Recently, data management and processing for wireless sensor networks (WSNs) has become a topic of active research in several fields of computer science, such as the distributed systems, the database systems, and the data mining. The main aim of deploying the WSNs-based applications is to make the real-time decision which has been proved to be very challenging due to the highly resource-constrained computing, communicating capacities, and huge volume of fast-changed data generated by WSNs. This challenge motivates the research community to explore novel data mining techniques dealing with extracting knowledge from large continuous arriving data from WSNs. Traditional data mining techniques are not directly applicable to WSNs due to the nature of sensor data, their special characteristics, and limitations of the WSNs. This work provides an overview of how traditional data mining algorithms are revised and improved to achieve good performance in a wireless sensor network environment. A comprehensive survey of existing data mining techniques and their multilevel classification scheme is presented. The taxonomy together with the comparative tables can be used as a guideline to select a technique suitable for the application at hand. Based on the limitations of the existing technique, an adaptive data mining framework of WSNs for future research is proposed.

Social media postings are increasingly being used in modern days disaster management. Along with the textual information, the contexts and cues inherent in the images posted on social media play an important role in identifying appropriate emergency responses to a particular disaster. In this paper, we proposed a disaster taxonomy of emergency response and used the same taxonomy with an emergency response pipeline together with deep-learning-based image classification and object identification algorithms to automate the emergency response decision-making process. We used the card sorting method to validate the completeness and correctness of the disaster taxonomy. We also used VGG-16 and You Only Look Once (YOLO) algorithms to analyze disaster-related images and identify disaster types and relevant cues (such as objects that appeared in those images). Furthermore, using decision tables and applied analytic hierarchy processes (AHP), we aligned the intermediate outputs to map a disaster-related image into the disaster taxonomy and determine an appropriate type of emergency response for a given disaster. The proposed approach has been validated using Earthquake, Hurricane, and Typhoon as use cases. The results show that 96% of images were categorized correctly on disaster taxonomy using YOLOv4. The accuracy can be further improved using an incremental training approach. Due to the use of cloud-based deep learning algorithms in image analysis, our approach can potentially be useful to real-time crisis management. The algorithms along with the proposed emergency response pipeline can be further enhanced with other spatiotemporal features extracted from multimedia information posted on social media.

The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart cities due to its great potential for intelligent management of infrastructures and facilities and the enhanced delivery of services in support of sustainable cities. Smart cities typically rely on IoT, where a wide variety of devices communicate with each other and collaborate across heterogeneous and distributed computing environments to provide information and services to urban entities and urbanites. However, leveraging the IoT within software applications raises tremendous challenges, such as data acquisition, device heterogeneity, service management, security and privacy, interoperability, scalability, flexibility, data processing, and visualization. Middleware for IoT has been recognized as the system that can provide the necessary infrastructure of services and has become increasingly important for IoT over the last few years. This study aims to review and synthesize the relevant literature to identify and discuss the core challenges of existing IoT middleware. Furthermore, it augments the information landscape of IoT middleware with big data applications to achieve the required level of services supporting sustainable cities. In doing so, it proposes a novel IoT middleware for smart city applications, namely Generic Middleware for Smart City Applications (GMSCA), which brings together many studies to further capture and invigorate the application demand for sustainable solutions which IoT and big data can offer. The proposed middleware is implemented, and its feasibility is assessed by developing three applications addressing various scenarios. Finally, the GMSCA is tested by conducting load balance and performance tests. The results prove the excellent functioning and usability of the GMSCA.

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