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.

/pdf/multimedia-internet-of-things-a-comprehensive-survey-2fjhk0x8a8.pdf

Multimedia Internet of Things: A Comprehensive Survey

Internet of Things (IoT) paradigm links physical objects in the real world to cyber world and enables the creation of smart environments and applications. A physical object is the fundamental building block of the IoT, known as a Smart Device , that can monitor the environment. These devices can communicate with each other and have data processing abilities. When deployed, smart devices collect real-time data and publish the gathered data on the Web. The functionality of smart devices can be abstracted as a service and an IoT application can be built by combining the smart devices with these services that help to address challenges of day-to-day activities. The IoT comprises billions of these intelligent communicating devices that generate enormous amount of data, and hence performing analysis on this data is a significant task. Using search techniques, the size and extent of data can be reduced and limited, so that an application can choose just the most important and valuable data items as per its necessities. It is, however, a tedious task to effectively seek and select a proper device and/or its data among a large number of available devices for a specific application. Search techniques are fundamental to IoT and poses various challenges like a large number of devices, dynamic availability, restrictions on resource utilization, real time data in various types and formats, past and historical monitoring. In the recent past, various methods and techniques have been developed by the research community to address these issues. In this paper, we present a review of the state-of-the-art search methods for the IoT, classifying them according to their design principle and search approaches as: IoT data and IoT object-based techniques. Under each classification, we describe the method adopted, their advantages and disadvantages. Finally, we identify and discuss key challenges and future research directions that will allow the next generation search techniques to recognize and respond to user queries and satisfy the information needs of users.

Searching for the IoT Resources: Fundamentals, Requirements, Comprehensive Review, and Future Directions

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. 

/pdf/ai-based-modeling-techniques-applications-and-research-1fa5zqym.pdf

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

/pdf/ai-based-modeling-techniques-applications-and-research-gp3mohvs.pdf

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

This paper exhibits a novel context-aware service framework for IoT based Smart Traffic Management using ontology to regulate smooth traffic flow in smart cities by analyzing real-time traffic environment. The proposed approach makes smarter use of transport networks to achieve objectives related to performance of transport system. This requires efficient traffic planning measures which relate to the actions designed to adjust the demand and capacity of the network in time and space by use of IoT technologies. The adoption of sensors and IoT devices in Smart Traffic System helps to capture the user's preferences and context information which can be in the form of travel time, weather conditions or real-life driving patterns. We have employed multimedia ontology to derive higher level descriptions of traffic conditions and vehicles from perceptual observation of traffic information which provides important grounds for our proposed IoT framework. The multimedia ontology encoded in Multimedia Web Ontology Language(MOWL) helps to define classes, properties, and structure of a possible traffic environment to provide insights across the transportation network. MOWL supports Dynamic Bayesian networks (DBN) to deal with time-series data and uncertainties linked with context observations which fits the definition of an intelligent IoT system. Thus, our proposed smart traffic framework aggregates information corresponding to traffic domain such as traffic videos captured using CCTV cameras and allows automatic prediction of dynamically changing situations which helps to make traffic authorities more responsive. We have illustrated use of our approach by utilizing contextual information, to assess real-time congestion situation on roads thus allowing to visualize planning services. Once the congestion situation is predicted, alternate congestion free routes which are in accordance with the coveted criteria are suggested that can be propagated through text-messages or e-mails to the users.

An IoT approach for context-aware smart traffic management using ontology

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

This paper presents a context-aware ontology driven approach to water resource management in smart cities for providing adequate water supply to the citizens. The appropriate management of water requires exploitation of efficient action plan to review the prevailing causes of water shortage in a geospatial environment. This involves analysis of historical and real-time water specific information captured through heterogeneous sensors. Since the gathered contextual data is available in different formats so interoperability across diverse data requires converting it into a common perceivable RDF format. As the perceptual model of the Smart Water domain comprises of observable media properties of the concepts so to achieve context-aware data fusion we have employed multimedia ontology based semantic mapping. The multimedia ontology encoded in Multimedia Web Ontology Language (MOWL) forms the core of our IoT based smart water application. It supports Dynamic Bayesian Network based probabilistic reasoning to predict the changing situations in a real-time irregular environment patterns. Ultimately, the paper presents a context-aware approach to deal with uncertainties in water resource in the face of environment variability and offer timely conveyance to water authorities by circulating warnings via text-messages or emails. To illustrate the usability of the presented approach we have utilized the online available sample water data-sets.

Smart Water Management: An Ontology-Driven Context-Aware IoT Application

Sentiment analysis is concerned with extracting sentiment to ascertain the attitudes, and emotions associated with the text. It is broadly applied to voice of the customer as they convey their experience and feelings more blatantly which is why comprehending customer’s emotions is a must. To identify and classify these unstructured emotions natural language processing (NLP) and machine learning approaches have been adopted in recent times. The main issue with the existing techniques is the inability to deal with the correct interpretation of context owing to lack of labeled data. In this paper, we studied deep neural network based language models to interpret and classify textual sequences into positive, negative or neutral emotions which remove the bottleneck of explicit human labeling. These models were analyzed and evaluations were performed on the Twitter US Airline Sentiment dataset. We have observed a considerable amount of improvements with respect to prior state-of-the-art approaches which closes the gap with supervised feature learning.

Sentiment Analysis Using Language Models: A Study

This paper proposes a novel recommendation engine to suggest coordinated outfits to the users that complements each other. The proposed recommendation model encodes subjective knowledge of clothing experts in Multimedia Web Ontology Language (MOWL) and makes use of evidential and causal reasoning scheme to deal with the media properties of concepts. Our approach automatically identifies the user visual personality and interprets the contextual meaning of media features of the garments in the context of input query image. As a result, personalized complementary garments based on occasion of wear are recommended to the user. We have validated our approach with garment preferences of various models with a large collection of shirts and trousers, collected from various websites.

Deepti Goel

Papers

An IoT approach for context-aware smart traffic management using ontology

An ontology-driven context aware framework for smart traffic monitoring

Smart Water Management: An Ontology-Driven Context-Aware IoT Application

Sentiment Analysis Using Language Models: A Study

Recommendation of complementary garments using ontology