How to Ensure Drone Delivery Failure? Cheat at IoT testing!

Primarily used in military operations, drones have been around since as early as 1910s. Realizing the potential of drones in the commercial sector, Federal Aviation Administration (FAA) started releasing permits in 2006 for non-military applications. This window of opportunity opened by FAA has gushed in an overwhelming wave of enthusiasm, innovations, and investments in the drone technology.

With Amazon preparing to employ a drone-delivery system and Uber Eats collaborating with Uber Elevate to facilitate food delivery, we can expect to see unmanned, autonomous drones flying in the skyline in the near future. As flying cars are already being tested, self-driving cars are all set to get on the road, smart cities and smart homes are becoming a reality, we are well on our way to create our very own ‘Orbit City’, straight out of The Jetsons.

If a drone delivery system can be successfully implemented, it will be beneficial in transmitting critical packages such as temperature-dependent medicines to remote or disaster-struck areas. Note that we are not talking about a very distant future. It is happening right now, even as we speak. Many products are under trial, others are being developed, striving to revolutionize the logistics industry as we know it.

A global e-commerce enabling company, UPS has applied to obtain FAA permit to operate its commercial drone-delivery subsidiary known as ‘UPS Flight Forward’. UPS is looking to utilize this system to perform healthcare-related deliveries. Drone delivery system can end the reliance on courier cars while providing a faster, cheaper alternative to carry out time-bound, urgent logistic operations.

Recently, fully-autonomous drone trials were conducted by a consortium involving Direct Relief, Merck, Softbox, AT&T, and Volans-I to deliver cold-chain medicines and vaccines to hard-to-reach locations, crossing over even open water bodies. For ascertaining an effective delivery, the drones were monitored using remote IoT sensors while using continuous temperature tracking and cloud-based, real-time analysis.

In the past, drones were controlled and navigated by a manual operator. Today, with advancements in Artificial Intelligence and IoT, drone operations can become completely autonomous. A fully-autonomous drone would require a working internet connection, GPS system, vehicle-to-vehicle as well as non-collaborative sensor-based sense-and-avoid systems to realize a delivery successfully. The numerous sensors needed for its functioning need to be in complete tandem with each other, relaying the relevant information as and when needed.

The IoT reliance for drone-delivery systems

The need to realize expectations placed on the modern drones mandates capturing and processing of real-time data. In order to achieve such a level of sophistication in the drone delivery systems, a plethora of IoT components involved different sensors and cameras. These components engage in a collaborative relationship where they empower the existence and functioning of each other. For drone deliveries to become the norm, the most critical aspect is guaranteeing a flawless fulfilment of Beyond Visual Line of Sight (BVLOS) operations.

An IoT system of a full-functional, completely autonomous, UAV with computer vision would include two main components – Sensors and wireless connectivity – to facilitate self-navigation, object detection, and collision avoidance.

• Light Detection and Ranging (LIDAR) Sensor: Also used in the autonomous vehicles, LIDAR is used to measure distance by a drone to any object. LIDAR uses laser reflection mechanism to understand how far an object is, thus, maintaining the necessary gap.
• Thermal Imaging Camera: Essentially an extension of the heat detection technology, a thermal imaging camera detects the heat radiated by surrounding objects based on their temperature. This allows drones to form a 3D image of the landscape in which it is operating.
• Hyperspectral sensors: These sensors place the vicinal objects on the electromagnetic spectrum based on the wavelengths on which they interact with the light. The spectral and radiometric accuracy yielded by these sensors inculcate precision to a drone’s flight.
• Photogrammetry: Drone photogrammetry technique creates a roadmap for the UAV for the entire flight based on the images captured. The images are processed and stitched together to layout the route.
• Obstacle detection sensors: The legacy drones are incapable of detecting road obstacles, causing them to collide with them and crash. Obstacle detection sensors utilize any combination of vision, ultrasonic, infrared, ToF to identify obstacles on the path and divert their route.

Complete autonomy of drones for near as well as BVLOS deliveries can become a far-fetched dream if all these components fail to interact with each other optimally. Entrusting the Unmanned Aerial Vehicles (UAVs) or parcelocopter, as the drones used for delivery are commonly called, is a huge gamble. The interconnected web of devices and network have a heavy dependability on the smooth functioning of each element involved in the IoT ecosystem. If even one segment were to go off or to stop working at a moment, the entire drone delivery system can go haywire, causing chaos everywhere – resulting in non-traceable drones, missing couriers, and lost faith in the entire system.

Conclusion

IoT testing is necessary to maintain a seamless operational flow. The drone delivery systems under trial at the moment underwent rigorous testing phases before reaching the current stage. If the manufacturers slacked in performing their part of IoT testing, the drone delivery systems will become a failure before they even reach the market. Or if they do, the subpar performance will cost the drone companies their reputation. These failures will not only cause aversion of customers to that particular company but will also fuel the ongoing skepticism regarding the capability of drone delivery systems.

Cigniti’s experience in IoT app Testing as a Service (TaaS), a team of IoT-skilled testers, and a robust IoT testing infrastructure (– labs, simulators, test racks etc.,) support real-time testing of Big Data, Compatibility, IoT Security, Performance, Pilot, Regulatory, Reliability, Upgrade, Usability, and smart devices in a dynamic environment (RFID, Sensors). Connect with our IoT testing experts today!