Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find unknown nodes in Three-Dimensional environment, only single anchor node is used. In the simulation-based environment, the nodes with unknown locations are moving at middle & lower layers whereas the top layer is equipped with single anchor node. A novel soft computing technique namely Adaptive Plant Propagation Algorithm (APPA) is introduced to obtain the optimized locations of these mobile nodes. These mobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity (Degree of Irregularity (DOI)) value set to 0.01. The simulation results present that proposed APPA algorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error, computational time, and the located sensor nodes.

Wireless Sensor Networks (WSNs) contain many small low-power sensor nodes (SNs) deployed randomly in the environment to determine the physical behavior. Sensors are often used to obtain measurements of location, temperature, humidity, irradiance, sound, and pressure [

The following section in this work is as described: Section 2 illustrates challenges which deal with 3D localization. Section 3, introduces a novel approach named APPA. In Section 4 the process of deploying only one anchor node in the sensing field is explained. Section 5 concludes results and discussions. At last, the Future Scope and the conclusive part is discussed in Section 6.

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This Algorithm is comprised of a population of shoots, and every shoot presents a solution in the search space. It is assumed that each shoot has taken root which is equivalent to the objective function being assessed. Each shoot will then send runners out to explore the space around the solution.

A plant is considered to be in a location

The objective function values at different positions

The effect of this mapping function is that, it provides a means of emphasizing further better solutions over those which are not as good.

The number of runners that are found out by the solution and the distance of propagation of each of them are described. There exists a direct relationship between the number of runners produced by a candidate solution and its fitness given by

Here,

where

The algorithm is modified to be an adaptive one in view of the limits of the search domain. Hence, the name is given as Adaptive Plant Propagation Algorithm (APPA). In the event that the limits are disregarded the point is changed in accordance to lie within the search space. Essentially,

In this 3D localization problem, a single anchor node with known location information is considered and this location information of anchor is utilized to find out the locations of randomly placed mobile nodes. These mobile nodes are grouped into three different layers with anchor placed at the top most position and unknown nodes are moving at middle and the bottom layers. Anchor nodes transmits a beacon signal that will be sensed by mobile nodes and using the concept of virtual anchors, three of these virtual anchors and anchor node itself are selected to locate all the mobile nodes. Based on received RSSI the approximated distance between anchor and target node is estimated. The complete flow of localization procedure is given by

The proposed algorithm has below mentioned properties and further steps for estimating location information have been discussed in this section.

Using the APPA algorithm, a new method for projecting virtual nodes in the field to determine the exact locations of deployed sensor nodes in a three dimensional scenario.

Line of Sight (LoS) problems will be reduced to a greater extent with virtual anchor nodes.

Flip ambiguity issues in range-based methods are also minimized.

Firstly, the anchor and moving targets distance is determined in 3D scenarios using RSS measures. Further, the anchor nodes which are virtual (six in number) are placed with same distance at an angle difference of sixty degrees, given by

Here in three dimensions, the position of the nodes which are targets is given by

It has been shown by

Here, the estimated position of the target node is given by

Error in the process of localization is given by

Here, a novel technique APPA is used for three dimensional localization problem where the concept one anchor and six virtual anchors assumed in six directions placed at

Algorithm | Parameters |
---|---|

PSO | _{max} |

HPSO | _{max} |

BBO | _{max} |

FA | _{max} |

GWO | _{max} |

APPA | _{max} |

Here, NP is number of population, D is dimension of problem, Gmax is number of iteration.

Where (c1), (c2) and (c3) are the cognitive, social and neighborhood learning parameters. Here w is the inertia weight and Pm is the probability of mutation. In FA x and

The average localization error for all competitive algorithms is computed in

Algorithms | Movements number | Max localization error | Min localization error | Average error | Number of located targets |
---|---|---|---|---|---|

PSO | 1 | 3.9358 | 0.0554 | 0.9958 | 80 |

2 | 5.3379 | 0.0831 | 0.9839 | 80 | |

3 | 5.0108 | 0.0800 | 0.9267 | 80 | |

4 | 5.1655 | 0.0367 | 0.9757 | 80 | |

5 | 5.1325 | 0.0812 | 0.9612 | 80 | |

HPSO | 1 | 3.1204 | 0.1044 | 0.6742 | 80 |

2 | 5.0134 | 0.0647 | 0.4876 | 80 | |

3 | 4.8279 | 0.0976 | 0.4032 | 80 | |

4 | 5.2376 | 0.0230 | 0.5546 | 80 | |

5 | 5.2134 | 0.0316 | 0.5324 | 80 | |

BBO | 1 | 5.8904 | 0.1822 | 1.1892 | 80 |

2 | 5.3500 | 0.3318 | 1.2560 | 80 | |

3 | 5.5989 | 0.1822 | 1.1585 | 80 | |

4 | 5.6348 | 0.1528 | 1.2818 | 80 | |

5 | 5.9014 | 0.1911 | 1.1916 | 80 | |

GWO | 1 | 3.1101 | 0.0944 | 0.6442 | 80 |

2 | 4.9834 | 0.0547 | 0.4776 | 80 | |

3 | 4.8134 | 0.0876 | 0.3932 | 80 | |

4 | 4.7976 | 0.0430 | 0.4946 | 80 | |

5 | 4.9776 | 0.0513 | 0.4713 | 80 | |

FA | 1 | 6.1101 | 0.1922 | 2.2234 | 80 |

2 | 6.3120 | 0.3412 | 2.3124 | 80 | |

3 | 6.6990 | 0.1923 | 2.4651 | 80 | |

4 | 6.8912 | 0.1627 | 2.5123 | 80 | |

5 | 6.9036 | 0.2010 | 2.2013 | 80 | |

The localization optimization using algorithms viz. PSO, HPSO, BBO, GWO and FA are already available in the literature with static scenarios. In this paper, these algorithms are also implemented with the proposed technique having single anchor node with umbrella based projection. Further these algorithms are compared with APPA algorithm, given by

The performances of all algorithms have been compared with the proposed scheme in dynamic scenarios. It has been analyzed from the results given in

The single anchor node method was used to obtain three Dimensional positions of unknown nodes with range-based technique using a meta-heuristic algorithm called APPA. The idea of an anchor and virtual anchor node forms an umbrella projection for finding all unknown nodes. When the mobile target nodes come under the range of the known node, further, with the help of anchor as well as virtual anchors, position of unknown nodes is determined (to find out three dimensional positions, at least four anchor nodes are required). A variety of applications exists where sensor node location is essential and the proposed algorithm is helpful, including logistics, underwater scenarios, tracking of coal mine workers, monitoring of environmental aspects, localization of occurring events in remote and hilly regions etc. Performance of APPA algorithm proposed in this work in order to find out the exact location of the nodes is found out to be better than its competitive algorithms. It has been proved with the help of the results that using APPA, accurate locations are being found as compared to other algorithms and convergence characteristics are also faster. In future, with the help of hybridization of few optimized algorithms, more accuracy could be achieved.