As noted by the venture capital firm Andreessen Horowitz, AI and Web3 are converging technologies that leverage decentralized networks, self-sovereign identities (SSI), autonomous agents, and machine learning. These innovations enable new business models and streamline business processes, particularly in the realm of decentralized mobility solutions. A crucial success factor lies in integrating these technologies with existing business processes. Collaboration between innovative AI and Web3 startups and established industry leaders is essential for driving impact and fostering innovation.

At IAA Mobility, AI and Web3 startups collaborate with industry players and universities in the moveID consortium. They are showcasing a state-of-the-art smart city solution called MOBIX Park & Charge. To meet the project’s goals, positioning innovative cutting-edge solutions is vital. These solutions leverage data to redefine the technological landscape, heralding a data-driven revolution. The synergistic trio of Fetch.ai, a long-time partner of Datarella, along with peaq and Ocean Protocol as moveID partners, is making significant strides forward. This initiative is further supported by solution providers and integrators like Datarella, DeltaDAO, 51Nodes, and university teams from htw saar and Zeppelin University.

Fetch.ai: Bridging AI and Blockchain

Datarella partner Fetch.ai seamlessly combines artificial intelligence (AI) with blockchain technology, unlocking new possibilities across various sectors. These possibilities include autonomous machine economies, smart cities, and efficient resource management. The Fetch.ai platform provides a decentralized infrastructure for Microagents capable of executing tasks autonomously.

In the context of MOBIX Park & Charge, Microagents act as bridges between different software components on edge devices. This setup enables autonomous control over various systems, such as traffic lights.

Microagents in Action

Fetch.ai Microagents serve as multifunctional intermediaries. They communicate and initiate processes in external systems, handling complex tasks like:

• Managing crypto wallets
• Ensuring secure payment transfers
• Verifying events
• Controlling access to hardware such as gates, traffic lights, and chargers

In essence, Fetch.ai Microagents automate tasks on behalf of human clients, streamlining processes and enhancing efficiency. These functionalities contribute significantly to the development of decentralized mobility solutions.

peaq: Empowering the Economy of Things

As automation surges, the peaq network emerges as a key player in sharing its benefits with everyone. It fosters the Economy of Things by establishing a crucial layer-one blockchain infrastructure for decentralized mobility applications.

Peaq’s technology stack and economic incentives enable the development of applications for machines in this emerging economy. For instance, it powers electric vehicle (EV) charging applications. The peaq token fuels the entire ecosystem, facilitating digital identities for machines and enabling seamless payments.

In MOBIX Park & Charge, peaq allows communication with charging points, using peaq tokens to initiate and pay for EV charging processes.

Ocean Protocol: A New Data Economy

Ocean Protocol aims to unleash its potential for individuals and organizations alike. It empowers secure data sharing (Compute-to-Data), selling, and monetization while ensuring control and privacy. Users retain the power to determine who accesses their data and how it is used.

This establishes a novel paradigm in the data economy, especially for AI models in the mobility sector. In MOBIX Park & Charge, Ocean Protocol integrates its decentralized data marketplace technology. This facilitates sovereign and privacy-preserving data exchange within mobility applications.

Key Components of Ocean Protocol

Key components of Ocean’s data pricing mechanisms include Compute-to-Data and a Gaia-X-compliant Self-Sovereign Identity (SSI) approach. Additionally, Ocean solution provider DeltaDAO collaborates to implement the Ocean Tech Stack. In future versions of MOBIX Park & Charge, the goal is to simplify data sales for IoT devices. These devices will automatically sell data directly on the Ocean Marketplace or through Compute to Data. Fetch.ai agents possess built-in intelligence, allowing IoT devices in cars to decide when and how to post data to the Ocean Marketplace based on traffic situations.

Conclusion: Paving the Way for a Data-Driven Future

The versatile infrastructure of peaq, the automated decision-making capabilities of Fetch.ai’s Microagents, and Ocean Protocol’s sovereign data exchange mechanism synergistically converge to facilitate MOBIX Park & Charge. As we stride toward a data-driven future, these technologies pave the way for decentralized mobility solutions that could profoundly reshape mobility.

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Introduction to Ocean Protocol’s decentralized data marketplace

The Ocean Protocol is a decentralized data exchange protocol that aims to unlock the value of data and enable secure and privacy-preserving on-chain data sharing and monetization ⛓️. It provides a framework for building data-centric applications that facilitate the exchange of data assets while ensuring data privacy, security, and compliance.

The Ocean Protocol marketplace is the heart of the entire Ocean Protocol ecosystem. These data sets can be bought and sold along with access to computation on those data. What Uniswap did for Finance, Ocean Protocol is doing for data. Ocean goes beyond just merely trading data in the clear though and makes it possible to order computation on data you never see and get actionable results while maintaining data privacy and secrecy. That in many ways is the holy grail of data privacy and security!

The Ocean Protocol approach to data markets is already gaining substantial traction in industries through the MoveID consortium and GAIA-X where Datarella is a partner. Specifically, there are already industry focused GAIA-X compliant Marketplaces utilizing Ocean protocol already released in the wild (see Pontus-X)

Fetch.ai Agents + Ocean Protocol: A Perfect Match

Switching gears, you may have also heard of Fetch.ai. It provides a next-generation protocol enabling a digital world where Autonomous Economic Agents and Microagents can perform proactive economic activity. AEAs have some unique attributes. Fetch Agents (AEAs) can find resources on their own, they have private keys so they can transact autonomously, and, more importantly, they can decide to execute business logic autonomously based on their programmed skills and behaviors. AEAs are perfect for delegating on-chain tasks that you need to make sure actually happen technically but don’t want to have to handle manually. A great example of this is the stop loss agents that Fetch.ai has released in order to allow people to provision liquidity on DeFi protocols autonomously. Another real-life use case is the kind of behavior that we demonstrated with MOBIX at the International Automobile Show (IAA) in 2021.

In that context, we demonstrated how an Autonomous Economic Agent, running on an edge device and accessible through a specialized version of the MOBIX app, could enable privacy-preserving ad workflows. That sounds super complicated, but its utility is really simple.

Smart Recommendations without Data-Lake-Honeypots

Consumers have come to expect really smart recommendations in their apps based on their current context and previous actions. We’ve come to take this for granted everywhere. It extends from the autocomplete suggestions in your search field to Netflix recommendations to mobility recommendations embedded in scooter rental apps. There’s a shadow side of this intelligence though. Simply put, in order to offer that kind of intelligence someone is invariably building a vast data lake full of your user data to power those convenient algorithms. We all know that this kind of data accumulation has massive privacy ramifications, not to mention the security risks associated with those giant data-lake-honeypots. By connecting Fetch.ai Autonomous Economic Agents with Ocean Compute to Data workflows and the Ocean Marketplace we are laying the cornerstone for enabling that kind context aware convenience without sacrificing any user privacy.

Moreover, outside of the consumer sector in the world of IoT devices, this code makes it really convenient for any number of machines to automatically sell their data either directly on the Ocean Marketplace or via Compute to Data! AEAs have a certain amount of built-in intelligence. This enables IoT devices from cars to weather stations to decide, based on their situation, when and how to post the data to the Ocean Marketplace for sale. Additionally, it allows consuming devices to autonomously search out and purchase the data that they need. Together, this approach provides the basis for autonomous adaptive machine learning without having to own your own ample data sets.

Privacy-Preserving Prediction Engines

TLDR: By enabling Fetch Agents to post data to Ocean Marketplace Compute to Data recommendation engines can be driven without violating privacy.

Datarella has joined forces with Fetch.ai and Ocean protocol over the last few months to build an Ocean Connection Agent to take private data available to itself, store it confidentially, and then spin up a fixed-rate exchange on the Ocean Market or with compute to data enabled as shown in the previous figure. You can imagine that in the near future, this might be your current location data or data about your preferences or searches performed locally within an app (the MOBIX app for example). This data never leaves the sovereign control of the user and the user doesn’t have to worry about handling or organizing or authorizing the data because the AEA does that on behalf of the user well, autonomously! The same applies to companies running AEAs on IoT devices too. You might be asking now why this is even beneficial. It’s useful because it solves the problems associated with those vast data lakes that are driving the recommendation engines behind all sorts of “smart” services.

The Solution for Automating Private Business Intelligence

Ocean Marketplace is an open-source decentralized market that you can fork to develop your own marketplace. It comes equipped with all the features right out of the box. The “Compute to Data” part enables the person, or in this case, AEA is posting the data to stipulate that it cannot be downloaded in the clear – but rather that you’re selling the results of selected algorithms which you or your agent will run (automatically) against the private data on behalf of a purchasing entity (person or AEA). They get the results of the computation – the basis for those context-aware recommendations, but they don’t have the opportunity to abuse the data because they never actually see it despite gaining actionable business intelligence based on the contents of said data. That’s what we mean by “Automating Private Business Intelligence”.

The world we’re building is one where your data remains yours but the results of computations on your data will enable a host of new context-aware smart services that respect your privacy by design but still feel like magic. Bringing the unique properties of the Ocean Marketplace together with Fetch.ai Agents means for the everyday person that it’s increasingly going to be possible that you can have your cake and eat it too. You won’t have to choose between amazing functionality and privacy – you’ll be able to have both – and get paid for it.  Your data – your profits!

Developer Resources

If you’re a developer and want to dive into the code there are two spots you’ll want to check out.  First of all, you’ll find the code open-sourced on github here. Additionally, if you’re already an AEA developer you’ll want to check out the package in the AEA registry. In the near future, we’re also planning to release the Storj AEA but for now, you can take any S3-compatible storage you choose. Let’s start automating private business intelligence generation!

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The demonstration involving Tesla and Jaguar electric vehicles will allow the visitors to experience the moveID’s vision of a vendor-neutral open ecosystem of data-driven mobility services with integrated payment capabilities. The live demo will show how autonomous transactions between connected devices streamline expenses and add new revenue streams.

In preparation for the big event, several moveID consortium members, namely BOSCH, DENSO, ITK Engineering,peaq, htw saar, Ocean, and 51Nodes,  have met for integration workshops in the Datarella offices in Munich. The goal of the first integration workshop was to connect the different software and hardware components provided by the partners and test the first set of workflows for the IAA demo from a user’s perspective. Datarella was entrusted with the planning and organization of the event as well as the integration of the different workflows into a seamless user experience via the MOBIX app.

The critical path that was completed, included three workflows, namely issuance of a Verifiable Credential to a vehicle, access to a parking space via a Verifiable Presentation to a Signal Head, and checkout of a parking space via a Verifiable Presentation to a Signal Head. The workflows are enabled by state-of-the-art Self-Sovereign Identity (SSI), Vehicle-to-Everything (V2X) as well as Fetch.aI micro-agent technology, all controlled via the MOBIX mobile application.

“Leveraging our long-time partner Fetch.ai’s micro-agents to connect different technologies and seeing the distilled result of the agent’s work in our MOBIX app is living proof of the power of the moveID tech stack,” says Datarella CEO Michael Reuter.

While the demonstration focuses on the mobility industry, the business model behind it can be implemented in a wide array of industries, such as Public-to-Private power grid interplay and smart supply chain management. The moveID project invites all stakeholders in the EV charging ecosystem to participate and make their charging solution part of the overall charging network. If you are interested in test drives, please contact our IAA Mobility team!

The post Preparing the MOBIX Park & Charge Experience at IAA Mobility 2023 appeared first on DATARELLA.

Use Cases for the Mobility of the Future

The research project moveID comprises various scenarios of smart traffic management, s.a. parking, charging, or zoning. To create a trusted environment and connect all traffic participants, vehicles, and infrastructure components, s.a. traffic lights, access gates, and parking meters, obtain unique digital identities. All identities are managed as self-sovereign identities, a concept known as SSI, for maximum privacy and compliance with GDPR and the Gaia-X framework.

Datarella will co-create the standards and infrastructure for the future of mobility as part of the Gaia-X 4 Future Mobility moveID project, with an overall size of more than €20 million, by Gaia-X, the European Association for Data and Cloud. The three-year project brings together top industry players to build a sovereign digital infrastructure enabling secure, connected, and open mobility, with Datarella, conducting intense research and development as well as leveraging its expertise in creating successful mobility applications, s.a. Deep Parking, and MOBIX. To make moveID‘s decentralized digital identity infrastructure usable and to develop winning business models on top of it, B2B and B2C applications will be developed.

„With moveID, we will prove that European research projects like Gaia-X can be managed in an agile, dynamic way, and produce significant meaningful results for all mobility participants. Working with industry leaders like Bosch, or Continental on the one hand, and with Web3 projects and research institutes, on the other hand, collaboration is not a simple phrase – it‘s living reality“, says Datarella CEO Michael Reuter.

The Power of Web3

Besides Datarella, other project participants include Bosch, Ocean Protocol, Airbus, Continental, Fetch.ai, Materna, Denso, WOBCOM, ecsec, htw saar, Atos, Chainstep, ZU, peaq, 51nodes,  ITK, DLR, and deltaDAO. The project will feature a strong Web3 component with contributions from leading projects in the space: 

• peaq, the Web3 network powering the Economy of Things, will conduct intense research and development within the project. peaq will further build up its layer-one blockchain in line with the co-created requirements and standards, aiming to grant Gaia-X’s moveID the perfect infrastructure for decentralized mobility applications. peaq will also provide its core functions: peaq ID – Self-Sovereign Machine Identities (SSMIs), peaq access, and peaq pay. peaq is built using Substrate and will soon be live on both Kusama as well as Polkadot. It is working on enabling collaboration and interoperability with the Ethereum and Cosmos ecosystems for other Web3 consortia collaborators in Gaia-X.
• 51nodes, a Web3 developing and integration company brings to the project its experience with SSI architecture and implementation in the context of decentralized mobility infrastructure. In addition to the development and integration of various SSI technologies, the focus of work will be on SSI interoperability challenges.
• Through BigchainDB, Ocean Protocol – the Web3 platform to unlock data services for AI and business innovation – is providing key technical infrastructure for moveID to expedite the development of decentralized digital identity infrastructure for European mobility. Essential components of Ocean’s decentralized data marketplace technology, Compute-to-Data, and data pricing mechanisms are being leveraged to build a system architecture that ensures a seamless exchange of information between providers and customers of mobility applications. 
• deltaDAO, a web3 software development, integration, and consulting company, will co-develop decentralized data infrastructure and federation services in the context of the broader Gaia-X ecosystem. deltaDAO contributes its extensive knowledge regarding Gaia-X compliance, interoperability, and integration

Peter Busch, Product Owner for Distributed Ledger Technologies (Mobility) at Bosch, said: “Web3 technologies offer a promising digital foundation for future mobility solutions. With moveID, we are setting the foundation for a hyper-connected mobility infrastructure by leveraging self-sovereign device identities and decentralized data sharing to enable hundreds of potential use cases. We are thrilled to be exploring this exciting prospect side by side with some of the leading projects in the decentralized space such as peaq, Ocean Protocol, Datarella, 51nodes, and Chainstep.”

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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.

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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.

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The project’s overall goal will be the creation of a decentralized ecosystem of data & services that allows autonomous & networked vehicles to integrate into smart infrastructures and third-party services, following GAIA-X’s design principles. With mutual trust as its core concept, its stated goal will be achieving data sovereignty.

For Datarella, technology is an instrument to increase the quality of living, by supporting human beings in all kinds of professional and private activities. The growing complexity and diversity of our ecosystem require technological infrastructures that facilitate collaboration and cooperation. By planning, developing and implementing blockchain solutions worldwide, Datarella meets this requirement: in the fields of Finance, Supply Chain, and Mobility, we enable industry participants to join forces and create sustainable, crisis-proof business models. GAIA-X is the perfect environment for true collaboration.  – Michael Reuter, CEO of Datarella

Data generated by network actors or Autonomous Economic Agents (AEAs) in the ecosystem can be useful to 3rd parties, and thus has value as an asset. Ocean Market unlocks the value of these by enabling every authenticated user to put their data up for sale and to monetize it. Vehicle data, for example, could be (re)used for traffic analysis for the generation of better traffic models such as optimized traffic light switching.

Different market actors will price data differently, depending on the method of data generation, data quality, and its utility to the potential buyer. Ocean Market allows for dynamic pricing and thus for optimal value generation for data generators and data curators, in a privacy-preserving manner.

This project will use Datarella’s Enterprise Blockchain Solutions, which serve as the foundational, underlying protocol for digital business transformation with converging technologies such as AI and autonomous machines. Datarella’s future mobility solutions are fueled by blockchain technology, real-time decentralized data management, transparency and GDPR compliance. Given the natural synergies between Ocean and Datarella, this partnership is the first of many that will drive enterprise adoption of Ocean Protocol technology in Datarella’s client base. 

Our mission is for data to be treated as an asset. By tapping into underutilized data from mobility IoT sensors, AI can uncover mobility patterns and optimize services / reduce waste. Machines can speak to each other and algorithms can make optimizations – all in real-time. Ocean can tokenize mobility data from devices, thereby creating an opportunity for these tokens to be used as instruments in DeFi projects. This is one of the many ways in which we enable data owners to share in the value their data creates. – Razvan Olteanu, COO of Ocean Protocol

Ocean Protocol is a Day 1 Member of GAIA-X Association AISBL, an international non-profit organisation established to achieve the GAIA-X project goals for the development of an efficient, competitive, secure and trustworthy federation of data infrastructure and service providers for Europe – fostering digital sovereignty of European cloud service users.

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About Ocean Protocol
Ocean Protocol’s mission is to kickstart a Web3 Data Economy that reaches the world, giving power back to data owners and enabling people to capture value from data to better our world. Data is a new asset class; Ocean Protocol unlocks its value. Data owners and consumers use the Ocean Market app to publish, discover, and consume data assets in a secure, privacy-preserving fashion. Ocean datatokens turn data into data assets. This enables data wallets, data exchanges, and data co-ops by leveraging crypto wallets, exchanges, and other DeFi tools. Projects use Ocean libraries and OCEAN in their own apps to help drive the Web3 Data Economy. The Ocean token is used to stake on data, to govern Ocean Protocol’s community funding, and to buy & sell data. Its supply is disbursed over time to drive near-term growth and long-term sustainability. OCEAN is designed to increase with a rise in usage volume.

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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.

Software Components (from Architecture Diagram above)

• Parking Agents: The parking agent is responsible for the edge processing. Each one runs as a fetch.ai autonomous 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!

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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!

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