Search Results (19)

We develop a polynomial time algorithm for the single-source all destinations shortest paths problem for interval temporal graphs ($ITG$s). While a polynomial time algorithm for this problem is known for contact sequence temporal graphs ($CSG$s), no such prior algorithm is known for $ITG$s. We benchmark our $ITG$ algorithm against that for $CSG$s using datasets that can be solved using either algorithm. Using synthetic datasets, experimentally, we show that our algorithm for $ITG$s obtains a speedup of up to $32.5$ relative to the state-of-the-art algorithm for $CSG$s. Full article

The Single-Source Shortest Paths (SSSP) graph problem is a fundamental computation. This study attempted to characterize concretely the energy behaviors of the two primary methods to solve it, the Bellman–Ford and Dijkstra algorithms. The very different interactions of the algorithms with the hardware may have significant implications for energy. The study was motivated by the multidisciplinary nature of the problem. Gaining better insights should help vital applications in many domains. The work used reliable embedded sensors in an HPC-class CPU to collect empirical data for a wide range of sizes for two graph cases: complete as an upper-bound case and moderately dense. The findings confirmed that Dijkstra’s algorithm is drastically more energy efficient, as expected from its decisive time complexity advantage. In terms of power draw, however, Bellman–Ford had an advantage for sizes that fit in the upper parts of the memory hierarchy (up to 2.36 W on average), with a region of near parity in both power draw and total energy budgets. This result correlated with the interaction of lighter logic and graph footprint in memory with the Level 2 cache. It should be significant for applications that rely on solving a lot of small instances since Bellman–Ford is more general and is easier to implement. It also suggests implications for the design and parallelization of the algorithms when efficiency in power draw is in mind. Full article

This research paper introduces an optimization methodology for the strategic electric sources’ placement at multiple positions in a DC islanded microgrid characterized by a mesh network, aiming to minimize line losses while considering minimal cable weight. The DC microgrid studied in this paper is composed of PV panels, batteries, a diesel generator, and 20 residential loads. Employing Dijkstra’s algorithm, a graph algorithm used in Google Maps, the study identifies the shortest path (resistance) between potential source nodes and various variable loads within a predefined electric distribution mesh network topology. This study focuses on active power considerations and offers valuable insights into the placement optimization of multiple sources’ positions in DC microgrid mesh networks. A key contribution of this paper lies in the ranking of source node positions based on minimal to maximal line losses, taking into consideration optimal cable weights, while using MATPOWER to validate sources’ ranking based on Dijkstra’s hypothesis. The research further includes a techno-economic study to assess the viability of sources’ placement at multiple positions within the mesh network, comparing it with the optimal placement scenario involving a single position for all sources. This methodology serves as a valuable resource for system designers and operators aiming to minimize line losses and optimize energy distribution in DC microgrids in a mesh topology. Full article

Finding the shortest path from a source vertex to any other vertices on a graph (single-source shortest path, SSSP) is used in a wide range of applications. With the rapid expansion of graph data volume, graphs are too large to be stored and processed in a standalone machine. Therefore, performing SSSP distributively in the computer clusters becomes an inevitable way. We found that the performance of existing distributed SSSP algorithms is limited by the communication cost between workers, which is caused by global relaxation. To eliminate the expensive communication cost, we propose an efficient distributed SSSP algorithm LR-SSSP that replaces global relaxation with local relaxation. Furthermore, we propose two optimizations, i.e., lazy synchronization and forward relaxation, to reduce invalid synchronization and communication. Our results show that LR-SSSP can achieve up to 6–20× speedup over the state-of-the-art $\Delta$-stepping++ algorithm. Full article

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R² of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35–0.48 m, while the R² of the DBH fit was increased to a range of 0.97–0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001–0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data. Full article

Recent years have been characterized by increasing interest in graph computations. This trend can be related to the large number of potential application areas. Moreover, increasing computational capabilities of modern computers allowed turning theory of graph algorithms into explorations of best methods for their actual realization. These factors, in turn, brought about ideas like creation of a hardware component dedicated to graph computation; i.e., the Graphcore Intelligent Processor Unit (IPU). Interestingly, Graphcore systems are a hardware implementation of the Bulk Synchronous Parallel paradigm, which seemed to be a mostly theoretical concept from the end of last century. In this context, the question that has to be addressed experimentally is as follows: how good are Graphcore systems in comparison with standard systems that can be used to run graph algorithms, i.e., CPUs and GPUs. To provide a partial response to this broad question, in this contribution, PageRank, Single Source Shortest Path and Breadth-First Search algorithms are used to compare the performance of IPU-deployed algorithms to other parallel architectures. Obtained results clearly show that the Graphcore IPU outperforms other devices for the studied heterogeneous algorithms and, currently, provides best-in-class execution time results for a range of graph sizes and densities. Full article

This paper proposes a new time-respecting graph (TRG) representation for contact sequence temporal graphs. Our representation is more memory-efficient than previously proposed representations and has run-time advantages over the ordered sequence of edges (OSE) representation, which is faster than other known representations. While our proposed representation clearly outperforms the OSE representation for shallow neighborhood search problems, it is not evident that it does so for different problems. We demonstrate the competitiveness of our TRG representation for the single-source all-destinations fastest, min-hop, shortest, and foremost paths problems. Full article

The optimal path problem is an important topic in the current geographic information system (GIS) and computer science fields. The Dijkstra algorithm is a commonly used method to find the shortest path, which is usually used to find the least cost path from a single source. Based on the analysis and research of the traditional Dijkstra algorithm, this paper points out the problems of the Dijkstra algorithm and optimizes it to improve its storage capacity and operation efficiency. Then, combined with the traffic elements, a new network-based optimal path planning method is established. However, the existing network is far from actual operation in terms of the expansion of the transportation network, the uncertainty of the transportation environment, and the differences in the transportation area. Therefore, this paper proposes an optimal transshipment path planning method based on deep learning, which is oriented to multimodal transportation scenarios. This paper mainly introduces the intelligent transportation system and intelligent navigation system, and then conducts in-depth research on optimal path planning. This paper also uses the deep neural network algorithm to optimize the calculation, and finally analyzes its use and application. Simulation experiments were also performed to analyze the relationship between energy consumption, emissions, speed, load cost, and other factors under the optimal path. The final experimental results show that within the range of the emission limit of [100,200], the emission is 50%, the emission is less than 100%, but the emission is higher than 75%. In [100,200], 75% of the loading rate emits no less than 100%. In [200,300], the 50% and 100% emissions are the same. This also means that the emissions are the same but the paths are not necessarily the same. Full article

Betweenness centrality evaluates the importance of nodes and edges in networks and is one of the most pivotal indices in complex network analysis; for example, it is widely used in centrality ordering, failure cascading modeling, and path planning. Existing algorithms are based on single-source shortest paths technology, which cannot show the change of betweenness centrality with the growth of paths, and prevents deep analysis. We propose a novel algorithm that calculates betweenness centrality hierarchically and accelerates computing via GPUs. Based on the novel algorithm, we find that the distribution of shortest path has an intrinsic correlation with betweenness centrality. Furthermore, we find that the betweenness centrality indices of some nodes are 0, but these nodes are not edge nodes, and they characterize critical significance in real networks. Experimental evidence shows that betweenness centrality is closely related to the distribution of the shortest paths. Full article

In order to obtain the optimal perspectives of the recognition target, this paper combines the motion path of the manipulator arm and camera. A path planning method to find the optimal perspectives based on an A* algorithm is proposed. The quality of perspectives is represented by means of multi-view recognition. A binary multi-view 2D kernel principal component analysis network (BM2DKPCANet) is built to extract features. The multi-view angles classifier based on BM2DKPCANet + Softmax is established, which outputs category posterior probability to represent the perspective recognition performance function. The path planning problem is transformed into a multi-objective optimization problem by taking the optimal view recognition and the shortest path distance as the objective functions. In order to reduce the calculation, the multi-objective optimization problem is transformed into a single optimization problem by fusing the objective functions based on the established perspective observation directed graph model. An A* algorithm is used to solve the single source shortest path problem of the fused directed graph. The path planning experiments with different numbers of view angles and different starting points demonstrate that the method can guide the camera to reach the viewpoint with higher recognition accuracy and complete the optimal observation path planning. Full article

This paper proposes a framework for the wireless sensor data acquisition using a team of Unmanned Aerial Vehicles (UAVs). Scattered over a terrain, the sensors detect information about their surroundings and can transmit this information wirelessly over a short range. With no access to a terrestrial or satellite communication network to relay the information to, UAVs are used to visit the sensors and collect the data. The proposed framework uses an iterative k-means algorithm to group the sensors into clusters and to identify Download Points (DPs) where the UAVs hover to download the data. A Single-Source–Shortest-Path algorithm (SSSP) is used to compute optimal paths between every pair of DPs with a constraint to reduce the number of turns. A genetic algorithm supplemented with a 2-opt local search heuristic is used to solve the multi-travelling salesperson problem and to find optimized tours for each UAVs. Finally, a collision avoidance strategy is implemented to guarantee collision-free trajectories. Concerned with the overall runtime of the framework, the SSSP algorithm is implemented in parallel on a graphics processing unit. The proposed framework is tested in simulation using three UAVs and realistic 3D maps with up to 100 sensors and runs in just 20.7 s, a 33.3× speed-up compared to a sequential execution on CPU. The results show that the proposed method is efficient at calculating optimized trajectories for the UAVs for data acquisition from wireless sensors. The results also show the significant advantage of the parallel implementation on GPU. Full article

In long distance sensor nodes, propagation delay is the most crucial factor for the successful transmission of data packets in underwater acoustic sensors networks (UWAs). Therefore, to cope with the problem of propagation delay, we propose examining and selecting the best relay node (EBRN) technique based on checking the eligibility and compatibility of RN and selecting the best RN for UWAs. In the EBRN technique, the source node (S) creates a list of the best RNs, based on the minimum propagation delay to the midpoint of a direct link between S and the destination node (D). After that, the S attaches the list of selected RNs and transmit to the D along with data packets. Finally, from the list of selected RNs, the process of retransmission is performed. To avoid collision among control packets, we use a backoff timer that is calculated from the received signal strength indicator (RSSI), propagation delay and transmission time, whereas the collision among data packets is avoided by involving single RN in a particular time. The performance of the proposed EBRN technique is analyzed and evaluated based on throughput, packet loss rate (LR), packet delivery ratio (PDR), energy efficiency, and latency. The simulation results validate the effectiveness of the proposed EBRN technique. Compared with the existing schemes such as underwater cooperative medium access control (UCMAC) and shortest path first (SPF), the proposed EBRN technique performs remarkably well by increasing the throughput, PDR, and energy efficiency while decreasing the latency and LR in UWAs. Full article

With the growing complexity of indoor living environments, people have an increasing demand for indoor navigation. Currently, navigation path options in indoor are monotonous as existing navigation systems commonly offer single-source shortest-distance or fastest paths. Such path options might be not always attractive. For instance, pedestrians in a shopping mall may be interested in a path that navigates through multiple places starting from and ending at the same location. Here, we name it as the indoor traveling salesman problem (ITSP) path. As its name implies, this path type is similar to the classical outdoor traveling salesman problem (TSP), namely, the shortest path that visits a number of places exactly once and returns to the original departure place. This paper presents a general solution to the ITSP path based on Dijkstra and branch and bound (B&B) algorithm. We demonstrate and validate the method by applying it to path planning in a large shopping mall with six floors, in which the QR (Quick Response) codes are assumed to be utilized as the indoor positioning approach. The results show that the presented solution can successfully compute the ITSP paths and their potentials to apply to other indoor navigation applications at museums or hospitals. Full article

The shortest path-searching with the minimal weight for multiple destinations is a crucial need in an indoor applications, especially in supermarkets, warehouses, libraries, etc. However, when it is used for multiple item searches, its weight becomes higher as it searches only the shortest path between the single sources to each destination item separately. If the conventional Dijkstra algorithm is modified to multi-destination mode then the weight is decreased, but the output path is not considered as the real shortest path among multiple destinations items. Our proposed algorithm is more efficient for finding the shortest path among multiple destination items with minimum weight, compared to the single source single destination and modified multi-destinations of Dijkstra’s algorithm. In this research, our proposed method has been validated by real-world data as well as by simulated random solutions. Our advancement is more applicable in indoor environment applications based on multiple items or destinations searching. Full article

Consider a communication network represented by a directed graph $G=(V,E)$ of n nodes and m edges. Assume that edges in E are partitioned into two sets: a set C of edges with a fixed non-negative real cost, and a set P of edges whose costs are instead priced by a leader. This is done with the final intent of maximizing a revenue that will be returned for their use by a follower, whose goal in turn is to select for his communication purposes a subnetwork of Gminimizing a given objective function of the edge costs. In this paper, we study the natural setting in which the follower computes a single-source shortest paths tree of G, and then returns to the leader a payment equal to the sum of the selected priceable edges. Thus, the problem can be modeled as a one-round two-player Stackelberg Network Pricing Game, but with the novelty that the objective functions of the two players are asymmetric, in that the revenue returned to the leader for any of her selected edges is not equal to the cost of such an edge in the follower’s solution. As is shown, for any $ϵ>0$ and unless $\mathsf{P}=\mathsf{NP}$, the leader’s problem of finding an optimal pricing is not approximable within $n^{1/2-ϵ}$, while, if G is unweighted and the leader can only decide which of her edges enter in the solution, then the problem is not approximable within $n^{1/3-ϵ}$. On the positive side, we devise a strongly polynomial-time $O\left(n\right)$-approximation algorithm, which favorably compares against the classic approach based on a single-price algorithm. Finally, motivated by practical applications, we consider the special cases in which edges in C are unweighted and happen to form two popular network topologies, namely stars and chains, and we provide a comprehensive characterization of their computational tractability. Full article