A Disaster Management-Oriented Path Planning for Mobile Anchor Node-Based Localization in Wireless Sensor Networks

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.

Localization of sensor node is imperative in Wireless Sensor Networks (WSN) as data gathered at the user end become meaningless without location information of the node. In this work, impact of anchor position errors on WSN localization using Mobile Anchor Positioning (MAP) Algorithm has been investigated by evaluating two deployment strategies. In deployment I sensor nodes are randomly spread in the entire chosen area (1000 χ 1000) whilst deployment II has nodes spread in the central region of the chosen area. Firstly, MAP Algorithm employing three anchor nodes is investigated for the two deployments. To study the impact of mobile anchors on MAP, anchor number is increased to four and the two deployments are investigated again. Further, this work also investigates the effect of anchor position errors on localization accuracy of sensor nodes. Error term is modelled using Additive White Gaussian Noise (AWGN) Model. Performance metrices considered for validation are Average Root Mean Square Error and Execution time.

In the various applications of wireless sensor networks (WSN), the event occurrence information or data sensed by the sensor node has no significance until the location of an event/sensor node is not known. Therefore, localization is an important parameter in WSN for tracking these deployed sensor nodes in the sensing area. To localize these sensor nodes, there are various existing localization models in available detailed literature. One solution to this problem is to deploy a set of static anchor nodes (GPS equipped) in the sensing area, that broadcast their coordination information to help unknown nodes to localize themselves. Another way is to use a single mobile anchor node that will follow a specific path and localize unknown nodes. But a mobile anchor node must follow an optimal path that helps to achieve high accuracy and able to localize a large number of unknown nodes in less computational time. In this paper, the author design and developed a localization algorithm in which a GPS equipped single mobile anchor node follows a new path trajectory i.e. spiral pentagon. Once the target node falls under the communication diameter of the virtual node, six virtual anchor nodes are projected in the shape of a ring around the anchor node and localize all the unknown nodes in the sensing area by using the application of Salp Swarm Algorithm (SSA) for optimization. The hypothesis of six virtual anchor nodes is to avoid the problem of Line of Sight. This proposed method assured that each target node can estimate their node coordinates at least once with a low localization error.

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.

Many applications require high degree of sensor node to identify their locations in wireless sensor network. Location information is gathered from manual setting or GPS device. Since manual setting requires huge cost of human time, and GPS requires expensive device cost. Both approaches are not applicable for large scale WSN. The mobile anchor node is used to finding the position of unknown location node. The optimal path planning mechanism is evaluated to minimize the time for determining the localization and should increase the accuracy. In proposed system the circle based path planning mechanism can be implemented, because it covers four corners of the sensing field by increasing the diameter of the concentric circles. The sensor node is located at the center of this circle, a single mobile anchor node moves randomly through the sensing field to determine the localization and also detecting the hacker node in WSN.

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

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.

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.

Knowing the position of sensor nodes in an environmental monitoring is useful to identify the location of events. However deploying GPS receivers or other anchor sensors is expensive, since the role of anchor nodes ends after localizing sensor nodes' positions and they are transferred into ordinary sensor nodes. In this paper, we introduce a new localization scheme for a wireless sensor network that can localize sensor nodes using a collinear and non-collinear mobile anchor node. This scheme benefits from the estimated distance between neighbor nodes and additional information provided by the anchor node about the flow direction of the message. Each node localizes it's position from two independent directions. A Kalman Filter is then used to improve the location accuracy for each node. Through simulation studies, we show that the scheme using a Kalman Filter decreases the estimation errors than using single direction by 31% and 16% better than using weighted averages. As well, our new scheme overcomes the collinearity problem that appears from using mobile anchor nodes.

In applications like patient monitoring and surveillance systems, the sensed data becomes meaningful only when they are accompanied by the location information. Thus accurate localization is highly inevitable. But in the same time, a Wireless Sensor Network is expected to live long to justify the effort and cost involved in establishing the network. Energy stringent standards are the need of the day in WSN. Much work has been done to improve the localization accuracy and fewer in improving the energy efficiency. Deployed sensor nodes often make use of mobile anchor assisted localization techniques. An important challenge often overlooked in these schemes is the path taken by the mobile anchor. The path can greatly influence the time needed for localization and most importantly the energy needed. In this paper, a novel approach, called Hex-Path, for anchor path planning has been proposed. The proposed strategy has been applied to a high precision localization technique called MAP-GSA and has also been compared with existing path planning mechanisms. Simulation results show the efficiency of the proposed approach in minimizing the energy needed for localization, thus improving the Network Lifetime.

In wireless sensor networks, it is crucial to support the collected data of sensor nodes with position information. One of the promising ways to acquire the position of unknown nodes is using a mobile anchor node that traverses throughout the network, stops at determined points, and sends its position to aid in obtaining the location of other unknown nodes. The main challenge in using mobile anchor nodes lies in designing the path model with the highest localization accuracy, shortest path length, full coverage area, and minimal power consumption. In this paper, a path model named the Arrow-Curve path model is proposed for mobile node aided localization. The proposed path model can effectively localize all the static unknown sensor positions in the network field with high positioning accuracy and low power consumption while pledging full coverage area. The sensor network is implemented using MATLAB simulation and MCU node in both static unknown nodes and the mobile anchor node. The realtime environment guarantees a realistic environmental model with reliable results. The path model is implemented in realtime in indoor and outdoor environments and compared to the H-Curve path model using a trilateration technique. The results show that the suggested path model achieves better results compared to H-Curve model. The proposed path model achieves an average position error less than that of H-Curve by 10.6% in a simulation environment, 5% in an outdoor realtime environment, and 9% in an indoor realtime environment, and it decreases power consumption by 62.65% in the simulation environment, 50% in the outdoor realtime environment, and 75% in the realtime environment in indoor compared to H-Curve.

Confidentiality, authentication, and the integrity services of wireless sensor networks (WSNs) are the concerns at present in an era of Internet of Things (IoT) or Internet of Everything (IoE). In this context, a secured location estimation technique is developed for advanced WSN with lightweight lattice signcryption. The method inherently uses range-free DV-hop algorithm to ensure no extra burden on the hardware cost with low power consumption, low complexity (time and space), and desirable accuracy. The anchor nodes and unknown sensor nodes of the WSNs are considered mobile in nature and use the random mobility model. The evaluation and comparison of the developed “SECure LAttice based Signcryption for LOcalization (SECLASLO)” have been done in terms of location error analysis, memory, time, and energy consumption.

At present, most underwater positioning algorithms improve the positioning accuracy by increasing the number of anchor nodes which resulting in the increasing energy consumption. To solve this problem, the paper proposes a localization algorithm assisted by mobile anchor node and based on region determination (LMRD), which not only improves the positioning accuracy of nodes positioning but also reduces the energy consumption. This algorithm is divided into two stages: region determination stage and location positioning stage. In the region determination stage, the target region is divided into several sub-regions by the region division strategy with the smallest overlap rate which can reduce the number of virtual anchor nodes and lock the target node to a sub-region, and then through the planning of mobile nodes to optimize the travel path, reduce the moving distance, and reduce system energy consumption. In the location positioning stage, the target node location can be calculated using the HILBERT path planning and trilateration. The simulation results show that the proposed algorithm can improve the positioning accuracy when the energy consumption is reduced.

In wireless sensor networks (WSN) high-accuracy localization is crucial for both of WNS management and many other numerous location-based applications. Only a subset of nodes in a WSN is deployed as anchor nodes with their locations a priori known to localize unknown sensor nodes. The accuracy of the estimated position depends on the number of anchor nodes. Obviously, increasing the number or ratio of anchors will undoubtedly increase the localization accuracy. However, it severely constrains the flexibility of WSN deployment while impacting costs and energy. This paper aims to drastically reduce anchor number or ratio of anchor in WSN deployment and ensures a good trade-off for localization accuracy. Hence, this work presents an approach to decrease the number of anchor nodes without compromising localization accuracy. Assuming a random string WSN topology, the results in terms of anchor rates and localization accuracy are presented and show significant reduction in anchor deployment rates from 32% to 2%.

Improved DV-HOP localization algorithm based on grey wolf optimization