Learning Impulsive Pinning Control of Complex Networks

In this paper, we present an impulsive pinning control algorithm for discrete-time complex networks with different node dynamics, using a linear algebra approach and a neural network as an identifier, to synthesize a learning control law. The model of the complex network used in the analysis has unknown node self-dynamics, linear connections between nodes, where the impulsive dynamics add feedback control input only to the pinned nodes. The proposed controller consists of the linearization for the node dynamics and a reorder of the resulting quadratic Lyapunov function using the Rayleigh quotient. The learning part of the control is done with a discrete-time recurrent high order neural network used for identification of the pinned nodes, which is trained using an extended Kalman filter algorithm. A numerical simulation is included in order to illustrate the behavior of the system under the developed controller. For this simulation, a 20-node complex network with 5 different node dynamics is used. The node dynamics consists of discretized versions of well-known continuous chaotic attractors.

Energy-Efficient Clustering and Localization Technique Using Genetic Algorithm in Wireless Sensor Networks

Localization is recognized among the topmost vital features in numerous wireless sensor network (WSN) applications. This paper puts forward energy-efficient clustering and localization centered on genetic algorithm (ECGAL), in which the residual energy, distance estimation, and coverage connection are developed to form the fitness function. This function is certainly fast to run. The proposed ECGAL exhausts a lesser amount of energy and extends wireless network existence. Finally, the simulations are carried out to assess the performance of the proposed algorithm. Experimental results show that the proposed algorithm approximates the unknown node location and provides minimum localization error.

Localization in Wireless Sensor Networks for Accurate Event Detection

Event detection at its initial stage is considerably most demanding and more importantly challenging to reduce the causes and damages. The GPS-enabled sensor nodes are possibly a solution for the location estimation, but having GPS receiver in each sensor node makes the network costly. In this paper, the authors have presented a UNL, unknown node localization, method for the estimation of sensor location. The proposed method is based on RSSI, and there is no requirement of extra hardware and communication of data among the sensor nodes. The experiments are conducted in order to investigate the localization accuracy of UNL method, and they analyzed that the proposed method is simple as there is less computation and communication overhead. The proposed algorithm is further compared with other existing localization methods for the accurate estimation of unknown nodes. The experimental results show the effectiveness of the algorithm and its capability for locating the unknown nodes in a network more accurately.

An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model

Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

Underwater Localization Techniques

Underwater acoustic communication uses sound waves to trans-receive information, diving deep inside water, environment scanning, undersea explorations, disaster prevention, etc. In this chapter, an attempt has been made to cover stationary and mobile localization algorithm. They are further subdivided into distributed and centralized. Each one is further subcategorized into estimation-based and prediction-based schemes. The category therefore extends on the basis of ranging method, communication, and synchronization, some of which are area localization, sensor-based localization, forming a sensor array, motion-aware self-localization, silent localization. Each one will be discussed in detail in this chapter. At last, hybrid technique is also discussed, which combines stationary and mobile techniques. The discussion includes various nodes including anchor node, unknown node, sink node, and reference node. Various methods to follow the techniques are also discussed, which include anchor-based method, ranging method, and message communication.

Fast and Accurate Hop-based Positioning based on Anchor-skeleton for Anisotropic Networks

Background: The hop-based positioning method is a straightforward, low-cost, and feasible positioning method. Methods: Most previous hop-based algorithms assume that the network is isotropic and uniformly distributed, which often does not reflect real-world conditions. In practice, the network may be anisotropic, which makes the hop count between nodes may not match the real distance well. Results: As a result of this issue for hop-based positioning methods, in this paper, we propose a novel scheme that builds a skeleton model between anchor nodes to represent the anisotropy of a network. During the process of building the skeleton model, we use the corrected Akaike's Information Criterion (AICc), which can assist in the construction of a reliable and high accuracy skeleton model. With the help of the skeleton model with AICc, an unknown node can get a more accurate and reliable estimated position. Conclusion: The results of both theoretical analysis and experimental simulation show that the optimal hop-distance conversion model can be achieved, and compared to other similar algorithms, the proposed algorithm can obtain the position estimation result in a fast and accurate manner.

Mapping Property of Bilateration and Its Application to Human-Following Robot

Objective of this paper is twofold. The first one is to study the mapping property and unified form of the component equations of the unknown node in bilateration, and the second one is to introduce the concept model for human-following robot based on bilateration. Bilateration needs only two known nodes and two distances’ data. Because of the simple sensor arrangement in bilateration, it needs less computation and uses less number of unavoidable erroneous distances compared to the trilateration.

Improved DV-Hop Location Algorithm Based on Mobile Anchor Node and Modified Hop Count for Wireless Sensor Network

To improve the performance of location accuracy for wireless sensor network, a new location algorithm based on mobile anchor node and modified hop count is proposed. Firstly, we set different communication powers for all nodes to make them have different communication ranges. This makes the relationship between the hop count and real distance more accurate. Secondly, the unknown node computes the mean distance per hop between it and the three anchor nodes that are the nearest to the unknown node and uses the mean value as the mean distance per hop. Finally, we suppose that some anchor nodes can move. Once the position of some anchor nodes changes, we recalculate the positions of unknown nodes and use the mean value of recorded positions as position of unknown nodes. Simulation results show that the proposed method has lower location error than other methods.

Taxonomy on Localization Issues and Challenges in Wireless Sensor Networks

Background: In Wireless Sensor Networks, Localization is the most dynamic field for research. The data extracted from the sensor nodes that carries physical location information is very much helpful in WSNs as it is useful in major applications such as for the purpose of monitoring of any environment, tracking and for the detection purpose. Localization is known as the estimation of unknown node locations and its positions by communicating through localized nodes as well as unlocalized nodes. Objective: The aim of this study is to present classification of various localization algorithms and to compare them. Methods: The prime consideration is to know that how localization affects the network lifetime and how these algorithms work for increasing the lifetime of a network in a severe. Results: This paper also aims for finding the position of the node with respect to range based, anchor based and distributed localization techniques for harsh environments. Additionally, this paper also features the concern that occurs with these localization techniques. Conclusion: The technique that gives highly accurate location coordinates and having less hardware cost is distributed RSSI based localization algorithm.

Trilateration Method For Estimating Location in RSSI-Based Indoor Positioning System Using Zigbee Protocol

Wireless network technology that is used today is developing rapidly because of the increasing need for location information of an object with high accuracy. Global Positioning System (GPS) is a technology to estimate the current location. Unfortunately, GPS has a disadvantage of low accuracy of 10 meters when used indoors. Therefore, it began to be developed with the concept of an indoor positioning system. This is a technology used to estimate the location of objects in a building by utilizing WSN (Wireless Sensor Network). The purpose of this study is to estimate the location of the unknown nodes in the lecturer room as an object and obtain the accuracy of the system being tested. The positioning process is based on the received signal strength (RSSI) on the unknown node using the ZigBee module. The trilateration method is used to estimate unknown node located at the observation area based on the signal strength received at the time of testing. The result shows that the path loss coefficient value at the observation area is 0.9836 and the Mean Square Error of the test is 1.251 meters, which indicates that the system can be a solution to the indoor GPS problem.