Node (computer science)

Abstract: Summary We present an r package, ggtree, which provides programmable visualization and annotation of phylogenetic trees. ggtree can read more tree file formats than other softwares, including newick, nexus, NHX, phylip and jplace formats, and support visualization of phylo, multiphylo, phylo4, phylo4d, obkdata and phyloseq tree objects defined in other r packages. It can also extract the tree/branch/node-specific and other data from the analysis outputs of beast, epa, hyphy, paml, phylodog, pplacer, r8s, raxml and revbayes software, and allows using these data to annotate the tree. The package allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion. A two-dimensional tree can be drawn by scaling the tree width based on an attribute of the nodes. A tree can be annotated with an associated numerical matrix (as a heat map), multiple sequence alignment, subplots or silhouette images. The package ggtree is released under the artistic-2.0 license. The source code and documents are freely available through bioconductor (http://www.bioconductor.org/packages/ggtree).

Abstract: Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.

Abstract: Distributed Sensor Networks (DSNs) are ad-hoc mobile networks that include sensor nodes with limited computation and communication capabilities. DSNs are dynamic in the sense that they allow addition and deletion of sensor nodes after deployment to grow the network or replace failing and unreliable nodes. DSNs may be deployed in hostile areas where communication is monitored and nodes are subject to capture and surreptitious use by an adversary. Hence DSNs require cryptographic protection of communications, sensor-capture detection, key revocation and sensor disabling. In this paper, we present a key-management scheme designed to satisfy both operational and security requirements of DSNs. The scheme includes selective distribution and revocation of keys to sensor nodes as well as node re-keying without substantial computation and communication capabilities. It relies on probabilistic key sharing among the nodes of a random graph and uses simple protocols for shared-key discovery and path-key establishment, and for key revocation, re-keying, and incremental addition of nodes. The security and network connectivity characteristics supported by the key-management scheme are discussed and simulation experiments presented.

Abstract: Position information of individual nodes is useful in implementing functions such as routing and querying in ad-hoc networks. Deriving position information by using the capability of the nodes to measure time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA) and signal strength have been used to localize nodes relative to a frame of reference. The nodes in an ad-hoc network can have multiple capabilities and exploiting one or more of the capabilities can improve the quality of positioning. In this paper, we show how AOA capability of the nodes can be used to derive position information. We propose a method for all nodes to determine their orientation and position in an ad-hoc network where only a fraction of the nodes have positioning capabilities, under the assumption that each node has the AOA capability.