New Radar-Like-Algorithm (RLA) A Radial sweep-line is used to scan the terrain: a = 0 ; d = small fixed angle (say P

1. Approximating Radio Maps Boaz Ben-Moshe School of Computing Science, Simon Fraser University Experiment Parameters: Hi resolution maps: 100*100 km of different types of terrains. Radii [5-30] km, antenna height [10-50]m, sampling set [1000-20000] Platform: Java 1.5 Win XP RF-Propagation models: knife edge (Bullington, Epstein, Deygout). Results 3-5 faster runtime. Improved accuracy. Goal given a terrain T and an antenna A, associate a signal strength to each point on T as received from A - approximate the radio map of A over T in an accurate and efficient manner. Basic Algorithms Given a terrain T and an antenna A: Sample T with n sample points (SP) For each point p (in SP) compute the signal-strength (A,p) Extrapolate SP using a triangulation. The Sample set (SP) can be Random, Grid Base Or an outcome of a 3D simplification of T. The Brighter the stronger the signal is. • Motivation • Facility location optimization algorithms, which involve locating large scale wireless networks, use approximated radio maps algorithms. In fact, computing such radio maps is often there runtimebottle-neck. • Methods • Implement existing methods for approximating radio maps. • Develop a new Radar-like-algorithm (RLA). • Conduct a large scale experiment, comparing the methods: runtime, accuracy, robustness. • Basic elements • Map (terrain): higher  brighter • Cross-section: convex-hull  • Diffractions Signal computation, • (Generalization of visibility) • Signal Strength: continuance\discrete function: • Extrapolation: using triangulation, • Transforming a set of samples into a • continuance surface. Average runtime (milliseconds) for constructing a radio map. Main Obstacle: runtime– too many samples (cross sections) Solution: pipe-line computation along a ray using 2D terrain simplification sampling Examples Sampling (1000 samples): Grid (left) Random +TS (mid) Radar (right) Extrapolation (5000 samples): Random: Uniform – hi noise, blare edge detection. S-Radar: Sensitive angle and density. None uniform – low noise, nice edge detection. • New Radar-Like-Algorithm (RLA) • A Radial sweep-line is used to scan the terrain: • a = 0 ; d = small fixed angle (say P/45); • S1 = signal-section (T,A, a); • PQ = new priorityQueue (); • while (a < 360) { • S2 = signal-section (T,p, a +d); • Pz = pizza-slice (S1, S2); • PQ.add(Pz); • S1 = S2; a = a + d; • for (i=0; i < BUDGET;i++) { • Pz = PQ.top(); // most distant pizza • Pz1 = Pz.leftSplit(); Pz2 = Pz.rightSplit(); • PQ.remove(Pz); PQ.add(Pz1); PQ.add(Pz2); • Distance(S1, S2): • max vertical distance • average distance • RMS • Note: the distance is also multiplied by angle between S1 and S2. Pizza-slice (middle): composed from two signal-sections • Conclusions & future work • The new Radar-Like-Algorithm is: • Significantly faster and more accurate. • The sensitive version perform better than the fixed one. • Highly robust (fits to any terrain and parameters). • Current research involves an alternative extrapolation method based on the radial order of the pizza-slices.