Internet of things (IOT) connects physical to digital
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1) The document discusses the topic of Internet of Things (IoT). It defines IoT as a network of physical objects embedded with sensors that can collect and exchange data. 2) The document outlines some key characteristics of IoT including connectivity, data collection, communication, intelligence, and action. It also discusses how IoT works by collecting data via sensors, communicating data through networks, analyzing the data, and taking action. 3) Several potential research topics in IoT are proposed, including applying deep learning for intrusion detection in IoT networks, finding dead zones in large IoT networks, and developing governance models for machine learning algorithms within IoT.
Internet of things (IOT) connects physical to digital
- 1. Internet of Things By Islam Nader
- 2. Put Data at the Heart of Your IoT Strategy Internet of things (IoT) connects physical to digital
- 3. Contents of Introduction What’s Internet of Things? State of the Art of IoT. How IoT Works? Few Applications of IoT Current Status & Future Prospect of IoT Technological Challenges of IoT IOT PHD Research opportunities 3
- 4. What’s the Internet of Things • Definition • The Internet of Things, also called The Internet of Objects, refers to a wireless network between objects. • The Internet of Things (IoT) is the network of physical objects or "things“ embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data • A “Thing” in the context of the Internet of things (IoT), is an entity or physical object that has a Unique identifier, an embedded system and the ability to transfer data over a network • Heart monitoring implants • Biochip transponders on farm animals • Automobiles with built-in sensors • DNA analysis devices & Other Wearbles etc 4
- 6. There are 7 crucial Internet of Things characteristics: 1. Connectivity. This doesn’t need much further explanation. Devices, sensors, they need to be connected: to an item, to each other, actuators, a process and to ‘the Internet’ or another network. 2. Things. Anything that can be tagged or connected as such as it’s designed to be connected. From sensors and household appliances to tagged livestock. Devices can contain sensors or sensing materials can be attached to devices and items. 3. Data. Data is the glue of the Internet of Things, the first step towards action and intelligence. 4. Communication. Devices get connected so they can communicate data and this data can be analyzed. 5. Intelligence. The aspect of intelligence as in the sensing capabilities in IoT devices and the intelligence gathered from data analytics (also artificial intelligence). 6. Action. The consequence of intelligence. This can be manual action, action based upon debates regarding phenomena (for instance in climate change decisions) and automation, often the most important piece. 7. Ecosystem. The place of the Internet of Things from a perspective of other technologies, communities, goals and the picture in which the Internet of Things fits. The Internet of Everything dimension, the platform dimension and the need for solid partnerships. 6
- 7. Why Internet of Things • Dynamic control of industry and daily life • Improve the resource utilization ratio • Integrating human society and physical systems. • Flexible configuration. • Universal transport & internetworking • Acts as technologies integrator 7
- 8. IOT lifecycle Collect Communicate Analyze Action 8
- 9. 1-Collect At Your Home In your Car At the Office Device and Senones are collecting Data everywhere . 9
- 10. 2-Communicate A Cloud Platform Private data center Home network Sending Data and Events through networks to some destination 10
- 11. 3-Analyze Visualizing Data Building reports Filtering data ( drill down and Up ) Creating Information form Data 11
- 12. 4-Action Communicate with another machine Send a notification ( Sms , Email , Text) Talk to another system Taking action based on information and data 12
- 13. How IoT Works? RFID Sensor Smart Tech Nano technologies To identify and track the data of things To collect and process the data to detect the changes in the physical status of things To enhance the power of the network by developing processing capabilities to different part of the network. To make the smaller and smaller things have the ability to connect and interact. 13
- 14. Applications of IoT Structural Health of Buildings Waste Management Air Quality Noise Monitoring Traffic Congestion City Energy Consumption Smart Parking Smart Lighting Automation and Salubrity of Public Buildings 14
- 15. Conceptual representation of an IoT network 15
- 16. Current Status & Future Prospect of IoT 16
- 17. Current Status & Future Prospect of IoT 17
- 18. Current Status & Future Prospect of IoT 18
- 19. Current Status & Future Prospect of IoT 19
- 20. Current Status & Future Prospect of IoT 20
- 21. Sensor Vision 21
- 22. TECHNOLOGICAL CHALLENGES OF IoT At Present IoT is faced with many challenges 22
- 23. IOT PHD Research opportunities For whom Interesting in IOT PHD Research 23
- 24. Applying Deep Learning for Intrusion Detection System in the Internet of Things (IoT) Network • Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services. By 2020, 50 billions of IoT heterogeneous devices are connected to the internet. These connected IoT devices form an intelligent system of systems that transfer the data without human-to- computer or human-to-human interaction. • While enjoying the convenience and efficiency that IoT brings to us, threats to the IoT devices and applications are on the rise; however patterns within recorded data can be analysed to help predict threats. There are different types of attacks and threats that may diffuse the IoT architecture, such as spoofing, DDoS attacks, unauthorized access, man-in-the-middle attacks and ad hoc networks. One feasible countermeasure against targeted attacks is to apply Intrusion Detection System (IDS) on the IoT network to detect and report intrusion, policy violations, and unauthorized use. Hence a an efficient approach for detecting and predicting IoT attacks is needed. • This PhD project will develop an innovative IDS model for the application and distribution of deep Learning algorithms within IoT networks. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation. Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics will be useful, as will experience of planning and workflow scheduling systems. 24
- 25. Dead-Zone or Nearly-Dead-Zone Finder in Large IoT Networks • The Internet of Things (IoT) is aimed at connecting billions of things and enable communication and information exchanging among things, where many different types of large directed networks will arise. • In large directed networks, there may exist dead-zones, where only incoming edges are available but without outgoing edges from the zone. There may also exist nearly-dead-zones, where the number of outgoing edges is significantly smaller than the number of incoming edges for the whole zone. • Finding all such dead-zones and/or nearly-dead-zones in large directed networks is important to analyse and maintain the networks. Large (nearly-)dead-zones may also contain small (nearly-)dead-zones. For example, a given standalone directed network as a whole, is a dead-zone. This means that we can build up a hierarchical structure of (nearly-)dead-zones for any given large directed networks. • Your tasks in this project mainly include: • (1) Identify research gaps between existing approaches for finding dead-zones and our target - dead-zone finder in large directed networks; • (2) Design efficient algorithms to find (nearly-)dead-zones in large directed networks; and • (3) Evaluate your proposed algorithms against existing algorithms. • The application of the research results from this project will help us to understand the community structures and communication patterns across large directed networks in IoT. 25
- 26. Governing distributed learning algorithms within Internet of Things (IoT) networks • In terms of scale the Boston Consulting Group states that “IoT sensors and devices are expected to exceed mobile devices as the largest category of connect devices in 2018, growing at 23%”. Key markets that will benefit from this technology push are advanced robotics (digital manufacturing), connected and autonomous vehicles, and the emerging health and wellness monitoring market. • These large-scale networks present opportunities and challenges to learn from massive, distributed datasets. Algorithms to perform supervised and unsupervised learning can enable insight to be derived from data. Such data is collected and often analysed locally by individual IoT devices, presenting difficulties when governing the distribution of learning algorithms to arrive at robust conclusions for queries. • This PhD project will develop a governance model for the application and distribution of Machine Learning algorithms within IoT networks. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation. Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics will be useful, as will experience of planning and workflow scheduling systems. 26
- 27. Influence Analysis of Changes in Graph-Based IoT Data • Recent years have witnessed the fast emergence of massive graph data in many application domains, such as the World Wide Web, linked data technology, online social networks, and Internet of Things (IoT). • Most graphs are generally subject to changes in terms of connections and nodes. For example, the emerging Internet of Things calls for graph data management with connection and node changes because smart things are normally moving and their connectivity could be intermittent, leading to frequent and unpredictable changes in the corresponding graph models. • This project aims to explore what influence can be brought by changes (including both incremental and decremental changes) in the underlying graph models of big IoT data. New algorithms will be designed to identify and manage the most influential connections and nodes in an IoT data graph model (Some initial work has been presented at DASFAA 2017). • The success of this project will be able to help manage the dynamicity of an IoT system effectively and allocate resources and budgets wisely to the most critical parts of the system. 27
- 28. Multiagent Systems for Resilient Internet of Things (IoT) Architectures • Interest in the Internet of Things (IoT) is increasing the demand for new design approaches that can assist the specification of resilient, distributed architectures. A market analysis report by Boston Consulting Group (BCG) “Winning in IoT: It’s All About the Business Processes, https://www.bcg.com/perspectives/218353” predicts that $267 billion will be spent on IoT technologies by 2020. • IoT devices already produce massive volumes of data, which places significant demands upon network infrastructure. Mobile devices, that move in, out and between networks, place extraordinary demands upon network management services, which need to keep track of individual device identifiers, as well as knowing which devices to trust. • This PhD project will investigate Multiagent Systems approaches to the design, modelling and evaluation of IoT architectures, exploring the use of goal directed behaviour abstractions to model wired and wireless IoT nodes as part of a larger network. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation. • Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics will be useful. Experience of predicate/modal logic would be beneficial though not essential. 28