An efficient network‐coding based back‐pressure scheduling algorithm for wireless multi‐hop networks

Abstract

Back-pressure scheduling has been considered as a promising strategy for resource allocation in wireless multi-hop networks. However, there still exist some problems preventing its wide deployment in practice.One of the problems is its poor end-to-end (E2E) delay performance. In this paper, we study how to effectively use inter-flow network coding to improve E2E delay and also throughput performance of back-pressure scheduling. For this purpose, we propose an efficient network coding based back-pressure algorithm (NBP), and accordingly design detailed procedure regarding how to consider coding gain in back-pressure based weight calculation and how to integrate it into next hop decision making in the NBP algorithm. We theoretically prove that NBP can stabilize the networks. Simulation results demonstrate that NBP can not only improve the delay performance of back-pressure algorithm, but also achieve higher network throughput.To ease the understanding of how the back-pressure algorithm in [1] works, here we present a brief introduction. Consider that time is slotted. Each network node maintains a per-flow queue for each flow passing through it. For each time slot, the back-pressure algorithm works to schedule an optimal link set, that is a set of non-interference links whose total link-weights can contribute to a global maximum sum, to transmit data packets. Here, the weight of a link (also called link-weight) is assigned as the largest flow-weight on the link, where the weight of a flow (flow-weight) equals the differential of the flow's queue backlogs between the two endpoints of the link. Obviously, to obtain a global optimal schedule set, global network state knowledge is required. It was shown in [11] that the complexity for computing the optimal schedule is O(|V| 3 ) under 1-hop interference model (where |V| represents the number of nodes in the network) and in general NP-Hard under K-hop interference model (K≥2). Recently, much progress has been made in reducing the computational complexities and also designing distributed implementations of back-pressure algorithms. For more details along this direction, please refer to [12][13][14] and references cited therein.Back-pressure based scheduling usually leads to poor E2E delay performance because of the following two reasons. First, routing decisions made by pure back-pressure algorithm often causes unnecessarily long data paths and thus results in large E2E delay. To address this issue, some remedies have been proposed. For example, the Back-pressure Collection Protocol (BCP) in [7] uses a punish factor to avoid routing loop. In [9], Athanasopoulou et al. proposed a packet-bypacket adaptive back-pressure routing and scheduling algorithm PARN. To encourage packets to go along shorter paths, PARN introduces two strategies: (i) calculation of link weight based on backlog differential of shadow packets, whose total amount is (1 + ε) times that of the native packets and are injected into network from the flow sources; and (ii) M-back-pressure such that a link can o...

Experimental assessment of LNC‐based cooperative communication schemes using commercial off‐the‐shelf wireless sensor network nodes

“…Typical drawbacks are the loss of connectivity or message corruption and, as a consequence, a loss of data packets, an increase of the delay jitter, an increased number of missed deadlines, and the loss of synchronization between network nodes . The use of network coding (NC) techniques to support cooperative communication has been proposed as a mean to increase the reliability of WSNs communication, for optimizing its bandwidth usage or for reducing its overall energy consumption . In essence, some cooperative relay nodes in the network combine multiple overheard messages into single messages using NC techniques, and at the destination arrival, the independent information will be reliably extracted.…”