Localization of Sensor Nodes in a Wireless Sensor Network Using the nanoLOC TRX Transceiver

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.

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

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.

Underwater acoustic communication is a method of sending and receiving messages beneath the water’s surface that has been extensively researched for long-distance communication and localization. While, in this paper we will focus on medium range optical communication localization and short range Magneto Inductive (MI) communication localization. Both optical wireless sensor networks (OWSNs) and magneto inductive wireless sensor networks (MIWSNs) are investigated for localization of sensor nodes using a received signal strength indicator (RSSI) based localization technique for underwater environment/medium. Location information for MI nodes is obtained directly from mutual coupling measurement with a proposed localization scheme. Where as the localization of optical sensor nodes is done by exploiting the Lambert cosine law which gives values for luminous intensity in different distances and orientations. A simulation environment for both underwater optical wireless sensor networks (UOWSNs), and underwater magneto inductive wireless sensor networks (UMIWSNs) consisting of eight Tx anchor nodes and ten Rx sensor nodes is created to elaborate the localization estimation results.

Multi-sensor data fusion methods for indoor localization under collinear ambiguity

Localization of sensor nodes is one of the key issues in wireless sensor networks. Next to the ability, to assign a phenomenon to a position, localization is a precondition for sensor network algorithms like geographic clustering and routing. A simple approach for coarse grained localization is centroid localization (CL), which was firstly presented by Bulusu et al. and assumes regularly arranged beacons. The localization accuracy was improved by various centroid-based algorithms, which use approximate distances to improve location estimation through weighting beacons in range, e.g. weighted centroid localization (WCL). Nevertheless, all these approaches have in common an increased localization error near network borders. In this work, we investigate this error and present two strategies to reduce the localization error of border area nodes.

Sensor nodes' locations perform a vital role in many sensor network applications. Localization of sensor node in Wireless Sensor Networks adds security issues such as node re-authentication and tracing the movement. A number of techniques have been proposed recently to solve the locations issues of regular sensors but inadequate to support hostile and dynamic environments since their designs are only for the static environments. In this paper, we propose the well-organized node authentication and key exchange protocol that trim downs the overhead in node re-authentication and also facilitates untraceability of localized nodes. The proposed protocol will be the efficient solution to increase the lifetime of sensor network.

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

Accuracy of unknown sound source localization and lifetime of sensor nodes is one of the important and critical issue and challenge in wireless sensor network. Localization plays major role when there is an uncertainty of the exact source location of some fixed or mobile devices. The main problem is source localization in an environment where they try to localize and focus on a source of unknown sound and noisy distance. This paper presents a new approach on the indoor localization of an acoustic sensor source. In this localization system, the environment will be scattered with a swarm of acoustic motes. The motes used in this localization system are the LOTUS motes from MEMSIC. In order to get the system working, We utilize a modular sensor board to plug on the mote. This sensor board captures acoustic signals and then digitalizes them. This localization algorithm uses the incoming Time Difference of Arrival (TDOA) values of a swarm ZigBee modules. The location of the source of the acoustic signal is determined by the multilateration technique. A sensor network is used in order to collect the signals. The evident advantage of the algorithm is that it requires synchronization between the motes mutually and not between the motes and acoustic source. The algorithm for these methods is written in MATLAB. The motes communicate in a ZigBee mesh network.

The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.

Wireless sensor network (WSN) is an emerging technology which find useful in several application areas such as healthcare, environmental monitoring, border surveillance, etc. Several issues that exist in the designing of WSN are node localization, coverage, energy efficiency, security, and so on. In spite of the issues, node localization is considered an important issue, which intends to calculate the coordinate points of unknown nodes with the assistance of anchors. The efficiency of the WSN can be considerably influenced by the node localization accuracy. Therefore, this paper presents a modified search and rescue optimization based node localization technique (MSRO-NLT) for WSN. The major aim of the MSRO-NLT technique is to determine the positioning of the unknown nodes in the WSN. Since the traditional search and rescue optimization (SRO) algorithm suffers from the local optima problem with an increase in number of iterations, MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique. The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm. In order to validate the effective node localization performance of the MSRO-NLT algorithm, a set of simulations were performed to highlight the supremacy of the presented model. A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

Wireless sensor networks are being used in a variety of ways - from reconnaissance and detection in military to biomedical applications and a wide variety of commercial endeavors. We introduce a position-adaptive direction finding method using mobile sensor networks and present recent experimental results in localization of a non-cooperative sensor node using static and mobile sensor networks. Electromagnetic (EM) direction finding is a technique in which a group or a swarm of Micro-Aerial Vehicles (MAVs) cooperate their sensing missions, adapt their position in real-time autonomously, and localize an unknown, hidden EM source based on optimal detection algorithms. The MAVs are equipped with IRIS wireless sensor nodes that serve as the sensing agents by providing mobility to the otherwise stationary nodes. In order to localize the transmitter, we use the Received Signal Strength Indicator (RSSI) data to approximate distance from the transmitter to the revolving receivers. We provide an algorithm for on-line estimation of the Path Loss Exponent (PLE) estimation that is used in modeling the distance based on received signal strength (RSS) measurements. The emitter position estimation is calculated based on surrounding sensors RSS values using Least-Square Estimation (LSE).

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Accurate localization of nodes in a sensor network is a crucial step before the sensor network can be utilized for various applications. This paper proposes a successive estimation method to self-localize the sensor nodes using time of arrival (TOA) measurements, through the aid of a few anchor nodes whose positions are known <emphasis emphasistype="italic">a priori</emphasis>. The successive nature of the proposed algorithm makes it attractive for distributed computation in a resource constrained environment. The proposed technique uses subsets of TOAs to obtain coarse sensor node estimates. Depending on available computation and transmission resources, the node positions are refined to improve their accuracies. The solution of the proposed node localization algorithm is algebraic and closed-form, which simplifies computation and avoids possible local convergence or divergence problems as in the traditional iterative approaches. A main benefit of the proposed algorithm is that although it is closed-form and performs successive estimation of the node locations, it is able to reach the CramÉr–Rao lower bound (CRLB) accuracy under white Gaussian measurement noise with sufficient SNR. This is confirmed by the theoretical analysis and corroborated by simulations. </para>

The effective localization of sensor nodes can ensure stable operation of sensor networks. The sensor node localization in traditional method does not considers the produced sparse of sensor channel due to the uneven distribution of nodes, resulting in much meaningless zero tap estimation in the positioning process, thereby reducing the efficiency of localization. In this paper, an improved node localization algorithm based on DV-Hop is proposed. In this method, the traditional DV-Hop localization algorithm is improved. Using particle swarm algorithm to optimize the positioning process, the positioning results of sensor nodes are finally obtained. The experimental results show that the improved algorithm can improve the positioning accuracy of sensor nodes, which are satisfactory.

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of sensor nodes, a challenging task because of severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, we present a Multi-Sequence Positioning (MSP) method for large-scale stationary sensor node localization in outdoor environments. The novel idea behind MSP is to reconstruct and estimate two-dimensional location information for each sensor node by processing multiple one-dimensional node sequences, easily obtained through loosely guided event distribution. Starting from a basic MSP design, we propose four optimizations that work together to increase localization accuracy. We address several interesting issues such as incomplete (partial) node sequences and sequence flip, found in the Mirage test-bed we built. We have evaluated the MSP system through theoretical analysis, extensive simulation as well as two physical systems (an indoor version with 46 MICAz motes and an outdoor version with 20 MICAz motes). Evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. In fact, it provides a nice tradeoff between physical cost (anchors) and soft cost (events) while maintaining localization accuracy.