Link Scheduling Problem Research Articles

Multi-agent reinforcement learning enabled link scheduling for next generation Internet of Things

In next generation Internet of Things (NG-IoT) networks, numerous pieces of information are aggregated from the user devices and sensor nodes to the local computing units for further computing to support high-level applications. Those multitudinous transmission demands have raised new challenges for current link scheduling protocols. The centralized link scheduling protocols are inappropriate in some large-scale NG-IoT scenarios. The previously distributed link scheduling uses the randomized transmission scheme to avoid interference, making it hard to utilize the bandwidth resources fully. The multi-agent machine learning (MAML) technique is a potential approach to finding the most optimal link scheduling strategy. At the same time, the over-large state space will take a long time for them to approach the optimal solution, which reduces the practicality of the MAML. To fully utilize the bandwidth resource and improve the efficiency of link scheduling, this paper studies a multi-agent reinforcement learning enabled link scheduling problem. Different from the conventional MAML techniques that randomly select a state to do their exploration, in our multi-agent reinforcement learning algorithm, a good state is firstly obtained within polynomial time steps by executing a distributed and randomized sub-algorithm. We say a state is good if it is not far from the optimal state. Then, our multi-agent reinforcement learning scheme starts from the good state and does its exploration with an ɛ greedy scheme, which significantly reduces the time steps to get close to the optimal link scheduling strategy. Extensive simulations are conducted to investigate the performance of our work.

AI-federated novel delay-aware link-scheduling for Industry 4.0 applications in IoT networks

Purpose With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy. Design/methodology/approach With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. Findings Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0. Originality/value The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Efficient Link Scheduling Solutions for the Internet of Things Under Rayleigh Fading

Link scheduling is an appealing solution for ensuring the reliability and latency requirements of Internet of Things (IoT). Most existing results on the link scheduling problem were based on the graph or SINR (Signal-to-Interference-plus-Noise-Ratio) models, which ignored the impact of the random fading gain of the signals strength. In this paper, we address the link scheduling problem under the Rayleigh fading model. Both Shortest Link Scheduling (SLS) and Maximum Link Scheduling (MLS) problems are studied. In particular, we show that a set of links can be activated simultaneously under Rayleigh fading model if all link SINR constraints are satisfied. Based on the analysis of previous Link Diversity Partition (LDP) algorithm, we propose an Improved LDP (ILDP) algorithm and a centralized algorithm by localizing the global interference (denoted by CLT), building on which we design a distributed CLT algorithm (denoted by RCRDCLT) that converges to a constant approximation factor of the optimum with the time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\ln n)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of links. Furthermore, executing repeatedly RCRDCLT can solve the SLS with an approximation factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta (\ln n)$ </tex-math></inline-formula> . Extensive simulations indicate that CLT is more effective than previous six popular link scheduling algorithms, and RCRDCLT has the lowest time complexity while only losses a constant fraction of the optimum schedule.

Hybrid Satellite-UAV-Terrestrial Networks for 6G Ubiquitous Coverage: A Maritime Communications Perspective

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. Current maritime communication networks (MCNs) mainly rely on marine satellites and on-shore base stations (BSs). The former generally provides limited transmission rate, while the latter lacks wide-area coverage capability. Due to these facts, the state-of-the-art MCN falls far behind terrestrial fifth-generation (5G) networks. To fill up the gap in the coming sixth-generation (6G) era, we explore the benefit of deployable BSs for maritime coverage enhancement. Both unmanned aerial vehicles (UAVs) and mobile vessels are used to configure deployable BSs. This leads to a hierarchical satellite-UAV-terrestrial network on the ocean. We address the joint link scheduling and rate adaptation problem for this hybrid network, to minimize the total energy consumption with quality of service (QoS) guarantees. Different from previous studies, we use only the large-scale channel state information (CSI), which is location-dependent and thus can be predicted through the position information of each UAV/vessel based on its specific trajectory/shipping lane. The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. We solve it suboptimally by using Min-Max transformation and iterative problem relaxation, leading to a process-oriented joint link scheduling and rate adaptation scheme. As observed by simulations, the scheme can provide agile on-demand coverage for all users with much reduced system overhead and a polynomial computation complexity. Moreover, it can achieve a prominent performance close to the optimal solution.

Shortest link scheduling in wireless networks with oblivious power control

Link scheduling has always been a fundamental problem in wireless networks for its direct impacts on the performance of wireless networks such as throughput capacity, transmission delay, lifetime, etc. Existing work is mainly established under graph-based models, which are not only impractical but also incorrect due to the essentially fading characteristics of signals. In this paper, we study the shortest link scheduling problem under two more realistic models, namely the signal to interference plus noise ratio (SINR) model and the Rayleigh fading model. We propose a centralized square-based scheduling algorithm (CSSA) with oblivious power control under the SINR model and prove its correctness under both the SINR model and the Rayleigh fading model. Furthermore, we extend CSSA and propose a distributed square-based scheduling algorithm (DSSA). Note that DSSA adopts CSMA/CA so that a wireless node can compete for the wireless channel before starting its communication. We also show theoretical analysis and conduct extensive simulations to exhibit the correctness and efficiency of our algorithms.

A Minimized Buffered Link Scheduling Algorithm for WirelessHART Networks Based on Graph Route

WirelessHART network is an emerging wireless sensor network technology, famous for simplicity, safety, and reliability. WirelessHART network uses Time Division Media Access (TDMA) and Frequency Hopping Spread Spectrum (FHSS) access mechanism to avoid collisions and mitigate interference from other wireless networks. Many formulations of the link scheduling problem have been proven to be NP-complete, which is about how to assign slots and channels to links. In this paper, we propose a minimized buffered link scheduling algorithm for WirelessHART networks based on graph route (LBLSGR), which utilizes edge coloring to scheduling time slots according to the resource demand of nodes. We supply more communication resource to the nodes located nearer to the gateway, because of frequent packet forwarding. Utilizing this algorithm, the WirelessHART networks can make the average network delay least, the average network power assumption lower, and the packet buffers minimized. At last, the validity of our algorithm is proved and the test result is provided.

Emerging intelligent big data analytics for cloud and edge computing

Emerging intelligent big data analytics for cloud and edge computing

Efficient Link Scheduling in Wireless Networks Under Rayleigh-Fading and Multiuser Interference

Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity $O(n\ln n)$ , where $n$ is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of $O(\ln n)$ is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.

Graph Embedding-Based Wireless Link Scheduling With Few Training Samples

Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this article, we propose a novel graph embedding based method for link scheduling in D2D networks. We first construct a fully-connected directed graph for the D2D network, where each D2D pair is a node while interference links among D2D pairs are the edges. Then we compute a low-dimensional feature vector for each node in the graph. The graph embedding process is based on the distances of both communication and interference links, therefore without requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling strategy can be learned in a supervised manner based on the graph embedding results for each node. We also propose an unsupervised manner to train the graph embedding based method to further reinforce the scalability and develop a K-nearest neighbor graph representation method to reduce the computational complexity. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods but is with only hundreds of training network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios.

Backbone colouring and algorithms for TDMA scheduling

We investigate graph colouring models for the purpose of optimizing TDMA link scheduling in Wireless Networks. Inspired by the BPRN-colouring model recently introduced by Rocha and Sasaki, we introduce a new colouring model, namely the BMRN-colouring model, which can be used to model link scheduling problems where particular types of collisions must be avoided during the node transmissions. In this paper, we initiate the study of the BMRN-colouring model by providing several bounds on the minimum number of colours needed to BMRN-colour digraphs, as well as several complexity results establishing the hardness of finding optimal colourings. We also give a special focus on these considerations for planar digraph topologies, for which we provide refined results.

Localized and distributed link scheduling algorithms in IoT under rayleigh fading

The Internet of Things (IoT) is rapidly gaining ground in future wireless communications. Transmission reliability and latency are two significant measurements for the utilization of the IoT. In this paper, we aim to improve reliability and latency requirements by solving the link scheduling problem. Under the Rayleigh fading model, a more realistic interference model, we first localize the global interference by ignoring the interference outside some certain distance, and obtain the success probability of a transmission at least 1−ϵ, where ϵ is an acceptable error probability of a transmission. Based on this key result, we then design two localized and distributed algorithms for one-slot scheduling problem (i.e., how to ensure that the selected links have high transmission reliability or can be scheduled successfully). In addition, we design a localized and distributed algorithm with time complexity of O(Δmax′T,rlogn) to resolve latency minimization problem (i.e., minimize the number of time slots until all transmissions were successful), where Δmax′T,r is the maximum number of senders within RT centered at a receiver in the network, where RT is transmission range of a node. Theoretical analysis and extensive simulations demonstrate that the proposed algorithms can improve the reliability and latency requirements significantly.

MLOPS: A SIC-Based Minimum Frame Length With Optimized Power Scheduling for UANs

Due to the large propagation delay and scarce spectrum resource of the underwater wireless acoustic channels, it is essential to design efficient and reliable multiuser scheduling scheme for underwater network communication systems. The successive interference cancellation (SIC) technology that supports multiple parallel transmissions can improve spectrum efficiency, which is vital for the underwater acoustic networks (UANs). The SIC technology has been widely studied in the underwater network communication systems. However, there is no appropriate technology solving the link scheduling problem in the SIC-based UANs. In this paper, we propose a link-scheduling model for SIC-based UANs and formulate the problem of minimizing the transmission delay and overall network power consumption by combining transmitters scheduling and power allocating. We also present a polynomial-time algorithm named UMLOPS with the complexity of O(n) for unified traffic load, and for weighted traffic load, we present a universal algorithm named WMLOPS with the complexity of O(n 3 ), where n denotes the number of source nodes. The extensive simulation results reveal that both scheduling frame length and aggregate power consumption of MLOPS significantly outperform those of the existing time-division multiple access protocols in underwater sensor equipped aquatic swarm architecture networks.

Asynchronous Distributed Greedy Link Scheduling in Multihop Wireless Networks

We consider the problem of link scheduling in multihop wireless networks subject to interference constraints. We consider a graph-based representation of wireless networks where scheduled links adhere to the K-hop link interference model. For this scheduling problem, we develop an asynchronous and distributed greedy heuristic. Furthermore, we show that this distributed heuristic computes the exact same schedule as the well-known centralized greedy link scheduling algorithm.

Delay-Aware Optimization Framework for Proportional Flow Delay Differentiation in Millimeter-Wave Backhaul Cellular Networks

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements.

Maximizing Capacity in Cognitive Radio Networks Under Physical Interference Model

A fundamental problem in cognitive radio networks (CRN) is the following capacity maximization in CRN (CM-CRN) problem: given a set of primary links with a common transmitter, together with a set of secondary links, select a maximum cardinality subset of the links that can concurrently transmit successfully under the constraint that all primary links are selected. This problem is intrinsically different from the well-known link scheduling (LS) problem in wireless mesh networks, which does not have the constraint to select all primary links. In this paper, we make both theoretical and practical contributions to the CM-CRN problem. To achieve deep theoretical understanding of the problem, we show that CM-CRN is NP-hard and design a polynomial time approximation algorithm with a constant approximation ratio. In addition, we extend the designed algorithm to find approximate solutions to two variations of CM-CRN, one with the objective of maximizing the number of selected secondary links and the other with multiple primary users. To achieve good performance in practice, we design a simple but effective heuristic algorithm based on a greedy strategy. We also design an optimal algorithm based on integer linear programming, which serves as a benchmark for evaluating the performance of the approximation algorithm and heuristic algorithm, for problem instances of small sizes. Extensive evaluations show that our proved constant ratio of the approximation algorithm is considerably conservative and our heuristic algorithm produces results that are very close to the optimal solution. Our approximation algorithm for CM-CRN is motivated by and can be viewed as a non-trivial extension of the elegant approximation algorithm for the LS problem by Wan et al. to CRNs.

Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network

As a promising future network architecture, named data networking (NDN) has been widely considered as a very appropriate network protocol for the multihop wireless network (MWN). In named-data MWNs, congestion control is a critical issue. Independent optimization for congestion control may cause severe performance degradation if it can not cooperate well with protocols in other layers. Cross-layer congestion control is a potential method to enhance performance. There have been many cross-layer congestion control mechanisms for MWN with Internet Protocol (IP). However, these cross-layer mechanisms for MWNs with IP are not applicable to named-data MWNs because the communication characteristics of NDN are different from those of IP. In this paper, we study the joint congestion control, forwarding strategy, and link scheduling problem for named-data MWNs. The problem is modeled as a network utility maximization (NUM) problem. Based on the approximate subgradient algorithm, we propose an algorithm called ‘jointly optimized congestion control, forwarding strategy, and link scheduling (JOCFS)’ to solve the NUM problem distributively and iteratively. To the best of our knowledge, our proposal is the first cross-layer congestion control mechanism for named-dataMWNs. By comparison with the existing congestion control mechanism, JOCFS can achieve a better performance in terms of network throughput, fairness, and the pending interest table (PIT) size.

QC2LinQ: QoS and Channel-Aware Distributed Link Scheduler for D2D Communication

We study a distributed link scheduling problem for device-to-device (D2D) communication considering the quality-of-service (QoS) requirements and time-varying channel conditions of D2D links. To this end, we first study an optimal centralized link scheduling problem maximizing the total average sum-rate while satisfying the QoS requirements of D2D links. We then abstract the important scheduling principles of the optimal link scheduling, i.e., giving more chance to be scheduled to the links which have a good channel condition and do not satisfy the QoS requirement, in order to utilize them to develop distributed link scheduling algorithms. With the scheduling principles, we develop a procedure with which D2D links can share their degree of QoS unsatisfaction and channel condition with each other and generate their scheduling priorities according to the shared information in a distributed manner. We also develop a novel distributed link scheduling criterion with which D2D links determine their link scheduling. By using them, we propose distributed link scheduling algorithms, QCLinQ and QC $^{2}$ LinQ, which have significantly smaller signaling overhead and low computational complexity compared with the centralized optimal link scheduling algorithm. Moreover, they closely meet the QoS requirements of D2D links while achieving significant sum-rate improvement over conventional distributed algorithms.

Fast Link Scheduling Policies for Persistent Data Ferrying

The persistent data-ferrying problem has a naturally cascaded structure between vehicle and communication dynamics, allowing the wireless link scheduling optimization to be encapsulated within a path-planning optimization problem. This work derives and analyzes policies to solve the inner link scheduling problem quickly. Given a path and properties about the communication environment, the link scheduling problem is formulated as a binary integer linear program with unique ferrying constraints. The performances of several heuristic link scheduling policies are analyzed in terms of computation effort and achievable throughput of the solution. The performance characteristics are then validated through simulation, showing that the greedy knapsack solution achieves within 99% of the optimal allocation in less than 2% of the computation time compared to traditional solvers. The quickness of this policy reduces the computational complexity needed to solve the full data-ferry problem.

Channel assignment, link scheduling, routing, and rate control for multi-channel wireless mesh networks with directional antennas

The wireless mesh network (WMN) has attracted significant interests as a broadband wireless network to provide ubiquitous wireless access for broadband services. Especially with incorporating multiple orthogonal channels and multiple directional antennas into the WMN, each node can communicate with its neighbor nodes simultaneously without interference between them. However, as we allow more freedom, we need a more sophisticated algorithm to fully utilize it and developing such an algorithm is not easy in general. In this paper, we study a joint channel assignment, link scheduling, routing, and rate control problem for the WMN with multiple orthogonal channels and multiple directional antennas. This problem is inherently hard to solve, since the problem is formulated as a mixed integer nonlinear problem (MINLP). However, despite of its inherent difficulty, we develop an algorithm to solve the problem by using the generalized Benders decomposition approach [2]. The simulation results show the proposed algorithm provides the optimal solution to maximize the network utility, which is defined as the sum of utilities of all sessions.

Resource allocation for real-time traffic in unreliable wireless cellular networks

Providing reliable transmission for real-time traffic in wireless cellular networks is a great challenge due to the unreliable wireless links. This paper concentrates on the resource allocation problem aiming to improve the real-time throughput. First, the resource allocation problem is formulated as a Markov Decision Process and thus the optimal resource allocation policy could be obtained by adopting the value iteration algorithm. Considering the high time complexity of the optimal algorithm, we further propose an approximate algorithm which decomposes the resource allocation problem into two subproblems, namely link scheduling problem and packet scheduling problem. By this method, the unreliable wireless links are only constrained in the link scheduling problem, and we can focus on the real-time requirement of traffic in packet scheduling problem. For the link scheduling problem, we propose the maxRel algorithm to maximize the long-term network reliability, and we theoretically prove that the maxRel algorithm is optimal in scenarios with dynamic link reliabilities. The Least Laxity First algorithm is adopted for the packet scheduling problem. Extensive simulation results show that the proposed approximate resource allocation algorithm makes remarkable improvement in terms of time complexity, packet loss rate and delay.