The internet of tags: energy-harvesting adaptive algorithms

In this thesis, we will focus on the design and performance evaluation of networking algorithms for energy-harvesting tags. We will build upon our recent work on developing Energy-Harvesting Active Networked Tags (EnHANTS) [3– 5]. In this capacity, we have taken a bottom-up approach and integrated ultra-low power Ultra-Wideband ImpulseRadio (UWB-IR) transceivers with energy harvesting circuitry. In this thesis, we will take a top-down approach and develop energy harvesting adaptive algorithms to support the Internet of Tags (IoTags). We believe that IoTags will be a key component of the Internet of Things (IoT). In the near future, objects equipped with heterogeneous devices such as sensors, actuators, and tags, will be able to interact with each other and cooperate to achieve common goals. The IoT has been gaining increased attention from academia and industry [1], with applications in healthcare, smart buildings, assisted living, manufacturing, supply chain management, and intelligent transportation. Small, flexible, and energetically self-reliant, IoTags will be attached to objects that are traditionally not networked, such as books, furniture, walls, doors, toys, produce, and clothing. In their capacity as active tags, IoTags will provide infrastructure for novel tracking applications. We have already taken steps toward developing IoTags in our ongoing EnHANTs project. Our goal is to build upon our ongoing research to design and evaluate energyharvesting adaptive algorithms to enable IoTag networks.