Time series anomaly detection (TSAD) is essential for ensur-ing the safety and reliability of intelligent and autonomousvehicles. In edge–cloud systems, this task is challenging dueto limited on-board resources and real-time constraints. Deeplearning (DL) models offer high accuracy but are too compu-tationally demanding for embedded devices, whereas light-weight models are efficient but less precise. To address thistrade-off, we propose a hierarchical cascaded framework forunsupervised multivariate TSAD, consisting of two light-weight Gaussian Mixture Models (GMMs) on the edge anda fully connected Variational Autoencoder (FC-VAE) in thecloud. An adaptive offloading mechanism based on onlineregret minimization dynamically decides when to escalate in-puts, balancing inference cost and detection accuracy. Exper-iments on real-world sensor data from Scania’s autonomousmining trucks show that the proposed method achieves ac-curacy within 1% of a cloud-only FC-VAE while reducingcomputation cost by over 85%. These results demonstratethat cost-aware hierarchical inference enables scalable andefficient real-time anomaly detection in edge-centric intelli-gent transportation systems.

On-device inference offers significant benefits in edge ML systems, such as improved energy efficiency, responsiveness, and privacy, compared to traditional centralized approaches. However, the resource constraints of embedded devices limit their use to simple inference tasks, creating a trade-off between efficiency and capability. In this context, the Hierarchical Inference (HI) system has emerged as a promising solution that augments the capabilities of the local ML by offloading selected samples to an edge server/cloud for remote ML inference. Existing works, primarily based on simulations, demonstrate that HI improves accuracy. However, they fail to account for the latency and energy consumption in real-world deployments, nor do they consider three key heterogeneous components that characterize ML-enabled IoT systems: hardware, network connectivity, and models. To bridge this gap, this paper systematically evaluates HI against standalone on-device inference by analyzing accuracy, latency, and energy trade-offs across five devices and three image classification datasets. Our findings show that, for a given accuracy requirement, the HI approach we designed achieved up to 73% lower latency and up to 77% lower device energy consumption than an on-device inference system. Despite these gains, HI introduces a fixed energy and latency overhead from on-device inference for all samples. To address this, we propose a hybrid system called Early Exit with HI (EE-HI) and demonstrate that, compared to HI, EE-HI reduces the latency up to 59.7% and lowers the device’s energy consumption up to 60.4%. These findings demonstrate the potential of HI and EE-HI to enable more efficient ML in IoT systems.

Finding an optimum trade-off between event detection and network lifetime is a major problem in the sensor-based Internet of Things framework. Further, reliable, effective, and accurate event detection is a perennial research problem explored in the domain of Sensor Based Internet of Things (SBIoT). Major research problems focusing on event detection depend upon models like Boolean and probabilistic sensing models. However, event detection is practically dependent upon the intensity and persistence of the event. The traditional non-intensity-based event sensing models fix a predefined sensing radius. Any occurrence outside the sensing radius is not considered an event, independent of its severity. The present work argues that the intensity and persistence of the event are also relevant parameters for event detection. This paper proposes two novel event intensity and persistence-based models for detecting different types of events and improving upon the quality of detection. The proposed ‘Improved’ model proves to be more efficient than the proposed ‘Conventional’ model. Further, the simulation results indicate the proposed algorithm's efficiency and effectiveness, and compare it with Non-intensity based models. Additionally, the results are compared in terms of detection accuracy, node activation, and network lifetime to show the efficiency and trade-offs of the proposed scheme.

Deep learning (DL) applications have rapidly evolved to address increasingly complex tasks by leveraging large-scale, resource-intensive models. However, deploying such models on low-power devices is not practical or economically scalable. While cloud-centric solutions satisfy these computational demands, they present challenges in terms of communication costs and latencies for real-Time applications when every computation task is offloaded. To mitigate these concerns, hierarchical inference (HI) frameworks have been proposed, enabling edge devices equipped with small ML models to collaborate with edge servers by selectively offloading complex tasks. Existing HI approaches depend on immediate offloading of data upon selection, which can lead to inefficiencies due to frequent communication, especially in time-varying wireless environments. In this work, we introduce Batch HI, an approach that offloads samples in batches, thereby reducing communication overhead and improving system efficiency while achieving similar performance as existing HI methods. Additionally, we find the optimal batch size that attains a crucial balance between responsiveness and system time, tailored to specific user requirements. Numerical results confirm the effectiveness of our approach, highlighting the scenarios where batching is particularly beneficial.