Ambient Internet of Things (A-IoT) Ambient Internet of Things (A-IoT) refers to a class of IoT devices primarily powered by harvesting ambient energy from radio waves, light, motion, heat, or any other viable ambient energy source. Ambient IoT is an evolution of legacy IoT & RFID technologies that promises lower costs & high scalability thru support by global telecommunications standards, including Bluetooth, 5G Advanced, & 802.11bp. en.wikipedia.org/wiki/Ambien… A-IoT aims to connect hundreds of billions of ultra-low-power & battery-free devices, which has been included in the agenda for 6G standardization by 3GPP. A-IoT solutions are composed of several interlocking components that together create an intelligent & autonomous sensing & communication network. A-IoT devices communicate over standard wireless protocols—Bluetooth & cellular—& connect thru the existing network of smartphones, routers, access points, & mobile towers. This eliminates the need for proprietary readers or line-of-sight scanning & allows tags to be read automatically, wherever connectivity exists. wiliot.com/ambient-iot#get-s… Ambient IoT Alliance (AIoTA) The Ambient IoT Alliance (AIoTA) is a cross-industry group dedicated to accelerating the adoption of Ambient IoT. It supports an open, multi-standard ecosystem that connects manufacturers, suppliers, integrators, & users. Founding members include Atmosic, Infineon Technologies AG, Intel, PepsiCo, Qualcomm, VusionGroup, & Wiliot. ambient-iot.org/ The vast data generated by ambient IoT will also enable advanced artificial intelligence (AI) apps. Ambient IoT & AI are symbiotic technologies, each unlocking the promise of the other. uploads.strikinglycdn.com/fi… Bluetooth SIG The Bluetooth Special Interest Group (SIG) develops the core specs that allow Ambient IoT devices to communicate using Bluetooth Low Energy (BLE). bluetooth.com/wp-content/upl… These standards define how data is formatted, transmitted, & received—ensuring that Ambient IoT devices can be read by a wide variety of consumer & commercial Bluetooth-enabled devices. bluetooth.com/blog/the-role-… IEEE 802.11 Ambient Power (AMP) TIG/SG ieee802.org/11/Reports/amp_u… 3GPP In the Release 19 specs, 3GPP initiated a study on AIoT in Service & Systems Aspect working group 1 (SA1) to capture use case, traffic scenarios, service requirements, & KPIs. The results of this study were captured in TR 22.840. Other SA working groups are currently looking into service architecture, security aspects, & the charging aspects of Ambient IoT devices. 3GPP Device To standardize Ambient IoT across global networks, 3GPP classifies devices into types: Type 1 Devices Fully passive, powered exclusively through energy harvesting (e.g., radio frequency). These devices use backscatter communication to broadcast data & are best suited for mass-scale deployment in disposable or single-use apps. 3gpp.org/technologies/rel19-… Type 2a Devices Semi-passive, combining energy harvesting w/ a small energy store (capacitor or battery) for limited extended functionality. These devices support occasional two-way communication or onboard data storage. Type 2b Devices Active tags that use ambient energy for recharge but feature a larger power reserve & higher performance, allowing them to transmit data directly using RF or interact w/ more complex systems. 3gpp.org/technologies/anbien… Tech Report ITU-T YSTR.Ambient IoT itu.int/dms_pub/itu-t/opb/tu… NanoScatter NanoScatter is the first backscatter network for Ambient IoT that uses a custom application-specific integrated circuit (ASIC) on each tag to achieve ultra-low power & long-range communication. A nanowatt wake-up receiver design & a sensitivity-driven downlink/uplink modulation mechanism to carry out the ASIC, which enables minimizing the tag's power consumption & long-range communication. It supports concurrent communication of 6 IC-based tags achieving communication distances of 66m indoors & 100m outdoors. dl.acm.org/doi/10.1145/36802…

The AMBIENT-6G project ENERGY-NEUTRAL IOT DEVICES Such devices exploit ambient energy harvesting (e.g., based on vibration, thermal, photovoltaic, or radio frequency harvesting) or intentional radio frequency wireless power transfer (RF-WPT) to power their operations. This allows energy-neutral devices to achieve decades-long energy autonomy. The project considers the whole spectrum of energy-neutral devices, both powered by rechargeable batteries, as well as battery-less devices that use a (super)capacitor to store a small amount of energy. Moreover, we will study both devices that can actively communicate, as well as passive devices that make use of backscatter technologies. The AMBIENT-6G project targets the secure integration of both active and passive energy-neutral devices in the 6G network architecture and end-to-end cloud-edge-device continuum. To achieve this, the project aims to provide technological innovations on both the device and infrastructure side. The innovations encompass three areas: (i) circuits for energy-neutral devices, (ii) secure Ambient-IoT network protocols, and (iii) cloud-edge-device operations and resource allocation. The project's vision spans across the (i) devices & infrastructure, (ii) the intelligence & applications, and (iii) device operations. Overall objective Design, prototype, validate, and standardize hardware and software technology solutions for intelligent Artificial Intelligence (AI) enabled energy-neutral devices, and the 6G network infrastructure that connects them to the Internet. ambient-6g.eu/about.php Dataset Several (RF) Harvesters This repository presents the evaluation and data collection of multiple energy harvesters, focusing on their performance under representative operating conditions. The measurements aim to provide a comparative overview of the capabilities and limitations of the different harvesting solutions. github.com/AMBIENT-6G/datase… Harvester CSV Export Select one or more harvesters and parameters, then download a CSV export or plot a chart. ambient-6g.github.io/dataset… Modular Energy-Neutral Device (END) Platform A modular, research-oriented hardware and software platform for energy-neutral and backscatter-based IoT systems. This platform is developed to enable rapid prototyping, fair comparison, and reproducible research across sensing, energy harvesting, power management, storage, and RF backscatter communication. The core idea is simple:split an IoT node into clearly defined, subsystems, each with its own lifecycle, power domain, and research focus. Most existing battery-free or energy-neutral IoT platforms lock in key design choices early: 🔸fixed MCU 🔸fixed harvesting IC 🔸fixed RF front-end 🔸limited observability This platform does the opposite. You can swap: 🔸MCUs vs FPGAs 🔸harvesters (RF, solar, vibration, capacitive) 🔸storage (caps, supercaps, batteries) 🔸backscatter architectures (fully digital → IQ-based) The platform consists of a main board and multiple daughter boards connected via standardized 12-pin interfaces inspired by the Digilent PMOD specification. Architecture Each daughter board represents one function only. Typical combinations: 🔸sensing + harvesting + storage 🔸harvesting + power management + RF backscatter 🔸MCU + RF + debug github.com/AMBIENT-6G/END-Pl… Open Hardware/Software github.com/ambient-6g

JCVI-syn3A JCVI-syn3A is a minimized synthetic bacterial cell created by the J. Craig Venter Institute (JCVI) that contains a pared-down genome of 493 genes and 543 kilobase pairs (kbp). jcvi.org/research/first-mini… The JCVI-syn3.0 sequence has been deposited in Genbank under the accession number CP014940. ncbi.nlm.nih.gov/nuccore/CP0… Developed from Mycoplasma mycoides by stripping away non-essential genes and adding back 19 genes from syn3.0 to stabilize cell division and morphology. pmc.ncbi.nlm.nih.gov/article… Scientists Develop “Digital Cell” to Simulate Whole Cell Cycle in 4D, Signaling Era of Genomic Digital Twins A validated 4D whole-cell simulation of the minimal bacterium JCVI-syn3A now reproduces a complete cell cycle in space and time. The result behaves like a working digital twin of a cell, one researchers can interrogate across metabolism, DNA replication, gene expression, and division. A breakthrough establishes a new standard for genomic digital twins, providing a dynamic map of life that was previously theoretical. intelligentliving.co/digital… Bringing the genetically minimal cell to life on a computer in 4D cell.com/cell/fulltext/S0092… MC4D This repository contains the 4D whole-cell model for the genetically minimal cell, JCVI-syn3A. Below, you will find instructions on required programs that need to be installed, instructions on how to run the model, and descriptions of individual simulation files. github.com/Luthey-Schulten-L… National Science Foundation (NSF) From 1 cell to 2, researchers have simulated the entire cell division process A 4D computer simulation accurately shows every single molecule and reaction inside a cell as it divides over the course of about 100 minutes. The study builds on years of experimental results that systematically modeled the essential metabolism and other subcellular networks of a cell. Using this accumulated data, researchers determined the identity, location and function of thousands of molecules inside the cell and created 3D spatial views of the crowded cellular interior. By adding time as a fourth dimension across the 105 minutes it takes for the cell to grow and divide, the team gained unprecedented insights into cellular dynamics. The resulting 4D simulated cell captures how cellular components are made and degraded, how they function and move, and how they partition into daughter cells. nsf.gov/bio/updates/1-cell-2…

Human Identification at a Distance (HumanID) program To develop automated biometric identification technologies to detect, recognize and identify humans at great distances. Methods for fusing biometric technologies into advanced human identification systems will be developed to enable faster, more accurate and unconstrained identification of humans at significant standoff distances. HumanID will determine the critical factors that affect performance of biometric components, and identify the limits of range, accuracy, and reliability. The program will also conduct multi-modal fusion experiments and performance evaluations, and will demonstrate advanced human recognition capabilities in multiple force protection and/or homeland defense environments. web.archive.org/web/20021017…