Kuhn-Munkres Research Articles

An Intelligent Offloading System Based on Multiagent Reinforcement Learning

Intelligent vehicles have provided a variety of services; there is still a great challenge to execute some computing-intensive applications. Edge computing can provide plenty of computing resources for intelligent vehicles, because it offloads complex services from the base station (BS) to the edge computing nodes. Before the selection of the computing node for services, it is necessary to clarify the resource requirement of vehicles, the user mobility, and the situation of the mobile core network; they will affect the users’ quality of experience (QoE). To maximize the QoE, we use multiagent reinforcement learning to build an intelligent offloading system; we divide this goal into two suboptimization problems; they include global node scheduling and independent exploration of agents. We apply the improved Kuhn–Munkres (KM) algorithm to node scheduling and make full use of existing edge computing nodes; meanwhile, we guide intelligent vehicles to the potential areas of idle computing nodes; it can encourage their autonomous exploration. Finally, we make some performance evaluations to illustrate the effectiveness of our constructed system on the simulated dataset.

Kuhn-Munkres Algorithm-Based Matching Method and Automatic Device for Tiny Magnetic Steel Pair.

The tiny magnetic steel pair (TMSP), composed by two tiny magnetic steel blocks (TMSBs), is critical for some precision instruments. Incorrect matching of TMSP may result in insufficient instrument performance. Herein, the matching method of TMSP based on the Kuhn–Munkres algorithm is proposed. Further, an automatic TMSP matching device is developed. Especially, an ingenious clamp for multiple constraints of TMSB is presented and a visual/magnetism/force hybrid control strategy is realized for the safe and efficient manipulation of TMSBs in a magnetic environment. Moreover, with the TMSBs of a pendulum accelerometer, the matching experiments are conducted to validate the comprehensive performance. The result of the numerical experiment shows that the Kuhn–Munkres algorithm-based method is stable and efficient. The results of measurement and TMSP matching experiments show that the device has good repeatability (<1 mT) and practicability. The proposed matching method has great application prospect in various matching and microassembly of TMSPs.

Optimizing bipartite matching in real-world applications by incremental cost computation

The Kuhn-Munkres (KM) algorithm is a classical combinatorial optimization algorithm that is widely used for minimum cost bipartite matching in many real-world applications, such as transportation. For example, a ride-hailing service may use it to find the optimal assignment of drivers to passengers to minimize the overall wait time. Typically, given two bipartite sets, this process involves computing the edge costs between all bipartite pairs and finding an optimal matching. However, existing works overlook the impact of edge cost computation on the overall running time. In reality, edge computation often significantly outweighs the computation of the optimal assignment itself, as in the case of assigning drivers to passengers which involves computation of expensive graph shortest paths. Following on from this observation, we observe common real-world settings exhibit a useful property that allows us to incrementally compute edge costs only as required using an inexpensive lower-bound heuristic. This technique significantly reduces the overall cost of assignment compared to the original KM algorithm, as we demonstrate experimentally on multiple real-world data sets, workloads, and problems. Moreover, our algorithm is not limited to this domain and is potentially applicable in other settings where lower-bounding heuristics are available.

Semantic loop closure detection based on graph matching in multi-objects scenes

Robust loop-closure detection is essential for visual SLAM. Traditional methods often focus on the geometric and visual features in most scenes but ignore the semantic information provided by objects. Based on this consideration, we present a strategy that models the visual scene as semantic sub-graph by only preserving the semantic and geometric information from object detection. To align two sub-graphs efficiently, we use a sparse Kuhn–Munkres algorithm to speed up the search for correspondence among nodes. The shape similarity and the Euclidean distance between objects in the 3-D space are leveraged unitedly to measure the image similarity through graph matching. Furthermore, the proposed approach has been analyzed and compared with the state-of-the-art algorithms at several datasets as well as two indoor real scenes, where the results indicate that our semantic graph-based representation without extracting visual features is feasible for loop-closure detection at potential and competitive precision.

Resource allocation for offloading-efficiency maximization in clustered NOMA-enabled mobile edge computing networks

In this paper, the problem of resource allocation for multi-carrier clustered non-orthogonal multiple-access (NOMA)-enabled mobile edge computing (MEC) networks is considered. In particular, the goal is to maximize the network sum-offloading-efficiency (SOE) via joint subcarrier assignment, power allocation, and computing resource allocation (J-SA-PA-CRA) for all user clusters, under the partial offloading mode, and subject to transmit power, delay and computing resource constraints. However, the formulated J-SA-PA-CRA problem is non-convex, and happens to be NP-hard, and thus is computationally-prohibitive. In turn, a low-complexity solution procedure is proposed, which decouples problem J-SA-PA-CRA into two subproblems: (1) J-PA-CRA per (user cluster, subcarrier) pair, and (2) one-to-one subcarrier assignment. The first subproblem is optimally solved via a successive convex approximation algorithm. As for the second subproblem, the polynomial-time complexity Kuhn–Munkres (KM) algorithm is proposed for optimal subcarrier assignment. Also, the stable marriage matching (SMM) algorithm is devised for a sub-optimal subcarrier assignment solution, but with less complexity than the KM algorithm. Extensive simulation results are provided to validate the proposed solution procedure, illustrating that the KM-based (SMM-based) solution procedure efficiently yields the optimal (a sub-optimal) network SOE, in comparison to the J-SA-PA-CRA scheme (solved via a global optimization package), while being superior to their OMA counterpart schemes (e.g. FDMA and TDMA).

Three-Tier Architecture Supporting QoS Multimedia Routing in Cloud-Assisted MANET with 5G Communication (TCM5G)

Recently, evolving technologies such as 5G and cloud computing have offered new prospects in mobile ad hoc networks (MANETs). However, achieving a high quality of service (QoS) in multimedia routing over MANET–cloud using 5G networks remains challenging owing to the dynamic nature of mobile devices. The present study addresses this problem by proposing a three-tier architecture in cloud-assisted MANETs with 5G (TCM5G) communication. The network comprises the MANET, cloudlet, and cloud tiers. In the proposed scheme, partitioning and clustering are performed to optimize the cluster size. Specifically, partitioning is first performed by the improved monarch butterfly optimization algorithm. Here, the cluster head (CH) is first selected by computing the importance rate. The selected CH then forms a cluster around itself by broadcasting its selection message. Device-to-device (D2D) communication is established using the Kuhn–Munkres algorithm, which determines the optimal device for each D2D communication in the network in order to increase data transmission efficiency. Network performance depends on effective routing; hence, we considered two routing types: inter-cluster and cloudlet. The former is performed through chaotic flower pollination, and the latter is achieved by using the improved Type-2 Takagi Sugeno fuzzy algorithm. To improve the QoS in multimedia routing, we employ a full-interpolation, scalable video-coding algorithm for effective multimedia data encoding. Task offloading among cloudlets is performed based on a load criterion to balance the cloudlet load. The performance of the proposed system is evaluated using five metrics: throughput, packet delivery ratio (PDR), end-to-end delay, task-completion time, and bandwidth consumption. The results demonstrate that in comparison with the existing DCRM, PCA, and HRM methods, the proposed TCM5G scheme enhances throughput and PDR by 30%, and reduces end-to-end delay, task-completion time, and bandwidth consumption by up to 40%.

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.

A Satellite Handover Strategy Based on MIMO Technology in LEO Satellite Networks

This letter proposes a handover strategy for the links between satellites and gateway stations based on weighted bipartite graph in low earth orbit (LEO) satellite networks. To maximize the overall communication quality and balance the load of satellite networks, a maximum weight matching method is investigated by using Kuhn-Munkres (KM) algorithm. Then, we enhance KM algorithm, which is more suitable for the scene of satellite handover, and employ multiple-input multiple-output (MIMO) technology to further improve communication quality. Finally, we derive the achievable rate and validate the proposed handover strategy.

Outage performance analysis and resource allocation algorithm for energy harvesting D2D communication system

In this paper, we consider an energy harvesting device-to-device (D2D) communication system, where D2D transmitter can use mode A to directly communicate with D2D receiver or use mode B as a relay to assist cellular communication while communicating with D2D receiver by adopting non-orthogonal multiple access technology. Firstly, the outage probability expression in two modes is derived, and the communication mode is determined according to outage performance. Then, assuming that the full system information is available, the channel allocation and relay selection are completed by Kuhn–Munkres algorithm, and the offline power allocation of D2D users is realized by reinforcement learning. Next, the offline optimization results are taken as the training data set to train the neural network, and the optimal model of the transmission power is obtained. Considering the transmission power constraint, the online power allocation optimization algorithm is further proposed. Numerical results demonstrate the accuracy of derived outage probability, and the proposed resource allocation algorithm can improve the performance of hybrid system.

A Two-Layer, Energy-Efficient Approach for Joint Power Control and Uplink-Downlink Channel Allocation in D2D Communication.

Energy efficiency (EE) is a critical performance indicator for the device-to-device (D2D) communication underlaying cellular networks due to limited battery capacity and serious interference between user equipment. In this study, we proposed a power control and channel allocation scheme for the EE maximization of the D2D pairs, while jointly reusing uplink–downlink resources and guaranteeing the cellular users’ (CUs) quality of service (QoS). The formulated problem was a mixed-integer nonlinear programming (MINLP) problem, which is generally an unsolved non-deterministic polynomial-time hardness (NP-hard) problem within polynomial time. To make it tractable to solve, the original problem was divided into two sub-problems: power control and channel allocation. A power control algorithm based on the Lambert W function was proposed to maximize the EE of the individual D2D pair. Assigning either an uplink or downlink resource to reuse, the EE of each D2D pair was calculated using the power control results. A channel allocation scheme based on the Kuhn–Munkres algorithm utilized the EE weights to optimize the overall EE of the D2D pairs. The simulation results verified the theoretical analysis and proved that the proposed algorithm could remarkably improve the EE of D2D pairs while guaranteeing the QoS of the CUs.

Energy-Efficient Joint Power Allocation and User Selection Algorithm for Data Transmission in Internet-of-Things Networks

The Internet-of-Things (IoT) system is a novel networking technology that connects smart communication devices through Internet-enabled infrastructure to enhance wireless communications. The explosive growth of IoT devices connectivity has increased energy consumption drastically that raises economic and physical environment concerns. To meet the challenges posed by high energy consumption, energy efficiency has become an urgent need for IoT networks recently. This article examines resource allocation and formulates the joint optimization problem for power allocation and user selection subject to the maximum transmit power and different Quality-of-Service (QoS) requirements, to achieve an improved energy efficiency performance in IoT networks under channel uncertainty. Furthermore, the formulated optimization problem is mixed-integer nonlinear programming (MINLP) with no practical solutions. Due to the nonconvexity and NP-hardness of the MINLP problem, the primal optimization problem is transformed into a convex problem and solved optimally for power allocation and user selection, by applying the Lagrangian dual decomposition method and the Kuhn–Munkres algorithm, respectively. An efficient joint iterative algorithm is proposed to maximize energy efficiency performance with guaranteed convergence within few numbers of iterations. The numerical results validate the robustness of the proposed algorithm and significantly show its superior performance as compared with the baseline algorithms.

Energy-Efficient Barrier Coverage With Probabilistic Sensors in Wireless Sensor Networks

Barrier coverage is an important research issue in wireless sensor networks (WSNs). Barrier gaps may exist due to deployment randomness or battery depletion. It is critical to address the problem with an energy-efficient method due to the high cost of mobile sensors. Besides, it is a challenge to introduce the realistic probabilistic sensing model of sensors while mending barrier gaps. In this paper, we study how to efficiently use mobile sensors with a probabilistic sensing model to improve barrier coverage. Firstly, we define safe cell and design algorithms to find safe cells and barrier gaps. Secondly, we study how to improve the barrier coverage with minimum number of mobile sensors . Thirdly, we divide minimum movement of mobile sensors problem into the simple case and general case, and the general case is resorted to Kuhn-Munkres (KM) algorithm. Simulation results are conducted to demonstrate the effectiveness and advantage of our proposed algorithms.

Joint Relay Assignment and Power Allocation for Multiuser Multirelay Networks Over Underwater Wireless Optical Channels

Multiuser multirelay network is a potential scenario to fulfill the transmission requirements of various sources and high-volume traffic for the Internet of Underwater Things. To efficiently complete concurrent transmissions for multiple users, this article investigates the joint relay assignment and power allocation problem for multiuser multirelay networks based on the underwater optical wireless communication (UOWC). Specifically, the multiuser multirelay network for UOWC based on decode-and-forward relaying is modeled, where the absorption, scattering, solar radiation noise, and oceanic turbulence of UOWC are all considered. The joint optimization problem of relay assignment and power allocation is formulated as a mixed-integer programming problem, where the average outage probability is minimized with the constraint of total transmitted power. To solve this joint problem, an alternating optimization method is employed, which alternately optimizes the relay assignment and power allocation subproblems. The relay assignment subproblem is modeled as a weighted bipartite matching problem and solved by an improved Kuhn–Munkres algorithm, whereas the power allocation subproblem is proved to be quasiconvex and solved by an iterative bisection algorithm. The simulation results indicate that the proposed schemes significantly reduce the average outage probability with fast convergence.

An Optimal Guidance Strategy for Moving-Target Interception by a Multirotor Unmanned Aerial Vehicle Swarm

To solve the problem of intercepting a moving target by a multirotor unmanned aerial vehicle (UAV) swarm, an optimal guidance strategy is proposed. The proposed guidance law is based on the integration of the classic pure pursuit guidance law and Kuhn-Munkres (KM) optimal matching algorithm, and virtual force potential functions are used to avoid collision. The proposed optimal guidance strategy is demonstrated by simulation experiments. The simulation results indicate that with the proposed optimal guidance strategy, a UAV swarm can intercept a moving target while maintaining the predetermined formation, and during the formation flight, the collisions between UAVs or the target can be avoided. Through a comparative experiment, the proposed optimal matching algorithm is proven to significantly reduce the average per-sampling-period total flight distance of all the UAVs and accelerate the interception process, and the formation completion degree is improved.

Assembly consistency improvement of straightness error of the linear axis based on the consistency degree and GA-MSVM-I-KM

Fluctuation on the assembly quality of the linear axis of machine tools (LA-MT) at the same batch is urgent problem need to be solved in assembly of machine tools. In this paper, a new concept of assembly consistency degree was introduced for defining the fluctuation degree of assembly quality. Based on assembly consistency degree, a hybrid machine learning method, genetic algorithm optimized multi-class support vector machine and improved Kuhn–Munkres (GA-MSVM-I-KM) was proposed for improving assembly consistency of LA-MT. The assembly of linear axis of a three-axis vertical machining center was regarded as an example, and the assembly consistency influence factors on straightness error of Y-axis (SE-YA) were analyzed through the Kruskal–Wallis statistical method. The main factors affected on the assembly consistency of SE-YA turned out to be the machining errors of bed and the assembly team technical levels. Based on this, the assembly consistency improvement model was established. Then, the prediction model of SE-YA based on assembly experiment data and genetic algorithm optimized multi-class support vector machine (GA-MSVM) was constructed, and I-KM method was applied for improving assembly consistency of SE-YA. The results show that the GA-MSVM-I-KM method can effectively enhance the assembly consistency of SE-YA, and the assembly consistency degree is reduced from 0.19 to 0.08.

Control plane delay minimization-based capacitated controller placement algorithm for SDN

Software-defined networking (SDN) is proposed as a new network paradigm which decouples control plane from data plane and provides flexible network management. In this paper, we consider the capacitated controller placement problem in SDN, which jointly determines the number, location, and the capacity matching strategy of SDN controllers. redTo stress the importance of control plane delay which is composed of both the transmission and processing delay between controllers and switches, and the inter-controller delay; we formulate control plane delay minimization problem subject to controller-switch association constraints, controller capacity constraints, etc. Since the formulated optimization problem is a complicated nonlinear integer programming problem which cannot be solved easily, we transform it into controller-switch association subproblem and controller capacity matching subproblem. To solve the controller-switch association subproblem, we propose a heuristic algorithm based on Dijkstra algorithm and K-means algorithm. Given the controller-switch association strategy, we then apply the Kuhn-Munkres (K-M) algorithm to solve the controller capacity matching subproblem and obtain the capacitated controller placement strategy. Simulation results are shown to demonstrate the effectiveness of the proposed algorithm.

Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement

The development of new concepts for smart cities and the application of drones in this area requires different architecture for the drones’ stations (nests) and their placement. Drones’ stations are designed to protect drones from hazards and utilize charging mechanisms such as solar cells to recharge them. Increasing the number of drones in smart cities makes it harder to find the optimum station for each drone to go to after performing its mission. In classic ordered technique, each drone returns to its preassigned station, which is shown to be not very efficient. Greedy and Kuhn–Munkres (Hungarian) algorithms are used to match the drone to the best nesting station. Three different scenarios are investigated in this study; (1) drones with the same level of energy, (2) drones with different levels of energy, and (3) drones and stations with different levels of energy. The results show that an energy consumption reduction of 25–80% can be achieved by applying the Kuhn–Munkres and greedy algorithms in drone–nest matching compared to preassigned stations. A graphical user interface is also designed to demonstrate drone–station matching through the Kuhn–Munkres and greedy algorithms.

Stackelberg Game Based Social-Aware Resource Allocation for NOMA Enhanced D2D Communications

Device-to-device (D2D) communication and non-orthogonal multiple access (NOMA) have been considered promising techniques to improve system throughput. In the NOMA-enhanced D2D scenario, a joint channel and power allocation algorithm based on the Stackelberg game is proposed in this paper. The social relationship between the cellular and D2D users is utilized to define their utility functions. In the two-stage Stackelberg game, the cellular user is the leader and the D2D group is the follower. Cellular users and D2D groups are matched via the Kuhn–Munkres (KM) algorithm to allocate channels for D2D groups in the first stage. The power allocation of D2D users is optimized through a penalty-function-based particle swarm optimization algorithm (PSO) in the second stage. The simulation results show that the proposed algorithm can effectively strengthen the cooperation between cellular and D2D users and improve their utility.

Abnormal Water Quality Monitoring Based on Visual Sensing of Three-Dimensional Motion Behavior of Fish

In the context of the problem of water pollution, the movement characteristics and patterns of fish under normal water quality and abnormal water quality are clearly different. This paper proposes a biological water quality monitoring method combining three-dimensional motion trajectory synthesis and integrated learning. The videos of the fish movement are captured by two cameras, and the Kuhn-Munkres (KM) algorithm is used to match the target points of the fish body. The Kalman filter is used to update the current state and find the optimal tracking position as the tracking result. The Kernelized Correlation Filters (KCF) algorithm compensates the targets that are lost in the tracking process and collision or occlusion in the movement process, reducing the errors caused by illumination, occlusion and water surface fluctuation effectively. This algorithm can directly obtain the target motion trajectory, avoiding the re-extraction from the centroid point in the image sequence, which greatly improves the efficiency. In order to avoid the one-sidedness of the two-dimensional trajectory, the experiment combines the pixel coordinates of different perspectives into three-dimensional trajectory pixel coordinates, so as to provide a more authentic fish swimming trajectory. We then select a representative positive and negative sample data set; the number of data sets should have symmetry. The base classifier capable of identifying different water quality is obtained by training. Finally, support vector machine(SVM), eXtreme Gradient Boosting (XGBoost) and pointnet based classifiers are combined into strong classifiers through integrated learning. The experimental results show that the integrated learning model can reflect the water quality effectively and accurately under the three-dimensional trajectory pixel coordinates of fish, and the recognition rate of water quality is above 95%.

An Efficient Resource Allocation Algorithm for Device-To-Device Communications

In order to solve the problem of interference and spectrum optimization caused by D2D (device-to-device) communication multiplexing uplink channel of heterogeneous cellular networks, the allocation algorithm based on the many-to-one Gale-Shapley (M21GS) algorithm proposed in this paper can effectively solve the resource allocation problem of D2D users multiplexed cellular user channels in heterogeneous cellular network environments. In order to improve the utilization of the wireless spectrum, the algorithm allows multiple D2D users to share the channel resources of one cellular user and maintains the communication service quality of the cellular users and D2D users by setting the signal to interference and noise ratio (SINR) threshold. A D2D user and channel preference list are established based on the implemented system’s capacity to maximize the system total capacity objective function. Finally, we use the Kuhn–Munkres (KM) algorithm to achieve the optimal matching between D2D clusters and cellular channel to maximize the total capacity of D2D users. The MATLAB simulation is used to compare and analyze the total system capacity of the proposed algorithm, the resource allocation algorithm based on the delay acceptance algorithm, the random resource allocation algorithm and the optimal exhaustive search algorithm, and the maximum allowable access for D2D users. The simulation results show that the proposed algorithm has fast convergence and low complexity, and the total capacity is close to the optimal algorithm.