Monte Carlo Analysis of nodes deployment for large-scale Wireless Sensor Network using range-free location methods

Abstract

In this paper a Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneousWireless Sensor Networks, has been done. We ran simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes (special sensors with known position) and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to 'feed' them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.

Analysis of Multi-hop Wireless Sensor Networks using Probability Propagation Models

“…A method for analysing the collected data in multi-hop WSNs with cluster-tree topology is to examine the probability of collected data in each hop independently. In this study, a probability propagation model was created using a pseudo-random Monte Carlo simulation [14,15]. Such Monte Carlo simulation has been used to compute the concrete probability of successful collection rates to deploy node sensors in a network.…”

Holistic Approach for Characterizing the Performance of Wireless Sensor Networks

“…In another study [7], the researchers performed simulations of random node deployments over a square area of varying densities and assumed that their network was composed of simple sensor nodes. The research also proposes a model for simulating a random sensor deployment and other features to empirically calculate the connectivity probability between a certain number of anchor and sensor nodes.…”

“…The transmit power can be adjusted depending on the design and needs of a WSN. To obtain the transmit power, each terrain path loss model and equation (2) are combined: P r_short_grass_node_at_0m = Pt Gt Gr 11.534532 × 10 6 × d 3.401 (3) P r_short_grass_node_at_17cm = Pt Gt Gr 0.00685488 ×10 6 × d 3.9 (4) P r _ tall _ grass _ node _ at _ 3cm = Pt Gt Gr 0.2133× 10 6 × d 3.131 (5) P r_tall_grass_node_at_50cm = Pt Gt Gr 0.005035 × 10 6 × d 3.533 (6) P r_dense_tree_node_at_0m = Pt Gt Gr 0.167 × 10 6 × d 2.811 (7) P r_dense_tree_node_at_0m = Pt Gt Gr 3.162 × 10 6 × d 3.274 (8) The amplifying energy on the transmitter side depends on two factors: receiver sensitivity and noise figure. To obtain the minimum transmitted power, a backward process is performed starting from the power threshold to ensure that the received power must be higher than the threshold.…”