The Internet of Things (IoT) envisions the interconnection of billions of devices to the Internet to support daily life, infrastructure and humanity in general. By 2025, there will be over 8 connected devices per human in the world, driving automation and powering the data economy. In other words, there will be over 67 billion connected IoT devices in 6 years. Currently, these devices are widely powered by batteries, which wear out after a few years, even if they are rechargeable. Considering an optimistic 2-year battery lifetime, we will need to replace and dispose of over 33 billion dead batteries per year, or 91 million per day. This will require around 3 million people changing IoT device batteries 8 hours per day, every day. Even for a single smart building, equipped with 10 000 sensors, over 3 hours of daily battery maintenance will be required. As the number of connected battery-powered devices grows, this problem will only get worse. This is financially costly in terms of maintenance, ecologically irresponsible, and unsustainable. Moreover, batteries are bulky, dangerous, and sensitive to temperature changes.
IoBaLeT aims to bring the performance (i.e. throughput, scalability and range) of battery-less IoT networks on-par with its battery-powered counterparts, by enabling active rather than passive communications and computing. An end-to-end networking solution for battery-less IoT will be developed that will achieve this goal through inter-device cooperation and cross-layer energy-awareness. IoBaLeT pursues specific 4 scientific objectives:
Accurate energy prediction models to estimate the short-term energy budget of a device, encompassing the interplay between energy storage (e.g., (super)capacitors or hybrid capacitors), harvesting (e.g., voltaic, piezo, electromagnetic) and consumption (e.g., computing, radio, peripherals) processes.
Hardware design (i.e., antennas, rectifiers) and multi-antenna transmission techniques for highly efficient cooperative SWIPT, able to power IoT devices in a 5x5x3m room in both line-of-sight (LoS) and non-LoS conditions. This will be extended to a hybrid harvesting solution, combining SWIPT with solar and vibration energy to achieve more harvested power.
Scalable channel access and routing protocols for multi-hop SWIPT-enabled battery-less IoT networks, able to handle the unpredictable intermittently-powered behaviour of battery-less devices.
Energy-aware task scheduler for intermittent devices that intelligently decides which application and network tasks to execute at which time, considering task deadlines, data freshness, expected energy consumption of interconnected tasks and available and expected harvested energy.
The envisioned IoBaLeT solution is shown in the figure, with the planned innovations highlighted in green. The monitoring and prediction component will be developed in Work Package WP2 and collects energy-related parameters from the device to get an accurate view on the current and near-future energy stored, consumed and generated.
In WP3, we will study the design of novel SWIPT hardware and SWIPT-optimized physical (PHY) layer modulation schemes within the standard constraints to support wireless power transfer (WPT). This will enable the simultaneous transmission of both data and power between devices. SWIPT can be used both for the redistribution of energy among battery-less devices, as well as power transfer from a dedicated (more powerful) transmitter (e.g., a mains-powered AP).
The MAC protocols that optimize the trade-off between data and power transfer will be studied in WP4. In addition, this work package will study the use of wake-up radios (WuRs) to coordinate the wake-up of battery-less receivers, which are not able to listen for incoming transmissions for a long duration, or even in a scheduled manner. This will be extended to multi-hop network protocols, enabling the range between battery-less devices and the access point to be increased. We will consider both a dedicated WuR (not depicted), or the use of the passive SWIPT circuit as a WuR.
Finally, WP5 will investigate energy-aware task scheduling algorithms for intermittent devices, making use of the output of the energy monitoring and prediction component. Based on the predicted available energy, an estimate of the required energy per task and the deadline of each task (flow), the scheduler will decide when to execute each task, maximizing the probability of successful completion of as many task flows as possible. Moreover, WP5 will study the possibility of offloading computations to other battery-less devices or edge cloud servers.