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The Internet of Things (IoT) aims to connect everyday physical objects to the internet. These objects will produce a significant amount of data. The traditional cloud computing architecture aims to process data in the cloud. As a result, a significant amount of data needs to be communicated to the cloud. This creates a number of challenges, such as high communication latency between the devices and the cloud, increased energy consumption of devices during frequent data upload to the cloud, high bandwidth consumption, while making the network busy by sending the data continuously, and less privacy because of less control on the transmitted data to the server. Fog computing has been proposed to counter these weaknesses. Fog computing aims to process data at the edge and substantially eliminate the necessity of sending data to the cloud. However, combining the Service Oriented Architecture (SOA) with the fog computing architecture is still an open challenge. In this paper, we propose to decompose services to create linked-microservices (LMS). Linked-microservices are services that run on multiple nodes but closely linked to their linked-partners. Linked-microservices allow distributing the computation across different computing nodes in the IoT architecture. Using four different types of architectures namely cloud, fog, hybrid and fog+cloud, we explore and demonstrate the effectiveness of service decomposition by applying four experiments to three different type of datasets. Evaluation of the four architectures shows that decomposing services into nodes reduce the data consumption over the network by 10% - 70%. Overall, these results indicate that the importance of decomposing services in the context of fog computing for enhancing the quality of service., Comment: IEEE TRANSACTIONS SUSTAINABLE COMPUTING, 2019

Smart cities are cyber-physical-social systems, where city data from different sources could be collected, processed and analyzed to extract useful knowledge. As the volume of data from the social world is exploding, social media data analysis has become an emerging area in many different disciplines. During crisis events, users may post informative tweets about affected individuals, utility damage or cautions on social media platforms. If such tweets are efficiently and effectively processed and analyzed, city organizations may gain a better situational awareness of the affected citizens and provide better response actions. Advances in deep neural networks have significantly improved the performance in many social media analyzing tasks, e.g., sentiment analysis, fake news detection, crisis data classification, etc. However, deep learning models require a large amount of labeled data for model training, which is impractical to collect, especially at the early stage of a crisis event. To address this limitation, we proposed a BERT-based Adversarial Domain Adaptation model (BERT-ADA) for crisis tweet classification. Our experiments with three real-world crisis datasets demonstrate the advantages of the proposed model over several baselines.

Social media platforms such as Twitter are increasingly used to collect data of all kinds. During natural disasters, users may post text and image data on social media platforms to report information about infrastructure damage, injured people, cautions and warnings. Effective processing and analysing tweets in real time can help city organisations gain situational awareness of the affected citizens and take timely operations. With the advances in deep learning techniques, recent studies have significantly improved the performance in classifying crisis-related tweets. However, deep learning models are vulnerable to adversarial examples, which may be imperceptible to the human, but can lead to model's misclassification. To process multi-modal data as well as improve the robustness of deep learning models, we propose a multi-modal adversarial training method for crisis-related tweets classification in this paper. The evaluation results clearly demonstrate the advantages of the proposed model in improving the robustness of tweet classification.

Rapid urbanization has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy consumption, public safety, and so on. Research on smart cities aims to address these issues with various technologies developed for the Internet of Things. Very recently, the research focus has shifted toward processing of massive amount of data continuously generated within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and health care. Techniques from computational intelligence have been applied to process and analyze such data, and to extract useful knowledge that helps citizens better understand their surroundings and informs city authorities to provide better and more efficient public services. Deep learning, as a relatively new paradigm in computational intelligence, has attracted substantial attention of the research community and demonstrated greater potential over traditional techniques. This paper provides a survey of the latest research on the convergence of deep learning and smart city from two perspectives: while the technique-oriented review pays attention to the popular and extended deep learning models, the application-oriented review emphasises the representative application domains in smart cities. Our study showed that there are still many challenges ahead for this emerging area owing to the complex nature of deep learning and wide coverage of smart city applications. We pointed out a number of future directions related to deep learning efficiency, emergent deep learning paradigms, knowledge fusion and privacy preservation, and hope these would move the relevant research one step further in creating truly distributed intelligence for smart cities.

Mobile sensing techniques have been increasingly deployed in many Internet of Things-based applications because of their cost efficiency, wide coverage, and flexibility. However, these techniques are unreliable in many situations due to noise of different kinds, loss of communication, or insufficient energy. As such, datasets created from mobile sensing scenarios are likely to contain large amount of missing data, which makes further data analysis difficult, inaccurate, or even impossible. We find that the existing estimation models and techniques developed for static sensing do not work well in the mobile sensing scenarios. To address the problem, we propose a spatio-temporal method, which is specifically designed for answering queries in such applications. Experiments on a real-world, incomplete mobile sensing dataset show that the proposed method outperforms the state-of-the-art noticeably in terms of estimation errors. More importantly, the proposed model is tolerant to datasets with extremely high-missing data rates. Training with the proposed model is also efficient, which makes it suitable for deployment on computationally constrained devices and platforms that need to process massive amounts of data in real time.

The Internet of Things (IoT) paradigm connects everyday objects to the Internet and enables a multitude of applications with the real world data collected from those objects. In the city environment, real world data sources include fixed installations of sensor networks by city authorities as well as mobile sources, such as citizens’ smartphones,¬ taxis and buses equipped with sensors. This kind of data varies not only along the temporal but also the spatial axis. For handling such frequently updated, time-stamped and structured data from a large number of heterogeneous sources, this paper presents a data-centric framework that offers a structured substrate for abstracting heterogeneous sensing sources. More importantly, it enables the collection, storage and discovery of observation and measurement data from both static and mobile sensing sources.

The VISIon of service personalization for mobile communication environments entails context sensitive service provisioning. The realization of such customizable smart spaces necessitates acquisition and processing of modality context information from a variety of devices in the ambient environment. The heterogeneity of available device capabilities and description formats brings new challenges for a context reasoning engine that formulates content delivery decisions. Specifically, to ensure interoperability with existing application logic, the enabling components should support semantic queries. Secondly, situations where variously formatted context input may not provide enough information to answer queries, should be intelligently handled. Towards this aim, this paper discusses a context reasoning and query interface component as part of a Service Context Manager (SCM) framework that supports semantic querying and handles incomplete context information through a rule-based mechanism. The validation of the approach is provided by showing the mapping of disparate UAProf and UPnP descriptions into the framework and querying of supported modality services.

The current research on context-aware systems in ubiquitous environments opens a number of interlinked research challenges. On the lowest level of such systems, discovery mechanisms and flexible semantic descriptions of available devices and services form the basis for end-user service personalization. The challenge is to design a description model that leverages implicit semantic information obtained, while being cognizant of resource constraints of a device. To encounter the different device characteristics and personalization challenges, this paper proposes a device and service description approach that provides a high level contextual view of device information. The work has been performed as part of the Personal Distributed Environment concept, also described in the paper. Further, a user-centric view of multiple user interface devices to access services in a heterogeneous and dynamic networked environment has been implemented by extending UPnP device discovery. A comparison with existing state of the art approaches concludes the work.