Accurate mobility prediction has become pivotal for a wide range of smart city applications including optimizing electric vehicles (EV) charging management, traffic management, infrastructure planning, etc. However, traditional mobility prediction models face significant challenges including ineffective integration of geographical information, inability to dynamically adapt to changing location popularity, and struggle with long-term dependency. Furthermore, these models are susceptible to catastrophic forgetting, losing previously learned knowledge when exposed to new data.To overcome these challenges, we propose the Lifelong Edge-cloud Adaptive Prediction (LEAP) framework, a fresh approach that integrates lifelong learning into mobility prediction. LEAP improves prediction accuracy and reliability in dynamic real-world environments by fusing a central cloud model for capturing long-term trends with multiple local edge models that process real-time data. LEAP employs Spatially Adaptive LSTM (SA-LSTM) and Temporal Adjustment LSTM (TA-LSTM) to incorporate dynamic spatio-temporal patterns, along with Global Context Operations (GC Ops) to manage long-term dependencies. To prevent catastrophic forgetting, LEAP uses Learning without Forgetting (LwF), enabling on-device continuous learning and adaptation at the edge.Extensive evaluations demonstrate that LEAP surpasses ten state-of-the-art methods, including Scikit-Learn’s LSTM, with average improvements of 2.61x in Recall-5, 2.35x in Recall-10, 2.80x in NDCG-5, and 3.06x in NDCG-10. These results highlight LEAP’s superior effectiveness, accuracy, and adaptability, proving it a worthy choice for dynamic real-world mobility prediction tasks while effectively addressing catastrophic forgetting.

As machine learning moves towards edge deployment, lifelong learning becomes crucial due to evolving data distributions and new tasks. Yet, applying traditional methods to learn from vast, complex IoT data streams poses challenges. These include excessive CPU usage, RAM overflow, prolonged convergence times disrupting device operation, and difficulties in adapting to concept drift. Consequently, models trained on devices struggle to handle frequently changing data, affecting their ability to respond effectively to new inputs.To address these issues, we introduce Poly Instance Lifelong Learning (PILL), an algorithm designed for real-time on-device model training and inference at the edge under lifelong learning settings. PILL is lightweight, operating efficiently on the CPUs of low-power single-board computers (SBCs). It achieves this by partitioning input data into manageable instances, filtering out label noise, and applying early stopping for rapid predictions.PILL was evaluated on three popular low-power SBCs as well as a high-end Windows 10 machine using four datasets of different sizes and features. The results indicate that despite the superior resources of the Windows 10 machine, models trained using PILL on SBCs differ in accuracy by only ±0.05%. Additionally, PILL’s LSTM trains 2.41 - 2.85 times faster than the widely used Scikit-Learn’s LSTM. Additionally, when compared to ten state-of-the-art methods, PILL demonstrated superior performance across key metrics (Precision, Recall, and F1-Score) while minimizing computational overhead, making it an ideal choice for efficient, real-time edge deployment.

Typically a Neural Networks (NN) is trained on data centers using historic datasets, then a C source file (model as a char array) of the trained model is generated and flashed on IoT devices. This standard process impedes the flexibility of billions of deployed ML-powered devices as they cannot learn unseen/fresh data patterns (static intelligence) and are impossible to adapt to dynamic scenarios. Currently, to address this issue, Online Machine Learning (OL) algorithms are deployed on IoT devices that provide devices the ability to locally re-train themselves -continuously updating the last few NN layers using unseen data patterns encountered after deployment.In OL, catastrophic forgetting is common when NNs are trained using non-stationary data distribution. The majority of recent work in the OL domain embraces the implicit assumption that the distribution of local training data is balanced. But the fact is, the sensor data streams in real-world IoT are severely imbalanced and temporally correlated. This paper introduces Imbal-OL, a resource-friendly technique that can be used as an OL plugin to balance the size of classes in a range of data streams. When Imbal-OL processed stream is used for OL, the models can adapt faster to changes in the stream while parallelly preventing catastrophic forgetting. Experimental evaluation of Imbal-OL using CIFAR datasets over ResNet-18 demonstrates its ability to deal with imperfect data streams, as it manages to produce high-quality models even under challenging learning settings.

The concept of ML model aggregation rather than data aggregation has gained much attention as it boosts pre- diction performance while maintaining stability and preserving privacy. In a non-ideal scenario, there are chances for a base model trained on a single device to make independent but complementary errors. To handle such cases, in this paper, we implement and release the code of 8 robust ML model combining methods that achieves reliable prediction results by combining numerous base models (trained on many devices) to form a central model that effectively limits errors, built-in randomness and uncertainties. We extensively test the model combining performance by performing 15 heterogeneous devices and 3 datasets based experiments that exemplifies how a complicated collective intelligence can be derived from numerous elementary intelligence learned by distributed, ubiquitous IoT devices.