Simulation of Swarm Intelligence-Based Message Broadcast in Highly Mobile Ad Hoc Networks

This paper explores the potential for the efficient broadcasting of messages in highly mobile multi-hop ad hoc networks by treating the nodes in the network as autonomous agents for which only a simple set of rules governing the passing of messages from one node to another is defined. The general problem presented by highly mobile systems is one of coordinating communication between nodes in settings in which the details of their connections to each other – the portions of the network that are within radio range at any given time – can change. Several scenarios in which a swarm intelligence-based paradigm is applied to this problem are simulated and the results are compared in order to analyze the performance of various approaches relative to each other. A baseline is established in which agents in the system can potentially move after each transmittal of a message to their immediate neighbors. This is then used to evaluate the relative gains in performance achieved by fully exploiting the ad hoc networks as they emerge. The effects of the shape of the deployment area on communication efficiency are also examined. A "biologically inspired" heuristic for broadcasting is introduced. This heuristic has low computational overhead making it especially suitable for "power-limited" systems. INTRODUCTION With the proliferation of portable computing and communication has come an increasing need for communication networks that can support devices that move around as they are working. This has been addressed in part by radio based local networks within buildings and by stationary antennas in telephone cells. Each of these types of methods, however, relies on a portion of the network * Michael Kirkpatrick is currently at West Virginia University in Morgantown, WV. being fixed in place. For example, the “on air” network hub connecting laptops moving within an office is installed in a fixed location, both physically and in terms of its logical connection to the larger network. In such a setting a computer wishing to communicate with another must pass a message to the hub which may then send it directly to the intended destination or may route it through other parts of the stationary network. This will be done even if the sending and receiving computers are physically within inches of each other. Work has been done to develop communication protocols that exploit the fact that computers in radio range of a central hub may also be in radio range of each other and to allow direct communication to occur in such cases [1,2]. In some situations, such as when all of the nodes of the network are mobile, it may even be desirable to eliminate the reliance on fixed network elements entirely. Examples of such networks include robots exploring the surface of a distant planet, soldiers moving on a battlefield, robots searching for avalanche victims [3], or the elimination of fixed towers in cellular telephone systems. The general problem presented by these mobile systems is that the details of their connections to each other can change. Inouye, et al. [2], describe algorithms that test for connectedness and route messages accordingly, adapting to changes in the configuration of the system. A common feature of such algorithms is an attempt to establish and maintain some type of global knowledge about the current connections either within the system as a whole or on a more localized sub-system, called an ad hoc network. Nature provides us with examples of mobile, independently operating agents that seemingly work together to perform tasks in a highly efficient manner without complex communication networks and without global knowledge of the locations of individuals. One such example can be found in flocks of birds flying in formation. No individual bird is aware of the positions of all of the other birds. No specific bird directs the movement of the flock. Instead, each bird takes its cue to turn in one direction or another from those immediately surrounding it. Yet, when viewed as a whole the flock appears to be moving in an intelligent, directed manner [4]. This method of groups performing tasks effectively by using only a small set of rules for individual behavior – called swarm intelligence – has shown promise when applied to routing problems such as those encountered in transmitting messages through telephone and computer networks [5]. This paper considers the possibilities for applying similar techniques to the problem of communicating in ad hoc mobile networks. Specifically, we are interested in exploring the potential for providing reasonably efficient and reliable communication between nodes in an entirely mobile network – one without any necessarily stationary parts – by defining only a simple set of rules governing the passing of messages from one node to another. In such a network, messages may be passed between two nodes within radio range of each other. A message intended for a third node not in range of either of them may be passed from the first node to the second and held by the second until it either encounters the intended recipient or is able to pass it on to yet another node to carry. As a starting point for studying this problem, a simulation was developed which models a number of agents moving independently and at random within a defined space. When two agents encounter (come within range) of each other, if one of them has a message and the other does not, the message is passed between them so that both of them have it. So two simple rules govern the entire operation of the agents: 1. Move according to a (possibly random) schedule independent of other agents; 2. When encountering another agent, pass a message to or receive a message from that agent, as appropriate. A third rule, which determines whether an agent retains a message indefinitely, "times it out," replaces it with a newer message, or drops it upon an acknowledgement of receipt, can also be defined to control the retention of a message by an agent. LITERATURE REVIEW Networks in a mobile environment are characterized primarily by the fact that the connections between nodes can change. A common view of such a network is of nodes which are connected to each other and/or to some stationary network by a wireless medium such as radio [6, 7]. At any point in time a given node will be within range of some subset of the nodes in the network. The number of nodes in this subset can range from zero (in which case the node is isolated) to n – 1, where n is the total number of nodes in the system. Viewing this connectivity from the system’s perspective, rather than from that of a node, gives a picture of a partially connected graph made up of connected subgraphs, or groups [7]. These subgraphs, referred to in the literature as ad hoc networks, allow communication to take place for a while but do not guarantee the existence of a link at arbitrarily selected times. Most communication in these networks tends to be either broadcast (flooding) or point-to-point [6]. Perkins and Bhagwat [8] describe a method in which each node maintains a routing table of the nodes it can reach and periodically broadcasts this table (or otherwise makes it available) to its neighbors. Johansson, et al. [9], compare the efficiency of maintaining such tables proactively, as done by Perkins and Bhagwat, to building them as needed and, in so doing, saving the overhead of maintaining tables with links to nodes which are no longer in communication. They also use the past history of routes in the ad hoc network to make predictions of the stability of paths and attempt to use more stable paths. Chandy and Lamport [10] describe a method of taking “snapshots” of a distributed system showing its state at a given point in time and Murphy, et al. [11], adapt this technique for use in delivering messages in an ad hoc network. A good summary of issues relating to data communications in a mobile setting is given by Perkins [12]. Of particular interest, he says that the overall bandwidth can be improved by increasing the number of base stations in a system which includes stationary base stations. Adler and Scheideler [6] consider powercontrolled ad-hoc networks. In such a system, the ability of a given node to connect with another is not entirely a function of the chance that the nodes are positioned in such a way that they can communicate (either directly or through other nodes in the ad hoc network). Instead, a node may be able to boost or reduce its signal, allowing it to communicate farther or to restrict the recipients. Work has also been done studying the passing of messages between vehicles traveling on highways [13, 14, 15], allowing a braking car, for example, to notify the cars behind it that it was slowing, giving more reaction time than that provided by the driver simply seeing the brake lights of the car immediately ahead. One of the more significant problems common to message delivery in mobile settings, in general, and in highly mobile settings, in particular, is the overhead of maintaining routing information. The nature of highly mobile environments is such that this information is often not available “naturally. ” Das, et al. [16] offer an algorithm for multicasting in a highly mobile network which reduces overhead and increases multicast efficiency by assigning specific forwarding tasks to certain nodes and Chen and Liestman [17] explore an approach which uses weakly connected dominating sets to cluster nodes, simplifying the ad hoc networks. Williams and Camp [18] provide a good summary of twelve broadcast protocols with comparisons between them. And Peng and Lu [19] describe ways of reducing redundancy of message transmissions in mobile ad hoc networks. They suggest, for example, to delay relaying a message until an agent has had a chance to move out of the area in which it received the message and Hass, et al. [20] suggest a probability-based approach to determining whether or not to relay