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.

While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-and-error process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities. However, most deep learning-based HAR requires a large amount of labelled data and extracting HAR features from unlabelled data for effective deep learning training remains challenging. We, therefore, introduce a deep semi-supervised HAR approach, MixHAR, which concurrently uses labelled and unlabelled activities. Our MixHAR employs a linear interpolation mechanism to blend labelled and unlabelled activities while addressing both inter- and intra-activity variability. A unique challenge identified is the activity-intrusion problem during mixing, for which we propose a mixing calibration mechanism to mitigate it in the feature embedding space. Additionally, we rigorously explored and evaluated the five conventional/popular deep semi-supervised technologies on HAR, acting as the benchmark of deep semi-supervised HAR. Our results demonstrate that MixHAR significantly improves performance, underscoring the potential of deep semi-supervised techniques in HAR.

Multivariate time series data (Mv-TSD) portray the evolving processes of the system(s) under examination in a “multi-view” manner. Factorization methods are salient for Mv-TSD analysis with the potentials of structural feature construction correlating various data attributes. However, research challenges remain in the derivation of factors due to highly scattered data distribution of Mv-TSD and intensive interferences/outliers embedded in the source data. The proposed Enhanced Bayesian Factorization approach (Enhanced-BF) addresses the challenges in three phases: (1) variant scale partitioning applies to Mv-TSD according to degree of amplitude and obtains the blocks of variant scales; (2) hierarchical Bayesian model for tensor factorization automatically derives the factors of each block with interferences suppressed; (3) Bayesian unification model merges those block factors to construct the final structural features.Enhanced-BFhas been evaluated using a case study of brain data engineering with multivariate electroencephalogram (EEG). Experimental results indicate that the proposed method manifests robustness to the interferences and outperforms the counterparts in terms of operation efficiency and error when factorizing EEG tensor. Besides,Enhanced-BFexcels in factorization-based analysis of ongoing autism spectrum disorder (ASD) EEG: 3 times speed-up in factorization and$87.35\%$accuracy in ASD discrimination. The latent factors (“biomarkers”) can distinctly interpret the typical EEG characteristics of ASD subjects.

We observe significant interest in reinforcement learning methods for real-world sensing-control scenarios driven by the sensor data streams. However, the delay introduced to the data by the communication channels may degrade the system's performance. It is especially crucial in the internet of things (IoT), where devices with constraint resources and low throughput networks are used.

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.

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.

Containerized web-applications have gained popularity recently due to the advantages provided by the containers including light-weight, packaged, fast start up and shut down and easy scalability. As there are more than 267 cloud providers, finding a flexible deployment option for containerized web-applications is very difficult as each cloud offers numerous deployment infrastructure. Benchmarking is one of the eminent options to evaluate the provisioned resources before product-level deployment. However, benchmarking the massive infrastructure resources provisioned by various cloud providers is a time consuming, tedious and costly process and is not practical to accomplish manually.

Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may lead to various challenges for the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for the data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, first the Frobenius norm is applied on high-order-tensors (used for data representation) to minimize the reconstruction error of the reduced tensors. Then, an empirical probability-based control algorithm is designed to estimate an optimal path to forward the reduced data using software-defined networks for minimization of the network load and effective bandwidth utilization. The proposed scheme minimizes the transmission delay incurred during the movement of the dimensionally reduced data between different nodes. The efficacy of the proposed scheme has been evaluated using extensive simulations carried out on the data traces using `R' programming and Matlab. The big data traces considered for evaluation consist of more than two million entries (2,075,259) collected at one minute sampling rate having hetrogenous features such as-voltage, energy, frequency, electric signals, etc. Moreover, a comparative study for different data traces and a real SG testbed is also presented to prove the efficacy of the proposed scheme. The results obtained depict the effectiveness of the proposed scheme with respect to the parameters such asnetwork delay, accuracy, and throughput.

A data analytics flow typically operates on three layers: ingestion, analytics, and storage, each of which is provided by a data-intensive system. These systems are often available as cloud managed services, enabling the users to have pain-free deployment of data analytics flow applications such as click-stream analytics. Despite straightforward orchestration, elasticity management of the flows is challenging. This is due to: a) heterogeneity of workloads and diversity of cloud resources such as queue partitions, compute servers and NoSQL throughputs capacity, b) workload dependencies between the layers, and c) different performance behaviours and resource consumption patterns. In this demonstration, we present Flower, a holistic elasticity management system that exploits advanced optimization and control theory techniques to manage elasticity of complex data analytics flows on clouds. Flower analyzes statistics and data collected from different data-intensive systems to provide the user with a suite of rich functionalities, including: workload dependency analysis, optimal resource share analysis, dynamic resource provisioning, and cross-platform monitoring. We will showcase various features of Flower using a real-world data analytics flow. We will allow the audience to explore Flower by visually defining and configuring a data analytics flow elasticity manager and get hands-on experience with integrated data analytics flow management.

The service delivery model of cloud computing acts as a key enabler for big data analytics applications enhancing productivity, efficiency and reducing costs. The ever increasing flood of data generated from smart phones and sensors such as RFID readers, traffic cams etc require innovative provisioning and QoS monitoring approaches to continuously support big data analytics. To provide essential information for effective and efficient bid data analytics application QoS monitoring, in this paper we propose and develop CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework: (a) performs multi-cloud monitoring, and (b) addresses the issue of cross-layer monitoring of applications. We implement and demonstrate CLAMS functions on real-world multi-cloud platforms such as Amazon and Azure.

Cloud computing is the latest computing paradigm that delivers hardware and software resources as virtualized services in which users are free from the burden of worrying about the low-level system administration details. Migrating Web applications to Cloud services and integrating Cloud services into existing computing infrastructures is non-trivial. It leads to new challenges that often require innovation of paradigms and practices at all levels: technical, cultural, legal, regulatory, and social. The key problem in mapping Web applications to virtualized Cloud services is selecting the best and compatible mix of software images (e.g., Web server image) and infrastructure services to ensure that Quality of Service (QoS) targets of an application are achieved. The fact that, when selecting Cloud services, engineers must consider heterogeneous sets of criteria and complex dependencies between infrastructure services and software images, which are impossible to resolve manually, is a critical issue. To overcome these challenges, we present a framework (called CloudGenius) which automates the decision-making process based on a model and factors specifically for Web server migration to the Cloud. CloudGenius leverages a well known multi-criteria decision making technique, called Analytic Hierarchy Process, to automate the selection process based on a model, factors, and QoS parameters related to an application. An example application demonstrates the applicability of the theoretical CloudGenius approach. Moreover, we present an implementation of CloudGenius that has been validated through experiments.