Geo-logical routing in wireless sensor networks

Abstract

Abstract-Geo-Logical Routing (GLR) is a novel technique that combines the advantages of geographic and logical routing to achieve higher routability at a lower cost. It uses topology domain coordinates, derived solely from virtual coordinates (VCs), as a better alternative for physical location information. In logical domain, a node is characterized by a VC vector consisting of minimum number of hops to a set of anchors. VCs contain information derived from connectivity of the network, but lack physical layout information such as directionality and geographic voids. Disadvantages of geographic routing, which relies on physical location information, include cost of node localization or/and use of GPS, as well as misrouting due to physical voids. With the ability to generate topological maps from virtual coordinates via a Singular Value Decomposition based technique, it is now possible to characterize a network with topological coordinates, which we demonstrate to be more effective than physical coordinates for making routing decisions. By switching between geographic routing and logical routing, GLR overcomes local minima in the respective domains. Performance results presented indicate that GLR significantly outperforms existing logical routing schemes -Convex Subspace Routing (CSR) and Logical Coordinate Routing (LCR) -as well as geographic scheme, Greedy Perimeter Stateless Routing (GPSR).

Network Topology Mapping From Partial Virtual Coordinates and Graph Geodesics

“…Reconstruction of the graph from its VCs is attempted in [10] using an algorithmic approach. TC based schemes have demonstrated performance comparable, or better than the corresponding geographic coordinate based counterparts [23], [26], [34].…”

“…Thus all the information about the network layout such as shapes, voids, and boundaries are lost in a VCS. This issue leads to natural generalizations of VCSs, such as Topology Coordinates (TCs) and Topology Preserving Maps (TPMs) [25], where one performs an eigen-decomposition of P or H similar to that preformed in Principal Component Analysis (PCA) [23], [26]. Such methods can recover the general shapes and boundaries of the physical network layout, thus providing an effective alternative for representing 2-D and 3-D sensor networks and carrying out operations such as routing and boundary detection, but without the need for physical distance measurements [23], [26].…”

The neighborhoods method and routing in sensor networks

“…In this case, the geographic position of the node is selected as the location [8], [9], [10]; greedy forwarding technology is most commonly used, through which a data packet is directed to a neighboring node that is closer to the destination than the node holding the packet [11], [12]. Greedy forwarding technology allows the transmission of packets over the shortest route, but its use may give rise to a situation called the problem of local minimum [11].…”

Clueless nodes to network-cognizant smart nodes: Achieving network awareness in wireless sensor networks

“…The circular network with three voids with 496 nodes (see Fig. 2), one of the CSU Sensor-Net benchmarks [7] that has been used for evaluating different WSN algorithms [8], [9], [10], is used to illustrate the effectiveness of the learning scheme. In addition, we also considered a similar shape with 1081 and 2048 nodes to evaluate the scalability of the scheme.…”

“…Any VC based routing proposed in literature is possible to use. When a packet is routed from to , nodes which are in VCS mode will use VCS based routing scheme while nodes in Topological mode will use a routing scheme called Geo-Logical routing (GLR) [10]. TC mode that uses topology based coordinates for distance evaluation; VC mode, which uses VCs and a VC based distance, and AM (Anchor Mode) which routes packets toward the anchor that is closest to the destination.…”