Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Efficient Binary scheme for Training Heterogeneous Sensor Actor Networks

International audienceSensor networks are expected to evolve into long-lived, autonomous networked systems whose main mission is to provide in-situ users – called actors – with real-time information in support of specific goals supportive of their mission. The network is populated with a heterogeneous set of tiny sensors. The free sensors alternate between sleep and awake periods, under program control in response to computational and communication needs. The periodic sensors alternate between sleep periods and awake periods of predefined lengths, established at the fabrication time. The architectural model of an actor-centric network used in this work comprises in addition to the tiny sensors a set of mobile actors that organize and manage the sensors in their vicinity. We take the view that the sensors deployed are anonymous and unaware of their geographic location. Importantly, the sensors are not, a priori, organized into a network. It is, indeed, the interaction between the actors and the sensor population that organizes the sensors in a disk around each actor into a short-lived, mission-specific, network that exists for the purpose of serving the actor and that will be disbanded when the interaction terminates. The task of setting up this form of actor-centric network involves a training stage where the sensors acquire dynamic coordinates relative to the actor in their vicinity. The main contribution of this work is to propose an energy- efficient training protocol for actor-centric heterogeneous sensor networks. Our protocol outperforms all know training protocols in the number of sleep/awake transitions per sensor needed by the training process. Specifically, in the presence of $k$ coronas, no sensor will experience more than$\lceil log(k)\rceil$ sleep/awake transitions and awake periods

Twins:Device-free Object Tracking using Passive Tags

Without requiring objects to carry any transceiver, device-free based object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects such as intruders. However, existing device-free solutions mainly use sensors and active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a newly observed phenomenon called critical state caused by interference among passive tags. We contribute to both theory and practice of such phenomenon by presenting a new interference model that perfectly explains this phenomenon and using extensive experiments to validate it. We design a practical Twins based intrusion detection scheme and implement a real prototype with commercial off-the-shelf reader and tags. The results show that Twins is effective in detecting the moving object, with low location error of 0.75m in average