Time Allocation Problem Research Articles

Research on key aircraft identification method based on time-effective network

For the problem of identifying key aircraft in air traffic situation, existing research has failed to fully consider the spatiotemporal effects in the actual operation of air traffic. Therefore, a key aircraft identification method based on time-effective network is proposed. The convergence relationship and complexity between aircraft are used to construct a time-effective network model through the neighbor topology overlap coefficient, and the key aircraft is determined based on the eigenvector centrality. A network attack is carried out on the key aircraft nodes to observe the changes in sector complexity, and compared with the attack based on static network indicators, an improved genetic algorithm is used to assign new entry time to the aircraft nodes deleted by the network attack, so as to verify the selection effect of key aircraft. The actual data verification shows that compared with the static network attack, this method is more efficient in reducing the average complexity of the sector when removing key aircraft. The improved genetic algorithm has higher convergence when solving the key aircraft entry time allocation problem, making the sector complexity more stable within a certain period of time. Analysis of the control effect of key aircraft shows that the time-effective network method can more accurately identify aircraft that have a greater impact on sector complexity within a period of time than the static network.

Robust joint multiple resources allocation algorithm for cooperative underwater acoustic communication networks

AbstractIn this article, a joint relay selection, power control and time allocation problem is studied to maximize the energy efficiency for the cooperative underwater acoustic communication networks. The joint optimization problem is full of challenges due to the strong coupling of multiple resources and the uncertain characteristics of the underwater acoustic communication scenario. To address this issue, the worst‐case method is employed to transform an original uncertain problem into a deterministic problem. Furthermore, we propose the block coordinate descent‐based method to decouple the strongly coupling multi‐resource allocation problem into three relatively independent sub‐problems. The coupling of multiple resources is completely decoupled, thereby greatly reducing the solving difficulty. In addition, given that the sub‐problems with the fractional objective function are still non‐convex and hard to solve, the Dinkelbach‐based method is proposed to transform the fractional objective function into a subtractive form. At last, the relay selection sub‐problem is transformed into a integer programming problem, and the time allocation sub‐problem is transformed into a linear programming problem, whose optimal solutions can be obtained by some well‐established solution methods. The power allocation problem is transformed into a convex optimization problem, which can be solved by the Lagrangian dual method. Finally, in the proposed iteration structure, the three sub‐problems are alternatingly solved until convergence. Simulation results are presented to demonstrate the efficiency and robustness of the proposed algorithm.

Joint Optimal Data Collection Scheduling and Time Allocation in Full-Duplex WP-IoT Networks

This paper considers a full-duplex wireless powered internet of things (WP-IoT) network, supporting simultaneous wireless power transfer of a hybrid access point in the downlink and collected data transfer of devices in the uplink. Compared to existing works where the data collection process is mostly ignored, the problem of joint data collection scheduling and time allocation is investigated under two objectives, i.e., total throughput maximization subject to a total time constraint and total time minimization subject to minimum collected data constraints. For both objectives, the optimal solution is derived in closed-form. It is revealed that the proposed algorithms outperform various baseline algorithms in existing literature.

Model Predictive Contouring Control for Time-Optimal Quadrotor Flight

In this article, we tackle the problem of flying time-optimal trajectories through multiple waypoints with quadrotors. State-of-the-art solutions split the problem into a planning task—where a global time-optimal trajectory is generated—and a control task—where this trajectory is accurately tracked. However, at the current state, generating a time-optimal trajectory that considers the full quadrotor model requires solving a difficult time allocation problem via optimization, which is computationally demanding (in the order of minutes or even hours). This is detrimental for replanning in the presence of disturbances. We overcome this issue by solving the time allocation problem and the control problem concurrently via Model Predictive Contouring Control (MPCC). Our MPCC optimally selects the future states of the platform at runtime, while maximizing the progress along the reference path and minimizing the distance to it. We show that, even when tracking simplified trajectories, the proposed MPCC results in a path that approaches the true time-optimal one, and which can be generated in real time. We validate our approach in the real world, where we show that our method outperforms both the current state of the art and a world-class human pilot in terms of lap time achieving speeds of up to 60 km/h.

Throughput Guarantees for Multi-Cell Wireless Powered Communication Networks With Non-Orthogonal Multiple Access

The emerging non-orthogonal multiple access (NOMA) technology can effectively improve the throughput performance of Internet of Things (IoT) devices. Besides throughput maximization, ensuring throughput fairness is a practical design issue when implementing NOMA in wireless powered communication networks (WPCN). To this end, we investigate the joint transmission time and power allocation problem for NOMA communication, aiming to improve the sum-throughput while guaranteeing different wireless devices’ (WDs’) throughput in multi-cell WPCN. In particular, we first analyze the feasibility of the problem by deriving the necessary and sufficient conditions for the existence of feasible solutions and propose an efficient algorithm to obtain the set of feasible values of transmission time allocation. We then propose an efficient algorithm for the transmission time allocation to improve the sum-throughput. During each search iteration, we adopt the successive convex approximation (SCA) approach to transform the non-convex power allocation problem into a sequence of convex problems and obtain the locally optimal transmit power under a fixed transmission time. Numerical simulations show that the proposed algorithm can improve the sum-throughput while guaranteeing each WD’s throughput.

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Unmanned Aerial Vehicles (UAVs) play an essential role in information collection where routing and time scheduling are two critical factors and are considered sequently. Given the paths of UAVs, metaheuristic algorithms are explored to allocate searching time of UAVs, which are hard to guarantee the optimal solution for time allocation. In this paper, a novel searchinG timE allocatioN undEr coopeRative pAth pLaning (GENERAL) is proposed to solve the optimal solution of the time allocation given paths of UAVs. GENERAL adopts a semi-greedy construction and a repair procedure to initialize and amend the routing solutions during iterations. Motivated by the Newton’s method from convex optimization domains, we introduce a new Perturbed Parametric Nonlinear Complementarity Problem function (PPNCP-function), which reformulates the time allocation problem as a smoothing system of equations according to Karush-Kuhn-Tucker (KKT) theorem. Then a smoothing Newton method is introduced to obtain the optimal time allocation solution with superlinear convergence. Experimental results empirically indicate the GENERAL’s effectiveness compared to Ant Colony Optimization with Simulated Annealing (ACO-SA) and Genetic Algorithm (GA). Besides, some numerical results indicate that the smoothing Newton method with PPNCP function is promising. The emerging aspects of this paper include: 1) it integrates the path planning with time allocation problem for maximizing the information collection reward; 2) a new PPNCP-function and a smoothing Newton method have been proposed to solve the optimal time allocation under path planning of UAVs; 3) the theoretical convergence analysis of the smoothing Newton method has been provided in this paper.

Counselling Needs Assessment of Students of WA Polytechnic in The Upper West Region, Ghana

Individuals may seek counselling for various reasons, however, whatever the reason may be, clients seek counselling in order to be able to address certain issues they may be faced in their lives. The study is about the counselling needs of students in the Polytechnic in the Wa Municipality. The data was analysed using both descriptive (frequencies and percentages) and inferential statistics (Independent t-test and One-Way ANOVA). The findings revealed that, there were significant differences in age concerning students’ ratings and needs for guidance and counselling in services. The study revealed that, students needed guidance and counselling as there were challenged in terms of time allocation problems, inability to discuss with lecturers about their problems, coping with workload issues as well as struggling to meet deadlines. Due to the absence of guidance and counselling services, the results of the study led the researchers to the conclusion that students attending Wa Polytechnic were facing a number of challenges that need answers in order to be resolved. Based on the findings and conclusion drawn from the study is recommended that the defunct guidance and counselling unit should be revived and properly resourced for students to benefit from it.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Energy-Aware Hybrid MAC Protocol for IoT Enabled WBAN Systems

Energy efficiency is an important quality-of-service requirement that needs to be considered when designing an efficient MAC protocol for a WBAN system due to the limited power resources of biomedical sensor devices. To address this, an energy-aware multi-group hybrid MAC (MG-HYMAC) protocol is proposed in this work to improve energy efficiency as well as the lifetime of the biomedical sensor devices in a personalized healthcare system. The proposed protocol combines both the advantages of the CSMA/CA and the TDMA schemes to enable the biomedical sensors to efficiently contend for transmission opportunities and to allow them to efficiently transmit health data. The MG-HYMAC protocol is combined with a transmission scheduling technique to duty cycle the operations of the biomedical devices with less critical data to determine when and how the biomedical sensor devices will transmit their health data packets in order to reduce collisions to save energy and prolong the battery lifetime of the biomedical sensor devices so as to improve the overall network lifetime. Also, a stochastic probability model and a heuristic-based power control scheme are developed to solve time allocation and power control problems to improve energy efficiency and the biomedical sensor devices lifetime. To validate the MG-HYMAC protocol, it was compared with other related protocols (including HyMAC and CPMAC) and simulated in MATLAB. The simulation results proved that the proposed MG-HYMAC protocol outperformed the existing MAC protocols using standard metrics like energy efficiency, biomedical sensor devices lifetime, and convergence speed.

The Effect of Class Scheduling on The Students’ English Performance

The study focuses on the effect of the class scheduling toward the students’ English performance during the latest policy of new normal face to face learning. The research method used was case study research design. The researchers used an interview and questionnaire as the research instrument to gain the data. The subject of the research was X MIPA 1 to X MIPA 8 in SMA Negeri 1 Gedangan; in specific number were 15 students as the interviewee and 130 students who were willing to participate filling the survey. The data analysis technique was descriptive analysis with the percentage technique from the questionnaire analysis result to validate the outcome of the research. The results of this study showed that the school has prepared an effective schedule yet the indiscipline of teachers and students who are not able to implement the class scheduling in accordance with the provisions of the applicable rules. Therefore, students’ English performance was disrupted due to the implementation of inefficient time allocations. Thus, the school must optimize teacher performance in managing time and improve student performance so that the short lesson hours can be more effective and help students learn without any interference from time allocation problems, class scheduling, and other internal factors.

Emotions as Abstract Evaluation Criteria in Biological and Artificial Intelligences.

Biological as well as advanced artificial intelligences (AIs) need to decide which goals to pursue. We review nature's solution to the time allocation problem, which is based on a continuously readjusted categorical weighting mechanism we experience introspectively as emotions. One observes phylogenetically that the available number of emotional states increases hand in hand with the cognitive capabilities of animals and that raising levels of intelligence entail ever larger sets of behavioral options. Our ability to experience a multitude of potentially conflicting feelings is in this view not a leftover of a more primitive heritage, but a generic mechanism for attributing values to behavioral options that can not be specified at birth. In this view, emotions are essential for understanding the mind. For concreteness, we propose and discuss a framework which mimics emotions on a functional level. Based on time allocation via emotional stationarity (TAES), emotions are implemented as abstract criteria, such as satisfaction, challenge and boredom, which serve to evaluate activities that have been carried out. The resulting timeline of experienced emotions is compared with the “character” of the agent, which is defined in terms of a preferred distribution of emotional states. The long-term goal of the agent, to align experience with character, is achieved by optimizing the frequency for selecting individual tasks. Upon optimization, the statistics of emotion experience becomes stationary.

Payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger in IoT

In order to maximize the work efficiency of wireless mobile charger, a payoff-maximization-based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi-node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade-off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.

Time-optimal planning for quadrotor waypoint flight.

Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential because of their inherent smoothness. Recent works resorted to numerical optimization but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time allocation is a priori unknown and renders previous works incapable of producing truly time-optimal trajectories. To generate truly time-optimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotor's actuator potential. We achieve this by introducing a formulation of progress along the trajectory, which enables the simultaneous optimization of the time allocation and the trajectory itself. We compare our method against related approaches and validate it in real-world flights in one of the world's largest motion-capture systems, where we outperform human expert drone pilots in a drone-racing task.

Magic-Matrix-Based Power Allocation for Downlink Nonorthogonal Multiple Access

In a wireless cellular system, resource allocation among active users is a challenging task. The cellular systems with nonorthogonal multiple access (NOMA) solve the problem of bandwidth and time allocation. All the active users in the same NOMA cluster are allowed to access the complete cluster bandwidth at the same time. However, power allocation in NOMA is a multiconstraint optimization problem. The complexity of power allocation increases with the increase in the number of users inside the cluster. This article presents a novel technique for power allocation in a NOMA downlink cluster, using the magic sum of the magic matrix. The sum throughput (SUMTP) and the min–max fairness index (MMFI) are calculated and compared with an existing method for the downlink NOMA system. The proposed method shows comparable SUMTP performance to the conventional optimized method while ensuring better MMFI. We observed an inverse association between SUMTP and MMFI. In addition, the execution time of the proposed method is much lesser than that of the conventional optimized method owing to lower number of flops required for magic-matrix-based power allocation.

AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks

This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV's trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV's trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush-Kuhn-Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

Platform-Oriented Event Time Allocation

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement, i.e. events are scheduled at the reasonable time to attract maximum number of participants. Existing approaches usually focus on assigning a set of events organized by the same group to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this paper, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. Unfortunately, we find that the PETA problem is NP-hard due to the global conflict constraints on events. Thus, we propose design a greedy algorithm and two approximation algorithms to solve the PETA problem. Finally, we conduct extensive experiments on both real and synthetic datasets to test the effectiveness and efficiency of the proposed algorithms.

Throughput Optimization Based Joint Access Point Association and Transmission Time Allocation in WLANs

In this paper, the joint access point (AP) association and transmission time allocation problem in densely deployed multirate WLANs based on time sharing medium access control (MAC) is considered. In our study, the centralized coordinated joint AP association and transmission time allocation problem with heterogeneous station (STA) throughput demands is first formulated as an NP-hard single non-zero programming (SNZP) optimization problem with proportionally fair (PF) throughput based objective function. To solve the NP-hard SNZP problem, two novel algorithms, the SNZP relaxation (SNZPR) algorithm and the iterative SNZPR (iSNZPR) algorithm, are proposed. The property and complexity of the proposed algorithms are analyzed. In addition, for dynamic network scenarios where there are new STAs arriving at and trying to join the network, a distributed joint admission, AP association and transmission time allocation (DAAA) algorithm is proposed to determine whether to admit a new STA arriving at the network or not. The performance of the proposed algorithms is then investigated and compared with that of some existing algorithms based on numerical results.

Time Allocation for Integrated Bi-Static Radar and Communication Systems

Integrated radar and communications systems are increasingly important for applications requiring both sensing and information exchange. In this letter, we study the time allocation problem for an integrated bi-static radar and communications system. A closed-form expression for the achievable rate considering radar detection accuracy is derived analytically for this problem and optimized. Numerical results reveal that the optimum time allocation is characterized by the signal-to-noise ratio and the prior probability of target.

An exact algorithm for the unidirectional quay crane scheduling problem with vessel stability

This paper addresses the quay crane scheduling problem (QCSP) with vessel stability constraints. Vessel stability is essential to improve quay crane operations in container terminals, but it significantly complicates the basic QCSP and the corresponding solutions methods. We describe a novel mathematical formulation for the unidirectional QCSP with vessel stability, and we propose an exact algorithm based on logic-based Benders decomposition to solve the problem efficiently. The problem is decomposed into two subproblems, e.g., a task-assignment master problem without vessel stability constraints, and a time-allocation problem, aimed at determining the operation time of each task under the premise of the vessel stability requirements. The proposed algorithm is tested on benchmark instances derived from the literature, and the effectiveness of the proposed model and solution approach is demonstrated.

Minimum Length Scheduling for Full Duplex Time-Critical Wireless Powered Communication Networks

Radio frequency (RF) energy harvesting is key in attaining perpetual lifetime for time-critical wireless powered communication networks (WPCNs) due to full control on energy transfer, far field region, small and low-cost circuitry. In this paper, we propose a novel minimum length scheduling problem to determine the optimal power control, time allocation and schedule subject to data, energy causality and maximum transmit power constraints in a full-duplex WPCN. We first formulate the problem as a mixed integer non-linear programming problem and conjecture that the problem is NP-hard. As a solution strategy, we demonstrate that the power control and time allocation, and the scheduling problems can be solved separately in the optimal solution. For the power control and time allocation problem, we derive the optimal solution by evaluating Karush-Kuhn-Tucker conditions. For the scheduling, we introduce a penalty function allowing reformulation of the problem as a sum penalty minimization problem. Upon derivation of the optimality conditions based on the characteristics of the penalty function, we propose two polynomial-time heuristic algorithms and a reduced-complexity exact algorithm employing smart pruning techniques. Via extensive simulations, we illustrate that the proposed heuristic schemes outperform the schemes for predetermined transmission order of users and achieve close-to-optimal solutions.