Multi-mode Tracking of a Group of Mobile Agents

We consider the problem of tracking a group of mobile nodes with limited available computational and energy resources given noisy RSSI measurements and position estimates from group members. The multilateration solutions are known for energy efficiency. However, these solutions are not directly applicable to dynamic grouping scenarios where neighbourhoods and resource availability may frequently change. Existing algorithms such as cluster-based GPS duty-cycling, individual-based tracking, and multilateration-based tracking can only partially deal with the challenges of dynamic grouping scenarios. To cope with these challenges in an effective manner, we propose a new group-based multi-mode tracking algorithm. The proposed algorithm takes the topological structure of the group as well as the availability of the resources into consideration and decides the best solution at any particular time instance. We consider a clustering approach where a cluster head coordinates the usage of resources among the cluster members. We evaluate the energy-accuracy trade-off of the proposed algorithm for various fixed sampling intervals. The evaluation is based on the 2D position tracks of 40 nodes generated using Reynolds' flocking model. For a given energy budget, the proposed algorithm reduces the mean tracking error by up to $20\%$ in comparison to the existing energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as accurate as the individual-based tracking while using almost half the energy.Comment: Accepted for publication in the 20th international symposium on wireless personal multimedia communications (WPMC-2017

Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components

In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multiple-output (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to the IEEE Transaction on Wireless Communications for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Indoor Cooperative Localization for Ultra Wideband Wireless Sensor Networks

In recent years there has been growing interest in ad-hoc and wireless sensor networks (WSNs) for a variety of indoor applications. Localization information in these networks is an enabling technology and in some applications it is the main sought after parameter. The cooperative localization performance of WSNs is ultimately constrained by the behavior of the utilized ranging technology in dense cluttered indoor environments. Recently, ultra-wideband (UWB) Time-of-Arrival (TOA) based ranging has exhibited potential due to its large bandwidth and high time resolution. However, the performance of its ranging and cooperative localization capabilities in dense indoor multipath environments needs to be further investigated. Of main concern is the high probability of non-line of sight (NLOS) and Direct Path (DP) blockage between sensor nodes, which biases the TOA estimation and degrades the localization performance. In this dissertation, we first present the results of measurement and modeling of UWB TOA-based ranging in different indoor multipath environments. We provide detailed characterization of the spatial behavior of ranging, where we focus on the statistics of the ranging error in the presence and absence of the DP and evaluate the pathloss behavior in the former case which is important for indoor geolocation coverage characterization. Parameters of the ranging error probability distributions and pathloss models are provided for different environments: traditional office, modern office, residential and manufacturing floor; and different ranging scenarios: indoor-to-indoor (ITI), outdoor-to-indoor (OTI) and roof-to-indoor (RTI). Based on the developed empirical models of UWB TOA-based OTI and ITI ranging, we derive and analyze cooperative localization bounds for WSNs in the different indoor multipath environments. First, we highlight the need for cooperative localization in indoor applications. Then we provide comprehensive analysis of the factors affecting localization accuracy such as network and ranging model parameters. Finally we introduce a novel distributed cooperative localization algorithm for indoor WSNs. The Cooperative LOcalization with Quality of estimation (CLOQ) algorithm integrates and disseminates the quality of the TOA ranging and position information in order to improve the localization performance for the entire WSN. The algorithm has the ability to reduce the effects of the cluttered indoor environments by identifying and mitigating the associated ranging errors. In addition the information regarding the integrity of the position estimate is further incorporated in the iterative distributed localization process which further reduces error escalation in the network. The simulation results of CLOQ algorithm are then compared against the derived G-CRLB, which shows substantial improvements in the localization performance

Soft information for localization-of-things

Location awareness is vital for emerging Internetof- Things applications and opens a new era for Localizationof- Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.RYC-2016-1938

Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

Localization Of Sensors In Presence Of Fading And Mobility

The objective of this dissertation is to estimate the location of a sensor through analysis of signal strengths of messages received from a collection of mobile anchors. In particular, a sensor node determines its location from distance measurements to mobile anchors of known locations. We take into account the uncertainty and fluctuation of the RSS as a result of fading and take into account the decay of the RSS which is proportional to the transmitter-receiver distance power raised to the PLE. The objective is to characterize the channel in order to derive accurate distance estimates from RSS measurements and then utilize the distance estimates in locating the sensors. To characterize the channel, two techniques are presented for the mobile anchors to periodically estimate the channel\u27s PLE and fading parameter. Both techniques estimate the PLE by solving an equation via successive approximations. The formula in the first is stated directly from MLE analysis whereas in the second is derived from a simple probability analysis. Then two distance estimates are proposed, one based on a derived formula and the other based on the MLE analysis. Then a location technique is proposed where two anchors are sufficient to uniquely locate a sensor. That is, the sensor narrows down its possible locations to two when collects RSS measurements transmitted by a mobile anchor, then uniquely determines its location when given a distance to the second anchor. Analysis shows the PLE has no effect on the accuracy of the channel characterization, the normalized error in the distance estimation is invariant to the estimated distance, and accurate location estimates can be achieved from a moderate sample of RSS measurements

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future