Towards packet-less ultrasonic sensor networks for energy-harvesting structures

Bluetooth Low Energy (BLE) signals are attractive for indoor positioning due to their low power and ease of deployment. Most existing studies have investigated the BLE Received Signal Strength Indicator (RSSI) from all channels; however, only a few studies have given attention to the separation of BLE signal information in the three different advertisement channels. In this paper, an algorithm is proposed that can characterize the BLE RSSI based on an online path loss exponent (PLE) estimation and correction for human body shadowing in all BLE advertisement channels. Corrected RSSI values are applied to both ranging and fingerprinting methods and compared to uncorrected RSSI values. The implementation results show that RSSI characterization based on online PLE estimation and human body effect in separate advertisement channels can significantly improve the positioning accuracy.

Trilateration With BLE RSSI Accounting for Pathloss Due to Human Obstacles

Passive human sensing approaches based on the analysis of the radio signals emitted by the most common wireless communication technologies have been steadily gaining momentum during the last decade. In this context, the Bluetooth technology, despite its widespread adoption in mobile and IoT applications, so far has not received all the attention it deserves. However, the introduction of the Bluetooth direction finding feature and the application of Artificial Intelligence techniques to the processing and analysis of the wireless signal for passive human sensing pave the way for novel Bluetooth-based passive human sensing applications, which will leverage Bluetooth Low Energy features, such as low power consumption, noise resilience, wide diffusion, and relatively low deployment cost. This paper provides a reasoned analysis of the data preprocessing and classification techniques proposed in the literature on Bluetooth-based remote passive human sensing, which is supported by a comparison of the reported accuracy results. Building on such results, the paper also identifies and discusses the multiple factors and operating conditions that explain the different accuracy values achieved by the considered techniques, and it draws the main research directions for the near future.

/pdf/a-perspective-on-passive-human-sensing-with-bluetooth-24o1i5fg.pdf

A Perspective on Passive Human Sensing with Bluetooth

Queueing systems are used to estimate the attributes of waiting lines, for example the number of people in line or the waiting time. This information helps to reduce the time spent waiting by balancing people amongst multiple lines or aids in the decision making about opening a new line. However, current queueing systems are often mounted at fixed positions, require user participation, or need time consuming manual calibration after each layout change. In this work, we introduce CutiQueue, a flexible and portable queueing system that is battery powered, needs no manual calibration or user participation. CutiQueue is based on passive presence detection using the RSSI of low cost Bluetooth Low Energy transceivers. We analyze the performance of the system in experiments using a prototype implementation to estimate the number of people waiting as well as the length of the line. The experimental evaluation indicates an average accuracy of 97.96% when compared to counting people walking or standing in the waiting line using a laser barrier.

CutiQueue: People Counting in Waiting Lines Using Bluetooth Low Energy Based Passive Presence Detection

To Appear in IEEE International Conference on Robotics and Automation (ICRA), New Orleans, USA, 2004 Autonomous Deployment and Repair of a Sensor Network using an Unmanned Aerial Vehicle P. Corke ∗ , S. Hrabar ‡ R. Peterson † , D. Rus †§ , S. Saripalli ‡ and G. Sukhatme ‡ , CSIRO Manufacturing & Infrastructure Technology, Queensland, Australia peter.corke@csiro.au † Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA Computer Science and Artiﬁcial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA {rapjr,rus}@cs.dartmouth.edu ‡ Center for Robotics and Embedded Systems, University of Southern California, Los Angeles, California, USA {shrabar,srik,gaurav}@robotics.usc.edu Abstract— We describe a sensor network deployment method using autonomous ﬂying robots. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data we collected from ﬁeld trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for a second, repair, pass to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.) I. I NTRODUCTION We investigate the role of mobility in sensor networks. Mobility can be used to deploy sensor networks, to maintain and repair connectivity, and to enable applications such as monitoring and surveillance. We examine sensor networks that consist of static and dynamic nodes. The static sensor nodes are “Motes” and the mobile nodes are autonomous helicopters. Integrating static nodes with mobile robots en- hances the capabilities of both types of devices and enables new applications. Using networking, the sensors can provide the Unmanned Aerial Vehicle (UAV) with information which is out of the range of the robot. Using mobility, the robot can deploy the network, localize the nodes in the network, maintain connectivity by introducing new nodes as needed, and and act as “data mules” to relay information between disconnected wireless clouds. We combine ad-hoc networking, sensing, and control to deploy and use a sensor network. We use an autonomous heli- copter to deploy a sensor network with a controlled topology, for example a star, grid, or random. The helicopter deploys the sensors one at a time at designated locations. Once on the ground, the sensors establish an ad-hoc network and compute their connectivity map in a localized and distributed way. The helicopter is equipped with a sensor node so that it is a mobile component of the sensor network and it can com- municate to the ground. This system can handle on-demand node deployment. The connectivity map is used to determine ground locations that require additional nodes (for example to repair connectivity or to increase bandwidth). The helicopter responds by ﬂying to that location and deploying a new Fig. 1. AVATAR Autonomous Helicopter node, which is incorporated into the existing network. This approach to autonomous deployment of sensor networks will allow the on-demand instrumentation of remote environments that may be inaccessible by ground methods, and supporting applications to navigation, monitoring, and search and rescue. We have implemented the deployment algorithms on a hardware platform that integrates hardware and software from three labs: USC’s helicopter, Dartmouth’s sensor network, and CSIRO’s interface between a helicopter and a sensor network. The three groups conducted joint experiments which demonstrate, for a desired network topology, (1) autonomous sensor network deployment with a helicopter, (2) ground to ground and ground to air connectivity measurements for the resulting network and (3) uses the results to repair the network connectivity when the deployed topology does not match the desired topology. II. R ELATED W ORK Our work builds on important previous work in sensor networks and robotics, and bridges the two communities by integrating autonomous control of ﬂying vehicles with multi- hop message routing in ad-hoc networks. Autonomous aerial vehicles have been an active area of research for several years. Autonomous model helicopters

Autonomous Deployment and Repair of a Sensor Network Using an Unmanned Aerial Vehicle

Knowledge about the presence of persons in a waiting line can help estimating the waiting time or guiding the decision about opening a second line. However, existing presence detection systems for waiting lines are either mounted at fixed positions, take a long time to deploy, need a power connection or require users to carry devices. Past research on the analysis of the Received Signal Strength Indicator (RSSI) of a radio transmission indicates that it can be used to detect the presence of persons. Here, the accuracy of the detection is directly linked to the quality of the radio link. Radio links based on Bluetooth Low Energy (BLE) offer a stable connection, but implement a mandatory frequency hopping scheme, with the information about the current channel typically not accessible. In this work we extend the concept of passive presence detection to work on BLE radio links. We adapt two RSSI based presence detection techniques and evaluate their performance in experiments. The experimental results indicate that it is possible to achieve a 92% accuracy using BLE when compared to the ground truth.

RSSI based passive detection of persons for waiting lines using Bluetooth Low Energy

Wireless sensor networks offer a pragmatic solution for monitoring in a variety of scenarios. For efficient and practical data gathering, especially in large-scale systems deployed in inaccessible areas, unmanned vehicles are becoming a compelling solution. The added infrastructure flexibility comes at the cost of limited contact time between the mobile entity and the stationary devices. The channel fading caused by mobility further decreases the data yield.We address this challenge by analysing the relevant classes of data transfer schemes and identifying adaptation conditions that enable the selection of the best fitting strategy. The result of this analysis, ICELUS, provides an integrated protocol that exploits the available communication resources.

ICELUS: investigating strategy switching for throughput maximization to a mobile sink

http://digital.csic.es/bitstream/10261/156284/1/kassandra.pdf.pdf

KASSANDRA: A framework for distributed simulation of heterogeneous cooperating objects

Radio signals can be used to detect the presence of a person (target) in an environment by analysing the fluctuations in the Received Signal Strength Indicator (RSSI). The velocity of the target can be estimated by examining the sequence of disturbances in consecutive radio links over a period of time. This requires knowledge of the deployment of the radio transceivers and the time when the target crosses the Line of Sight (LoS) of each radio link. However, it is not trivial to precisely estimate the exact time of the link crossing due to the broad range of RSSI fluctuations generated as the target approaches the link. In this paper, we evaluate and compare 15 techniques for estimating the velocity of the target and propose enhancements to some of the techniques. In our experiments the techniques perform with an average accuracy in the range between 13.02% and 96.18%, which corresponds to an average error of 0.05m/s for a moving target.

/pdf/accurate-event-detection-and-velocity-estimation-in-wireless-4yx57fges8.pdf

Falk Brockmann

Papers

CutiQueue: People Counting in Waiting Lines Using Bluetooth Low Energy Based Passive Presence Detection

RSSI based passive detection of persons for waiting lines using Bluetooth Low Energy

ICELUS: investigating strategy switching for throughput maximization to a mobile sink

KASSANDRA: A framework for distributed simulation of heterogeneous cooperating objects

Accurate event detection and velocity estimation in wireless environments