Opportunistic Localization Scheme Based on Linear Matrix Inequality

Enabling self-localization of mobile nodes is an important problem that has been widely studied in the literature. The general conclusions is that an accurate localization requires either sophisticated hardware (GPS, UWB, ultrasounds transceiver) or a dedicated infrastructure (GSM, WLAN). In this paper we tackle the problem from a different and rather new perspective: we investigate how localization performance can be improved by means of a cooperative and opportunistic data exchange among the nodes. We consider a target node, completely unaware of its own position, and a number of mobile nodes with some self-localization capabilities. When the opportunity occurs, the target node can exchange data with in-range mobile nodes. This opportunistic data exchange is then used by the target node to refine its position estimate by using a technique based on Linear Matrix Inequalities and barycentric algorithm. To investigate the performance of such an opportunistic localization algorithm, we define a simple mathematical model that describes the opportunistic interactions and, then, we run several computer simulations for analyzing the effect of the nodes duty-cycle and of the native self-localization error modeling considered. The results show that the opportunistic interactions can actually improve the self-localization accuracy of a strayed node in many different scenarios

Group behavior impact on an opportunistic localization scheme

In this paper we tackled the localization problem from an opportunistic perspective, according to which a node can infer its own spatial position by exchanging data with passing by nodes, called peers. We consider an opportunistic localization algorithm based on the linear matrix inequality (LMI) method coupled with a weighted barycenter algorithm. This scheme has been previously analyzed in scenarios with random deployment of peers, proving its effectiveness. In this paper, we extend the analysis by considering more realistic mobility models for peer nodes. More specifically, we consider two mobility models, namely the Group Random Waypoint Mobility Model and the Group Random Pedestrian Mobility Model, which is an improvement of the first one. Hence, we analyze by simulation the opportunistic localization algorithm for both the models, in order to gain insights on the impact of nodes mobility pattern onto the localization performance. The simulation results show that the mobility model has non-negligible effect on the final localization error, though the performance of the opportunistic localization scheme remains acceptable in all the considered scenarios

An opportunistic indoors positioning scheme based on estimated positions

The localization requirements for mobile nodes in wireless (sensor) networks are increasing. However, most research works are based on range measurements between nodes which are often oversensitive to the measurement error. In this paper we propose a location estimation scheme based on moving nodes that opportunistically exchange known positions. The user couples a linear matrix inequality (LMI) method with a barycenter computation to estimate its position. Simulations have shown that the accuracy of the estimation increases when the number of known positions increases, the radio range decreases and the node speeds increase. The proposed method only depends on a maximum RSS threshold to take into account a known position, which makes it robust and easy to implement. To obtain an accuracy of 1 meter, a user may have to wait at the same position for 5 minutes, with 8 pedestrians moving within range on average

Stochastic Cooperative Decision Approach for Studying the Symmetric Behavior of People in Wireless Indoor Location Systems

[EN] Nowadays, several wireless location systems have been developed in the research world. The goal of these systems has always been to find the greatest accuracy as possible. However, if every node takes data from the environment, we can gather a lot of information, which may help us understand what is happening around our network in a cooperative way. In order to develop this cooperative location and tracking system, we have implemented a sensor network to capture data from user devices. From this captured data we have observed a symmetry behavior in people's movements at a specific site. By using these data and the symmetry feature, we have developed a statistical cooperative approach to predict the new user's location. The system has been tested in a real environment, evaluating the next location predicted by the system and comparing it with the next location in the real track, thus getting satisfactory results. Better results have been obtained when the stochastic cooperative approach uses the transition matrix with symmetry.This work is supported by the "Universitat Politecnica de Valencia" through "PAID-05-12".Tomás Gironés, J.; García Pineda, M.; Canovas Solbes, A.; Lloret, J. (2016). Stochastic Cooperative Decision Approach for Studying the Symmetric Behavior of People in Wireless Indoor Location Systems. Symmetry (Basel). 8(7):1-13. https://doi.org/10.3390/sym8070061S11387Gu, Y., Lo, A., & Niemegeers, I. (2009). A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys & Tutorials, 11(1), 13-32. doi:10.1109/surv.2009.090103Maghdid, H. S., Lami, I. A., Ghafoor, K. Z., & Lloret, J. (2016). Seamless Outdoors-Indoors Localization Solutions on Smartphones. ACM Computing Surveys, 48(4), 1-34. doi:10.1145/2871166Li, F., Zhao, C., Ding, G., Gong, J., Liu, C., & Zhao, F. (2012). 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Collaborative Indoor Positioning Systems: A Systematic Review

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified

Distributed Kalman Filters over Wireless Sensor Networks: Data Fusion, Consensus, and Time-Varying Topologies

Kalman filtering is a widely used recursive algorithm for optimal state estimation of linear stochastic dynamic systems. The recent advances of wireless sensor networks (WSNs) provide the technology to monitor and control physical processes with a high degree of temporal and spatial granularity. Several important problems concerning Kalman filtering over WSNs are addressed in this dissertation. First we study data fusion Kalman filtering for discrete-time linear time-invariant (LTI) systems over WSNs, assuming the existence of a data fusion center that receives observations from distributed sensor nodes and estimates the state of the target system in the presence of data packet drops. We focus on the single sensor node case and show that the critical data arrival rate of the Bernoulli channel can be computed by solving a simple linear matrix inequality problem. Then a more general scenario is considered where multiple sensor nodes are employed. We derive the stationary Kalman filter that minimizes the average error variance under a TCP-like protocol. The stability margin is adopted to tackle the stability issue. Second we study distributed Kalman filtering for LTI systems over WSNs, where each sensor node is required to locally estimate the state in a collaborative manner with its neighbors in the presence of data packet drops. The stationary distributed Kalman filter (DKF) that minimizes the local average error variance is derived. Building on the stationary DKF, we propose Kalman consensus filter for the consensus of different local estimates. The upper bound for the consensus coefficient is computed to ensure the mean square stability of the error dynamics. Finally we focus on time-varying topology. The solution to state consensus control for discrete-time homogeneous multi-agent systems over deterministic time-varying feedback topology is provided, generalizing the existing results. Then we study distributed state estimation over WSNs with time-varying communication topology. Under the uniform observability, each sensor node can closely track the dynamic state by using only its own observation, plus information exchanged with its neighbors, and carrying out local computation

An accurate RSS/AoA-based localization method for internet of underwater things

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors

Range-only SLAM schemes exploiting robot-sensor network cooperation

Simultaneous localization and mapping (SLAM) is a key problem in robotics. A robot with no previous knowledge of the environment builds a map of this environment and localizes itself in that map. Range-only SLAM is a particularization of the SLAM problem which only uses the information provided by range sensors. This PhD Thesis describes the design, integration, evaluation and validation of a set of schemes for accurate and e_cient range-only simultaneous localization and mapping exploiting the cooperation between robots and sensor networks. This PhD Thesis proposes a general architecture for range-only simultaneous localization and mapping (RO-SLAM) with cooperation between robots and sensor networks. The adopted architecture has two main characteristics. First, it exploits the sensing, computational and communication capabilities of sensor network nodes. Both, the robot and the beacons actively participate in the execution of the RO-SLAM _lter. Second, it integrates not only robot-beacon measurements but also range measurements between two di_erent beacons, the so-called inter-beacon measurements. Most reported RO-SLAM methods are executed in a centralized manner in the robot. In these methods all tasks in RO-SLAM are executed in the robot, including measurement gathering, integration of measurements in RO-SLAM and the Prediction stage. These fully centralized RO-SLAM methods require high computational burden in the robot and have very poor scalability. This PhD Thesis proposes three di_erent schemes that works under the aforementioned architecture. These schemes exploit the advantages of cooperation between robots and sensor networks and intend to minimize the drawbacks of this cooperation. The _rst scheme proposed in this PhD Thesis is a RO-SLAM scheme with dynamically con_gurable measurement gathering. Integrating inter-beacon measurements in RO-SLAM signi_cantly improves map estimation but involves high consumption of resources, such as the energy required to gather and transmit measurements, the bandwidth required by the measurement collection protocol and the computational burden necessary to integrate the larger number of measurements. The objective of this scheme is to reduce the increment in resource consumption resulting from the integration of inter-beacon measurements by adopting a centralized mechanism running in the robot that adapts measurement gathering. The second scheme of this PhD Thesis consists in a distributed RO-SLAM scheme based on the Sparse Extended Information Filter (SEIF). This scheme reduces the increment in resource consumption resulting from the integration of inter-beacon measurements by adopting a distributed SLAM _lter in which each beacon is responsible for gathering its measurements to the robot and to other beacons and computing the SLAM Update stage in order to integrate its measurements in SLAM. Moreover, it inherits the scalability of the SEIF. The third scheme of this PhD Thesis is a resource-constrained RO-SLAM scheme based on the distributed SEIF previously presented. This scheme includes the two mechanisms developed in the previous contributions {measurement gathering control and distribution of RO-SLAM Update stage between beacons{ in order to reduce the increment in resource consumption resulting from the integration of inter-beacon measurements. This scheme exploits robot-beacon cooperation to improve SLAM accuracy and e_ciency while meeting a given resource consumption bound. The resource consumption bound is expressed in terms of the maximum number of measurements that can be integrated in SLAM per iteration. The sensing channel capacity used, the beacon energy consumed or the computational capacity employed, among others, are proportional to the number of measurements that are gathered and integrated in SLAM. The performance of the proposed schemes have been analyzed and compared with each other and with existing works. The proposed schemes are validated in real experiments with aerial robots. This PhD Thesis proves that the cooperation between robots and sensor networks provides many advantages to solve the RO-SLAM problem. Resource consumption is an important constraint in sensor networks. The proposed architecture allows the exploitation of the cooperation advantages. On the other hand, the proposed schemes give solutions to the resource limitation without degrading performance

Communication Efficiency in Information Gathering through Dynamic Information Flow

This thesis addresses the problem of how to improve the performance of multi-robot information gathering tasks by actively controlling the rate of communication between robots. Examples of such tasks include cooperative tracking and cooperative environmental monitoring. Communication is essential in such systems for both decentralised data fusion and decision making, but wireless networks impose capacity constraints that are frequently overlooked. While existing research has focussed on improving available communication throughput, the aim in this thesis is to develop algorithms that make more efficient use of the available communication capacity. Since information may be shared at various levels of abstraction, another challenge is the decision of where information should be processed based on limits of the computational resources available. Therefore, the flow of information needs to be controlled based on the trade-off between communication limits, computation limits and information value. In this thesis, we approach the trade-off by introducing the dynamic information flow (DIF) problem. We suggest variants of DIF that either consider data fusion communication independently or both data fusion and decision making communication simultaneously. For the data fusion case, we propose efficient decentralised solutions that dynamically adjust the flow of information. For the decision making case, we present an algorithm for communication efficiency based on local LQ approximations of information gathering problems. The algorithm is then integrated with our solution for the data fusion case to produce a complete communication efficiency solution for information gathering. We analyse our suggested algorithms and present important performance guarantees. The algorithms are validated in a custom-designed decentralised simulation framework and through field-robotic experimental demonstrations

Detection performance and mitigation techniques in CR networks

Pervasive wireless communications rely enormously on spectrum utilization; the increase in demand for new wireless services and their application has led to spectrum scarcity. Spectrum limitations can be resolved by cognitive radio (CR) which is a technology that allows secondary users (SUs) to use the spectrum when it is not occupied by primary users (PUs). In this thesis, the security issues that decrease CR performance are discussed; there are two major threats i.e. primary user emulation attack (PUEA) and spectrum sensing data falsification attack (SSDF). Firstly, the CR network (CRN) is simulated whereby PUs and SUs are presented in the system with the presence of multiple malicious users that are randomly located within a circle of radius (R). The simulation results, based on an analytical model, show that the false alarm probability is significantly affected by the network radius Rand malicious users' number, and it is proved that there is a range of R over which the PUEAs are most successful. Secondly, a transmitter verification scheme (direct scheme) and indirect trust scheme that considers the users' history are presented; the results proved that if the signal to noise ratio (SNR) is raised, correspondingly the t:rnstworthiness of the PU is considerably increased. Based on these two schemes, the trnstworthiness of the PU is much higher than that of the malicious user and because the indirect scheme considers the historical behaviour of the user, it improves the user's trustworthiness.Finally, cooperative spectrum sensing (CSS) approaches are proposed, namely, a trust based approach, a punishment based approach and a dedicated punishment based approach. It is proved that these proposed CSS approaches outperform the traditional majority scheme despite a high number of malicious users. In addition, the dedicated punishment approaches which punish only the malicious users outperform the other approaches