The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delay-sensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT.

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Multimedia Internet of Things: A Comprehensive Survey

Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems according to today's needs. To solve real-world issues, various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on "AI-based Modeling" with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains. 

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AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

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The main vision of the Internet of Things IoT is to enable seamless connection between physical devices and information systems to improve the lives of people. One of the main obstacles to achieve this vision is the current lack of IoT interoperability. In this article, the authors are giving an overview on how semantics is used in IoT interoperability related research. To do this, they performed a systematic literature review and extracted data from 105 selected primary studies dealing with semantics in IoT interoperability. The authors have analysed the maturity level of this research field and when and where the relevant works were published. This article answers five main research questions about the following issues: what are semantics used for; what types of ontologies exist which are used to give semantical descriptions; what are the main themes and suggestions for future work in these research articles; and what are other related areas.

Using Semantic Web for Internet of Things Interoperability: A Systematic Review

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

In this paper, we present a comprehensive methodology to support IoT based semantic traffic monitoring in smart cities to derive the status of traffic on a road segment in order to optimize traffic flow during rush hours. Smart traffic monitoring involves real-time traffic scene analysis from CCTV cameras to detect and identify stalled vehicles and measure traffic density on roads. The scene analysis essentially necessitates tracking and counting the number of moving and stopped vehicles as they progress along the image sequence by utilizing computer vision techniques. The obtained features along with manually annotated traffic data are translated to a higher level information using semantic web technologies, thus providing a common platform to exchange the information. As the perceptual modelling of traffic data involves media patterns so we have employed Multimedia Web Ontology Language (MOWL) for the semantic interpretation and to tackle inherent uncertainties involved in these media documents. MOWL supports hierarchical probabilistic reasoning framework and utilizes time-varying Dynamic Bayesian Networks (DBN) to perform semantic comparison and predict the evolution of changing situations. In this paper, we discuss the key tasks of vision and probabilistic reasoning components that provide a feasible solution to identify the cause of traffic jam. The proposed approach can be used to trigger an automatic warning or alerts to traffic authorities in case any anomalous activity is encountered and guide users to take some different route to avoid jam. The experimental results shows effectiveness of real-time vehicle monitoring to assess congestion on road and offer user an assistive environment to operate.

An ontology-driven context aware framework for smart traffic monitoring

In this paper, we have proposed an ontology-based context-aware framework for providing intelligent services such as smart surveillance, which employ IoT technologies to ensure better quality of life in a smart city. An IoT network such as a smart surveillance system combines the working of Closed-circuit television (CCTV) cameras and various sensors to perform real-time computation for identifying threats and critical situations with the help of valuable context information. This information is perceptual in nature and needs to be converted into higher-level abstractions that can further be used for reasoning to recognize situations. Semantic abstractions for perceptual inputs are possible with the use of a multimedia ontology encoded using Multimedia Web Ontology Language (MOWL) that helps to define concepts, properties and structure of a possible environment. MOWL also allows for a dynamic modeling of real-time situations by employing Dynamic Bayesian networks (DBN), which suits the requirements of a intelligent IoT system. In this paper, we show the application of this framework in a smart surveillance system. Surveillance is enhanced by not only helping to analyze past events, but by predicting dangerous situations for which preventive actions can be taken. In our proposed approach, continuous video stream of data captured by CCTV cameras can be processed on the fly to give real-time alerts to concerned authorities. These alerts can be disseminated using e-mail, text messaging, on-screen alerts and alarms.

An Ontology-based Context-aware IoT Framework for Smart Surveillance

Events in multimedia objects represent landmarks of a story encoded in the media. This paper proposes a new specification for representing Events using MOWL (E-MOWL). Using this approach, higher-level complex events are detected based on the hierarchy of events present in the Event Ontology created using MOWL. The MOWL definition of Spatio-Temporal relations is extended to represent the Context of an event. The Context is any kind of information which gets defined with respect to an event instance wherein Context has its own structure and involves parameters like Time, Geographical location, and Actors/ Entities involved in the event. Such an extension would enhance event detection and facilitate complete semantics, besides providing scope for personalized multimedia information retrieval.

Extending MOWL for Event Representation (E-MOWL)

Forensic watermarking is a tool for secure transmission of forensic questioned documents. The reported cases of fraudulent manipulation of forensic case documents are basic motivation behind the application proposed in this paper. Earlier approaches in forensic watermarking embed watermark onto whole document which is highly susceptible to attack. We have proposed a RROI based forensic watermarking of case documents which basically represents the rectangular region of a document (fingerprint/signature) that needs to be watermarked. In this, DCT and DWT based transform domain watermarking techniques were implemented on RROI and the performance of the two were compared on the basis of PSNR. Moreover, some attacks like scaling and addition of noise have been performed to check the robustness of the watermark.

Nisha Pahal

Papers

An ontology-driven context aware framework for smart traffic monitoring

An Ontology-based Context-aware IoT Framework for Smart Surveillance

Extending MOWL for Event Representation (E-MOWL)

A Novel Approach for Securing Forensic Documents Using Rectangular Region-of-Interest (RROI)

Extending MOWL for Event Representation (E-MOWL).