Abstract
With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms). Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.
Highlights
Internet of Things (IoT) is becoming ubiquitous that connects a million devices such as sensors, actuators, gateways, and hubs over the Internet [1]
single-layer feedforward neural network (SLFN)-earliest hyper period first (EHF) task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods
The challenge that arises in real-time is (i) Identification of task criticality and selection of best resources for execution is still lagging, which degrades the performance at runtime. We addressed this issue by developing an intellectual learning neural network to predict the best hardware cluster for each autonomous vehicle workload on the heterogeneous MPSoC (HMPSoC) platform
Summary
IoT is becoming ubiquitous that connects a million devices such as sensors, actuators, gateways, and hubs over the Internet [1]. Self-driving cars are the primary example for the real-time embedded IoT system, which includes multiple hardware sensors, actuators are connected through the IoT for processing each function. The core components of autonomous vehicles are sensors, actuators, and processors to handle multiple functions based on the external environment conditions This requires an efficient hardware platform and software kernel to achieve high performance and secure driving. Homogeneous multiprocessor chip includes a similar category of CPUs on the same chip, and heterogeneous MPSoC (HMPSoC) comprises of distinct category such as CPUs, GPUs, AI processors on the same system-on-chip [7] These asymmetric multiprocessors are energy-efficient and perform multi-tasking with low power consumption. Many real-time embedded IoT applications adopt this energy-efficient MPSoC as a hardware configuration to improve the overall performance with low-power consumption. The output accuracy is enhanced that compared to conventional algorithms for the specified inputs [12]
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