Abstract Entity Patterns for Sensors and Actuators
Abstract
:1. Introduction
- AEPs for sensors and actuators. These patterns are paradigms for any concrete type of sensor or actuator, from which concrete patterns can be derived.
- Two concrete patterns derived from these AEPs: the Lidar Sensor and the Brake Actuator.
- The structure and behavior of the ESFs for sensors and actuators. These ESFs are the basis for SSFs, which are secure units that can be applied in the design of a variety of CPSs.
- A validation of the functional correctness of these AEPs and their derived patterns.
- A roadmap to derive concrete security patterns for sensors and actuators and build the corresponding SSFs.
2. Background
2.1. Cyber–Physical Systems (CPSs) and Internet of Things (IoT)
2.2. Autonomous Cars
2.3. Patterns
2.4. Security Solution Frames (SSFs)
3. Abstract Entity Patterns for Sensors and Actuators
3.1. Abstract Entity Sensor [7]
3.1.1. Intent
3.1.2. Context
3.1.3. Problem
- The measuring devices must collect physical values from their environment;
- The collected values should be able to be converted to digital data;
- The data collected must be sent accurately to a designated destination or stored;
- The device must have a sufficient amount of resources, such as computational capacity, power supply (battery life), etc.;
- There is a need to collect data from different types and numbers of devices. Complex systems need a variety of measurements;
- Devices used for data collection should not be very expensive, decreasing the cost of the systems that use them.
3.1.4. Solution
Structure
3.1.5. Known Uses
3.1.6. Consequences
- Sensors can collect physical values from their environment;
- Sensors have their own ADCs to convert information into digital data;
- Sensors have Interfaces and data collected by sensors can be sent accurately to a designated destination;
- Sensors have power supplies and may use them only when required. Sensor technologies have increased due to the inexpensive availability of computational resources;
- Various systems/applications use different types and numbers of sensors and collect data; however, sensor fusion technology can combine sensory data from disparate sources.
- Elaborated sensors are more expensive; their use must be justified by need;
- Since sensors may collect any activities around them, their use can raise concerns over the privacy of individuals. This point depends on the type of sensor.
3.2. Abstract Entity Actuator
3.2.1. Intent
3.2.2. Context
3.2.3. Problem
- Command faithfulness: Commands sent by controllers must be faithfully executed;
- Resource availability: There must be enough resources, e.g., batteries, to perform the actions;
- Functional availability: Systems must be available to act when needed;
- Action variety: There must be ways to perform different varieties of actions according to the needs of applications;
- Power heterogeneity: Different types of actions (electric, hydraulic, pneumatic, etc.) require different sources of power, such as electric or hydraulic power.
3.2.4. Solution
Structure
Dynamics
- The controller receives data with instructions for physical actions;
- The controller generates digital commands;
- The controller sends digital commands to the DAC;
- The DAC converts these digital commands into analog commands;
- The DAC sends the analog commands to the actuator;
- The actuator executes the requested physical action.
3.2.5. Known Uses
3.2.6. Consequences
- Actuators include mechanisms to perform physical actions following commands;
- Actuators can be provided with enough resources to perform their work, such as electrical power, fuel, or hydraulic/pneumatic energy;
- Different sources of power can be used in the actuators to perform different types of action. For example, electrically driven actuators may use power from the electricity grid [22];
- It is possible to build actuators appropriate for different types of applications.
- This pattern has the following disadvantage:
- Inexpensive concrete actuators have limited resources and cannot perform elaborate commands; thus, they may not meet design constraints on mass and volume. Advanced actuators may also be costly.
4. Concrete Patterns for Sensors and Actuators
4.1. Lidar Sensor
4.1.1. Intent
4.1.2. Example
4.1.3. Context
4.1.4. Problem
- Accuracy: The measurement must be accurate enough and produced in real-time;
- Cost: The solution must have a reasonable cost;
- Performance: The performance of the sensor should not be limited by poor weather conditions, such as heavy rain, cloud, snow, fog, atmospheric haze, and strong and dazzling light, which can attenuate the signal and negatively affect the detection of objects;
- Complementarity: Different types of sensors should complement the measures of other sensors; otherwise, they would be a waste of money and functions;
- Reliability: Data measured and collected by sensors must be reliable.
4.1.5. Solution
Structure
Dynamics
4.1.6. Implementation
4.1.7. Example Resolved
4.1.8. Known Uses
- Autonomous systems: Honda Motors’ new self-driving Legend luxury sedan uses lidar sensors. Moreover, Waymo and many other autonomous car manufacturers are using lidar. Some car manufacturers, such as Germany’s Daimler, Sweden’s Volvo, the Israel-based Mobileye, and Toyota Motors, have adopted lidar sensors (produced by Luminar Technologies) for their self-driving prototypes [30].
- Lidar sensors are used for Adaptive Cruise Control (ACC), Autonomous Emergency Brake (AEB), Anti-lock Braking System (ABS), etc. In addition, five lidar units produced by the German company Sick AG were used for short-range detection on Stanley, the autonomous car that won the 2005 DARPA Grand Challenge [31].
- Forestry: Lidar sensors are used to measure vegetation height, density, and other characteristics across large areas, such as forests, by capturing information about the landscape. Lidar directly measures vertical forest structure; this direct measurement of canopy heights, sub-canopy topography, and the vertical distribution of intercepted surfaces provides high-resolution maps and much data for forest characterization and management [32]. Aerial lidar was used to map the bush fires in Australia in early-2020 [31].
- Apple products: Lidar sensors are used in the iPhone 12 Pro, Pro Max, and iPad Pro to improve portrait mode photos and background shots in night mode.
- A Doppler lidar system was used in the 2008 Summer Olympics to measure wind fields during the yacht competition [31]
- A robotic Boeing AH-6 (light attack/reconnaissance helicopter) performed a fully autonomous flight in June 2010 using lidar to avoid obstacles [31].
4.1.9. Consequences
- Accuracy: Accuracy is high in lidar sensors because of their high spatial resolution, produced by the small focus diameter of their beam of light and shorter wavelength;
- Cost: The production of lidar sensors in high volume can bring their cost down. Luminar Technologies has developed low-priced lidar sensors priced at $500 to $1000 [30]. Some companies are trying to reduce the cost of lidars even more. Moreover, lidar manufacturing companies are using 905-nm diode lasers, which are inexpensive and can be used with silicon detectors, to bring lidar down costs [33];
- Performance: High-performance lidar sensors produced by Luminar Technologies can detect dark objects, such as debris or a person wearing black clothes [30]. Moreover, the performance of lidar sensors can be improved by combining them with other types of sensors, such as vision-based cameras, radar, etc.;
- Complementarity: All the data collected by lidar and other sensors are sent to a Sensor Fusion Processor to be combined and build a more accurate model;
- Heterogeneity in lidar sensors complicates their integration with other systems. Different types of lidar sensors are produced by different lidar manufacturing companies and there are no strict international protocols that guide the collection and analysis of the data using lidar [36].
- Lidar technology is not accepted by all auto manufacturing companies. For example, Tesla uses camera-based technology instead of lidar sensors.
- Computational requirements for real-time use are high [31].
4.1.10. See Also (Related Patterns)
- A pattern for Sensor Network Architectures describes sensor network configurations [37].
- Sensor Node Design Pattern [38] is a design pattern intended to model the architecture of a wireless sensor node with real-time constraints; it is designed and annotated using the UML/MARTE standard.
- Sensor Node Pattern [39] describes the architecture and dynamics of a Sensor Node that includes a processor, memory, and a radio frequency transceiver.
- This pattern is derived from the Sensor AEP.
4.2. Autonomous Emergency Braking System (AEB)
4.2.1. Intent
4.2.2. Context
4.2.3. Problem
- Accuracy: Systems in autonomous cars must be able to execute commands from Controllers accurately;
- Reliability: Autonomous cars must have reliable systems that perform correctly when needed;
- Resources: There must be enough resources, e.g., energy and computing power, to perform the actions;
- Safety: Systems must be able to stop cars in time with minimal delay;
- Availability: Systems must be available to act all the time in emergencies;
- Autonomy: Systems must be able to operate autonomously based on needs;
- Performance: There must be ways to perform different varieties of actions (slow down, speed up, or stop the systems if necessary) according to the needs of the systems.
4.2.4. Solution
Structure
Dynamics
4.2.5. Implementation
4.2.6. Known Uses
- Tesla Model 3 is designed to determine the distance from a detected object traveling in front of the car and apply the brakes automatically to reduce the speed of the car when there is a possible collision. When the AEB applies brakes, the dashboard displays a visual warning and sounds a chime [46]. The system is also active when the autopilot feature is disengaged.
- Subaru models have an AEB system called EyeSight. This system comprises two cameras mounted above the windscreen to monitor the path ahead and brings the car to a complete stop if it detects any issues [10]. Subaru’s AEB system is integrated with ACC and lane departure warning.
- Mercedes-Benz introduced an early version of AEB in their S-Class 2005 model, which was one of the first cars with AEB. The recent S-class model of Mercedes-Benz has short- and long-range radar, optical cameras, and ultrasonic detectors to detect the closest obstacles [47].
- The AEB system used in Volvo XC40 applies brakes automatically when objects such as cyclists, pedestrians, city traffic, and faster-moving vehicles are detected [48].
- Other examples are Ford F-150, Honda CR-V, Toyota Camry, etc. [49].
4.2.7. Consequences
- Accuracy: The units in the architecture described earlier are able to brake automatically in the presence of objects;
- Reliability: Autonomous braking systems can be built to be reliable by using high-quality materials and some redundancy;
- Resources: It is possible to provide enough resources, e.g., power, to perform the actions because cars generate energy that can be reused;
- Safety: Autonomous braking systems stop cars at a precise time (when the collision is expected) to avoid accidents. They are effective in reducing collisions;
- Availability: AEB systems give alerts and are available to act in emergencies all the time via the appropriate implementation;
- Autonomy: AEB systems operate autonomously and unattended;
- Performance: AEB systems can handle different situations, such as sensor errors, road conditions, obstacles, speed, velocity, position, direction, timing, etc., and slow down or stop cars completely if necessary.
- AEB systems are less effective in the dark, in glare from sunrise and sunset, and in bad weather because sensors may not be able to detect objects efficiently. They also may not be very effective at very high speeds.
- Each car manufacturer has a specific approach to braking system designs and names them differently; therefore, no two braking systems work in the same manner, except for fundamental characteristics. This may make maintenance complex.
4.2.8. See Also (Related Patterns)
- A Pattern for a Secure Actuator Node [50] describes how an actuator node performs securely on commands sent by a controller and communicates the effect of these actions to other nodes or controllers.
- A Real-Time Design Pattern for Actuators in Advanced Driver Assistance Systems [51] defines a design pattern for an action sub-system of an advanced driver assistance system to model the structural and behavioral aspects of the sub-system.
- Design Patterns for Advanced Driver Assistance Systems [52] describes three patterns—namely, (i) Sensing, (ii) Data Processing, and (iii) Action-Taking—to cover design problems related to sensing, processing, and control of sensor data, as well as taking actions for warning and actuation.
- This pattern is derived from the Actuator AEP.
5. Validation of AEPs and Their Derived Patterns
6. An SSF for Autonomous Driving
7. Related Work
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Thapa, B.; Fernandez, E.B.; Cardei, I.; Larrondo-Petrie, M.M. Abstract Entity Patterns for Sensors and Actuators. Computers 2023, 12, 93. https://doi.org/10.3390/computers12050093
Thapa B, Fernandez EB, Cardei I, Larrondo-Petrie MM. Abstract Entity Patterns for Sensors and Actuators. Computers. 2023; 12(5):93. https://doi.org/10.3390/computers12050093
Chicago/Turabian StyleThapa, Bijayita, Eduardo B. Fernandez, Ionut Cardei, and Maria M. Larrondo-Petrie. 2023. "Abstract Entity Patterns for Sensors and Actuators" Computers 12, no. 5: 93. https://doi.org/10.3390/computers12050093