Backpressure Algorithm Research Articles

Backpressure-Based Distributed Dynamic Route Control for Connected and Automated Vehicles

This study focuses on the dynamic route control (DRC) problem for connected and automated vehicles (CAVs). The problem is usually solved using two popular methods, i.e., dynamic shortest path (DSP) and dynamic system optimal assignment (DSO). However, the DSP algorithm is unproductive under congested conditions, while the DSO, although can achieve system optimal, possesses considerable algorithm complexity. Furthermore, the two methods are usually solved in a centralized system due to the need for global information of the entire network, which is both communicationally and computationally ineffective. With the emergence of mobile edge computing (MEC), a new distributed DRC method only relying on local information is more achievable and practical. In this context, this research developed a novel DRC algorithm based on the distributed Backpressure (BP) principle for the MEC-enabled CAVs. The BP herein is modified as a function of the real-time density, which avoids the unrealistic point queue assumption in the original BP algorithm. In addition, the real-time traffic state is pre-identified to determine whether re-routing is essential, thereby reducing the possibility of CAVs guided to unnecessarily long routes. Results from the case study simulated by the traffic simulation software Simulation of Urban MObility (SUMO) indicate that the proposed method outperforms the BP method without congestion identification in low-demand cases, and the performance is significantly better than the DSP while approximating the DSO in congested cases. More importantly, both communicational cost and algorithm execution time was greatly reduced when compared with the DSO.

TSBS: A Two-Stage Backpressure Scheduling scheme over multihop wireless networks

The backpressure algorithm has been considered a promising routing and scheduling scheme in multihop wireless networks. However, path detours or loop problems and the last packet problem have always troubled the performance improvement of the backpressure algorithm. Existing backpressure algorithms cannot simultaneously prevent both path detours or loop and the last packet problems and thus fail to improve network performance further. To this end, this paper proposes a Two-Stage Backpressure Scheduling (TSBS) scheme that presents a new link weight design and develops a two-stage execution algorithm to resolve the above two problems. In the first stage, we use the shortest path and queue backlog metrics to build link weight to prevent path detours or loop problems and obtain the scheduling link set with the maximum queue backlog. In the second stage, considering the delay differential of the Head-of-Line (HOL) packets in link weight design, an optimal scheduling link is selected from the scheduling link set to inhibit the last packet problem. By Lyapunov drift theory, our analysis proves that TSBS achieves network stability and optimal throughput within the network’s capacity region. Simulation results show that TSBS improves average end-to-end delay by up to 26.78%, the percentage of packet forwarding by up to 18.92%, average queue length by up to 27.91%, and network throughput by up to 283.86%, compared with the baseline D-BP algorithm, respectively.

Arrival-Based Backpressure Traffic Signal Control

Fluctuations in arrival rates significantly affect the performance of traffic signal controls. In this paper, we propose an arrival-based distributed control algorithm based on the backpressure control framework, and show that this arrival-based backpressure (ABP) algorithm preserves the attractive features of backpressure control, including queue mitigation and global stability. Moreover, numerical results show that the ABP algorithm outperforms the fixed, actuated, and original backpressure signal controllers in low, medium, and heavy loading demand levels in increasing throughput, reducing queue length and waiting time.The benefits are evident under periodic traffic scenarios.

MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication

With the rapid development of future wireless networks, device-to-device (D2D) technology is widely used as the communication system in the Internet of Things (IoT) fifth generation (5G) network. The IoT 5G network based on D2D communication technology provides pervasive intelligent applications. However, to realize this reliable technology, several issues need to be critically addressed. Firstly, the device’s energy is constrained during its vital operations due to limited battery power; thereby, the connectivity will suffer from link failures when the device’s energy is exhausted. Similarly, the device’s mobility alters the network topology in an arbitrary manner, which affects the stability of established routes. Meanwhile, traffic congestion occurs in the network due to the backlog packet in the queue of devices. This paper presents a Mobility, Battery, and Queue length Multipath-Aware (MBMQA) routing scheme for the IoT 5G network based on D2D communication to cope with these key challenges. The back-pressure algorithm strategy is employed to divert packet flow and illuminate the device selection’s estimated value. Furthermore, a Multiple-Attributes Route Selection (MARS) metric is applied for the optimal route selection with load balancing in the D2D-based IoT 5G network. Overall, the obtained simulation results demonstrate that the proposed MBMQA routing scheme significantly improves the network performance and quality of service (QoS) as compared with the other existing routing schemes.

Flow control oriented forwarding and caching in cache-enabled networks

The cache-enabled network architecture is very promising to improve the efficiency of content distribution and reduce the network congestion. In this paper, we propose a maximizing weighted throughput (MWT) algorithm for joint request forwarding, cache placement and flow control to dynamically optimize network performance. Specifically, a dual queue system that includes requests and data is established to retrieve the global content demands and traffic congestion information. In order to improve throughput performance as well as stabilize the queue, we formulate the flow-level throughput and design the request-level control policy by optimizing the throughput function and Lyapunov drift. The request forwarding policy adaptively allocates request forwarding rates for every link according to the differences among adjacent request queue backlogs. A novel cache priority function and a threshold-based request-dropping policy are used in the caching policy and flow control respectively to alleviate network overload. In addition, we prove the MWT algorithm achieves throughput near-optimal performance, and testify the dual queue system is stable by deducing the upper bound of the all queue backlogs. The experimental results verify the MWT algorithm stability and demonstrate the superiority of the MWT algorithm, compared to the state-of-the-art caching algorithms which are combined with back-pressure algorithm.

Approximate Gradient Descent Convergence Dynamics for Adaptive Control on Heterogeneous Networks

Adaptive control is a classical control method for complex cyber-physical systems, including transportation networks. In this work, we analyze the convergence properties of such methods on exemplar graphs, both theoretically and numerically. We first illustrate a limitation of the standard backpressure algorithm for scheduling optimization, and prove that a re-scaling of the model state can lead to an improvement in the overall system optimality by a factor of at most O(k) depending on the network parameters, where k characterizes the network heterogeneity. We exhaustively describe the associated transient and steady-state regimes, and derive convergence properties within this generalized class of backpressure algorithms. Extensive simulations are conducted on both a synthetic network and on a more realistic large-scale network modeled on the Manhattan grid on which theoretical results are verified.

A double-layer collaborative apportionment method for personalized and balanced routing

Improving the travel efficiency of citizens and the operation efficiency of urban has always been the goal of Intelligent Transportation System. Due to the neglect of strong traffic demand and driving behaviour preferences, coupled with the insufficient of communication and computing power, the existing measures based on the centralized control of vehicles or mandatory traffic restrictions lead to the traffic efficiency dramatically deviates from the system optimum. Which puts forward an urgent demand for multi-vehicle collaborative apportionment, but also brings challenges. In this paper, a double-layer collaborative apportionment method for connected vehicles, 2L-CoV for short, is proposed under the assistance of Space-air-ground integrated networks. 2L-CoV includes the traffic flow scheduling in global-layer and the vehicle routing planning in local-layer. Firstly, a distributed collaborative framework based SAGIN is presented to make a large-scale of virtual vehicles can interact with each other. Then, at the global-layer, traffic flow is guided speedily by an improved back-pressure algorithm to complete traffic flow scheduling; at the local-layer, considering the driving behaviour preferences, a game evolution online learning approach based on dominant strategy is proposed to plan the vehicle routing. Finally, the simulation results show that 2L-CoV can effectively balance the traffic network, improves the network throughput, and reduces the total travel time.

Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes.

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.

Integrated route planning and resource allocation for connected vehicles

Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities. As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly, it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds. Existing methods usually perform resource allocation in a fairly effective but still reactive manner, which is subject to the capacity of nearby edge clouds. To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity, we proactively balance the edge computing demands across edge clouds by appropriate route planning. In this paper, route planning and resource allocation are jointly optimized to enhance intelligent driving. We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality. In large-scale optimization, backpressure algorithm is used to conduct route planning and load balancing across edge clouds. In small-scale optimization, game-theoretic multi-agent learning is exploited to perform regional resource allocation. The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.

DeepWiFi: Cognitive WiFi with Deep Learning

We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE 802.11ac) with deep learning and sustains high throughput by mitigating out-of-network interference. DeepWiFi is interoperable with baseline WiFi and builds upon the existing WiFi's PHY transceiver chain without changing the MAC frame format. Users run DeepWiFi for: i) RF front end processing; ii) spectrum sensing and signal classification; iii) signal authentication; iv) channel selection and access; v) power control; vi) modulation and coding scheme (MCS) adaptation; and vii) routing. DeepWiFi mitigates the effects of probabilistic, sensing-based, and adaptive jammers. RF front end processing applies a deep learning-based autoencoder to extract spectrum-representative features. Then a deep neural network is trained to classify waveforms reliably as idle, WiFi, or jammer. Utilizing channel labels, users effectively access idle or jammed channels, while avoiding interference with legitimate WiFi transmissions (authenticated by machine learning-based RF fingerprinting) resulting in higher throughput. Users optimize their transmit power for low probability of intercept/detection and their MCS to maximize link rates used by backpressure algorithm for routing. Supported by embedded platform implementation, DeepWiFi provides major throughput gains compared to baseline WiFi and another jamming-resistant protocol, especially when channels are likely to be jammed and the signal-to-interference-plus-noise-ratio is low.

Developing a decentralized signal control strategy considering link storage capacity

This paper proposes a double pressure (DP) signal control strategy that takes into account finite link storage capacity. Two types of pressure measures, including an endogenous and an exogenous pressure, are defined. The endogenous pressure is associated with the estimated exit-flow during a signal phase in the next time period, while the exogenous pressure is a virtual pressure associated with the downstream links. The properties of the proposed signal control strategy, including localized waiting time minimization and queue length stabilization at each isolated intersection were discussed. The performance of the proposed signal control strategy was evaluated with two testing networks via simulation, including a network with an isolated signalized intersection and a network with multiple signalized intersections. We compared the performance of the proposed DP control strategy with three existing strategies, including the fixed signal control, adaptive signal control and a back-pressure algorithm based control strategy proposed by previous studies. The test results show promise of the proposed DP control strategy in improving network travel time and queue stability when compared with several state-of-art decentralized control strategies, even though we acknowledge that future efforts in extending the decentralized strategy towards a better system-wide coordination are warranted.

BACKPRESSURE BASED TRAFFIC SIGNAL CONTROL CONSIDERING CAPACITY OF DOWNSTREAM LINKS

Congestion is a kind of expression of instability of traffic network. Traffic signal control keeping traffic network stable can reduce the congestion of urban traffic. In order to improve the efficiency of urban traffic network, this study proposes a decentralized traffic signal control strategy based on backpressure algorithm used in Wi-Fi mesh networks for packets routing. Backpressure based traffic signal control algorithm can stabilize urban traffic network and achieve maximum throughput. Based on original backpressure algorithm, the variant parameter and penalty function are considered to balance the queue differential and capacity of downstream links in urban traffic network. For each traffic phase of intersections, phase weight is computed using queue differential and capacity of downstream links, which fixed the deficiency of infinite queue capacity in original backpressure algorithm. It is proved that the extended backpressure traffic signal control algorithm can maintain stability of urban traffic network, and also can prevent queue spillback, so as to improve performance of whole traffic network. Simulations are carried out in Vissim using Vissim COM programming interface and Visual Studio development tools. Evaluation results illuminate that it can get better performance than the backpressure algorithm just based on queue length differential in average queue length and delay of traffic network.

An optimal network coding based backpressure routing approach for massive IoT network

In order to mitigate the power consumption issue for the sensor’s nodes, an efficient power optimized routing protocol is needed. Therefore, this paper proposes a network coding backpressure routing (NCBPR) routing scheme for a large-scale Internet of Things (IoT) networks, which exploits the backpressure algorithm in order to divert packets flow. In the network, the packets are flowing from the highly congested node to low congested node, which helps to balance the load and optimized the fair use of the battery power of all the participating nodes. It divides the network into the small clusters, where the selection of clusters head depends upon an additional parameter of battery power apart from other optimum path parameters. It also employs an efficient data aggregation mechanism, which improves the throughput of the network by eliminating redundant packets. The network has been designed by considering 300 nodes in a network and the results have been drawn in terms of network throughput, packet delivery ratio and energy consumption. The results are presented in comparison with conventional well-known information-fusion-based role assignment (InFRA) and data routing for in-network aggregation (DRINA) routing schemes. The results prove that the proposed NCBPR scheme delivers significant improvement, such as throughput which increased by 21.38 and 12.13%, packet delivery ratio improved by 24.73 and 11.38%, and sensors node’s energy consumption is decreased by 61.46 and 44.35% as compared to conventional InFRA and DRINA schemes, respectively.

Channel congestion control model based on improved asynchronous back-pressure routing algorithm in wireless distributed networks

Due to the continuous increase of the network bandwidth, the traditional coarse-grained congestion control mechanisms and flow scheduling schemes are difficult to provide satisfying performance. Therefore, a distributed algorithm based on an improved asynchronous back-pressure routing algorithm is proposed for joint channel congestion control, routing and power allocation in this paper. Considering the application scenario of wireless distributed networks with node power constraints and independent buffers for traffic flow, this paper studies joint congestion control, routing and power allocation when channel state information is known. In order to improve the defect of Newton method, an algorithm with second-order convergence performance is designed and matrix decomposition method is used to realize the distributed updating of traffic source rate, link rate and link power in network nodes and links so as to maximize network utility. Compared with the known existing algorithms, our proposed algorithm has faster convergence speed, which improves the network utility and energy utility by optimizing power allocation, and it can control the queue backlog at a very low level.

Shortest‐path‐based back‐pressure routing with single‐FIFO queueing in ad hoc networks'

Owing to the limited network resources in ad hoc networks, improving network throughput is the key to solve network congestion and increase network transmission efficiency. Back-pressure routeing, as an optimal routeing strategy for throughput, has recently received substantial attention for joint routeing and scheduling over multi-hop wireless networks. However, the inefficient routeing decision and complex queueing management of back-pressure routeing make it unsuitable for practical applications. This study proposes an improved back-pressure routeing algorithm with one first-input-first-output (FIFO) queueing structure using the shortest-path decision. By making use of the shortest-path knowledge, the feasible routes quickly converge to the shortest path when the network is lightly loaded. On the other hand, when the network load is heavy, the proposed algorithm remains optimal for exploring all the feasible paths between each source and destination. The back-pressure routeing design based on a single-FIFO queue architecture greatly simplifies the complexity of packet queueing management. The authors' results indicate that the proposed routeing algorithm achieves lower end-to-end delay than that of some other back-pressure algorithms without reducing the network throughput performance.

Throughput-Optimal Broadcast in Wireless Networks with Dynamic Topology

We consider the problem of throughput-optimal broadcasting in a time-varying wireless network with an underlying Directed Acyclic (DAG) topology. Known broadcast algorithms route packets along pre-computed spanning trees. In large wireless networks with time-varying connectivities, the optimal trees are difficult to compute and maintain. In this paper, we propose a new online throughput-optimal broadcast algorithm, which takes packet-by-packet scheduling and routing decisions, obviating the need for any global topological structures, such as spanning trees. Our algorithm utilizes certain queue-like system-state information for making transmission decisions and hence, may be thought of as a generalization of the well-known back-pressure policy , which makes point-to-point unicast transmission decisions based on the local queue-length information. Technically, the back-pressure algorithm is derived by greedily stabilizing the queues. However, because of packet-duplications, the work-conservation principle is violated, and an analogous queueing process is non-trivial to define in the broadcast setting. To address this fundamental issue, we identify certain state variables whose dynamics behave like virtual queues . By stochastically stabilizing these virtual queues, we devise a throughput-optimal broadcast policy. We also derive new characterizations of the broadcast capacity of time-varying wireless DAGs and propose efficient algorithms to compute the capacity either exactly or approximately under various assumptions.

IEEE Access Special Section Editorial: Recent Advances on Radio Access and Security Methods in 5G Networks

Serviceability is the ability of a network to serve user equipments (UEs) within desired requirements (e.g., throughput, delay, and packet loss). High serviceability is considered as one of the key foundational criteria towards a successful fog radio access infrastructure satisfying the Internet of Things paradigm in the 5G era. In the article by Dao et al. , Adaptive resource balancing for serviceability maximization in fog radio access networks, the authors propose an adaptive resource balancing (ARB) scheme for serviceability maximization in fog radio access networks wherein the resource block (RB) utilization among remote radio heads (RRHs) is balanced using the backpressure algorithm with respect to a time-varying network topology issued by potential RRH motilities. The optimal UE selection for service migration from a high-RB-utilization RRH to its neighboring low RB-utilization RRHs is determined by the Hungarian method to minimize RB occupation after moving the service. Analytical results reveal that the proposed ARB scheme provides substantial gains compared to the standalone capacity-aware, max-rate, and cache-aware UE association approaches in terms of serviceability, availability, and throughput.

Network control and rate optimization for multiuser MIMO communications

We present a network control and rate optimization solution for multiuser multiple input multiple output (MU-MIMO) communications in wireless networks. This solution is decentralized and includes scheduling and routing of the MU-MIMO communication links that adapt to dynamic channel, interference, and traffic conditions. We start with analyzing the ergodic sum rates of MIMO multiple access channel (MAC) and interference channel (IC) configurations by considering the error, and overhead effects due to channel estimation (training) and quantization (feedback). By taking the practical considerations such as channel estimation, quantization error and in-network interference into account, we show the rate gain with an increasing number of antennas compared with single-input single-output (SISO) systems. Next, we present a distributed channel access protocol to select and activate MU-MIMO configurations with the maximum achievable sum rates using local information on channel, interference, and traffic conditions. Then, we extend this scheduling algorithm to routing via a cross-layer solution based on a decentralized version of the backpressure algorithm. After accounting for the control message overhead, we show that the proposed MU-MIMO scheduling and routing solution improves the stable throughput over the minimum distance routing based on frequency of encounters and single user MIMO communications in a mobile ad hoc network (MANET) setting.

A New Backpressure Algorithm for Joint Rate Control and Routing With Vanishing Utility Optimality Gaps and Finite Queue Lengths

The backpressure algorithm has been widely used as a distributed solution to the problem of joint rate control and routing in multi-hop data networks. By controlling an algorithm parameter, the backpressure algorithm can achieve an arbitrarily small utility optimality gap. However, this in turn brings in a large queue length at each node and hence causes large network delay. This phenomenon is known as the fundamental utility-delay tradeoff. The best known utility-delay tradeoff for general networks is $[O(\epsilon ), O(1/\epsilon )]$ and is attained by a backpressure algorithm based on a drift-plus-penalty technique. This may suggest that to achieve an arbitrarily small utility optimality gap, backpressure-based algorithms must incur arbitrarily large queue lengths. However, this paper proposes a new backpressure algorithm that has a vanishing utility optimality gap, so utility converges to exact optimality as the algorithm keeps running, while queue lengths are bounded throughout by a finite constant. The technique uses backpressure and drift concepts with a new method for convex programming.

Centralised versus decentralised signal control of large‐scale urban road networks in real time: a simulation study

Recently, signal control strategies with decentralised logic have been developed to tackle the traffic congestion problems of urban road networks. Such strategies aim at network-wide traffic flow efficiency improvement via local actions, thus low design effort and infrastructure investment. This study presents, compares, and evaluates two such innovative approaches: the job scheduling algorithm comprising the local control component of the scalable urban traffic control (SURTRAC) system and the max- or back-pressure algorithm. The approaches are also compared against traffic-responsive urban control (TUC), a well-established strategy with centralised logic. Evaluation is based on the AIMSUN simulation model of the city centre of Chania, Greece. The study results indicate that the TUC and max-pressure retain performance independently of the prevailing traffic conditions, while also being computationally simpler than job scheduling. Both decentralised approaches require frequent (high-resolution) and relatively accurate measurements; on the other hand, TUC, although less demanding in this respect, calls for communication lines between the junction controllers and the central computer. Finally, compared with both decentralised approaches, the TUC provides a signal plan sequence with less excessive differences between each other, thus fewer disturbances to the common network users. Nevertheless, for more comprehensive conclusions, more investigations, including field trials, would be needed.