Maximum Weight Matching Algorithm Research Articles

Traffic Classification and Packet Scheduling Strategy with Deadline Constraints for Input-Queued Switches in Time-Sensitive Networking

Deterministic transmission technology is a core key technology that supports deterministic real-time transmission requirements for industrial control in Time-Sensitive Networking (TSN). It requires each network node to have a deterministic forwarding delay to ensure the real-time end-to-end transmission of critical traffic streams. Therefore, when forwarding data frames, the switch nodes must consider the time-limited requirements of the traffic. In the input-queued switch system, an algorithm for clock-synchronized deterministic network traffic classification scheduling (CSDN-TCS) is proposed to address the issue of whether a higher-quality-of-service (QoS) performance can be provided under packet deadline constraints. First, the scheduling problem of the switch is transformed into a decomposition problem of the traffic matrix. Secondly, the maximum weight-matching algorithm in graph theory is used to solve the matching results slot by slot. By fully utilizing the slot resources, as many packets as possible can be scheduled to be completed before the deadline arrives. For two types of packet scheduling problems, this paper uses the maximum flow algorithm with upper- and lower-bound constraints to move packets from a larger deadline set to idle slots in a smaller deadline set, enabling early transmission, reducing the average packet delay, and increasing system throughput. When there are three or more types of deadlines in the scheduling set, this scheduling problem is an NP-hard problem. We solve this problem by polling the two types of scheduling algorithms. In this paper, simulation experiments based on the switching size and line load are designed, and the Earliest Deadline First (EDF) algorithm and the Flow-Based Iterative Packet Scheduling (FIPS) algorithm are compared with the CSDN-TCS algorithm. The simulation results show that under the same conditions, the CSDN-TCS algorithm proposed in this paper outperforms the other two algorithms in terms of success rate, packet loss rate, average delay and throughput rate. Compared with the FIPS algorithm, the CSDN-TCS algorithm has lower time complexity under the same QoS performance.

Joint resource scheduling and trajectory design for multi-UAV-assisted uplink NOMA networks

The combination of unmanned aerial vehicle (UAV) communication and non-orthogonal multiple access (NOMA) is of great significance to support massive connections in the Internet of things (IoT). This paper studies a multi-UAV-assisted communication network, where the UAVs are employed as aerial access points to receive data from IoT nodes with NOMA adopted for uplink transmission. The aim is to maximize the average sum rate of the network, with the requirements of the individual upload of each node. To handle the node clustering problem, the spectral clustering and maximum weight matching algorithm are first integrated to divide the IoT nodes. Then, the time-varying NOMA user grouping scheme is implemented accordingly. Furthermore, by considering the trajectory decomposition and successive hover-and-fly structure, the complex UAV trajectory problem is transformed into a linear programming and the optimal time-continuous trajectory is achieved. Extensive simulations validate the effectiveness of the proposed algorithms for the UAV-assisted NOMA network.

A Fine-Grain Batching-Based Task Allocation Algorithm for Spatial Crowdsourcing

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing.

Topology Design for GNSSs Under Polling Mechanism Considering Both Inter-Satellite Links and Ground-Satellite Links

Inter-satellite links (ISLs) are adopted in global navigation satellite systems (GNSSs) for high-precision orbit determination and space-based end-to-end telemetry telecommand control and communications. Due to limited onboard ISL terminals, the polling time division duplex (PTDD) mechanism is usually proposed for space link layer networking. By extending the polling mechanism to ground-satellite links (GSLs), a unified management system of the space segment and the ground segment can be realized. However, under the polling system how to jointly design the topology of ISLs and GSLs that are both time-slotted to improve data interaction has not been studied. In this paper, we formulate the topology design problem as an integer linear programming problem, aiming at minimizing the average delay of data delivery from satellites to ground stations while satisfying the ranging requirement for orbit determination. To tackle the computational complexity problem, we first present a novel modeling method of delay to reduce the number of decision variables. Furthermore, we propose a more efficient heuristic based on maximum weight matching algorithms. Simulation results demonstrate the feasibility of the proposed methods for practical operation in GNSSs with time-slotted GSLs. Comparing the two methods, the heuristic can achieve similar performance with respect to average delay but with significantly less complexity.

Financial Advisor Recruitment: A Smart Crowdsourcing-Assisted Approach

Successful portfolio management requires, in addition to advanced optimization strategies, effective recruitment of specialized financial advisors. Hiring the wrong ones can be detrimental to investors' financial goals. In this article, we propose an automated crowdsourcing system to organize cooperation between financial advisors and investors. Without interfering with their private portfolio optimization techniques, we design a recruitment framework that matches financial advisors to investors based on their profiles and features, as well as the previous activities of their peers. Using the database of the crowdsourcing platform, we employ an unsupervised technique to regroup advisors with a high degree of similarities into clusters and, hence, shrink the search space. Afterward, we train a machine learning regression model to predict the matching score that can be achieved if an investor hires a particular advisor. These scores are converted to weights of bipartite graphs to which we apply a double-phased many-to-many maximum weight matching algorithm to determine a suitable investor-advisor combination. In the simulations, we investigate the performance of the proposed recruitment approach and show that, compared with other traditional approaches, higher returns can be reached for both investors and financial advisors.

Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance

Authorities in modern cities are facing daily challenges related to traffic control. Due to the problem complexity caused by the urbanization growth, investing in developing traffic signal control systems (TSCS) to guarantee better mobility has taken more attention by these authorities. In the existing literature, the majority of TSCS offers only a real-time control for a detected traffic problem without considering early prediction and estimation of its occurrence. Furthermore, traffic problems related to the arrival and guidance of emergency vehicles are rarely considered. Based on these gaps, we rely on concepts and mechanisms from both, the Artificial and the convolution neural networks (ANN and CNN), coupled with the longest queue first maximal weight matching algorithm (LQF-MWM), to develop PANNAL, a predictive and reactive TSCS. PANNAL is a Multi-Agent based System, where each individual agent has ANN, CNN, and LQF-MWM to adapt signal sequences and durations and favor the crossing of emergency vehicles. Agents have a heterarchical architecture considered for coordination. We leant on V1SSIM, a state-of-the-art traffic simulation software for simulation and evaluation. We adopted algorithms, scenarios, key performance indicators, and evaluation results from the recent literature for benchmarking. These algorithms are pre-emptive and have a high performance and competitive results in traffic control of disturbed traffic condition.

Predicting Model and Algorithm in RNA Folding Structure Including Pseudoknots

The prediction of RNA structure with pseudoknots is a nondeterministic polynomial-time hard (NP-hard) problem; according to minimum free energy models and computational methods, we investigate the RNA-pseudoknotted structure. Our paper presents an efficient algorithm for predicting RNA structure with pseudoknots, and the algorithm takes O([Formula: see text]) time and O([Formula: see text]) space, the experimental tests in Rfam10.1 and PseudoBase indicate that the algorithm is more effective and precise. The predicting accuracy, the time complexity and space complexity outperform existing algorithms, such as Maximum Weight Matching (MWM) algorithm, PKNOTS algorithm and Inner Limiting Layer (ILM) algorithm, and the algorithm can predict arbitrary pseudoknots. And there exists a [Formula: see text] ([Formula: see text]) polynomial time approximation scheme in searching maximum number of stackings, and we give the proof of the approximation scheme in RNA-pseudoknotted structure. We have improved several types of pseudoknots considered in RNA folding structure, and analyze their possible transitions between types of pseudoknots.

Multiscale video sequence matching for near-duplicate detection and retrieval

As one of key technologies in content-based near-duplicate detection and video retrieval, video sequence matching can be used to judge whether two videos exist duplicate or near-duplicate segments or not. Despite a lot of research efforts devoted in recent years, how to precisely and efficiently perform sequence matching among videos (which may be subject to complex audio-visual transformations) from a large-scale database still remains a pretty challenging task. To address this problem, this paper proposes a multiscale video sequence matching (MS-VSM) method, which can gradually detect and locate the similar segments between videos from coarse to fine scales. At the coarse scale, it makes use of the Maximum Weight Matching (MWM) algorithm to rapidly select several candidate reference videos from the database for a given query. Then for each candidate video, its most similar segment with respect to the given query is obtained at the middle scale by the Constrained Longest Ascending Matching Subsequence (CLAMS) algorithm, and then can be used to judge whether that candidate exists near-duplicate or not. If so, the precise locations of the near-duplicate segments in both query and reference videos are determined at the fine scale by using bi-directional scanning to check the matching similarity at the segments’ boundaries. As such, the MS-VSM method can achieve excellent near-duplicate detection accuracy and localization precision with a very high processing efficiency. Extensive experiments show that it outperforms several state-of-the-art methods remarkably on several benchmarks.

Scheduling in Multi-Wavelength Ring-Based Optical Networks-on-Chip

Synchronous networks-on-chip (NoC) require tailored schedulers achieving low latency, high throughput, and fairness while avoiding packet collisions. Efficient schedulers exist for rearrangeable non-blocking NoC. However, NoCs fabricated in integrated photonics typically suffer a blocking behavior due to the limitations of the switching capabilities in the space and wavelength domains. This paper discusses a scheduler for an integrated optical NoC based on a ring topology and realized with multiple resonating microrings (multi-microring, MMR). Scheduling in MMR architecture consists of the conventional matching sub-problem and the wavelength assignment sub-problem. The paper’s contribution is twofold. An iterative parallel wavelength matching (iPWM) algorithm is presented for jointly addressing both sub-problems. iPWM achieves performance similar to a two-step scheduler based on sequential iSLIP and first-fit wavelength assignment, but with a computational complexity lower than and independent of the number of wavelengths. A comprehensive comparison with the two-step scheduler based on the more complex maximum weighted matching algorithm and with an optimal scheduler is also carried out for the case of fixed and tunable transmitters to assess the trade-off between scheduling complexity and performance. In addition, bidirectionality with one or two transceivers per port is proposed as a way to overcome the throughput degradation caused by the blocking behavior. Simulation results indicate that bidirectionality is very effective in improving the throughput and reducing the latency even when using a single transceiver.

An artificial immune network to control interrupted flow at a signalized intersection

To monitor and control interrupted flow at signalized intersections, several Traffic Signal Control Systems (TSCSs) were developed based on optimization and artificial intelligence techniques. Although learning can provide intelligent ways to deal with disturbances, existing approaches still lack concepts and mechanisms that enable direct representation of knowledge and explicit learning, particularly to capture and reuse previous experiences with disturbances. This article addresses this gap by designing a new TSCS based on innovative concepts and mechanisms borrowed from biological immunity. Immune memory enables the design of a Case-Based Reasoning (CBR) System in which cases provide a direct representation of knowledge about disturbances. Immune network theory enables the design of a Reinforcement Learning (RL) mechanism to interconnect cases, capture explicit knowledge about the outcomes (success and failure) of control decisions and enable decision-making by taking advantage of previous outcomes in reaction to new occurrences of disturbances. We provide a detailed description of new learning algorithms, both to create the case-base and to interconnect cases using RL. The performance of the suggested TSCS is assessed by benchmarking it against two standard control strategies from the literature, namely fixed-time and adaptive control using the Longest Queue First – Maximal Weight Matching (LQF-MWM) algorithm. The suggested TSCS is applied on an intersection simulated using VISSIM, a state-of-the-art traffic simulation software. The results show that the suggested TSCS is able to handle different traffic scenarios with competitive performance, and that it is recommended for extreme situations involving blocked approaches and high traffic flow.

Multi-agent immune networks to control interrupted flow at signalized intersections

Urban traffic is subject to disturbances that cause long queues and extended waiting times at signalized intersections. Although Multi-Agent Systems (MAS) were considered to control traffic at signalized intersections in a distributed way, their generic conceptual framework and lack of built-in adaptation mechanisms prevent them from achieving specific disturbance management capabilities. The traffic signal control problem is still a challenging open-ended problem for which learning and adaptation mechanisms need to be developed to deal with disturbances in an intelligent way. In this article, we rely on concepts and mechanisms inspired by biological immunity to design a distributed, intelligent and adaptive traffic signal control system. We suggest a heterarchical multi-agent architecture, where each agent represents a traffic signal controller assigned to a signalized intersection. Each agent communicates and coordinates with neighboring agents, and achieves learning and adaptation to disturbances based on an artificial immune network. The suggested Immune Network Algorithm based Multi-Agent System (INAMAS) provides intelligent mechanisms that capture disturbance-related knowledge explicitly and take advantage of previous successes and failures in dealing with disturbances through an adaptation of the reinforcement principle. To demonstrate the efficiency of the suggested control architecture, we assess its performance against two control strategies from literature, namely fixed-time control and a distributed adaptation of the Longest Queue First – Maximal Weight Matching (LQF-MWM) algorithm. Agents are developed using SPADE platform and used to control a network of signalized intersections simulated with VISSIM, a state-of-the-art traffic simulation software. The results show that INAMAS is able to handle different traffic scenarios with competitive performance (in terms of vehicle queue lengths and waiting times), and that it is particularly more successful than the other controllers in dealing with extreme situations involving blocked approaches and high traffic volumes.

A Case-Based Reasoning System to Control Traffic at Signalized Intersections

Traffic control systems (TCS) contribute to relieve congestion in cities. Although many optimization and intelligent approaches exist to develop TCS, only a few works have investigated Case Based Reasoning (CBR) to control traffic at signalized intersections. Existing works usually state that the case-base is created using experts’ knowledge but do not specify how this knowledge is acquired and how the case-base is built. In this article, we design a CBR system to control traffic at a single signalized intersection. We develop a hybrid methodology to create the case-base using simulation-optimisation, Condensed Nearest Neighbour algorithm (CNN) and a rule-based system. The algorithm is implemented in Python and applied on an intersection simulated using VISSIM, a state-of-the-art traffic simulation software. The performance of the system is assessed and compared to the Longest Queue First with Maximal Weight Matching (LQF-MWM) algorithm. Results show that the implemented system is able to handle different traffic scenarios with competitive performance.

A QOS Based Load Balanced Switch

The simple architecture with high forwarding capacity bandwidth is load balancing switch is build with two stage switch fabric to eliminate the mis sequencing problems and kept packets of the same flow in the order of load balanced two stage switch. Before load balancing two stages has perform with the Full frame first algorithm and use centralized scheduler to eliminate those mis sequencing problemsand getting the 100% throughput result. There comes at some additional cost and needs costly online scheduling algorithms and need for a complex scheduler and quality being reduced. In this paper we introduce the two stage switch fabric with round robin scheduling algorithmis used to solve mis sequencing problems and two maximum weight matching algorithms Longest queue first(LQF) and Oldest cell first(OCF) to achieve 100% throughput results. Theoretical analysis and simulation results shows that using these algorithms, achieve all the possible outcome results in the switch. � The simple architecture with the load balancing scheme perform with interesting two different stage switch fabric to eliminate the mis sequencing problems and kept packets of the same flow to the destination path in the order of load balanced two stage switch fabric. The two stage load balancing switch is more scalable than the any other switch in the network architecture. Hardly load balanced switch got some problem with that, on sending and receiving the data in wired or wireless networks(8), the major problem with that is mis sequencing problems has arised to loss of data packets or mis matched data formats. Somehow in earlier they are tired to clear those problems with the algorithms named, Full frame first algorithm to get mis sequencing problems and they put the centralized schedular to achieve the 100% throughput results(4)(2) in their data packets.Oftenly they got the perfect data packets arrival in destination, but the problem with that methodology is the quality of data is reduced to lower. And manually they they need costly online scheduling algorithms to rectify the mis matching of data and need more complex schedular to achieve the 100% throughput result of outcome data. We introduce in this paper to rectify the mis sequencing problems(1) and avoid the costly online scheduling algorithms, we use Round robin scheduling algorithms(RRS) to rectify the mis matching of data packets in the tcp applications and two maximum weight matching algorithms namely, Longest queue first(LOQ) and Oldest cell first(OCF) to achieve 100% throughput result for all independent arrival packets.(11)These algorithms are used to eliminate the complex schedular and needs of online costly scheduling algorithm to schedule the data packets in the destination node. The another approach of this proposed is to get the high quality of data packets in destination node which is the data which is send by sender, the receiver also got the same quality of data(9) without defragmentation and reduction of quality. We observed that in the algorithms and methodolgy of using those above like, RRS, LOQ and OCF, not only to solve mis sequencing problems and also get quality of data packets service of the same aaplication(10). Yet we are consider more algorithm to get the quality of data packets. Our idea is to just keep packets

Submodular maximization meets streaming: matchings, matroids, and more

We study the problem of finding a maximum matching in a graph given by an input stream listing its edges in some arbitrary order, where the quantity to be maximized is given by a monotone submodular function on subsets of edges. This problem, which we call maximum submodular-function matching (MSM), is a natural generalization of maximum weight matching (MWM), which is in turn a generalization of maximum cardinality matching. We give two incomparable algorithms for this problem with space usage falling in the semi-streaming range--they store only $$O(n)$$O(n) edges, using $$O(n\log n)$$O(nlogn) working memory--that achieve approximation ratios of 7.75 in a single pass and $$(3+\varepsilon )$$(3+?) in $$O(\varepsilon ^{-3})$$O(?-3) passes respectively. The operations of these algorithms mimic those of Zelke's and McGregor's respective algorithms for MWM; the novelty lies in the analysis for the MSM setting. In fact we identify a general framework for MWM algorithms that allows this kind of adaptation to the broader setting of MSM. Our framework is not specific to matchings. Rather, we identify a general pattern for algorithms that maximize linear weight functions over and prove that such algorithms can be adapted to maximize a submodular function. The notion of independence here is very general; in particular, appealing to known weight-maximization algorithms, we obtain results for submodular maximization over hypermatchings in hypergraphs as well as independent sets in the intersection of multiple matroids.

Linear-Time Approximation for Maximum Weight Matching

The maximum cardinality and maximum weight matching problems can be solved in Õ ( m √ n ) time, a bound that has resisted improvement despite decades of research. (Here m and n are the number of edges and vertices.) In this article, we demonstrate that this “ m √ n barrier” can be bypassed by approximation. For any ε > 0, we give an algorithm that computes a (1 − ε )-approximate maximum weight matching in O ( mε −1 log ε −1 ) time, that is, optimal linear time for any fixed ε . Our algorithm is dramatically simpler than the best exact maximum weight matching algorithms on general graphs and should be appealing in all applications that can tolerate a negligible relative error.

A reduction algorithm for the weighted stable set problem in claw-free graphs

In this article the Lovász–Plummer clique reduction is extended to the weighted case and used to find a maximum weight stable set in a claw-free graph G with n nodes in O(n2(n2+L(n))) time, where L(n) is the complexity of finding a maximum weight augmenting path in a line graph H with n nodes. The best algorithm known to date to solve the latter problem is Gabow’s maximum weight matching algorithm (applied to the root graph of H) which has a complexity of O(n2logn). It follows that our algorithm can produce a maximum weight stable set in a claw-free graph in O(n4logn) time.

On the channel usability of Wireless Mesh Networks: when stability plays with you

In this work, we are striving for a perfect balance on utilisation between reliable channels and unreliable channels, where the network throughput can be maximised. We propose 'p-stable', a distributed Multi-phase Maximum Weighted Matching algorithm. We also apply channel bundles to effectively improve stability in the network, and prove the problem NP-hard. The time complexity is O(Δ³ + (log. *n)²), and overhead complexity is O(Δ × (Δ + n) + nα + log n), with Δ denoting maximum number of node degree in an n nodes network. Simulation results show that p-stable design effectively improves network stability, especially upon the existence of large number of unreliable channels.

A new analysis of a self-stabilizing maximum weight matching algorithm with approximation ratio 2

The maximum weight matching problem is a fundamental problem in graph theory with a variety of important applications. Recently Manne and Mjelde presented the first self-stabilizing algorithm computing a 2-approximation of the optimal solution. They established that their algorithm stabilizes after O(2n) (resp. O(3n)) moves under a central (resp. distributed) scheduler. This paper contributes a new analysis, improving these bounds considerably. In particular it is shown that the algorithm stabilizes after O(nm) moves under the central scheduler and that a modified version of the algorithm also stabilizes after O(nm) moves under the distributed scheduler. The paper presents a new proof technique based on graph reduction for analyzing the complexity of self-stabilizing algorithms.

Fast Algorithm for Pseudoknotted RNA Structure Prediction

Existing polynomial time algorithms can handle only limited types of pseudoknots, or have too high time or space to predict long sequences. In this paper a heuristic algorithm is presented to maximize stems and predict arbitrary pseudoknots with O(n^3) time and O(n) space for a large scale of 5000 bases. Compared with maximum weighted matching algorithm, our algorithm reduce space complexity from O(n^2) to O(n); and the experimental results show that its sensitivity is improved from 80% to 87.8%, and specificity is increased from 53.7% to 75.9%. Compared with genetic algorithm with the accuracy of 83.3% and simulated annealing algorithm with the accuracy of 79.7%, our algorithm increases the predicted accuracy to 87.5%.

A Novel Signal-Scheduling Algorithm With Quality-of-Service Provisioning for an Isolated Intersection

There have been countless efforts directed toward efficiently controlling the flow of vehicular traffic through an intersection. This paper describes an algorithm designed for the signal control problem that employs concepts drawn from the field of packet switching in computer networks. The novel method proposed utilizes a maximal weight matching algorithm to minimize the queue sizes at each approach, yielding significantly lower average vehicle delay through the intersection. Of particular interest are scenarios in which differentiated services are offered to vehicle classes with differing priorities. Lyapunov function-based analysis is provided, deriving the conditions under which the system is guaranteed to be stable. The algorithm is compared to an optimized fixed-time controller and a vehicle-actuated controller using the VISSIM traffic simulation environment. Simulation results demonstrate the performance gain obtained when using the proposed scheme, particularly in the scenario in which vehicle routes are unequally distributed, and multiple classes of service are desired.