Intelligent Broadcasting in Mobile Ad Hoc Networks: Three Classes of Adaptive Protocols

Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2007, Article ID 10216, 16 pages doi:10.1155/2007/10216 Research Article Intelligent Broadcasting in Mobile Ad Hoc Networks: Three Classes of Adaptive Protocols Michael D. Colagrosso Department of Mathematical and Computer Sciences, Colorado School of Mines, Golden, CO 80401-1887, USA Received 10 February 2006; Revised 3 July 2006; Accepted 16 August 2006 Recommended by Hamid Sadjadpour Because adaptability greatly improves the performance of a broadcast protocol, we identify three ways in which machine learning can be applied to broadcasting in a mobile ad hoc network (MANET). We chose broadcasting because it functions as a foundation of MANET communication. Unicast, multicast, and geocast protocols utilize broadcasting as a building block, providing important control and route establishment functionality. Therefore, any improvements to the process of broadcasting can be immediately realized by higher-level MANET functionality and applications. While efficient broadcast protocols have been proposed, no single broadcasting protocol works well in all possible MANET conditions. Furthermore, protocols tend to fail catastrophically in severe network environments. Our three classes of adaptive protocols are pure machine learning, intra-protocol learning, and inter-protocol learning. In the pure machine learning approach, we exhibit a new approach to the design of a broadcast protocol: the decision of whether to rebroadcast a packet is cast as a classification problem. Each mobile node (MN) builds a classifier and trains it on data collected from the network environment. Using intra-protocol learning, each MN consults a simple machine model for the optimal value of one of its free parameters. Lastly, in inter-protocol learning, MNs learn to switch between different broadcasting protocols based on network conditions. For each class of learning method, we create a prototypical protocol and examine its performance in simulation. Copyright © 2007 Michael D. Colagrosso. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION: AD HOC NETWORK BROADCASTING We introduce three new classes of broadcast protocols that use machine learning in different ways for mobile ad hoc networks. A mobile ad hoc network (MANET) comprises wireless mobile nodes (MNs) that cooperatively form a network without specific user administration or configuration, allowing an arbitrary collection to create a network on demand. Scenarios that might benefit from ad hoc networking technology include rescue/emergency operations after a natural or environmental disaster, or terrorist attack, that destroys existing infrastructure, special operations during law enforcement activities, tactical missions in a hostile and/or unknown territory, and commercial gatherings such as conferences, exhibitions, workshops, and meetings. Network-wide broadcasting, simply referred to as “broadcasting” herein, is the process in which one MN sends a packet to all MNs in the network (or all nodes in a localized area). There has been considerable effort devoted to the development of network-wide broadcast protocols in an ad hoc network [1–14]. A performance evaluation of MANET broadcast protocols is available in [15]. Broadcasting is a building block for most other network layer protocols, providing important control and route establishment functionality in a number of unicast routing protocols. For example, unicast routing protocols such as dynamic source routing (DSR) [16, 17], ad hoc on-demand distance vector (AODV) [18, 19], zone routing protocol (ZRP) [20– 22], and location aided routing (LAR) [23] use broadcasting or a derivation of it to establish routes. Other unicast routing protocols, such as the temporally-ordered routing algorithm (TORA) [24], use broadcasting to transmit an error packet for an invalid route. Broadcasting is also often used as a building block for multicast protocols (e.g., [4, 25, 26]) and geocast protocols (e.g., [27, 28]). The preceding protocols typically assume a simplistic form of broadcasting called simple flooding, in which each MN retransmits every unique received packet exactly once. The main problems with simple flooding are that it often 2 EURASIP Journal on Wireless Communications and Networking causes unproductive and harmful bandwidth congestion (e.g., called the broadcast storm problem in [29]) and it wastes node resources. The goal of an efficient broadcast technique is to minimize the number of retransmissions while attempting to ensure that a broadcast packet is delivered to each MN in the network. The performance evaluation of MANET broadcast protocols in [15] illustrates that no single protocol for broadcasting works well in all possible network conditions in a MANET. Furthermore, every protocol fails catastrophically when the severity of the network environment is increased. In contrast to these static protocols, [15] proposed a handtuned rule to adapt the main parameter of one protocol and found it works well in many network environments. That adaptive rule, described further in Section 2.3, made strong assumptions that are specific to the network conditions under which it was tested; from a machine learning perspective, it is desirable for the protocol to tune itself in a systematic, mathematically principled way. In that spirit, we have identified three ways in which machine learning techniques can be incorporated naturally into broadcasting, and we use these ideas to create three new classes of broadcasting protocols: pure machine learning, intra-protocol learning, and inter-protocol learning. We believe that each class provides adaptability in a unique way, so we present example protocols from all three classes in this work. In Section 3, we train a probabilistic classifier and develop it into a pure machine learning-based protocol, which is an extension of our previous work [30]. We choose Bayesian networks [31] for our learning model because of their expressiveness and more elegant graphical representation compared to other “black box” machine learning models. Bayesian networks, sometimes called belief networks or graphical models, can be designed and interpreted by domain experts because they explicitly communicate the relevant variables and their interrelationships. In network-wide broadcasting, mobile nodes must make a repeated decision (to retransmit or not), but the input features that MNs can estimate (e.g., speed, network load, local density) are noisy and, taken individually, are weak predictors of the correct decision to make. Our results (Section 7) show that our Bayesian network combines the input features appropriately and often predicts whether to retransmit or not correctly. In Section 4, we devel (...truncated)