An FPGA implementation of an adaptive data reduction technique for wireless sensor networks

Wireless sensor networking (WSN) is an emerging technology that has a wide range of potential applications including environment monitoring, surveillance, medical systems, and robotic exploration. These networks consist of large numbers of distributed nodes that organize themselves into a multihop wireless network. Each node is equipped with one or more sensors, embedded processors, and low- power radios, and is normally battery operated. Reporting constant measurement updates incurs high communication costs for each individual node, resulting in a significant communication overhead and energy consumption. A solution to reduce power requirements is to select, among all data produced by the sensor network, a subset of sensor readings that is relayed to the user such that the original observation data can be reconstructed within some user-defined accuracy. This paper describes the implementation of an adaptive data reduction algorithm for WSN, on a Xilinx Spartan-3E FPGA. A feasibility study is carried out to determine the benefits of this solution.peer-reviewe

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

Dynamic Geospatial Spectrum Modelling: Taxonomy, Options and Consequences

Much of the research in Dynamic Spectrum Access (DSA) has focused on opportunistic access in the temporal domain. While this has been quite useful in establishing the technical feasibility of DSA systems, it has missed large sections of the overall DSA problem space. In this paper, we argue that the spatio-temporal operating context of specific environments matters to the selection of the appropriate technology for learning context information. We identify twelve potential operating environments and compare four context awareness approaches (on-board sensing, databases, sensor networks, and cooperative sharing) for these environments. Since our point of view is overall system cost and efficiency, this analysis has utility for those regulators whose objectives are reducing system costs and enhancing system efficiency. We conclude that regulators should pay attention to the operating environment of DSA systems when determining which approaches to context learning to encourage