Generating Random Graphs for Wireless Actuator Networks

Abstract

In this paper, we consider graphs created by actu-The maximum distance to which a node can transmit directly ators (people, robots, vehicles etc.) in sensor-actuator networks. is called the node's transmission range. It is usually assumed Most simulation studies for wireless ad hoc and sensor networks that all nodes have a common transmission range. Unit disk use connected random unit disk graphs generated by placing nodes randomly and independently from each other. However, rnido graphis aetenmused toreprse this topologyx.d in real life networks are created by actuators in a cooperative unit disk graph is determined by node positions and a fixed manner. Usually certain restrictions are imposed during the communication range, R, for all nodes. Any two nodes whose placement of a new node in order to improve network con-distance does not exceed R are connected by a bidirectional nectivity and functionality. This article is an initial study on edge. In simulation studies, it is desirable to generate conhow connected actuator graphs (CAG) can be generated by fast . .u algorithms and what kind of desirable characteristics can be n e end nit disk graphs to evaluate the performance achieved compared to completely random graphs, especially for of different network protocols. These graphs are normally sparse node densities. generated by placing nodes randomly and independently fromWe describe several CAG generation schemes where the next each other, and testing connectivity at the end. Sparse random node (actuator) position is selected based on the distribution of unit disk graphs have high probability of being partitioned, the nodes already placed. In our Minimum Degree Proximity which increases the generation time.algorithm (MIN-DPA), a new node is placed to be a neighbor of Assuming thepenerahon f a n s y an existing node with the lowest degree (number of neighbors). In Assuming independent positions for all nodes may be our Maximum Degree Proximity algorithm (MAX-DPA), a new appropriate for sensors dropped from air or thrown manually.node cannot be placed to increase the degree of any existing node However, in SANs, actuators create their own communication over a pre-specified parameter limit. We show that these new graph to enhance their coordination, data communication and algorithms are significantly faster than the well-known random . . ' unit graph generation scheme for sparse graphs. The graphs abiity os the nework etter. Simposes in generated by these new schemes are not necessarily drawn from restrictions on their location with each other. Similarly, in real the same distribution as those generated by the independent node life, we usually tend to choose our own locations depending placement. Thus, we explore their properties by studying their on the locations of other users. For instance, in a conference average node degree and partition patterns. We show that our with attendants forming a multihop ad hoc network with new schemes produce random unit disk graphs with reduced g degree (average number of neighbor...

Generating Realistic Node Mobility and Placement for Wireless Multi-Hop Network Simulation

“…Bettstetter et al [12] place nodes in accordance with the uniform process and then apply thinning to it. Onat and Stojmenovic [13] developed several algorithms that create connected topologies with high probability and allow user to choose the average node degree. Unfortunately, the properties of topologies generated according to these models were not analysed in these papers nor compared with real networks.…”

“…In order to provide good fit between the target degree distribution and the degree distribution of the end-result topology, before a node is actually added to the topology in an iteration, several candidate nodes are evaluated (lines [6][7][8][9][10][11][12][13][14]. The number of evaluated candidates per iteration is defined by the algorithm parameter retry.…”

Select-and-Protest-Based Beaconless Georouting with Guaranteed Delivery in Wireless Sensor Networks

“…The first assumption is fulfilled by the cheap GPS-receivers available at each sensor node. The second assumption is achieved by applying one of the existing location services [28].…”

“…The graphs are generated using Maximum Degree Proximity Algorithm (MAX-DPA) [28]. This section describes this algorithm.…”

“…When the destination location is not known in advance, it has to be requested by using a location service [28] which provides a mapping from node addresses to their physical locations.…”