Path Tree Research Articles

C3P-RPL: A collaborative and proactive approach for optimal peer to peer path selection and sustenance

Internet of Things is evolving from information gathering platforms into collaborative systems wherein, smart devices actively interact with each other in a seamless manner. For instance, Internet of Robotic Things is envisioned to provide augmented solutions through collaboration of varied smart devices and robots. These visions revolve around the ability of smart devices to directly communicate and cooperate with each other in real time. In this context, this paper is an attempt to study RPL’s (Routing Protocol for Low-power Lossy Networks) point to point routing that creates multi-hop paths between peer nodes. This standard routing protocol is known for robust and failsafe upward paths but its peer to peer (P2P) routes are reported to be suboptimal. This work assesses P2P performance of RPL’s storing mode in a network of new generation devices having higher memory. Further, a Collaborative and Proactive Peer to Peer (C3P) path selection and sustenance approach is proposed where, root node collates incremental topology from collaborative nodes and disseminates optimal single source shortest path trees SPT(n). A progressive node betweenness centrality score ensures spread out paths. Minor topology changes are accommodated through incremental node and edge updates to targeted SPT(n) locally. Storing SPTs in intermediate nodes reduces storage and packet size. Through simulations and testbed experiments, it is proven that C3P-RPL improves simultaneous peer to peer communication between all the nodes. Specifically, the path length is reduced by 30% and subsequently the network latency drops by 65% in an experimental testbed of 47 nodes, making it suitable for collaborations.

Quantum extensive-form games

We propose a concept of quantum extensive-form games, which is a quantum extension of classical extensive-form games. Extensive-form games is a general concept of games such as Go, Shogi, and chess, which have triggered the recent AI revolution, and is the basis for many important game theoretic models in economics. Quantum transitions allow for pairwise annihilation of paths in the quantum game tree, resulting in a probability distribution that is more likely to produce a particular outcome. This is similar in principle to the mechanism of speed-up by quantum computation represented by Grover’s algorithm. A quantum extensive-form game is also a generalization of quantum learning, including Quantum Generative Adversarial Networks. As an new example of quantum extensive-form games, we propose a quantum form of the Angel problem originally proposed by Conway in 1996. The classical problem has been solved but by quantizing it, the game becomes non-trivial.

Improved Learning-Based Design Space Exploration for Approximate Instance Generation

Design methodologies for approximation of medium to large-scale accelerators have largely relied on search-based design space exploration. Due to the enormously sized solution space, Artificial Intelligence (AI) based heuristic search has remained one of the most common techniques to explore cost and performance trade-offs. As a sub-class of AI techniques, Monte Carlo Tree Search (MCTS) has recently shown great potential as an intelligent stochastic search algorithm to solve computational problems with large branching factors. MCTS employs reinforced learning that combines past statistics and stochastic processes to traverse new paths in the search tree. Inspired by the success of MCTS in the games domain (AlphaGo), it has been recently applied to explore the solution space of approximate circuits. In this paper, a modified MCTS-based intelligent search technique is proposed that can handle huge search space for approximation of fairly large benchmark circuits. The proposed learning-based heuristic for MCTS directs the search towards deeper nodes in the search tree resulting in a rather asymmetric tree to efficiently utilize the search budget on the exploration of more promising nodes in the design space. The modified MCTS algorithm is based on reinforced learning where an agent uses past information to improve the overall gain. Experimental results confirm that the proposed heuristics can reach out to deeper nodes in the search tree which account for larger area savings in the context of approximate computing. The modified search algorithm enables 34.23% more area savings than the original search algorithm.

Tree-based Multicast Routing and Channel Assignment for Enhanced Throughout in Emerging Cognitive Radio Networks

In future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.

Connectivity Guarantee Within UAV Cluster: A Graph Coalition Formation Game Approach

Unmanned Aerial Vehicle (UAV) clustering is promising for performing large-scale missions because of high mobility and easy deployment. However, the connectivity problems caused by the high mobility of UAVs and the interferences are mostly ignored in existing UAV mission executions. Rapid restoration of UAV network connectivity is essential for preventing communication disruption and improving the overall network performance. Thus, a graph coalition formation game that integrates the UAV time-varying topology graph with the coalition formation game is proposed to rapidly restore the connectivity of the UAV network in need. The method improves the utility of UAV clustering and ensures the network connectivity. Then, a graph coalition formation game algorithm based on the shortest path tree (SPT-GCF) rapidly forms an approximately optimal coalition structure. The simulation results show that the proposed approach increases the average utility of UAV clustering by 6.5% and 14.5% respectively when compared with the existing non-overlapping coalition formation game (NOCFG) and the coalition formation game without considering the cluster connectivity.

Comparison on Routing Processing Performance between Shortest Paths Tree (SPT) and Minimum Spanning Tree (MST)

This study’s objective is to assess the performance of routing processing between Shortest Paths Tree (SPT) and Minimum Spanning Tree (MST). In this paper we propose and create software to calculate the efficiency path of routing processing in the network. Extensive simulations are being carried out with two different algorithms specific for Dijkistra’s algorithm and Kruskal’s algorithm. The network loads in the performance plots are characterized by two important parameters (a number of vertices) - 7 nodes and 10 nodes, more vertices could show good performance in the simulation results and showed that the performance of Kruskal’s algorithm is better than Dijkistra’s algorithm when the network loads are increased from 7 nodes to 10 nodes. In this simulation, Because of the total cost of distance matrix for Kruskal is less than Dijkistra when the number of routers or nodes is increased from 7 nodes to 10 nodes. This software namely Network Aids Design (NAD), is useful for calculation about the performance of routing processing in the network.

24X7 - Travelling Womens Safety in Metrocity Traffic

As technology has advanced quickly, more individuals are using smartphones for a variety of functions such as marketing, business, social media, surfing, etc. This has also increased the threat of information sharing across various sites, which is one of the major reasons for harassment cases and other security threats against women. Surveys reveal that, smartphone usage by women has risen upto 5 folds in the last 6 years which is an average increase by 27% per annum. The increase in usage means that, there are high chances for misuse of information which results in security threats for women. The proposed system aims to create awareness of smartphone usage in mobile computing among women to efficiently handle it by avoiding physical assaults and harassments against women. It focuses to provide a Global Positioning System (GPS) dependent, android based safety application exclusively designed for women who travel farther distances on daily basis. Here it is implemented with help of Dijkstra’s Algorithm to find out locations based on Breadth First Search to form shorted path tree for reachable locations.

On the Degree Distribution of Haros Graphs

Haros graphs are a graph-theoretical representation of real numbers in the unit interval. The degree distribution of the Haros graphs provides information regarding the topological structure and the associated real number. This article provides a comprehensive demonstration of a conjecture concerning the analytical formulation of the degree distribution. Specifically, a theorem outlines the relationship between Haros graphs, the corresponding continued fraction of its associated real number, and the subsequent symbolic paths in the Farey binary tree. Moreover, an expression that is continuous and piecewise linear in subintervals defined by Farey fractions can be derived from an additional conclusion for the degree distribution of Haros graphs.

ПОИСК ОПТИМАЛЬНЫХ ПУТЕЙ В НЕЧЕТКИХ ГРАФАХ

A large variety of problems in different fields, including the problems of the creation of optimal solutions of different kinds, can be formulated in graph theory language in the form of search tasks at given graphs of optimal paths of special character. Many results in these courses are now obtained which refer to classical graph theory. In this theory, a graph is assumed to be a deterministic object and there are no any uncertainties (fuzzinesses) in its description and in the description of its functioning process. Nowadays, science has come to the realization that the majority of our knowledge and links with the external world do not correspond to formerly established classical notions about them. New approaches to these areas are now being developed, which imply principal impossibility to do without fuzzinesses that are accepted as a reality of human existence. This requires the development of complex of concepts and methods which in, this fuzziness should be taken into account really in practical applications. In the proposed article, the tasks of searching optimal paths according to some criteria in the frames of currently accepted fuzzy graph model are considered. When solving the problem of the shortest paths, the rule of choosing the “best” arc is introduced and motivated. The method of solving the tasks with the use of the design of oriented graph path tree is proposed. The notion of path feasibility in fuzzy graph is introduced, the feasibility is evaluated as the probability of path real existence in given graph. The method for calculation of path feasibility, based on the reduction of belonging degree of each arc in the path of graph path to its probability, is proposed. The task on the search of path with maximum feasibility is solved on this basis.

Delay-constrained minimum shortest path trees and related problems

Motivated by some applications in communication networks of the diameter-constrained minimum spanning tree (Diamc-MST) problem, we address the delay-constrained minimum shortest path tree (Delayc-MSPT) problem, which is a variation of the Diamc-MST problem. Specifically, given a weighted graph G=(V,E;w,c) and a constant d0, where n=|V|, m=|E|, length function w:E→R+ and cost function c:E→R+, we are asked to determine a minimum shortest path tree among all shortest path trees (in G) whose delays are no more than d0, where the delay of a shortest path tree is the maximum distance (with respect to w(⋅)) from its source to every other leaf in that tree, and the cost of such a shortest path tree is the sum of costs of all edges (with respect to c(⋅)) in that tree. In addition, when a constant d0 is the radius of G, we refer to this version of the Delayc-MSPT problem as the minimum radius minimum shortest path tree (MinRadius-MSPT) problem, and when a constant d0 is the diameter of G, we refer to that version of the Delayc-MSPT problem as the maximum delay minimum shortest path tree (MaxDelay-MSPT) problem. In addition, we consider the diameter-constrained minimum shortest path tree (Diamc-MSPT) problem, except substituting the diameter for the delay of G in the Delayc-MSPT problem, where the diameter of a shortest path tree is the maximum distance (with respect to w(⋅)) between any two leaves in that tree.The main contribution of our paper is to show the following results. (1) We design an exact algorithm to solve the Delayc-MSPT problem and a similar exact algorithm to solve the MinRadius-MSPT problem, respectively, and these two algorithms run in time O(n3); (2) We present an exact algorithm to solve the MaxDelay-MSPT problem, and that algorithm runs in time O(n3); (3) We provide an exact algorithm to solve the special version of the Diamc-MSPT problem, where the constant d0 is exactly the diameter of G, and that algorithm runs in time O(mn2+n3log⁡n).

Walking as a research modality: the three paths of Francesco Careri in São Paulo - SP

This article approaches walking as a research modality in São Paulo from the analysis of tree paths by the italian architect Francesco Careri carried out in the capital of São Paulo at diferent times. In this sense, the text written through na analysis of the practice od the patch taken in the Comunidade de São Francisco (Zona Leste), in Praça Victor Civita (Zona Oeste) and in Bixiga (Centro). In this way, the main objective of this article is to reconstruct the path of the italian architect in the city of São Paulo, as well as to analyze his experiences, relating them to work in his book: Walkscapes: o caminhar como prática estética (2013) and Caminhar e Parar (2017), thus detailing this type of research in São Paulo.

Dynamic cognitive maps for robot route planning in complex workplaces represented by abstract mazes

Adaptive path planning and optimization for robots are a persistent challenge due to the lack of a cognitive map of the complex and dynamic environments. This paper presents a cognitive-map-based method for dynamic robot path planning by a formal maze model for workplace layouts representation. The maze model and global path trees provide a cognitive map for the robot to aware the environment. A theory of dynamic Paths Finding and Optimization (PFO) is developed. Powered by the PFO algorithm, a robot is able to autonomously explore the optimal path in a complex workplace by a single sight-read of its layout. The maze based PFO methodology provides robot for an efficient real time path cognition and optimization algorithm for dealing with dynamic workplaces. A set of experiments demonstrates the efficiency of the method, which extends traditional stepwise robot vision technologies to cognitive-map-driven global path optimization.

Conservation and restoration of riverine spawning habitats require fine-scale functional connectivity and functional heterogeneity

A severe extinction crisis of migratory fish caused by extensive hydropower development and climate change has attracted widespread environmental concern. Conserving and restoring riverine spawning habitat for migratory species is advantageous for population recovery. Depending on the reproductive characteristics of fish with adhesive eggs, functionally heterogeneous spawning habitats are required to support different stages of reproductive activity. However, few aquatic assessment models are available to consider the fine-scale functional connectivity between heterogeneous spawning habitats. This study developed a function-based framework that linked fine-scale functional connectivity modeling to habitat quality evaluations for the population recovery of migratory fish. The function path tree (FPT) model within the framework could identified the spatiotemporal dynamics of fine-scale connectivity patterns by emphasizing the attribute-dependence of patch arrangements. Here, we used the Chinese sturgeon, a well-known endangered anadromous fish producing adhesive eggs in the Yangtze River, as an example to demonstrate the applicability of the framework. Additionally, the ecological effectiveness of river restorations to overcome the detrimental influence of climate change on discharge decrease was also investigated. Compared to prior research, our methodology effectively enhanced the predictive performance of spatiotemporal distributions and quality assessments of spawning habitats. A strong correlation was discovered between the ecological profit indicator (HQI) and the estimated fecundity (R2 = 0.941) and field-collected eggs (R2 = 0.918). The minimum spawning discharge decreased from 8400 m3/s to 7000 m3/s by substrate restoration, with the optimal HQI growth rate of 52.7 % at Q < 8400 m3/s. This work will optimize long-term conservation for imperiled migratory species and help develop strategies to build resilience to ongoing environmental changes in flow-reduced rivers.

A multi-objective path planning method for the wave glider in the complex marine environment

Aiming to energy-efficient, short path, and safety, an online dynamic multi-objective path planning method for the wave glider (WG) is proposed to make the WG operate efficiently in the uncertain marine environment. In this paper, a novel kinematic constrained safe rapidly-exploring random tree (KS-RRT*) path planning algorithm is developed, which is subject to the safe area boundaries and kinematics. Biased random sampling method is introduced which the kinematic constraints is used as heuristic function and the KS-RRT* can reduce the extended area of random points. And a path safe (PS) scheme is developed, which can ensure that the WG safely passes through the planned path. In addition, energy cost, path length, and safety metric (SM) of each feasible trajectory are obtained by the KS-RRT*, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve multi-objective path optimization problem, and the final path is smoothed by the B-spline algorithm. The results of simulations show that the proposed KS-RRT* is more suitable for the WG's path planning than traditional path planning algorithm and the proposed multi-objective path optimization method can solve the path planning problem of the WG in complex marine environments.

An efficient data collection algorithm for partitioned wireless sensor networks

Data collection with mobile agent (MA) can balance the energy consumption of nodes in partitioned wireless sensor networks. However, the existing data collection algorithms for partitioned WSNs do not formalize the problem systematically. These algorithms are not efficient and difficult to meet the timeliness requirement. In order to solve the above shortcomings, we first formalize the Data Collection Problem (DCP). Then, it is transformed into a Generalized Traveling Salesman Problem (GTSP), and we propose a GTSP-Based data collection Algorithm (GBA) to calculate the rendezvous points (RPs) and the moving path of MA. GBA selects RPs by solving the GTSP problem. Furthermore, based on GBA, we take advantage of constructing convex hull to plan a more efficient moving path for MA. In addition, in order to reduce the energy burden on the RPs, we design an Improved Shortest Path tree (ISP tree) to aggregate data from nodes to RPs in partitioned WSNs. Finally, extensive simulations demonstrate the effectiveness and advantages of our algorithm in terms of the length of MA’s moving path and the amount of data collection per unit time.

The resource constrained clustered shortest path tree problem: Mathematical formulation and Branch&amp;Price solution algorithm

AbstractIn this article, the Resource Constrained Clustered Shortest Path Tree Problem is defined. It generalizes the classic Resource Constrained Shortest Path Tree Problem since it is defined on an undirected, complete and weighted graph whose set of nodes is partitioned into clusters. The aim is then to find a shortest path tree respecting some resource consumption constraints and inducing a connected subgraph within each cluster. The main support and motivation for studying this problem are related, among the others, to the design of telecommunication networks, and to Disaster Operations Management. In this work, we present a path‐based formulation for the problem, addressing the case of local resource constraints, that is, resource constraints on single paths. For its resolution, a Branch&amp;Price algorithm featuring a Column Generation approach with Multiple Pricing Scheme is devised. A comprehensive computational study is conducted, comparing the proposed method with the results achieved by the CPLEX solver, adopted to solve the mathematical model. The numerical results underline that the Branch&amp;Price algorithm outperforms CPLEX, both in terms of solution cost and time.

Haros graphs: an exotic representation of real numbers

AbstractThis article introduces Haros graphs, a construction which provides a graph-theoretical representation of real numbers in the unit interval reached via paths in the Farey binary tree. We show how the topological structure of Haros graphs yields a natural classification of the real numbers into a hierarchy of families. To unveil such classification, we introduce an entropic functional on these graphs and show that it can be expressed, thanks to its fractal nature, in terms of a generalized de Rham curve. We show that this entropy reaches a global maximum at the reciprocal of the Golden number and otherwise displays a rich hierarchy of local maxima and minima that relate to specific families of irrationals (noble numbers) and rationals, overall providing an exotic classification and representation of the reals numbers according to entropic principles. We close the article with a number of conjectures and outline a research programme on Haros graphs.

A comprehensive framework for risk probability assessment of landfill fire incidents using fuzzy fault tree analysis

Landfill fire is the most frequent type of incident in the waste management complexes. This paper presents a new framework for risk probability evaluation of major fires in landfills using the fuzzy fault tree analysis. The framework starts with construction of the fault tree of landfill fire comprised of 38 basic and 22 intermediate events with the corresponding type of faults under managerial, executive, human, and environmental conditions. Fault tree quantitative analysis is carried out through a combination of fuzzy set theory and experts' judgements to overcome the lack of data. Two new sensitivity analysis approaches are used to identify the critical fault type and critical paths in the fault tree. The proposed framework is demonstrated by its application to a real-world case of a landfill in Iran. The results show the probability of a major "fire incident" is 5.5 %, whereas "fire occurrence" stands for 25 % probability, higher than "lack of preparation for controlling fire" with 21.60 % probability. In addition, "Waste’s uncontrolled dumping" is recognised as the highest critical event with a failure probability of 6 % and importance degree of 24 %. "Executive fault" is also found as the most fault’s critical type through sensitivity analysis. The results also reveal the major impact of the experts’ weights, especially for events related to human or management faults. These results can give decision-makers a profound insight into providing effective intervention strategies for minimising the risk of major landfill fire incidents.

Clustering of Template-Generated Webpages Using DOM Tree Paths of URLs

Web templates are layouts for webpages that enable rapid and easy access to web content. Web data integration solutions use template based wrapper tools to extract product information from e-commerce websites. Given a collection of webpages, wrapper tools are used to discover the template portion of a webpage and extract data from it. These wrapper based data extraction techniques require pages created with the same template belong to the same cluster. Clustering these webpages based on their template is a significant challenge. While there are algorithms for clustering webpages based on their template, they are computationally intensive to be applied at web scale. By examining the DOM tree paths of URLs on a webpage, the proposed work presents a highly scalable methodology for clustering template-generated webpages. Further, the locality sensitive hashing (LSH) technique is used to reduce the cost of clustering. The proposed technique is found to be more precise and cost effective than the existing baseline methods when tested on three separate real-time data sets.

Immune Algorithm to Suppress Rumor Propagation Based on Influence Maximization

In order to effectively reduce the spread range of rumor information, an immune method based on two-stage influence maximization to suppress rumor propagation is proposed. Firstly, User and Clustering Influence Maximizing (UCIM) algorithm is proposed to obtain the most influence node set at the current moment in the initial stage of event evolution based on the network topology and user characteristics. Secondly, the node set is identified and classified based on the RNN rumor detection model, and Immunosuppression Strategy considering the Average Path Weights and User-Clustering (IS-APWUC) strategy is proposed for the identified rumor nodes. In this strategy, rumor node set is taken as root nodes, and neighbor nodes with weak influence are pruned to construct an effective rumor path tree. Thirdly, considering the total probability of being infected by the rumors and the comprehensive influence factors, the nodes with high influence in the propagation stage are calculated as immune nodes so as to block the spread of the rumor information. Finally, the proposed method is verified by experiments on four real-world datasets. The results show that the IS-APWUC method has a better rumor suppression effect than the similar correlation algorithms.