Backpressure Routing Research Articles

Biased Backpressure Routing Using Link Features and Graph Neural Networks

Biased Backpressure Routing Using Link Features and Graph Neural Networks

Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy.

With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols.

An Online Learning Approach to Shortest Path and Backpressure Routing in Wireless Networks

An Online Learning Approach to Shortest Path and Backpressure Routing in Wireless Networks

Analysis and Evaluation of Fully TCP-Compatible Backpressure-Driven Traffic Engineering

Throughput optimality is a core objective for the operation of modern networks. The backpressure routing and scheduling scheme is well-known for being analytically-optimal in terms of throughput maximization. Its operation is driven by packet queue build-ups at the network nodes, and constant status updates among neighbors in order to deduce the best next packet forwarding step. However, this operating principle is also incompatible to TCP, which uses congestion control to avoid queue build-ups. These factors have so far limited the use of backpressure at the edge only. The present work presents a novel combination of TCP and backpressure at the network layer, overcoming any compatibility issues and allowing for backpressure to be active at all network nodes without exceptions. The key idea is to apply the Lyapunov framework cumulatively, over a large set of router hardware clock ticks. This approach allows for the throughput-optimal backpressure decisions to be expressed via regular traffic engineering routing rules, instead of explicit per-packet forwarding directives. Simulation results validate the analytical findings, and demonstrate significant throughput gains over state-of-the-art related approaches. Proof of concept emulations showcase the simplicity in the implementation of the proposed scheme.

Distance-Based Back-Pressure Routing for Load-Balancing LEO Satellite Networks

Featuring wide coverage and high data rate, LEO satellite networks will be an important supplement to the traditional terrestrial networks, enabling the space-air-ground integrated network service. However, effective load balancing routing strategies for LEO satellite networks need to be designed, due to the bursty characteristic of the network traffic and imbalanced regional communication load. To achieve that, we propose a Distance-based Back-Pressure Routing (DBPR) strategy for LEO satellite networks. DBPR calculates the link weights based on a novel distance-based metric, which can select uncongested short-distance paths to the destinations and distribute network traffic dynamically with low delay. To control the number of forwardings in the network, we restrict the transmission range to a rectangle region between each source-destination pair. We design DBPR in the distributed fashion without collecting the global network load information, which is suitable for LEO satellite networks with limited resources, long propagation delay, dynamic topology, etc. We analyze the network stability and prove the throughput optimality of DBPR. Simulation results demonstrate that DBPR can achieve higher throughput and lower delay, compared with the state-of-the-art strategies, especially in the environments with limited cache resource.

CBPR: A Cluster-Based Backpressure Routing for the Internet of Things

The concept of the Internet of Things (IoT)-based Wireless Sensor Network (WSN) is rapidly gaining wide-spread recognition and acceptance in our day-to-day lives. Nowadays, the application of the IoT sensor nodes in various domains of endeavors such as health-care, smart homes, industrial and production sectors, control networks, and in many other fields has continued to increase steadily. In IoT-based WSN, sensor nodes dynamically join the internet and collaborate to accomplish a mission by sensing and collecting event data from the application field. The sensor nodes thus forward the collected information to the sink nodes or to the nearest base station for further transmission. However, one of the significant drawbacks of the IoT-based WSN networks is that the battery life of the sensor nodes is often short-lived due to the energy-limited nature of the electronic sensors, resulting in the network’s short lifetime. Thus, prolonging the lifetime duration of the sensor nodes becomes a fundamental task. Whether the battery life of a sensor node is extended for a reasonable length of time or depleted in a moment depends mainly on the energy efficiency of the underlying routing protocol. Therefore, the issue of network lifetime can be fundamentally addressed by implementing an efficient and robust energy-aware routing protocol for sustainable and prolonged network operation time in a WSN based IoT network scenario. In this paper, a cluster-based Backpressure routing (CBPR) scheme has been proposed, which targets to prolong the network lifetime and enhance the data transmission reliability using energy load-balancing mechanism. For every cluster of the sensor node, the CBPR scheme elects a cluster head which has the highest energy level and the shortest distance to the sink node. The proposed CBPR routing scheme further utilizes a very robust data aggregation algorithm to checkmate and prevent the circulation of redundant data packets in the network while also exploiting the Backpressure scheduling machine for data packets queueing and for route selection, which allows it to select the next-hop sensor node based on the queue length value of the sensor nodes. Extensive simulations have been performed to evaluate the efficiency of the proposed CBPR routing scheme, which was compared with that of other well-known routing schemes such as the Information Fusion Based Role Assignment and Data Routing for In-Network Aggregation in terms of throughput, energy consumption, and packet delivery ratio.

A General Framework for Intersection Traffic Control With Backpressure Routing

Traffic congestion has long been a worldwide difficult problem in metropolitan transportation networks, incurring tremendous time waste and exhaust pollution. Extensive efforts have been made to address this problem, among which traffic control at intersections is known to be especially crucial while challenging. Also, it is helpful to the recent advent of autopilot technologies by enhancing the schedule accuracy and reducing the infrastructure cost. In practice, however, existing researches can hardly work well in a pervasive manner since they are essentially limited to two ideal assumptions: 1) each intersection comprises four ways; 2) each way is homogeneously composed of two lanes. Through an in-depth examination of their basic models, we find that the two-fold ideal assumptions are largely compelled by the surprisingly high complexity of converting a practical intersection topology into a theoretical conflict graph. Moreover, existing works seldom consider the modeling of complex intersections in the scene of cooperative control of multiply intersections. Driven by the above understandings, our first effort towards a general framework (for handling multiple-way heterogeneous intersections) is to carefully transform a practical intersection topology into a homomorphic, regular conflict graph which is suited to theoretical modeling and further processing with an affordable complexity. Besides, a maximum weight independent set (MWIS) based approach is proposed to minimize the average waiting time of vehicles at an isolated intersection. In addition, we apply a backpressure-based algorithm to our framework to further optimize the global average waiting time of vehicles in a whole road network. Simulation results have demonstrated that the average waiting time achieved by our approach is merely 80 seconds while traditional traffic light control reaches 370 seconds.

Fundamental Limits of Volume-based Network DoS Attacks

Volume-based network denial-of-service (DoS) attacks refer to a class of cyber attacks where an adversary seeks to block user traffic from service by sending adversarial traffic that reduces the available user capacity. In this paper, we explore the fundamental limits of volume-based network DoS attacks by studying the minimum required rate of adversarial traffic and investigating optimal attack strategies. We start our analysis with single-hop networks where user traffic is routed to servers following the Join-the-Shortest- Queue (JSQ) rule. Given the service rates of servers and arrival rates of user traffic, we first characterize the feasibility region of the attack and show that the attack is feasible if and only if the rate of the adversarial traffic lies in the region.We then design an attack strategy that is (i). optimal: it guarantees the success of the attack whenever the adversarial traffic rate lies in the feasibility region and (ii). oblivious: it does not rely on knowledge of service rates or user traffic rates. Finally, we extend our results on the feasibility region of the attack and the optimal attack strategy to multi-hop networks that employ Back-pressure (Max-Weight) routing. At a higher level, this paper addresses a class of dual problems of stochastic network stability, i.e., how to optimally de-stabilize a network.

Applying improved K-means algorithm into official service vehicle networking environment and research

In order to improve the traffic efficiency of official vehicles in the traffic road network, a backpressure routing control strategy for multi-commodity flow (official traffic flow) using official vehicle network environmental data information is proposed. Firstly, the road network composed of official service vehicle-mounted wireless network nodes is used to collect information on road conditions and official service vehicles. In order to improve the real-time and forward-looking route control, an official service vehicle flow forecasting method is introduced to construct a virtual official service vehicle queue. A multi-commodity flow (official service vehicle flow) backpressure route method is proposed, and an official service vehicle control strategy is designed to improve the self-adaptive route of K-means algorithm. In addition, the weight of backpressure strategy is improved according to traffic pressure conditions, and the adaptability of backpressure route algorithm is improved by using optimized parameters. Finally, the simulation results show that the proposed method can effectively control traffic vehicles and improve traffic smoothness.

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.

Fundamental Limits of Volume-based Network DoS Attacks

Volume-based network denial-of-service (DoS) attacks refer to a class of cyber attacks where an adversary seeks to block user traffic from service by sending adversarial traffic that reduces the available user capacity. In this paper, we explore the fundamental limits of volume-based network DoS attacks by studying the minimum required rate of adversarial traffic and investigating optimal attack strategies. We start our analysis with single-hop networks where user traffic is routed to servers following the Join-the-Shortest-Queue (JSQ) rule. Given the service rates of servers and arrival rates of user traffic, we first characterize the feasibility region of the attack and show that the attack is feasible if and only if the rate of the adversarial traffic lies in the region. We then design an attack strategy that is (i).optimal: it guarantees the success of the attack whenever the adversarial traffic rate lies in the feasibility region and (ii).oblivious: it does not rely on knowledge of service rates or user traffic rates. Finally, we extend our results on the feasibility region of the attack and the optimal attack strategy to multi-hop networks that employ Back-pressure (Max-Weight) routing. At a higher level, this paper addresses a class of dual problems of stochastic network stability, i.e., how to optimally de-stabilize a network.

Differential back‐pressure routing for single‐queue time‐varying wireless networks

Dynamic resource control and routing are important for realising the intelligent control of data transmission in wireless multi-hop networks. It is well known that back-pressure routing based on a max-weight policy maximises network throughput and optimises resource allocation in multi-hop wireless networks with time-varying channels. Due to the slow routing convergence and complex control of data queues, however, back-pressure routing also results in large end-to-end delays and a waste of network resources, particularly when the network loads are light or moderate. In this study, a differential back-pressure routing scheme with single-queue management is proposed to improve the packet delay performance and simplify the management of data queues. Unlike in traditional back-pressure routing, the authors use the differences in the rates of change in data queue length to calculate data pressure. Compared with the method of data backlog calculation based on queue length differences, this method achieves faster routing convergence. They also consider the ceiling problem for a single queue and enhance the effect of the queue cap on the routing metric by means of dynamic weighting. Simulation results show that the proposed routing algorithm achieves a 20% decrease in end-to-end delay in grid and random networks.

DABPR: a large-scale internet of things-based data aggregation back pressure routing for disaster management

In this paper, we propose a data aggregation back pressure routing (DABPR) scheme, which aims to simultaneously aggregate overlapping routes for efficient data transmission and prolong the lifetime of the network. The DABPR routing algorithm is structured into five phases in which event data is sent from the event areas to the sink nodes. These include cluster-head selection, maximization of event detection reliability, data aggregation, scheduling, and route selection with multi attributes decision making metrics phases. The scheme performs data aggregation on redundant data at relay nodes in order to decrease both the size and rate of message exchanges to minimize communication overhead and energy consumption. The proposed scheme is assessed in terms of packet delivery, network lifetime, ratio, energy consumption, and throughput, and compared with two other well-known protocols, namely “information-fusion-based role assignment (InFRA)” and “data routing for in-network aggregation (DRINA)”, which intrinsically are cluster and tree-based routing schemes designed to improve data aggregation efficiency by maximizing the overlapping routes. Meticulous analysis of the simulated data showed that DABPR achieved overall superior proficiency and more reliable performance in all the evaluated performance metrics, above the others. The proposed DABPR routing scheme outperformed its counterparts in the average energy consumption metric by 64.78% and 51.41%, packet delivery ratio by 28.76% and 16.89% and network lifetime by 42.72% and 20.76% compared with InFRA and DRINA, respectively.

A Comprehensive Survey on RPL: Evolution and Challenges

LLNs (Low Power and Lossy Networks) are becoming hot research topics due to challenges posed by their limited battery life, computing power, memory and transmission power. LLNs require a routing protocol which can perform efficiently with their limitations. IETF has come up with RPL as standard routing protocol for LLNs. It builds energy efficient network. But it also has major limitations and several research have happened to overcome the same. This paper is survey of RPL challenges and recent research on RPL extensions. Major RPL limitations include packet loss under noisy scenario, increased DODAG depth causing high energy consumption. TTA (Trickle Timer Algorithm) is important part of RPL. It is used to manage control messages flow. Limitation of trickle algorithm results in problem of short time for listening and it can make few nodes crave for delay and higher latency. E-Trickle is proposed to overcome the listen only period, improve convergence time and energy consumption. QOI (Quality of Information) aware RPL reduces the energy consumption with less data transmission. RPL operates under one sink. Entire data flows towards the single sink. RPL doesn’t specify when, where and how more number of sinks need to be used. Dynamic rescue sink is RPL enhancement built with real timetracking of nodes’ performance in RPL networks to propose new sinks when required. AMI (Advanced metering infrastructure) is one of the application in smart grid for connecting smart metering devices at homes. It is critical to have efficient routing protocol for AMI as the smart meter nodes are resource constrained. AMI with high density networks suffer from high packet loss, network congestion retransmissions, increased latency, control traffic overhead and power consumption. LQE (Link Quality Estimation) influences quality of selected route and energy consumption. RL-probe measures link quality precisely with small overhead and energy consumption. RL-probe reduces packet loss by reacting to link quality variations and link failures due to mobility. Communication overhead needs to be as minimal as possible inLLNs with limited resources. Adaptive timing model uses dynamic method to decide frequency of executing objective function to construct DODAG based on degree of surrounding changes and reduces the control messages overhead. Objective is to bring down the PLR (packet loss ratio), overhead of control messages and battery usage. RPL doesn’t give good results for high throughput and changes in network conditions. This prevents use of RPL in high speed sensors and mobile sensing applications. BRPL is extension to RPL which combines RPL objective function (OF) with back pressure routing to handle dynamic traffic load and mobility.

Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection

With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same as the queue-based traffic signal control in the literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under the queue-based signal control. Our delay-based backpressure control can deal with the excessive delays and achieve better fairness with respect to delay while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.

BRPL: Backpressure RPL for High-Throughput and Mobile IoTs

RPL, an IPv6 routing protocol for Low power Lossy Networks (LLNs), is considered to be the de facto routing standard for the Internet of Things (IoT). However, more and more experimental results demonstrate that RPL performs poorly when it comes to throughput and adaptability to network dynamics. This significantly limits the application of RPL in many practical IoT scenarios, such as an LLN with high-speed sensor data streams and mobile sensing devices. To address this issue, we develop BRPL, an extension of RPL, providing a practical approach that allows users to smoothly combine any RPL Object Function (OF) with backpressure routing. BRPL uses two novel algorithms, QuickTheta and QuickBeta, to support time-varying data traffic loads and node mobility respectively. We implement BRPL on Contiki OS, an open-source operating system for the Internet of Things. We conduct an extensive evaluation using both real-world experiments based on the FIT IoT-LAB testbed and large-scale simulations using Cooja over 18 virtual servers on the Cloud. The evaluation results demonstrate that BRPL not only is fully backward compatible with RPL (i.e., devices running RPL and BRPL can work together seamlessly), but also significantly improves network throughput and adaptability to changes in network topologies and data traffic loads. The observed packet loss reduction in mobile networks is, at a minimum, 60 and up to 1,000 percent can be seen in extreme cases.

A novel implementation of backpressure algorithm in wireless ad hoc network

In the wireless networks, the routing technique is the one of the highest concern and it is the important procedure in the ad hoc networks. To aid this effort, we proposed a new valuation of backpressure appliances for wireless networks. By this proposed system, we will address numerous preparation and routing difficulties and also recover the throughput and delay that are essentially produced by the packets at the node transmission. The Backpressure routing is a dense and enlarged throughput for wireless networks, but endures improved delays. In routing, the backpressure algorithm is known to afford throughput optimality with active traffic. The significant supposition in the backpressure algorithm is that all nodes are kind and detect the algorithm rules leading the information conversation and principal optimization necessities. In the proposed system, we validate that how the node is steady at the backpressure algorithm routing and also by together easing the virtual trust line and the real package queue. The backpressure algorithm not only achieves flexibility, but also stands the throughput performance under safety attacks. This scheme is mostly enhances the node performance at the time of announcement and also it recovers the node security at the time of numerous threats in the wireless requests.

An Overlay Architecture for Throughput Optimal Multipath Routing

Legacy networks are often designed to operate with simple single-path routing, like the shortest path, which is known to be throughput suboptimal. On the other hand, previously proposed throughput optimal policies (i.e., backpressure) require every device in the network to make dynamic routing decisions. In this paper, we study an overlay architecture for dynamic routing, such that only a subset of devices (overlay nodes) need to make the dynamic routing decisions. We determine the essential collection of nodes that must bifurcate traffic for achieving the maximum multi-commodity network throughput. We apply our optimal node placement algorithm to several graphs and the results show that a small fraction of overlay nodes is sufficient for achieving maximum throughput. Finally, we propose a threshold-based policy (BP-T) and a heuristic policy (OBP), which dynamically control traffic bifurcations at overlay nodes. Policy BP-T is proved to maximize throughput for the case when underlay paths do no overlap. In all studied simulation scenarios, OBP not only achieves full throughput but also reduces delay in comparison to the throughput optimal backpressure routing.

Loop-Free Backpressure Routing Using Link-Reversal Algorithms

The backpressure routing policy is known to be a throughput optimal policy that supports any feasible traffic demand, but may have poor delay performance when packets traverse loops in the network. In this paper, we study loop-free backpressure routing policies that forward packets along directed acyclic graphs (DAGs) to avoid the looping problem. These policies use link reversal algorithms to improve the DAGs in order to support any achievable traffic demand. For a network with a single commodity, we show that a DAG that supports a given traffic demand can be found after a finite number of iterations of the link-reversal process. We use this to develop a joint link-reversal and backpressure routing policy, called the loop free backpressure (LFBP) algorithm. This algorithm forwards packets on the DAG, while the DAG is dynamically updated based on the growth of the queue backlogs. We show by simulations that such a DAG-based policy improves the delay over the classical backpressure routing policy. We also propose a multicommodity version of the LFBP algorithm and via simulation show that its delay performance is better than that of backpressure.