Wireless sensor network node localization research based on improved wolves 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

The wireless sensor network node localization algorithms are studied more and more at present, but most are based on two-dimensional localization algorithm for three-dimensional localization of too few. Based on the researches on the classic two-dimensional localization algorithm, and the trilateration was extended to three-dimensional localization algorithm. Error analysis of this algorithm was revealed in the classic two-dimensional and three-dimensional environment, and the node localization error with a linear relationship between average nodes ranging error was found.

The algorithm based on the beacon node localization is a research hotspot in the field of wireless sensor network. For circular distribution beacon group, we found that the optimal number of beacon is 5, and one is located in the centre of the circle, the remaining four evenly distributed on the circumference of a circle; For rectangular ROI, take advantage of the beacon group can pinpoint areas for the square, the whole network is divided into a grid, use a traveling salesman algorithm to get the beacon for optimal moving path; Based on five beacon node localization model in this paper, we put forward a novel region segmentation centroid localization algorithm (RDCLA), by comparing three groups of RSSI values, the node is locked into a triangle area, using centroid algorithm to locate the node; The algorithm principle is simple and has low computing complexity and has nothing to do with the network topology; The simulation results show that compared with TCLA and WCLA positioning accuracy has large improvement, and are suitable for all kinds of the size of the wireless sensor network. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

The localization of nodes in a wireless sensor network is milestone to the correct functioning of the network. This paper aims to evaluate by means of simulations the Malguki spring localization algorithm. The algorithm is simulated for three different anchor deployment scenarios: the classical scenarios of random and uniform anchors and a novel scenario called hybrid. This paper analyzes the algorithm from the point of view of the obtained average errors, as well as from the point of view of the parameters that influence its results.

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.

Motion capture might become one key feature of future Wireless Body Sensor Networks (WBSN), allowing new applications such as home activity monitoring, nomadic postural rehabilitation or sportive gesture recording through standard on-body communications. In this context we present herein a stand-alone solution that enables the localization of wearable wireless nodes relatively to a body-strapped Local Coordinate System (LCS). In particular, we consider adapting a Distributed Weighted Multi-Dimensional Scaling (DWMDS) algorithm fed by cooperative inter-node range measurements obtained through e.g., Time Of Arrival (TOA) estimation, where estimated nodes' locations are asynchronously updated based on their neighborhood information. Exploiting further the presence of constant-length radio links, a Constrained solution (CDWMDS) is thus proposed to improve localization accuracy, while reducing traffic and power consumption. Another novelty lies in the initialization step, which somehow benefits from the space-time correlation of nodes' locations under body mobility. Relying on a realistic biomechanical model, we provide preliminary simulation results to illustrate the relative gains observed in comparison with a nominal algorithm setting.

Wireless Sensor Network (WSN) is composed of sensor nodes deployed to achieve some specific task. Localization of individual mobile node is a challenging task in wireless sensor networks. Localization system is not only for location identification but also provides the base for routing, density control, tracking, and a number of other communication network aspects. In real time applications of outdoor or indoor environments, there are significant challenges for node localization in WSN. Keeping in view the fact that many precise and smart solutions have been proposed, an approach for range free localization is Concentric Anchor Beacon (CAB), which comes with the requirement of improvement. In this paper, we have presented a novel solution “Power Tuning Anchors” (PTA) algorithm that outperforms in terms of accuracy. In PTA, mobile node localizes its position on the basis of minimum received power levels transmitted by the neighboring anchors. Localization error, anchor density, radio range and Gitter between two consecutive power levels are the performance metrics used for evaluation of PTA. Initial empirical results have shown better precision and more accuracy in terms of aforementioned performance parameter.

Node localization is the basis of a variety of applications as an important technology in wireless sensor networks. This paper studies the existing localization algorithms, and puts forward a sensor node localization algorithm based on particle swarm algorithm, this algorithm uses the fitness value of the particle to adjust the inertia weight value, in order to achieve , and fast and accurate positioning of the target node. The simulation results show that the algorithm is an effective refinement algorithm in nodes localization.

For most wireless sensor network (WSN) applications, the positions of the sensor nodes need to be known. Global positioning systems have not fitted into WSNs very well owing to their price, power consumption, accuracy and limitations in their operating environment. Hence, the last decade has brought about a large number of proposed methods for WSN node localization. They show tremendous variation in the physical phenomena they use, the signal properties they measure, the resources they consume, as well as in their accuracy, range, advantages and limitations. This paper provides a high-level, comprehensive overview of this very active research area.

Wireless sensor node's localization is a funda-mental technology in Wireless Sensor Networks.There are only quite a few study on three-dimensional (3D) localization which is suffered in slow progress, actually, is one of the main difficulties in WSN localization.Based on the study of the existing two-dimensional positioning algorithm and the application of terrain modeling, localization algorithm for sensor nodes in (3D) condition has been focus on as well as the application of terrain model.Using the idea proposed by representative algorithm--APS multi-hop AOA (Angle of Arrival), this paper proposed a new algorithm named Multi-hop Three Dimensional AOA With Space-based Angle Trans-mission (MSAT3D AOA).Using this technology, target nodes can use information of anchor nodes which are more than one hop away form.This paper also combined MSAT3D AOA algorithm with Delaunay triangulation algorithm for terrain modeling.

In this paper, we propose an improved DV-Hop algorithm for wireless sensor network (WSN) node localization. Our algorithm applies a novel virtual node method to calculate the average hop size and a novel rule to correct unreasonable distance. Then it introduces a kind of weighted least squares method to calculate the locations of unknown nodes. Our algorithm doesn't require beacon nodes to broadcast additional information. We simulated our algorithm contrasting with the original DV-Hop algorithm under different conditions. The experiment results showed that our algorithm improved the localization accuracy obviously.

SummaryDespite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real‐time location‐based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best‐fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization.

A wireless sensor network consist of spatially distributed autonomous sensor nodes for data acquisition. Accurate localization of sensor nodes is a strong requirement in a wide area of applications. This paper describes the localization of sensor nodes using range measurements and trilateration with the Extended Kalman Filter. The commercially available nanoLOC sensor network is utilized for range measurements. Experimental results show, that a localization accuracy better than 0.5 m can be achieved in most cases.

Wireless Sensor Network (WSN) refers to a network of devices that can communicate the information gathered from a monitored field through wireless links. As a critical technology of WSN, the localization algorithm plays a vital role in improving node location accuracy and network efficiency. A hybrid Pigeon Inspired Optimization (PIO) with a typical localization model is proposed to solve the problem of node localization in WSN. The self-learning idea of PIO and speed formula are combined to improve exploring and exploiting agents of PIO. Fitness function for optimization is mathematically modeled based on analysis Pareto distances. The simulation results compared with the other approaches in the literature, e.g., the improved particle swarm optimization (PSO) and the cuckoo search (CS) show that the proposed method effectively improves the location accuracy of nodes and reduces the cumulative error caused by success positioning nodes.

With the development of electronic technology and communication protocols, wireless sensor network technology is developing rapidly. In a sense, the traditional static wireless sensor network has been unable to meet the needs of new applications. However, the introduction of mobile nodes extends the application of wireless sensor networks, despite the technical challenges. Because of its flexibility, the mobile wireless sensor network has attracted great attention, and even small, self-controlled mobile sensor devices have appeared. At present, mobile node localization has become one of the hotspots in wireless sensor networks. As the storage energy of wireless sensor network nodes is limited, and the communication radius is small, many scientists have focused their research direction on the location algorithm of mobile nodes. According to the continuity principle of mobile node movement, in this paper we propose an improved mobile node localization algorithm based on the Monte Carlo Location (MCL) algorithm, and the method can reduce the sampling interval effectively. First of all, this paper introduces the structure and classification of wireless sensor localization technology. Secondly, the principle of the Monte Carlo Location algorithm is described in detail. Thirdly, we propose an efficient method for mobile node localization based on the MCL algorithm. Finally, the effectiveness and accuracy of the new algorithm are verified by comparative analysis.