As a key characteristic for industrial wireless sensor networks, deterministic scheduling aims to ensure that real-time data flows arrive at destination devices under deadline constraints by allocating necessary commu...
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As a key characteristic for industrial wireless sensor networks, deterministic scheduling aims to ensure that real-time data flows arrive at destination devices under deadline constraints by allocating necessary communication resources, such as time slots and channels. Current research on deterministic scheduling mainly focuses on how to obtain a feasible scheduling solution. However, optimizing average transmission delays under deterministic flow deadlines is rarely considered when multiple scheduling solutions exist. To address this issue, in this paper we propose two scheduling algorithms: branch and bound based on link conflict classification, and least conflict degree first. The prior algorithm obtains optimal schedulable ratio by constructing a search tree and adopting necessary conditions of scheduling. The latter algorithm dynamically adjusts the scheduling order of flows to distribute channels in a heuristic manner, and achieves approximate optimal schedulable ratio in a short time with low complexity. Simulation results show that both of the proposed algorithms effectively reduce the average transmission delays of real-time data flows while guaranteeing that all flows are delivered before their deadlines.
The pervasiveness of public displays is prompting an increased need for "fresh" content to be shown, that is highly engaging and useful to passerbys. As such, live or time-sensitive content is often shown in...
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The pervasiveness of public displays is prompting an increased need for "fresh" content to be shown, that is highly engaging and useful to passerbys. As such, live or time-sensitive content is often shown in conjunction with "traditional" static content, which creates scheduling challenges. In this work, we propose a utility-based framework that can be used to represent the usefulness of a content item over time. We develop a novel scheduling algorithm for handling live and non-live content on public displays using our utility-based framework. We experimentally evaluate our proposed algorithm against a number of alternatives under a variety of workloads;the results show that our algorithm performs well on the proposed metrics. Additional experimental evaluation shows that our utility-based framework can handle changes in priorities and deadlines of content items, without requiring any involvement by the display owner beyond the initial setup.
With the rapid development of intelligent information technology, human-computer interaction for unmanned aerial vehicle (UAV) systems has gradually developed from single-mode interaction to multi-modal interaction. D...
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With the rapid development of intelligent information technology, human-computer interaction for unmanned aerial vehicle (UAV) systems has gradually developed from single-mode interaction to multi-modal interaction. Due to the problems of information ambiguity and operational redundancy in multi-modal interaction, and the inability to effectively realize the coupling of human-computer interaction of different modalities, a priority strategy for multi-modal interaction for UAV systems is proposed. Living cells or tissues, whether in a static or active state, will produce regular electrical phenomena called bioelectricity. Human-computer interaction intention recognition technology based on bioelectrical signals can significantly enhance the real-time performance and flexibility of human-computer interaction and has great development potential. The characteristics of human-computer interaction intention recognition methods based on bioelectrical signals, such as electroencephalograms (EEGs), electromyograms (EMGs), electrooculograms (EOGs), and other conventional methods are introduced and analyzed, and then the basic characteristics of single-mode interaction and multi-modal interaction are compared. Furthermore, the priority strategies for UAV systems under different interaction modes are studied based on the analysis of task value evaluation parameters. The cumulative value, urgency, and task complexity are comprehensively analyzed and evaluated, and a set of priority strategies for multi-modal interaction is integrated. Through the simulation and verification of the multi-modal human-computer interaction software of the UAV system, it is determined that the proposed priority strategy conforms to the operation intention of the user, which improves the interaction efficiency and the reliability. In noisy environments, the UAV system control can be combined with the complementary information of various interaction methods to achieve precise control, which has obvious
The time-triggered communication paradigm is a cost-efficient way to meet the real-time requirements of cyber-physical systems. It is a non-deterministic polynomial NP-complete problem for multi-hop networks and non-s...
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The time-triggered communication paradigm is a cost-efficient way to meet the real-time requirements of cyber-physical systems. It is a non-deterministic polynomial NP-complete problem for multi-hop networks and non-strictly periodic traffic. A two-level scheduling approach is proposed to simplify the complexity during optimization. In the first level, a fuzzy-controlled quantum-behaved particle swarm optimization (FQPSO) algorithm is proposed to optimize the scheduling performance by assigning time-triggered frame instances to the basic periods of each link. In order to prevent population from high aggregation, a random mutation mechanism is used to disturb particles at the aggregation point and enhance the diversity at later stages. Fuzzy logic is introduced and well designed to realize a dynamic adaptive adjustment of the contraction-expansion coefficient and mutation rate in FQPSO. In the second level, we use an improved Satisfiability Modulo Theories (SMT) scheduling algorithm to solve the collision-free and temporal constraints. A schedulability ranking method is proposed to accelerate the computation of the SMT-based incremental scheduler. Our approach can co-optimize the jitter and load balance of communication for an off-line schedule. The experiments show that the proposed approach can improve the performance of the scheduling table, reduce the optimization time, and reserve space for incremental messages.
A Simple Temporal Network with Uncertainty (STNU) is a data structure for reasoning about time constraints on actions that may have uncertain durations. An STNU is dispatchable if it can be executed in real-time with ...
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A Simple Temporal Network with Uncertainty (STNU) is a data structure for reasoning about time constraints on actions that may have uncertain durations. An STNU is dispatchable if it can be executed in real-time with minimal computation 1) satisfying all constraints no matter how the uncertain durations play out and 2) retaining maximum flexibility. The fastest known algorithm for converting STNUs into dispatchable form runs in O(n3) time, where n is the number of timepoints. This paper presents a faster algorithm that runs in O(mn + kn2 + n2 log n) time, where m is the number of edges and k is the number of uncertain durations. This performance is particularly meaningful in fields like Business Process Management, where sparse STNUs can represent temporal processes or plans. For sparse STNUs, our algorithm generates dispatchable forms in time O(n2 logn), a significant improvement over the O (n3)-time previous fastest algorithm.& COPY;2023 Elsevier Inc. All rights reserved.
The current works about MapReduce task scheduling with deadline constraints neither take the differences of Map and Reduce task, nor the cluster's heterogeneity into account. This paper proposes an extensional Map...
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The current works about MapReduce task scheduling with deadline constraints neither take the differences of Map and Reduce task, nor the cluster's heterogeneity into account. This paper proposes an extensional MapReduce Task scheduling algorithm for Deadline constraints in Hadoop platform: MTSD. It allows user specify a job's deadline and tries to make the job be finished before the deadline. Through measuring the node's computing capacity, a node classification algorithm is proposed in MTSD. This algorithm classifies the nodes into several levels in heterogeneous clusters. Under this algorithm, we firstly illuminate a novel data distribution model which distributes data according to the node's capacity level respectively. The experiments show that the node classification algorithm can improved data locality observably to compare with default scheduler and it also can improve other scheduler's locality. Secondly, we calculate the task's average completion time which is based on the node level. It improves the precision of task's remaining time evaluation. Finally, MTSD provides a mechanism to decide which job's task should be scheduled by calculating the Map and Reduce task slot requirements.
QoS provisioning and high capacity for high mobility users are considered as the distresses of broadband wireless communications (BWC) and specifically the key technology of WiMAX. Hence, the scheduling and resource a...
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QoS provisioning and high capacity for high mobility users are considered as the distresses of broadband wireless communications (BWC) and specifically the key technology of WiMAX. Hence, the scheduling and resource allocation algorithms play the main role in this regard. In the research conducted on scheduling algorithms in WiMAX network, two principal methods of AMC and PUSC are used. The high capacity in AMC mode algorithms is achieved by considering the low speed users. Conversely, in PUSC mode algorithms, speed does not affect the network performances;however, the capacity is low. To date, the importance of presenting QoS and maintaining the network capacity for the users with different speeds has not been acknowledged yet. This paper presents novel scheduling algorithms and also new frame partitioning scheme which are proper for the users with different mobility speeds. The new algorithm uses two modes of AMC and PUSC simultaneously to maintain the high capacity of the network. QoS is also provided. The simulation results reveal that our algorithm increases capacity while it presents low packet delay and packet loss rate in the presence of both high and low mobility speed users.
This paper details on the uplink scheduling algorithm for long term evolution advanced （LTE-A） system with relays. While emulating quality of service （QoS）-aware services with different bit-rate and delay budget r...
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This paper details on the uplink scheduling algorithm for long term evolution advanced （LTE-A） system with relays. While emulating quality of service （QoS）-aware services with different bit-rate and delay budget requirements for the upstream direction, a new QoS-aware scheduling algorithm for in-band relays is proposed. In this work, an improved scheduling metric calculation method and bit-rate guarantee scheme is applied. Moreover, this algorithm proposes an efficient scheme for the backhaul link allocation which allows information of the most backlogged users to be transmitted first. Finally, this paper concludes with simulation results to demonstrate how the proposed resource allocation strategy improves the performance of the system.
Electric vehicles (EV) are an environmentally friendly alternative to internal combustion engine vehicles. Despite their environmental benefits, the massive demand for electricity imposed by the expected growth of EVs...
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ISBN:
(纸本)9781728191393
Electric vehicles (EV) are an environmentally friendly alternative to internal combustion engine vehicles. Despite their environmental benefits, the massive demand for electricity imposed by the expected growth of EVs could affect the safe and economical operation of the electricity grid. For this reason, solar-powered electric vehicle charging stations are viewed as a promising solution for an energy supplement to cope with the increasing number of EVs. In this paper, we consider an autonomous photovoltaic charging station and propose an admission control algorithm and a charging schedule to solve the problem of limited energy storage in the station pack and reduced space in an EV charging station. The simulation results demonstrate that the proposed admission and scheduling algorithm outperformed Short Job First (SJF) algorithm. Besides, the introduced algorithm allowed maximizing the performance of the charging station.
We propose an efficient frequency-domain packet scheduling (FDPS) for situations where some of the queues have a small amount of data available for transmission in downlink OFDMA-based systems. Under such small-queue ...
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We propose an efficient frequency-domain packet scheduling (FDPS) for situations where some of the queues have a small amount of data available for transmission in downlink OFDMA-based systems. Under such small-queue condition, we first show that current FDPS proposals result in sub-optimal outcome, suffering from their isolated resource-assignment strategies. With our simple algorithmic solution, we demonstrate that better performance is achieved in terms of system throughput as well as fairness perspective, which is evaluated using 3GPP LTE system model simulations.
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