As drone technology advances, the use cases are evolving rapidly across the globe. Drones are supporting the COVID-19 pandemic by delivering test kits and disinfecting outdoor surfaces. They’re improving our response to hurricanes and floods by assessing damage and delivering aid to the most devastated areas. And they’re optimizing the oil and gas industry by inspecting pipelines and detecting leaks.
From retail and logistics to healthcare and energy, drone technology is disrupting a wide variety of industries and innovating old business models. But before we can realize its full potential, there are a few key challenges that must be addressed to solve unmanned traffic management (UTM) in the aviation industry at large:
- Enabling flight transparency: Real-time awareness of all unmanned flights is critical to optimize the airspace and avoid hazards that can put public safety at risk. This requires drone operators to share accurate, up-to-date flights plans with airspace authorities overseeing both manned and unmanned traffic. This becomes increasingly difficult as businesses operate a larger volume of drones to deliver packages, support emergency response, and conduct industrial inspections. We must simplify the process of sharing real-time flight data to enable better traceability and advance unmanned traffic management across the industry.
- Enforcing airspace compliance: Recent drone sightings near airports and critical infrastructure have exposed how drones can put lives at risk and cause major disruptions to operations. Due to rogue drones near the Gatwick Airport, flights were suspended for 30 hours and caused chaos for 140,000 passengers. Oftentimes, these incidents occur when drone operators unintentionally fly too close to an airport and too high in altitude. To avoid future incidents, it’s critical to minimize the potential for human error, particularly in high-risk areas near airports and urban environments.
- Advancing aircraft safety: The safety of our airspace also relies on the health of every drone, air taxi, or other unmanned aircraft in flight. A drone with a malfunctioning propeller or battery failure can unexpectedly interfere with the flight path of an airplane, helicopter, or another drone and put public safety in danger. As more aircraft begin sharing the sky, it’s important to ensure every drone is a healthy, high-performing vehicle.
- Protecting flight data integrity: In the wake of an incident, accurate flight data is critical to analyze the sequence of events and hold drone operators accountable. But authorities need assurances flight logs haven’t been tampered with by the drone operator or a third party. This requires the industry to ensure the integrity of data exchanged between operators, authorities, service suppliers, and other stakeholders.
- Improving industry collaboration: It’s also important to enable a common operating picture across the industry to solve unmanned traffic management. There are still many paper records used in manned aviation that can’t be relied on as the volume of unmanned flights grows. We must eliminate the need for paper documents and open the opportunity for more collaboration with digital records. However, it will be critical to maintain the privacy of confidential data, such as operator details and payload information, so it’s only accessible to authorized parties.
What’s the solution to these unmanned traffic management challenges?
Blockchain technology. In technical terms, blockchain is a distributed ledger of immutable records stored in a decentralized database. Although it sounds complex, this technology is the key to simplify flight transparency and create immutable audit trails.
In SkyGrid’s blockchain instance, each flight log can be stored in real-time and linked to the previous log with cryptography. That means all flight plans and historical drone data is tamper-proof and verifiable. The use of private keys ensures only authorized parties have access to confidential data.
Augmented with smart contracts, blockchain technology can have an even bigger impact in simplifying unmanned traffic management. It can help automate airspace compliance by encoding the rules as mandatory parameters in a flight planning system. And it can improve aircraft safety by requiring regular system checks and ensuring all maintenance needs are resolved.
Check out our latest whitepaper to learn more about blockchain and its ability to solve many of the biggest challenges in unmanned aviation.
From autonomous aerial taxis to cargo vehicles, hardware advancements in the urban air mobility market are underway. Regulatory standards and software platforms are also beginning to take shape. In fact, the FAA estimates 545,000 commercial drones will be in use by the end of 2020. These drones will be performing real commercial tasks – they’ll deliver packages, transport people, conduct industrial inspections and provide emergency assistance.
As more unmanned aircraft begin operating, there are four key technologies that will help ensure the safety and security of our airspace.
Drone Security: Preventing Malicious Activity with AI-Powered Cybersecurity
In the near future, there will be a network of flying computers in the sky. Just like the computer servers we use today, these drones could be hacked if not secured properly, posing dangers when they’re flying above a crowd of people or a busy highway.
And in this emerging environment, new security threats could also take the form of previously unseen, “zero-day” attacks. Traditional anti-malware software, dependent on signatures of known threats, won’t be adequate to detect this unknown malware.
AI-powered cybersecurity will be the key to detecting malicious activity on the edge and preventing it from making its way on to a drone or executing on it. An AI-based approach can learn the DNA of what a malicious file might look like instead of merely relying on an existing threat database. This type of technology can function even when network connectivity is non-existent or impaired and can defend drones against zero-day threats. AI-powered cybersecurity will be key in ensuring public safety by providing an adaptable system that protects against never-before-seen attacks.
Drone Data Integrity: Protecting Vehicle and Flight Data with Distributed Ledger Technologies (DLT)
The use of distributed data storage technologies that implement consensus and trust will also be essential to the urban air mobility market. A distributed ledger of immutable transactions can ensure drone data and flight logs are stored securely and accurately.
DLT, augmented with AI-powered “smart contracts”, which execute safely and under guarantees of performance, can create a verified data source airspace authorities can rely on when auditing drone operations or analyzing an incident. DLTs augmented into a future “Aviation OS” will allow flight logs to be stored securely and privately in real time. Since data can be offboarded from the aircraft rapidly and can’t be overwritten, authorities can determine a sequence of events with 100% certainty. Storing transactions on a shared digital ledger also eliminates the need for paper records and opens the opportunity for collaboration that hasn’t existed in the past. There are still many paper records and documents used in manned aviation that simply can’t be relied upon as we make the transition to a world with millions of autonomous aircraft in the sky.
From an operator’s perspective, digital ledgers can also help ensure all safety standards are being met. For example, if a business wants all drones to receive a system check after 100 hours of flight, they can encode this as a rule implemented by a smart contract that must be resolved with a private key before the drone can fly again.
Drone Maintenance: Managing Maintenance Requests with Predictive AI Analytics
Once businesses begin to scale their drone operations, it will no longer remain realistic for humans to safely monitor and track their performance. Predictive AI analytics will monitor the performance and behavior of drone fleets and return actionable insights. These insights can flag suboptimal operations and forecast vehicle health.
For example, predictive models might determine that a specific drone’s battery, under specific weather and usage patterns, is likely to degrade after flying for 200 hours. When a drone is close to hitting 200 hours, AI can be used to automatically generate a maintenance request for a battery replacement and assign the request to a technician upon landing at a facility. DTL can also ensure the maintenance request is resolved and signed off by a technician’s private key before the drone can operate again.
This approach to predictive maintenance can help alleviate the burden on humans and ensure drones are always safe to fly.
Drone Deconfliction: Avoiding In-flight Hazards with Intelligent Deconfliction
One of the biggest concerns when it comes to large-scale drone deployments is around how these drones will “sense and avoid” other aircraft and potential hazards in the airspace. Reliable “sense and avoid” is crucial to safely enable operations that go beyond visual line of sight. For example, another drone may suddenly enter a drone’s flight path, the wind may pick up unexpectedly or the FAA may issue a notice to airmen (NOTAM) that restricts the current route.
Intelligent deconfliction technology can help solve this challenge by constantly updating a drone’s route to account for new hazards and changes in operating conditions. As with drone maintenance needs, humans alone cannot be relied upon to avoid unexpected obstacles in the airspace.
Artificial intelligence will be necessary to safely sense and avoid new obstacles in-flight, or completely reroute the drone if the new conditions are extreme. This technology must also account for other aircraft to ensure there are no conflicting routes.
Ultimately, the possibilities enabled by urban air mobility will be transformative for industry and society in the not-too-distant future. But first, we must have the right technologies in place to ensure that every flight is a safe flight!
By Amir Husain, CEO & founder of SkyGrid. This article was originally published in Forbes.