Net fusion ant colony algorithm

An effective and controlled design change in product attributes can improve product quality, promote product innovation, and meet customer demands. During design changes, the links among product attributes are intricate. A change in the design of one attribute may lead to a series of changes in other attributes, which result in the change propagation as well as the re-planning and re-design of the propagation path. Therefore, an Expanded Petri Net (EPN) model is constructed to describe the propagation relationship of product attribute design change in this paper. Firstly, the attribute change propagation rules are established based on change propagation impact values. Secondly, to optimize the dynamic scheme of change propagation path, the ant colony path optimization algorithm is integrated into the Extended Petri Net model (Expanded Petri Ant Colony Optimization Algorithm, EP-ACOA). In addition, the constraint test factor and the upper and lower limits of pheromone values are introduced into the algorithm to ensure the whole network traversal of the change propagation path and accelerate the solving speed of the model. Finally, taking the attribute design change of Langevin piezoelectric transducer in an ultrasonic knife as an example, the Petri Net fusion ant colony algorithm was verified to be able to quickly and effectively provide the optimal solution or approximate optimal solution to the propagation path problem of product attribute design changes.

Analysis of Network Traversal and Qualification of the Testing Values of Trajectories

Analysis of Network Traversal and Qualification of the Testing Values of Trajectories

Exploring hardware accelerator offload for the Internet of Things

Abstract The Internet of Things is manifested through a large number of low-capability connected devices. This means that for many applications, computation must be offloaded to more capable platforms. While this has typically been cloud datacenters accessed over the Internet, this is not feasible for latency sensitive applications. In this paper we investigate the interplay between three factors that contribute to overall application latency when offloading computations in IoT applications. First, different platforms can reduce computation latency by differing amounts. Second, these platforms can be traditional server-based or emerging network-attached, which exhibit differing data ingestion latencies. Finally, where these platforms are deployed in the network has a significant impact on the network traversal latency. All these factors contributed to overall application latency, and hence the efficacy of computational offload. We show that network-attached acceleration scales better to further network locations and smaller base computation times that traditional server based approaches.

UAV communication system integrated into network traversal with mobility

In this paper, a secured unmanned aerial vehicle (UAV)-assisted heterogeneous network environment, and seamless IP prototype is proposed. UAV is controlled over the Internet to resolve a long-range communication barrier and to improve remote sensing. In the new technology, a pilot can control UAV via an end device (ED)-secured communication without any information about the network architectures, network address translation types, Internet protocol version. Network traversal with mobility (NTMobile) offers a secured communication for UAV through a public network infrastructure. NTMobile enhances the security and maintain mobility by generating keys and building tunnel using virtual IPs for two nodes inside the network. In the prototype the UAV flight controller can install a ground control station application on the UAV board. An ED can be any smart device connected to the Internet with the ability to run the remote protocols, which allows the pilot to send commands and receives data from UAV. Moreover, the pilot transmits commands through the remote desktop protocol or secure shell protocol. Experimentally, an autopilot mission is designed and uploaded to the flight controller where UAV and ED are forced to switch access network between cellular network LTE and 802.11a IEEE wireless local area network in specific waypoints. The novelty of this study is proposing secure continuous connectivity of the UAV communication and control system even during the occurrence of a network switch, which is one of the important factor in the heterogeneous network environment. The results revealed that NTMobile maintains continuous communication by re-establishing the created tunnel between UAV and ED in the proposed prototype while both are travelling in a heterogeneous network.

Computational Psycholinguistics

Computational Psycholinguistics

MINERVA API and plugins: opening molecular network analysis and visualization to the community.

SummaryThe complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platform’s visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network.Availability and implementationPlugins developed and maintained by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/. The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.

A comparison of methodologies: sibling identification using a relational versus a graph-based approach

IntroductionThe detection of siblings is an important pre-requisite for many research problems, yet is traditionally difficult or time-consuming owing to the way linked data is conventionally stored. We compare the methodology and results for an identification of siblings by SANT DataLink using a legacy relational approach and a graph-based approach.
 Objectives and ApproachAn existing project involving the identification of sibling clusters using relational techniques was replicated using the same core data in SANT DataLink’s Next Generation Linkage Managment System (NGLMS) which is graph-based. Data is stored in the NGLMS using richer relationships than just 'is W% similar to' or 'is part of group N'. Birth data was separated into children and parents and explicit child/parent relationships recorded. When coupled with electoral roll data using probabilistic and deterministic linkage, sibling structure can be identified by performing network traversal via parents, e.g.
 crecord—[MOTHER]⟶mrecord—[IS_SIMILAR_TO]⟶mrecord*⟵[MOTHER]—crecord*
 i.e. find the mother, find that mother’s cluster, find all children related to that cluster. The resulting records are siblings.
 ResultsA graph-based approach enabled the methology of 'finding siblings' to be more clearly described and communicated by mirroring genealogical structures natively within in the data. Comparable results were achieved in a shorter time with less manual effort using the same underlying data. Generating sibling clusters from the graph-based data required less manual intervention and review as explicit PARENT/CHILD relationships were stored in the data and able to be quickly traversed to assemble familial units and thus siblings. A focus on automated linkage quality rather than manual review was facilitated by the approach. Anomalous structures requiring detailed review, such as multiple fathers and mothers of a single child, were trivially identifiable and so manual effort was focussed on actions where a higher return in terms of end-product quality could be achieved.
 Conclusion/ImplicationsStoring data in a representation which more closely resembles the the underlying real-world situation allows greater fidelity with respect to data modeling. This in turn, enables the asking of richer questions and makes the answering of such questions much easier.

Shared Execution Approach to ε-Distance Join Queries in Dynamic Road Networks

Given a threshold distance ε and two object sets R and S in a road network, an ε-distance join query finds object pairs from R × S that are within the threshold distance ε (e.g., find passenger and taxicab pairs within a five-minute driving distance). Although this is a well-studied problem in the Euclidean space, little attention has been paid to dynamic road networks where the weights of road segments (e.g., travel times) are frequently updated and the distance between two objects is the length of the shortest path connecting them. In this work, we address the problem of ε-distance join queries in dynamic road networks by proposing an optimized ε-distance join algorithm called EDISON, the key concept of which is to cluster adjacent objects of the same type into a group, and then to optimize shared execution for the group to avoid redundant network traversal. The proposed method is intuitive and easy to implement, thereby allowing its simple integration with existing range query algorithms in road networks. We conduct an extensive experimental study using real-world roadmaps to show the efficiency and scalability of our shared execution approach.

A Proposal and Implementation of Communication Control Function for NTMobile

SUMMARYNTMobile (Network Traversal with Mobility) has been proposed to achieve end‐to‐end encryption communication supporting IP mobility in environments where IPv4/IPv6 networks coexist. However, since NTMobile unconditionally establishes an encrypted User Datagram Protocol (UDP) tunnel between NTMobile‐ready nodes (NTM nodes), a malicious NTM node can attack a target NTM node through the encrypted UDP tunnel without being detected by a firewall. Moreover, since communication with a general server always passes through a relay server (RS), the route becomes redundant even when IP mobility is not needed, and the communication delay increases. In order to solve these problems, this paper proposes an access control function using the name of the correspondent node and a “Route option” that can select whether the RS is used or not. As a result of implementation of the prototype system and evaluation of its performance, it was confirmed that the increase of the start‐up time and that of the overhead at the beginning of the communication were quite small, and there was little influence on practical use.

The show must go on: Fundamental data plane connectivity services for dependable SDNs

Software-defined network (SDN) architectures raise the question of how to deal with situations where the indirection via the control plane is not fast enough or not possible. In order to provide a high availability, connectivity, and robustness, dependable SDNs must support basic functionality also in the data plane. In particular, SDNs should implement functionality for inband network traversals, e.g., to find failover paths in the presence link failures. This paper shows that robust inband network traversal schemes for dependable SDNs are feasible, and presents three fundamentally different mechanisms: simple stateless mechanisms, efficient mechanisms based on packet tagging, and mechanisms based on dynamic state at the switches. We show how these mechanisms can be implemented in today’s SDNs and discuss different applications.

Exploiting location-aware social networks for efficient spatial query processing

In this paper, we introduce two watchtower-based parameter-tunable frameworks for efficient spatial processing with sparse distributions of Points of Interest (POIs) by exploiting mobile users' check-in data collected from the location-aware social networks. In our proposed frameworks, the network traversal can terminate earlier by retrieving the distance information stored in watchtowers. More important, by observing that people's movement often exhibits a strong spatial pattern, we employ Bayesian Information Criterion-based cluster analysis to model mobile users' check-in data as a mixture of 2-dimensional Gaussian distributions, where each cluster corresponds to a geographical hot zone. Afterwards, POI watchtowers are established in the hot zones and non-hot zones discriminatorily. Moreover, we discuss the optimal watchtower deployment mechanism in order to achieve a desired balance between the off-line pre-computation cost and the on-line query efficiency. Finally, the superiority of our solutions over the state-of-the-art approaches is demonstrated using the real data collected from Gowalla with large-scale road networks.

APINetworks Java. A Java approach to the efficient treatment of large-scale complex networks

We present a new version of the core structural package of our Application Programming Interface, APINetworks, for the treatment of complex networks in arbitrary computational environments. The new version is written in Java and presents several advantages over the previous C++ version: the portability of the Java code, the easiness of object-oriented design implementations, and the simplicity of memory management. In addition, some additional data structures are introduced for storing the sets of nodes and edges. Also, by resorting to the different garbage collectors currently available in the JVM the Java version is much more efficient than the C++ one with respect to memory management. In particular, the G1 collector is the most efficient one because of the parallel execution of G1 and the Java application. Using G1, APINetworks Java outperforms the C++ version and the well-known NetworkX and JGraphT packages in the building and BFS traversal of linear and complete networks. The better memory management of the present version allows for the modeling of much larger networks.

Research on the Network Traversal based on Spatial Influence

Research on the Network Traversal based on Spatial Influence