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

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).

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.

In this paper, we propose a cooperative routing in mobile ad-hoc wireless sensor networks with Rayleigh fading environments. The main features and contributions of the proposed cooperative routing are as follows. First, the construction of stable routing route by using entropy concepts in the view points of mobility of nodes. Second, the cooperative-aided transmission using the stable relay which can be selected by using entropy concepts. Third, we present a theoretical analysis model for the proposed cooperative routing with the outage probability of the end-to-end performance. Fourth, we consider a realistic approach based on mobile nodes as well as fixed nodes in the points of view of the mobile ad-hoc wireless sensor networks. The performance evaluation of our protocol is implemented via simulation and analysis.

The localization of sensor node is one of the key issues for sensor network systems. Therefore, several localization systems to obtain precise location information have been researched. However, they need completely configured space using a large number of anchor nodes of which location are well known, and are not suitable for sensor networks. To avoid the problems, we proposed the node localization method based on Self-Organizing Maps and needs no prepared space with a large number of anchor nodes. But, as in other similar precise localization methods, the proposed method needs advanced distance measurements unavailable in conventional sensor node systems. In this paper, the modification of the self-organizing localization for distance measurement that uses received signal strength available in conventional sensor node systems is described and its location estimation accuracy is shown.

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.

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

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.

In mobile sensor networks, sensor nodes are often required to move in order to complete various tasks, such as environment exploration and data collection. In these applications, the location-awareness information plays an important role. However, it is too expensive to equip each sensor node with special hardware for locating them from time to time. As a result, a distributed range-free localization scheme is very desirable in mobile sensor networks. Unlike previous work about locating mobile sensor nodes, this paper explores the static constraint set caused by the transmission range of both seed nodes and sensor nodes, and introduces the velocity constraint set caused by node movement. Specially, we have distinguished the transmission range of seed nodes and sensor nodes in order to generate strict boundaries. Once the constraint set is obtained, a numerical method is used to solve such seriously non-linear inequalities. The approach proposed in this paper is distributed and re-localizable, and performs well even when all sensor nodes are moving uncontrollably. Furthermore, we analyze the computation complexity of the proposed approach and discuss the communication protocols for seed nodes and sensor nodes respectively. Finally, we demonstrate by simulations that the proposed algorithm performs much better than existing algorithms.

ANR are featured by mobility and communication capabilities as they involve mobile robots and ad-hoc sensor networks enabling ubiquitous applications. This work proposes a low-cost ANR surveillance system for intruder and fire events within a large environment that is divided into operational zones with sensor nodes. Each sensor node comprises of low-cost odor, smoke, infrared fire, and motion detector sensors, radio frequency (RF) transceivers for networking and communication. Events detected within the zones are routed to the robots while suppression systems are activated based on the event criticality. On receiving the event signals from different zones, the robots navigate to these zones to assess the scenario as required. This assessment is then routed to a base station for further processing. Each robot is associated with one operational zone, but is not limited to it, since it performs other environment related functions. The network enables a distributed system with sensor nodes as the primary task distributor, while the robots negotiate and redistribute these tasks. Hence, path planning, and navigation would be executed locally and communicated among the robot team for effective collaboration. This work aims to advance research in the field of ANR through applicational approaches.

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of wireless sensor nodes, a challenging task because of the 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, which work together to increase the 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). This evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. It also provides a nice tradeoff between physical cost (anchors) and soft cost (events), while maintaining localization accuracy.

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.

This paper addresses the problem of the connectivity control and the self-organized deployment/dispersion of a team of mobile ad hoc sensor nodes. First, to reduce redundant communication links while preserving global connectivity, a distributed link removal algorithm is developed that only requires local information of no more than two-hop neighbors. Secondly, for the purpose of preserving essential links while avoiding collisions, a combined piecewise-continuous motion controller is designed to regulate the motion of mobile sensors between two consecutive switches. The proposed hybrid control system can autonomously disperse a team of mobile sensors towards their final configuration with guaranteed connectivity and collision avoidance. Theoretical analysis and computer simulations have confirmed the efficiency and scalability of the proposed schemes.

Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed to simultaneously impute missing range measurements and detect node anomaly in complex environments. Specifically, by utilizing inherent low-rank property of Euclidean Distance Matrix (EDM), we formulate range measurements imputation problem as a Robust -norm Regularized Matrix Decomposition (RRMD) model, where complex noise is fitted by Mixture of Gaussian (MoG) distribution, and node anomaly is sifted by -norm regularization. Meanwhile, an efficient optimization algorithm is designed to solve proposed RRMD model based on Expectation Maximization (EM) method. Furthermore, with the imputed EDM, all unknown nodes can be easily positioned by using Multi-Dimensional Scaling (MDS) method. Finally, some experiments are designed to evaluate performance of the proposed method, and experimental results demonstrate that our method outperforms three state-of-the-art node localization methods.

This paper presents a novel semantic model and a hybrid perceptive frame approach for the integration of sensing, computation, communication and control of reconfigurable mobile sensor networks composed of a collection of mobile robotic sensing nodes. In this paper, the entire mobile sensor network is modelled as a dynamical system, which has sensing, computation, communication and locomotion capabilities. A semantic model of mobile sensor networks is proposed to describe the relationship of different sensor nodes and the corresponding multi-modal sensory information at varying abstraction levels. Based on this model, the hybrid perceptive frame describes the hybrid nature of cooperative sensor networks and integrates sensing, communication and cooperation by perceptive references and perceptive semantics. The hybrid perceptive reference frame approach allows the mobile sensor nodes to respond to the unpredictable environments and coordinate with each other based on communication and the perception of environments.

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.