Datarella and its partners Weaver Labs and OroraTech are proud to announce: Track & Trust has successfully passed the Factory Acceptance Test of the European Space Agency (ESA)! The development of Track & Trust has been successfully completed, undergoing rigorous testing procedures. Now, it is time for Track & Trust to showcase its capabilities and create value in a real-world deployment.
Track & Trust combines modern Web3, communication, and satellite technologies, creating a robust supply chain solution that caters to the actual needs of users. By leveraging these cutting-edge advancements, Track & Trust allows logistics companies and humanitarian organizations to navigate complex scenarios, such as aid supply coordination in regions with compromised or non-existent communications infrastructure. Even in remote and disconnected areas, Track & Trust provides the transparency and authentic data required for efficient operations.
From the very beginning, efforts were made to understand the requirements in the field of logistics precisely. Real logistic processes were examined to identify their weaknesses. We have dedicated ourselves to developing a robust system that ultimately had to meet high stress criteria in the testing procedures required by the ESA, which funded the project with about EUR 1.5 million. We went through a complex development process where partners with three different technologies had to position their core competencies appropriately while integrating them seamlessly to become more than the sum of their parts.
The product – introducing Probabilistic Supply Chain Tracking
Unlike traditional supply chain solutions, Track & Trust offers a probabilistic approach to tracking goods. Any interaction with the shipment, including scanning a Track & Trust QR code, triggers a status update, providing as much information about the shipment as possible! It is ultimately up to the client which information to utilize. Track & Trust’s emphasis on data collection unlocks a world of possibilities for process innovation. By leveraging the data obtained from the operational area, logisticians can complement and expand their services in new and exciting ways. This data-driven approach opens doors to potential future extensions, such as training AI models to predict delivery shrinkage, stock levels, and route recommendations. By harnessing the power of data, Track & Trust generates added value for logistic companies’ customers, offering new competitive advantages in the industry
What lies ahead in the journey of Track & Trust?
We have successfully completed a significant milestone with the Factory Acceptance Test. Concrete processes with our partners are already being planned for the upcoming pilot phase. In the coming weeks, these plans will be further refined, and the delivery of Track & Trust system components will be carried out in the most suitable deployment area where it can achieve the greatest impact and benefit. We will announce the specific deployment scenario in our future blog posts. So, stay tuned!
Our next milestone is the Site Acceptance Test. Here we will conduct another series of tests under real conditions before the system is used for the first time by logisticians and partners.
We are excited to demonstrate the impact and benefits of Track & Trust very soon and to revolutionize the supply chain sector with its technological potential!
Track & Trust is ready to launch soon! Here’s what’s new: Unlike many other supply chain solutions, it offers a probabilistic rather than a deterministic approach to tracking goods. In short, this means the solution captures tons of data that logistics firms are currently missing out on, with Track & Trust – Probabilistic 360° Supply Chain Tracking.
Track & Trust combines a set of modern Web3, communication, and satellite technologies and aims to address significant unsolved challenges of today’s supply chains. Combining various communication systems creates a network for the supply chain even in the most remote areas of the world.
Any kind of shipment information from such areas is highly valuable for all actors along the supply chain, such as logistic firms, their clients, the end consumer, and the operating teams on the ground. One of the current challenges is the accessibility of information in fast-changing and remote areas – an issue the Track & Trust team seeks to facilitate. While other supply chain systems deterministically produce output information by requiring a specific input, Track & Trust does not rely on standardized initial input, minimizing hurdles in gaining knowledge. Our probabilistic approach, in contrast, allows maximum flexibility. Any kind of slightest interaction with the shipment, even if it is only the scanning of a T&T QR code with a smartphone, leads to the submission of a status update. And it doesn’t matter whether logistical staff or anyone else has carried out this scan or someone not related to it.
Additionally, users are free to report on the status of the shipment or their own circumstances, meaning they can communicate through a text field and provide any extra information on the shipment on their terms, e.g. “flooded route. Took a different route, delivery delayed by two hours”. It is ultimately at the discretion of the logistics operators to multiply the effectiveness of their tracking tools to decide how and what data they use.
Leveraging Data to Drive Process Innovation
Data from the operational area is the actual raw material – logisticians can use them to complement or expand their services in a new way. This leaves room for extensions in the future, for example, on a basis of such a broad database, AI models could be trained to ultimately make predictions about the shrinkage of deliveries, stock levels, and route recommendations. Doing so creates added value for logistic companies’ customers and new competitive advantages for the company itself. Thereby, while increasing efficiency and transparency, Track & Trust holds the potential to change the logistics industry. Lastly, we feel grateful that by investing in this project, the European Space Agency underscores the overall potential of our tracking tool.
As things are beginning to take shape, stay tuned for updates on Track & Trust as it is ready for deployment by 2023!
Track & Trust – the satellite-powered supply chain solution by Datarella is coming! Track & Trust combines a set of modern Web3, communication, and satellite technologies and aims to address major unsolved challenges of today’s supply chains. Once completed, it seeks to prove its reliability in the most challenging regions of the world.
The supply chain industry is facing massive changes through technological evolution. Led by Datarella, a consortium with our partners Weaver Labs and OroraTech has realized the opportunity to merge blockchain, network and satellite technologies to create a unique supply chain solution that meets the actual needs of its users. With its investment in Track & Trust, the European Space Agency (ESA) underlines the potential of this unique project. We at Datarella are proud of the progress the project has made since its kick-off in 2021. Track & Trust is currently in its late development phase and is scheduled to be ready for deployment by 2023!
What makes Track & Trust so innovative?
Today’s delivery processes face complex challenges – especially when dealing with crisis scenarios involving aid supplies and when the technical conditions on the ground are poor, but the demands for reliability and accountability are high. Logistics companies and humanitarian organizations, like our partner Aid Pioneers, are then faced with the challenge of coordinating aid supplies in regions where the communications infrastructure has most likely been destroyed. To ensure that goods arrive where they are supposed to, sufficient transparency, especially in these remote and disconnected areas, is required. Ultimately, it is not only a matter of transmitting information about the aid supplies but also of ensuring authentic undistorted data that reflects the actual facts on the ground.
What impact can Track & Trust have?
With the help of satellite technology and network technology, we intend to span the Internet over those separate areas that have no or insufficient Internet connection due to the crisis. In other words, we are putting a lot of effort into tracking deliveries right down to the last mile, which is unique in this way. Emerging from such a challenging demonstration, Track & Trust ultimately proves its suitability for any commercial application. Track & Trust holds the potential to change the logistics industry by increasing efficiency, transparency, and security.
So, it stays exciting! Stay tuned for updates on Track & Trust!
Datarella is proud to announce the signing of a piloting agreement with Aid Pioneers e.V. (Aid Pioneers) to further work on a shipment with the space-linked supply chain product Track and Trust (T&T). Track and Trust is funded by the European Space Agency as a 2-year Demonstration Project. In this regard, Datarella, as prime contractor, together with its partners Weaver Labs and OroraTech, recently successfully completed a significant milestone with ESA.
The consortium led by Datarella is currently working on a Blockchain-based enterprise solution to tackle complex supply chain challenges that humanitarian agencies across the world face in order to track aid in locations that lack access to reliable communications infrastructure. At the end of two years of development and commercial trials, Track & Trust aims to deliver a scalable cost-efficient communications platform & network combining satellite, IoT mesh and blockchain components serving mostly supply chain use cases.
Aid Pioneers agreed to join the Track and Trust initiative as a shipment partner. They will initiate and organize an aid supply for the demonstration of the Track and Trust service. The first shipment is planned for 2023 with the goal to support people in Ukraine. Aid Pioneers is a German association joining other NGOs to build logistics to foreign countries helping people in need.
“We are very proud to have Aid Pioneers on board as our piloting partner for Track and Trust. Aid Pioneers is a young but well-established aid organization active for aid shipments to various regions including Africa, Middle-East including Balkan and Ukraine. With Aid Pioneers as our shipment partner, we have a strong setup to make a success story out of Track and Trust.” Yukitaka Nezu, Co-Founder and CFO at Datarella.
We are very excited to have a piloting agreement with Aid Pioneers and are very much looking forward to a successful shipment with them, leveraging the integration of blockchain, space and network infrastructure technology into our product Track & Trust.
As an expression of our recent progress, we can also announce: Datarella, together with its partners, successfully passed the Critical Design Review (CDR) as one of the significant milestones with ESA. Our design for Track & Trust has been assessed as valid by ESA. We are looking forward to entering the development phase next, together with our partners, in order to start piloting soon!
The German Environment Agency (UBA) commissioned the Frankfurt School Blockchain Center, Capgemini and Datarella to elaborate a feasibility study on the use of DLT in today’s emissions trading registries. Datarella is happy to announce its successful completion.
The aim was to evaluate whether the DLT is suited to efficiently represent the current European emissions trading system (EU ETS). The insights generated by the project partners will support policymakers in informed decision making with regards to the question of whether to remain with a traditional architecture or with a central relational database. For this purpose, the technical concept of an emissions trading DLT and the consideration of the efficiency and sustainability of an emissions trading DLT were explored. As a result, we were able to demonstrate how Emission Allowances can be represented on the blockchain. Hereby, the scope of an emissions trading system can be significantly expanded without extensively altering users’ current user experience .
As a key benefit, our DLT solution offers a robust infrastructure that is secure against manipulation attempts. Additionally, with DLT, well-known advantages such as transparency and traceability can be established in emission trading systems. Our concept also provides sustainable and energy-saving operability of a DLT-based emissions trading system.
The Frankfurt School Blockchain Center, as coordinator of the project, has contributed dedicated scientific blockchain expertise. Capgemini provided a rich set of expertise in carbon trading, registries, and private blockchain implementation. Datarella as a Web3 solution provider contributed comprehensive implementation expertise of enterprise blockchain solutions.
The result of the feasibility study was highly satisfactory for the client. In the future, the result will be presented to a larger circle of interested stakeholders.
In this blog post, we look at the potential of service automation through autonomous economic agents in Blockchain-based systems. Datarella’s partner Fetch.ai has made it their mission to combine intelligent agents with blockchain technology in several use cases. Deep Parking is one of them, and using a specific example from MOBIX here you can get a feel for the potential of autonomous agents.
The path to the fourth industrial age is being paved by the interplay of Big Data-driven automation, robotics, IoT and Distributed Ledger Technologies, aka Blockchain. Given the increasing amount of data, and the number of digital services that go hand in hand with this progress, the need to automate them is also growing. Users should be relieved of unnecessary work and offered optimal results.
Agents take on the role of autonomously performing tasks on behalf of their clients (individuals or objects). For this purpose, they can also interact with each other. Intelligent agents can make complex decisions by using ML, i.e. AI-powered algorithms, based on large amounts of data.
A blockchain, with its data supply, offers a particularly favourable environment for intelligent agents. The data of a blockchain are permanently available and are logically related to each other. Decentralisation can offer robustness (no single point of failure) and lower transaction costs. Agents can assume a fully autonomous identity on a blockchain through private keys. They can use it to authenticate themselves and communicate their suitability for certain tasks. Agents can use shared protocols (possibly through smart contracts) to coordinate, collaborate efficiently, e.g. by distributing complex tasks among themselves. They can negotiate and make distributed decisions (even though voting processes use their blockchain). Tasks, goals or motives of agents can be recorded in the blockchain and economic incentives can be set for optimal task performance.
With regard to the IoT, a blockchain (as a single point of truth) can integrate various sub-systems, s.a. smart household appliances, smart buildings, smart districts and smart cities, and create added value for all agents participating in the network. Fetch.ai is an example of how intelligent agents can realize automated services based on blockchain technology.
Among the use cases of Fetch.ai, we would like to highlight agents for mobility services – traffic sign agents, parking agents for Deep Parking, agents for eMobility, agents for trains and stations that could even form a decentralised train network. In the process, increasingly intelligent autonomous agents interact on behalf of people or infrastructure, searching for each other, negotiating with each other in the interest of offering their users optimal solutions. In such a case, an autonomous agent of a car could, on behalf of its owner, seek and negotiate with agents working on behalf of parking lots to navigate the car and its owner to a quick and cheap place to park. With Deep Parking at the IAA in Munich 2021, the potential of agents for such use cases becomes clear.
There it was demonstrated how agents negotiate their resources on behalf of vehicles, their owners and the infrastructure to find an optimal solution for everyone without further efforts for the users. The following graphics show an excerpt from the exemplary communication between the agents involved.
In this case, a user named Jane is looking for available parking space in the city centre. Without Jane having to do this herself, the agent in her car (My Agent (Car)) looks for another agent who offers a parking space via a specific (agent-)network (SOEF). Using Blockchain technology, agents handle authentication, price negotiation, reservation and even payment, autonomously according to their client’s preferences. When Jane approaches the parking lot, access is automatically granted to her car without further ado.
As shown, the scope of tasks autonomous agents can perform and the added value they can contribute is without limits. So, by using autonomous agents, the potential of Blockchain technology can be leveraged for all use cases where handling of huge amounts of data in real-time or near-time is needed.
A bug bounty program is used to inspect protocol code and rewards inspectors if bugs are found successfully. Code and product quality can be increased significantly by such swarm intelligence. Therefore, MOBIX stands on a solid foundation as it leverages the Fetch.ai blockchain.
Even the best developers make mistakes. In order to gradually eliminate resulting bugs, a good solution is to motivate numerous competent inspectors to search through protocol code and identify weak spots in the code. Such vulnerabilities may be lucrative for blackhat hackers, so it is important to create appropriate incentives for whitehat inspectors to work as thoroughly as possible. Considering the follow-up costs that programing errors can result in, this can often be a very sensible investment.
Bug bounty programs are open to the public for this purpose, in order to acquire as many technically skilled inspectors as possible for a bug hunt. So-called “Full Disclosure” documentation discloses the program bugs completely publicly, while in the “Responsible Disclosure” model, only the originator is informed about the bugs for a limited time to have enough time to solve the problem. Responsible Disclosure is usually utilized when bug concerns a severe vulnerability to a live system which has not yet been exploited by attackers. One such case was the Zcash Counterfeiting bug discovered by the Electric Coin Co. in 2019.
Our partner, Fetch.ai launched a bug bounty program which ran from mid-2019 until the recent migration to the mainnet, which took place on 20 September 2021. There was a public call to inspect the code on Fetch.ai‘s Github ledger repository and report bugs as a Github issue, ranging from critical to low risk level. Depending on the severity of the bug, a reward of up to $10,000 in FET was available.
We mention this because our latest project, MOBIX is deployed to the Fetch.ai blockchain. In essence we’re able to leverage both the Cosmos SDK and Fetch.ai as a foundation for MOBIX. Due to the bug bounties run by Fetch and by the Interchain Foundation to assure code quality the chances of any kind of problem is significantly minimized.
Earlier this week, together with our partners at fetch.ai we released a driver walkthrough video that lets you come along for the ride during the M-Zone Field trials. For the first time, Datarella and Fetch.ai have field-tested an AI-powered Deep Parking solution installed at the Connex building complex in Munich. M-Zone provides automated incentives for efficient smart parking in metropolitan areas. It cuts C02 emissions by providing drivers with real-time options for parking and nudging them with tokenized incentives to park when and where demand is lowest without wasting time or energy driving in circles looking for a spot. Lots of people have asked for more details on how the system works so we decided to draft a technical deep-dive post explain how we built the system and what’s next for M-Zone.
First, let’s dive into a description of the hardware we used to make the M-Zone Smart Parking field trials possible. After that, we’ll delve into the software components and architecture as well as taking a look into how the fetch.ai agents interact with one another and what those interactions mean for cities, parking infrastructure providers, drivers, and the environment.
Edge Nodes
Two edge nodes powered up and scanning for plates. The display between them displays images captured during testing.
For the M-Zone field trials, we deployed two edge computers running computer vision software and fetch.ai autonomous economic agents to the Connex buildings at Frankfurter Ring 81 and 15. These edge computers have two main jobs. The first job is to monitor incoming and outgoing traffic and to read the license plates on incoming vehicles. The second job of the edge nodes is to keep track of the fill state of the parking lot and to publish data about available parking to their associated coordinator agent which is in turn registered on fetch.ai’s Simple Open Economic Framework.
Hardware
Compute: Raspberry Pi 4 Model B
Power: Uninterruptible Power Supply (Pi hat) and 1000 mAH Battery Pack
Connectivity: 4G router with OpenWrt
Cooling: Heat Sink with GPIO Risers and fans
Optional Video Output: 7” Display attached to casing with magnets
Enclosures: Waterproof aluminum boxes that have been modified for cable and camera routing as well as the addition of a plexiglass window for better LTE connectivity
Various USB A, C, and Micro HDMI cables for routing power and video
The hardware used in the field trials is based on cheap and ubiquitous raspberry pi computers and is intended to provide a plug and play upgrade to “dumb” parking infrastructure. Deployment is as simple as mounting the waterproof enclosures in a position where they have a good view of the entrances and exits of the parking garage allowing them to compute the fill level of the lot by observing the comings and goings of autos on a constant basis. Our computer vision solution uses the OpenALPR libraries to accomplish plate detection, edge recognition, binarization, deskewing, character segmentation, and finally optical character recognition to read out the license plates. This enables the nodes to authenticate autos on-the-fly. Future versions will contain a few hardware upgrade options include ruggedized custom enclosures and an improved embedded connectivity solution. The current version performed admirably and passed the field trial with flying colors.
Software Architecture
The real secret sauce doesn’t really come from the hardware though but rather through the software. One of the most critical design choices we made with M-Zone is to host all the cloud-based portions of the system using a Kubernetes cluster for orchestration. This design choice allows the Postgres database and our swagger API to be deployed in a distributed fashion, running on multiple pods within the cluster. This has multiple long term advantages.
It provides options for redundancy at the data state and application layers across multiple nodes located in multiple geographies and using multiple centralized and decentralized cloud/storage options simultaneously. Currently, our K8 cluster is hosted on an AWS EC2 instance but it could be hosted simultaneously across a number of infrastructures in the future. Another key benefit of this approach is the built-in ability to do auto-scaling the database to match load and available resources within the cluster. Building out the system for the M-Zone field trials would have been a lot easier if we had used a less elaborate traditional architecture without orchestration but we believe the investment will really pay off especially regarding the deployment of the IoT nodes. In our field trial, we only had to manage two parking agent nodes but we plan to scale the system and open source it so that such systems can be scaled to cover entire cities. At that scale, it becomes really critical to have an industrial orchestration system that allows you to deploy devices as fast as you can flash SD cards and then never touch them again. Our use of Kubernetes means that we can push updates to the nodes anytime we need to via an “over the air” 4G connection eliminating the need to interact with nodes physically once they’re deployed.
Your micro incentives and real-time parking lot states visualized.
Parking Agents: The parking agent is responsible for the edge processing. Each one runs as a fetch.aiautonomous economic agent inside a docker container running on a Kubernetes pod which is registered as part of the same cluster. The Parking Agents are responsible for identifying the autos that enter and exit the lot by their license plate and matching those plates against the accounts of registered drivers in a privacy-preserving manner. All the image data remains at the edge to eliminate any possibility for centralized malfeasance and prevents data siloization by design. You can check out our privacy design concept for M-Zone here.
Coordinator Agent: The coordinator registers as a service on fetch.ai’s search and discovery mechanism for autonomous economic agents (SOEF). This allows for the parking agents to find the coordinator. The agent also has responsibility for dynamically calculating the incentive payments due to the registered vehicles and executing these payments on the fetch.ai V2 Testnet. It also has the responsibility of periodically converting the issued V2 Testnet micro incentives into FET and sending settlement transactions out to wallet holders.
Settlement Wallet: We built a custom version of our XSC Smart Wallet to handle the receipt of FET settlement transactions.
Fetch V2 Testnet CLI Wallet & Account Visualization Web App: In order to handle the micro incentives on the Fetch V2 testnet we utilized a CLI wallet and visualized inputs from both our API and from the current wallet state to provide drivers with a real-time view of which parking lots have the most space and provide the best incentives.
Dashboard: Just for fun we also built a dashboard that provides an overview of the overall system. This is connected directly to the Postgres Database hosted within the cluster.
API: A swagger API provides the information consumed by the Account Visualization Web App.
High-Level Sequence Diagram
In the above sequence diagram, you can see that the Parking Agent edge nodes continually look for new images provided by our Kubernetes cluster. Next, they search for available agents on fetch.ai’s Open Economic Forum (OEF), the search and discovery mechanism for autonomous economic agents. The OEF returns the Coordinator Agent address to the Parking Agents after which the Parking Agents are able to register themselves with the coordinator. At that point, these agents start scanning for license plates. When they recognize the entry or exit of vehicles, they send a parking event to the Coordinator Agent which acts on the information by updating parking availability broadcast to the wallet via API and also dynamically adjusting the reward ratios and sending the rewards as needed (micro incentives & settlement transactions).
Parking Agent Skills
The parking agents are responsible for all the edge processing. License plate recognition is handled by the third-party library Open Alpr Upon agent instantiation. After the agent starts it performs an OEF search to locate the coordinator node. Once the agent has successfully located the coordinator agent, the agent sends an event packet to the coordinator agent. There are 2 types of events that parking agents can currently trigger.
Parking events: This event type provides information to link the entrance or exit of autos in the parking agent field of view to timestamps and map those autos to registered user addresses on the fetch blockchain.
Agent Update: This event type contains a status update from the parking agent. Within its main act function, the agent checks the most recent image saved from the raspberry camera. Any license plates are temporarily stored within the agent memory and deleted following processing. The size of the license plate detected relevant to the frame is also temporarily stored on the edge. Based on whether this frame size is increasing between snapshots or decreasing we can calculate whether a car is entering or exiting the garage. On each iteration, this memory bank is compared with the current image and if the agent detects a new plate, it sends an event update to the coordinator.
Coordinator Agent Skills
The coordinator registers as a service on the OEF which allows for the parking agents to find the coordinator. The agent also has responsibility for calculating the incentive payments due to the registered vehicles. Payments are incrementally made using the Fetchai v2 ledger due to low transaction costs and speed of settlement. Settlement payments are then periodically aggregated at a pre-determined interval to be batched and sent. Through this process, drivers receive payments of FET that are directly driven by their recent driving and parking behavior.
What’s the Economic Theory at Work?
Neoclassical economic models make a great of assumptions that often don’t hold up in the real world. Particularly under conditions of information asymmetry and in areas where public goods and externalities are present, “perfect competition” usually breaks down and inefficient markets are the outcome. This is what we currently observe in the parking market and it’s a big part of the reason why parking in cities is so annoying.
Public goods are defined as goods that are both non-excludable and non-rivalrous. Externalities are costs or benefits that are imposed on a third party who did not agree to incur that cost or benefit as part of an economic transaction. Market-based economies struggle to contain negative externalities like pollution and struggle to allocate public goods such as physical infrastructure because the assumptions of “perfect competition” don’t hold in the real world and markets don’t lead to efficient outcomes in the presence of these real-world issues.
At the risk of glossing over too much economic detail, essentially, in order for anything close to an efficient market for parking to exist, we need much better information. The M-Zone parking liquidity protocol is at its core a machine for improving market information levels and providing market participants on both the demand and supply sides of the parking equation with appropriate nudges to incentivize market participants toward more efficient market outcomes. The result is less CO2 emissions, better utilization of existing parking infrastructure, more efficient permitting processes and less time spent driving in circles looking for parking.
What’s Next for M-Zone Technically?
We’re currently in the process of defining the roadmap for building out M-Zone. The exact steps aren’t yet locked-in but there are some major topical areas that we can say are on the agenda.
Self Sovereign Identity-based Authentication
Payment gateways
Reservation pathways
Multichain search and discovery
Hardware “in the car”
More strategies for mobile agents and wallets
Improved UI and driver registration processes
Stay tuned over the next few months. There’s much more to come!
We’ve all been there. It seems like every time you go downtown you end up stuck in traffic and then have to drive in circles for ten minutes searching blindly for a parking spot. Even if you have one of the “digital” parking apps you can only park in a limited number of “in-network” spots. We think we’ve got a solution for this mess. In the video, above you’ll ride along with a real driver during one of our field tests leveraging fetch.ai autonomous economic agents and AI-enabled smart parking garages. Further down in this article we’ll examine the environmental, social, and technical aspects of our “M-Zone Parking Liquidity Protocol” approach to solving the parking riddle in cities.
Currently, Parking is Like Flying Half Empty Planes
Today, the vast majority of parking spaces in cities are locked up in various forms of reserved parking. Much of this capacity is reserved 100% of the time regardless of whether it is needed which leads to parking spaces sitting unoccupied mere meters away from where demand for parking is very high. High demand leads to more parking infrastructure being built. This in turn causes massive CO2 emissions for the building materials required (namely cement which requires 900 kg of CO2 per ton to produce). Cement is the source of about 8% of the world’s carbon dioxide (CO2) emissions. In addition drivers Just in Germany, drivers spend an average of 41 hours a year searching for the elusive parking spot at a cost of €896 per driver in wasted time, fuel, and emissions and the country as a whole €40.4 billion. One of our basic assumptions is that if parking infrastructure must be built it should be used as intensively and efficiently as possible to prevent additional unnecessary infrastructure from being constructed. For this to be possible we need intelligent parking systems that provide the correct incentives and nearly perfect information about usage without sacrificing privacy.
Most people wouldn’t compare parking infrastructure to airplanes but it’s actually a relatively good comparison. We all know that aviation is a major contributor to C02 emissions and airlines make every effort to ensure that every flight is as full as possible including “codesharing” where two airlines sell tickets on the same plane to ensure the flight doesn’t fly empty. They also use dynamic pricing to alter customers’ demand curves for particular flights at a particular time and price. What we’re proposing is analogous in the world of parking. Currently, the world of parking could be compared to flying all the planes half empty all the time and adding more capacity constantly despite increasing costs and environmental impact.
In this context, we can define waste as being any time that parking spaces are empty despite there being demand for those spots. Our Parking Liquidity Protocol allows us to recycle already existing capacity to meet current and future expected demand for parking instead of building new parking infrastructure and capacity.
Bringing the Vision of a Parking Liquidity Protocol to Life
Parking lots need to become aware of their full state and become able to communicate their fill state to users directly over a mobile wallet app AND to automatically incentivize these users to drive and park less by rewarding behaviors that are more sustainable. This vision led us to leverage the fetch.ai blockchain. The fetch blockchain includes “autonomous economic agents” which are essentially AI-powered programs that make economic decisions on behalf of users or machines and then execute economic transactions without human intervention on the blockchain. In partnership with the fetch.ai team, we conceived and built a number of edge computing devices with integrated uninterruptable power supplies, 4G modems for connectivity, and high-resolution cameras that can be deployed quickly and easily at parking garage entrances and exits.
Here we’re preparing the Autonomous Economic Agents for deployment on-site at the Connex buildings.
These edge computing devices (raspberry pi – based) are running computer vision algorithms that allow them to identify license plates on incoming and outgoing vehicles and to calculate how full the parking lot itself is. They are networked together with one another and with a “coordinator” agent which aggregates the information from daughter nodes and determines dynamically which micro-incentives should be sent to any individual driver at any one time. We’ve also built a web app that allows drivers to see the fill status of the lots how much their earned micro incentives, reward rate, and how much this earning rate will be reduced by parking in a particular lot at a particular time. Not parking at all is rewarded most but parking where and when parking demand is low also gets some rewards. Last but not least there is a settlement layer that sums up the micro-incentives that a driver has earned through parking less and parking more efficiently and makes payments in FET tokens to the driver wallet. These tokens are tradeable on the open market and are directly exchangeable for Euros or USD. It goes without saying that privacy by design is at the core of our system architechture.
Critically, these edge nodes are managed by a Kubernetes-based container orchestration system which allows us to do over-the-air updates to the hardware without retrieving it from the field. This greatly increases the scalability of our system because it allows us to install the hardware which provides intelligence to the parking garages once and never touch it again unless physical maintenance is required.
A two-node system has been field-tested successfully at the Connex building complex in Munich. These buildings are owned by Datarella Partner Hammer AG with whom we ready partnered to execute one of the first regulatory-compliant real estate tokenization projects last year (ConnexCoin). The money for the driver micro-incentives comes from the savings of both commercial real estate developers like Hammer AG and their tenants. Now with our system, they have the means to share parking capacity across nearby buildings. Hammer AG alone has 5 buildings on the same street in Munich within the Connex complex so it’s really realistic to encourage drivers to distribute parking load across the neighborhood and walk a few minutes further to reach their end destination.
What’s next?
We’ve got a lot on our plate for the next months. We’re looking to build on the success of the field trials to augment the parking liquidity protocol with a bunch of new components. We’re working on integrating a self-sovereign identity framework to beef up the privacy of our authentication methods. Parallel to this, we’re building out the user interfaces and onboarding processes working with our partners to expand the M-Zone parking liquidity protocol for payment and reservation. On top of that, we’re designing an open protocol tech stack to enable the search and discovery of parking lot ID’s and states in a chain agnostic manner. Keep an eye out for a technical deep dive in the coming days where we’ll get into the nitty-gritty of how the system works!
