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.

We demonstrate AHOM, a system that can Actively Observe Hazards via Monitoring Social Media Streams. AHOM proposes an active way to include the human in the loop of hazard information ac-quisition for social media. Different from state of the art, it supports bi-directional interaction between social media data processing system and social media users, which leads to the establishment of deeper and more accurate situational awareness of hazard events. We demonstrate how AHOM utilizes Twitter streams and bi-directional information exchange with social media users for enhanced hazard observation.