We proposed a localization algorithm named LSARSSI for mobile node based on RSSI (received signal strength indicator) between locating sensor node with inertia module built-in and the single anchor. Instead of directly mapping RSSI values into physical distance, contrasting RSSI values received from anchor in different visited locations, LSARSSI utilizes the geometric relationship of perpendicular intersection to compute node positions. Given that the values of RSSI among two visited locations are equal, we regard that their distances to anchor node are equal. After obtaining several sets of such visited locations, the relative location of mobile node and anchor node can be calculated. Because of the limitations of LSARSSI, we put forward an improved algorithm named ILSARSSI. Our scheme uses only one location-known anchor which is useful in low density environment without using additional hardware. The simulations show that LSARSSI achieves high accuracy and ILSARSSI performs high stability and feasibility.
Wireless sensor networks (WSNs) are such popular research fields that are highly interdisciplinary and state-of-the-art [
But these methods are not suitable for all the applications of WSNs. In some scenarios, however, there is only a stationary beacon node [
To address the previous issues, in this paper we proposed a localization algorithm named LSARSSI, an RSSI-based localization scheme using single stationary anchor. To increase the feasibility of our scheme, we further put forward an improved method called ILSARSSI. Major contributions of this paper are as follows. Our schemes can be used to locate mobile node in low density environment, which only need single anchor. In order to avoid errors from directly mapping absolute RSSI values to distances, we obtain the geometrical relationship of sensors by contrasting the measured RSSI values. We then design a novel localization scheme, LSARSSI, which has a better accuracy and low overhead. The simulation results show that our schemes perform high accuracy and feasibility, even in large-scale environment.
The rest of this paper is organized as follows. Section
According to the deployment of beacon nodes, localization technology can be classified into two categories: multiple stationary beacon nodes based approaches and mobile beacon node(s) based approaches.
Measurement techniques in WSNs based on multiple stationary beacon nodes can be broadly classified into two categories: range-based approaches and range-free approaches.
Range-based approaches assume that sensor nodes can measure the distance and/or the relative directions of neighbor nodes. Several mechanisms have been proposed to measure the node’s physical distance. For example, time of arrival (TOA) obtains range information through signal propagation times [
All the previous approaches require additional hardware equipment so as to increase the cost of the sensor nodes greatly. Such, TDOA needs at least two signal generators [
A popular and widely used ranging technique is the received signal strength (RSS) or quantified as the received signal strength indicator (RSSI). RSSI is utilized to estimate the distance between two nodes with ordinary hardware [
Given that range-based approaches are limited by hardware limitations and energy constraints, researchers have proposed range-free solutions as cost-effective alternatives.
Range-free approaches rely on the connectivity measurements between the measurement sensor nodes and a number of reference nodes, called seeds. For example, in the centroid algorithm [
Localization algorithms mentioned previously are in static sensor networks, which are not available in some scenarios. Recently, mobile-assisted localization approaches have been proposed to improve the efficiency of range-based approaches [
Several localization schemes are proposed in this field. For example, Bergamo and Mazzini propose a scheme to perform localization, based on the estimation of the power received by only two beacons placed in known positions [
In recent years, more researches are focusing on mobile sensor networks, which are mainly based on mobile nodes [
In this section, we first describe the application model in Section
For better implementing our algorithm, the hypotheses are as follows:
We can illustrate it by the following model assumption. The coordinate of anchor
A model assumption in 3D.
Table
Messages stored by sensor.
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RSSI( |
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RSSI( |
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RSSI( |
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RSSI( |
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RSSI( |
The 1001th message stored by sensor.
As the communication is not continuous, and the visited locations for our schemes needed are countable, the computation and communication costs are acceptable.
Typically, the ensemble mean received power in a real world obstructed channel decays proportional to
From (
An example of LSARSSI algorithm in 2D.
Node
The following are the formulas for calculating the location of locating node:
According to the geometry of vector, we can obtain that
Assume that
Here we extend our algorithm to three-dimensional space. As we know, if there are three noncoincident chords on the sphere, the intersection of three midvertical planes is the center of the sphere provided that these three planes can intersect.
The general idea of this algorithm in 3D utilizes the previous conjecture. This problem can be described as follows: the locations of locating node
Examples of LSARSSI algorithm in 3D.
As
According to the geometry of vector, we can obtain that
Assume that
then
In real scenarios, the measure of the RSS is discontinuous; it may be difficult to obtain several groups of locations with the same RSSI values. So the algorithm proposed previously cannot be successfully used to locate. However, the locating node can be located by comparing the RSSI according to the
Figure
An example of ILSARSSI algorithm in 2D.
The following are the formulas for calculating the location of locating node:
As
The approach can also be applied to the localization of mobile node in three-dimensional space. The simulation results show that ILSARSSI performs high feasibility and practicality.
To evaluate the performance of our proposed approaches, we use MATLAB 7.0 to conduct the simulations. In the following, the simulation parameters are listed in Table
Simulation parameters.
Parameters | Value(s) |
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Packet transmission period (s) | 1, 2, 3, 4, 5 |
Moving speed (m/s) | 2 |
Size of sensor field (m2) |
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A trajectory of the locating mobile node.
RSSI values received by locating node after time
In the first section, we mainly validate the feasibility of LSARSSI and LSARSSI algorithm, and in the second section, we discuss the localization error of LSARSSI and ILSARSSI algorithms.
Figure
Number of locations of LSARSSI and ILSARSSI algorithms.
Impact of packet transmission period
Impact of size of sensor field
Localization error of our LSARSSI algorithm.
Impact of packet transmission period
Impact of size of sensor field
Average localization error of LSARSSI and ILSARSSI.
In this paper, we propose a localization algorithm named LSARSSI for mobile node with single anchor by aid of inertia module. The simulation results demonstrate that our LSARSSI algorithm outperforms than other range-free localization mechanisms, for example, Ssu’s and BT algorithms. As the number of locations needed is approximately 200, we further proposed an improved algorithm named ILSARSSI. ILSARSSI utilize the
This work was supported by the National Natural Science Funds (Grant no. 60970129), the Public Science and Technology Research Funds Project of Ocean (Grant no. 201105033), and the Localization Algorithms of Underwater Sensor Networks Research Funds (Grant no. 61170258).