Ratio (IR) or the load at the gateways (GW1 and GW2). Obviously, solving ..... In fact, at data rate. 1500 kbps, the delay performance is around 43% better than.
Reinforcement Learning-based Best Path to Best Gateway Scheme for Wireless Mesh Networks Mustapha Boushaba, Abdelhakim Hafid, Abdeltouab Belbekkouche NRL, University of Montreal, Canada {boushamu, ahafid, belbekka}@iro.umontreal.ca Abstract— This paper addresses the problem of optimal routing in backbone wireless mesh networks (WMNs) where each mesh router (MR) is equipped with multiple radio interfaces and a subset of nodes serve as gateways to the Internet. Most routing schemes have been designed to reduce routing costs by optimizing one metric, e.g., hop count, load at routers and interference. However, when considering these metrics together, the complexity of the routing problem increases drastically. Thus, an efficient and adaptive routing scheme that takes into account several metrics simultaneously is needed. In this paper, we propose an efficient new routing scheme, called RLBPR (Reinforcement Learning-based Best Path Routing), that adaptively learns an optimal routing policy, depending on multiple optimization metrics such as loss ratio, interference ratio and load at the gateways. Simulation results show that RLBPR can significantly improve the overall network performance compared to schemes using either Metric of interference and channel switching (MIC), Best Path to Best Gateway (BP2BG), Expected Transmission count (ETX), nearest gateway (i.e., shortest path to gateway) or load at gateways as a metric for path selection. Reinforcement IR=0.1
=0 IR
.1 =0 IR
1
.1
.2
=0 IR
To increase WMN performance and capacity, nodes can be equipped with multiple radios and multiple channels. Otherwise, with a single channel, a node cannot transmit and receive simultaneously. Indeed, due to the scarce nature of wireless channel resources and to the limited number of channels supported by radios, network performance is highly impacted by interferences and congestion causing considerable packet losses and higher delays.
.1
INTRODUCTION
During last several years, wireless communications technologies have emerged in our daily lives. These technologies have given rise to several types of wireless networks, such as WSN (wireless sensor networks), VANET (vehicular area networks) and WMN (wireless mesh networks). Among these networks, WMNs [1] have attracted significant research due to their features that include dynamic self organization, self configuration, easy maintenance and low cost. In case of link failures, the network is able to automatically establish alternative routes. A WMN can be seen as a multi-hop Mobile Ad-hoc Network (MANET) with extended connectivity. In opposition to MANETs, WMNs are characterized by a relatively static architecture and low mobility.
IR =0 .
ETX;
IR=0.1
I.
interferences;
IR =0
Keywords: WMN; Learning; Routing.
Communications between two nodes in a multi-hop WMN can be supported by several intermediate, nodes called Mesh Routers (MRs). The role of MRs is to relay information from one node to another. Usually, MRs send traffic to the gateway (GW) that connects nodes to the Internet. Indeed, by equipping a WMN topology with a single GW to connect MRs to the Internet, the gateway selection problem becomes simple; this is because all upstream/downstream traffic flows must traverse the same GW; this node is more likely to become the bottleneck in the network [2]. To mitigate this problem, multiple gateways are installed to distribute load among them, and hence, improve performance. However, increasing the number of GWs does not necessarily increase the network capacity of WMNs. Indeed, network capacity is closely related to network connectivity and the placement of GWs; these are issues that are out of scope of this paper.
Fig. 1 WMN, multi-metrics routing case
The main goal of a routing protocol is to find better routes according to some requirements. These requirements vary according to client needs (e.g., bandwidth, delay, and packet loss). In general, routing protocols are designed to optimize only one of these goals [23, 24]; however, there exist several situations in WMNs where the choice of one metric, to optimize, impacts negatively the path choice. This situation is illustrated in Fig. 1 where nodes A, B, C, D, E are MRs, node S is the source MR and nodes GW1 and GW2 are gateways. In this example, we consider, as a routing metric, Interference Ratio (IR) or the load at the gateways (GW1 and GW2). Obviously, solving the optimization goals separately does not lead to an optimal solution. If S wants to send traffic to the Internet, the best path to choose, when using IR, will be S-D-
A-GW2; it is the path with the smallest interference ratio. On the other hand, the best path, when using load at the gateways, will be S-E-B-GW1; it is the path that traverses the least loaded gateway. This shows that choosing different metrics could yield different (may be conflicting) results. Thus, taking into consideration several metrics simultaneously is necessary to optimize multi-hop routing in WMNs. This paper proposes to use reinforcement learning, and more specifically, Q-learning algorithm to determine the best path to the best gateway in the context of multi-hop and multiradio wireless mesh networks. Our distributed routing scheme, called RLBPR, uses a learning agent in each node which learns, based on BP2BG [13], the best neighbor to send an incoming packet towards a given gateway. The remainder of the paper is organized as follows. In Section II, we present related work. Section III gives an overview of reinforcement learning techniques. In Section IV we present the network model, notations and definitions we use throughout the paper; then, we describe in details our routing scheme. Simulation results are presented and discussed in Section V. Section VI concludes the paper and presents future work. II.
RELATED WORK
Many routing schemes have been proposed, in the literature, for WMNs [3, 4]. These schemes could be roughly divided into three categories: (1) proactive routing protocols (e.g., Optimized Link State Routing Protocol OLSR [5]); (2) reactive routing protocols (e.g., Ad hoc On-demand Distance Vector AODV [6]), and (3) hybrid protocols (e.g., Temporallyordered routing algorithm TORA [7]). Based on one of the aforementioned routing techniques, several routing metrics have emerged. In general, a routing metric is used, by a routing protocol, to select the path having the highest throughput, the lowest delay and/or the lowest packet loss rate. Hop-count is the simplest metric in routing problems; however, for WMNs, hop-count is a poor choice; this is because there may be a shorter path between source and destination nodes that presents heavy interferences and high packet losses. ETX [23] is a routing metric that estimates the number of transmissions and retransmissions needed to successfully transmit a frame on a link. ETT [24] estimates the time a data frame needs to be successfully transmitted on a link. ETX is defined in Eq. (1) and ETT is defined in Eq. (2). The key shortcoming of ETX and ETT is that they do not consider interferences when selecting a path. ETX =
1 d f × dr
(1)
S B
(2)
ETT = ETX ×
where df and dr denote the forward and the reverse delivery ratio on the link, respectively. S represents the packet size and B the bandwidth of the link Among the metrics that are based on ETX and/or ETT, we have Weighted Cumulative Expected Transmission Time
(WCETT) [9], Metric of Interference and Channel switching (MIC) [10] and Best Path to Best Gateway (BP2BG) [13]. WCETT [9] has been proposed as an extension of ETT to take into account intra-flow interferences caused by channel diversity. WCETT, for a path, is defined as follows: n
WCETT = (1 − β ) × ∑ ETTi + β × max X j i =1
1≤ j ≤ k
(3)
where 0
