Wireless Sensor Networks Based on the DV-Hop Localization Algorithm

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches

In recent years node localization in wireless sensor networks (WSNs) has attracted much attention due to the increase of usage and applications of WSNs. Many algorithms and techniques for locating sensor nodes have been proposed in the literature. A recent algorithm, which is based on a given set of pairwise distance estimates among nodes and the target, generates a map of node locations. This algorithm, known as FastMap, uses projections onto orthogonal hyperplanes to find the coordinates successively. In this paper, we analytically study the performance of the FastMap algorithm for 2D positioning. Moreover a modified version of FastMap with better performance is proposed and analyzed. The analysis shows that the optimum anchor placement should be at the edge of the network.

Wireless sensor networks are an emerging technology deployed in many fields. Knowing the positions of sensor nodes is critical for many applications to perform their tasks. Hence, accurate node localisation in wireless sensor networks is an essential issue. The node localisation problem can be formulated as a multidimensional optimisation problem. In this study, the authors propose a node localisation scheme based on a sine–cosine algorithm. The proposed scheme addresses the flip ambiguity problem, which can badly affect the localisation accuracy of the entire network. The efficacy of the proposed scheme is evaluated through intensive experimentation under different scenarios. The obtained results show the superiority of the proposed scheme against other optimisation algorithms in terms of the localisation accuracy, the number of localised nodes, and the computation time.

Node localization is one of the main challenges in Wireless Sensor Networks (WSNs). In all sensor network applications, it is important to know the origin of collected data and reported events. This paper proposes a bio-inspired method for solving the WSN localization problem which is formulated as a non linear optimization problem. An Artificial immune inspired, population based optimization algorithm called Clonal Selection Algorithm (CSA) is used for locating nodes in a sensor network scenario. The performance of CSA based localization scheme is evaluated through simulations and results are compared with Particle Swarm Optimizer (PSO) based localization method. Also, evaluation is performed using data collected from a real WSn testbed.

Aiming at the problem of node localization in wireless sensor networks, a location algorithm for optimizing distance vector hopping (DV-hop) by constructing a quantum neural network model based on particle swarm optimization (PSO) is proposed. According to the average distance obtained by the traditional DV-HOP and the distance from the measured nodes, the quantum neural network model is constructed, and the average distance is trained by the particle swarm optimization algorithm which would shorten the training time of the traditional artificial neural network and accelerate the convergence speed. By using the proposed model, the optimal mean value is obtained, and the optimization of the DV-HOP algorithm is realized. The simulation results show that compared with the traditional DV-HOP algorithm, the proposed algorithm can reduce the positioning error by about 20%, and the positioning accuracy is significantly improved.

There has been a growing interest in the applications of wireless sensor networks in unattended environments. In such applications, sensor nodes are usually deployed randomly in an area of interest. Knowledge of accurate node location is essential in order to correlate the gathered data to the origin of the sensed phenomena and assure the relevance of the reported information. In this paper, we present an efficient anchor-free protocol for localization in wireless sensor networks. Each node discovers its neighbors that are within its transmission range and estimates their ranges. Our algorithm fuses local range measurements in order to form a network wide unified coordinate systems while minimizing the overhead incurred at the deployed sensors. Scalability is achieved through grouping sensors into clusters. Simulation results show that the proposed protocol achieves precise localization of sensors and maintains consistent error margins.

Localization is one of the significant field of research in wireless sensor networks (WSNs). It broadly refers to estimating the location of unidentified nodes in a WSN using anchor nodes (nodes whose location is known). Localization, along with the mobility of nodes in the network, makes this field more potent for practical purposes. Therefore, in this paper, we propose an algorithm for localization in WSNs using a mobile anchor and subordinate nodes (LWMS). In the proposed algorithm, we use a mobile anchor node to localize all the other unidentified nodes in the network. The mobile anchor node localizes the nodes in its path using RSSI technique, and these localized nodes are called the static anchor nodes. Using these static anchor nodes, the algorithm localizes all other nodes around them. The nodes localised using static anchor nodes are called subordinate anchor nodes. There are still some nodes left in the network to be localized due to holes or a small percentage of anchor nodes. Further, these subordinate nodes are used to localize all the remaining nodes in the network. We have also used a method of solving the system of distance equations, which reduces the propagation of error. The simulation results corroborate that LWMS performs better than the other two algorithms used for comparison.

Due to the un-consideration of random distribution of DV-Hop algorithm in wireless sensor network, a big error caused by using of hop count between unknown and anchor nodes and the nearest anchor nodes’ average hop distance instead of the average hop distance of all the anchor nodes. An improved DV-HOP localization algorithm is proposed based on the traditional DVHOP localization algorithm aimed to reduce the localization error in this paper. The proposed algorithm introduce RSSI technique to modify the Hop count of DV-Hop and use the original DV-HOP algorithm to estimate the positions of anchor nodes which are already known, the error between estimation and real distance of anchor nodes so obtained are then to modify the Hop size. The simulation result shows that the proposed algorithm has a better performance in localization precision.

The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the SOS algorithm, but also formed a novel global optimization algorithm called the MQSOS algorithm. It has speed and convergence ability and plays a good role in solving practical problems with multiple arguments. We also compared MQSOS with other intelligent algorithms under the CEC2013 large-scale optimization test suite, such as particle swarm optimization (PSO), parallel PSO (PPSO), adaptive PSO (APSO), QUasi-Affine TRansformation Evolutionary (QUATRE), and oppositional SOS (OSOS). The experimental results show that MQSOS algorithm had better performance than the other intelligent algorithms. In addition, we combined and optimized the DV-hop algorithm for node localization in wireless sensor networks, and also improved the DV-hop localization algorithm to achieve higher localization accuracy than some existing algorithms.

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

There are many optimisation problems in the real world. To solve these problems, different intelligent optimisation techniques/algorithms have been developed in the past several years. Among these algorithms, artificial bee colony (ABC) has received much attention in optimisation community. Compared to other similar algorithms, ABC has fewer control parameters. However, ABC shows poor local search ability and slow convergence rate. In this paper, we propose a novel ABC variant, called DyGABC, which employs two strategies including the global best solution guided solution model and a dynamic model for dimension updating. Experiments on 12 optimisation problems show that our DyGABC is better than the standard ABC and another improved ABC algorithm. In addition, DyGABC is used to improve the accuracy performance of node localisation in wireless sensor networks. Simulation results demonstrate that our approach is better than the original distance vector-hop (DV-Hop) algorithm.

ABSTRACTIn large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley & Sons, Ltd.

Wireless sensor network is a type of communication which gives a steadfast communication at the minimal expense.This paper highlights one of the fundamental concern namely node localization by using the combined effect of randomly generated tokens at anchor nodes and vector iterative process (RGT_VIP).Time synchronization is a critical aspect in the wireless sensor networks for their data transmission.But with this approach, we can range and estimate the position of deployed nodes without depending on central synchronized time reference at receivers end and at the anchors/transmitter end.Results show that this newly proposed method gives a greater accuracy in terms of average distance error on different variance of Gaussian values and performs better than synergetic localization algorithm on higher values of variance.

Improved DV-HOP localization algorithm based on grey wolf optimization

This chapter describes one of the major challenges in technology advancement of Wireless Sensor Networks (WSNs), i.e., Localization in WSNs. In recent years, sensor node localization is an emerging research area in WSNs. The sensor data become useless, if we do not know the location of the reporting node. Coordinates determination of the sensor node is a challenging problem and it is referred as localization problem. The nodes which has unknown coordinates is termed as target Nodes. Various localization methods can be utilized to find out the location of sensor nodes, those coordinates are not known in a system/network. Efficient WSN localization can be treated as multi-dimensional optimization problem which can be addressed through population based stochastic techniques, which involves the minimization of a function of differences between Euclidean and measured distance between sensor nodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optimization, Firefly Algorithm and Genetic Algorithm have been discussed. A choice between these algorithm is influenced by the localization accuracy expected to be and convergence rate.