Getting started with IoT

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Data repository 4 5 The edge IoT nodes of a network are limited in storage size and processing constraints. In an IoT application, you may have thousands of nodes collecting data. The solution is to move this data on to a database storage either locally (privately) hosted or on a cloud storage platform where it can be processed from a centralised location. Traditionally, most IoT devices will push all data up to the data repository, but with the emergence of edge computing, only the processed data or data of interest may be sent. The cloud ‘The cloud’ is a term used to describe a global network of powerful servers which are designed to store and manage data, run applications and deliver content or a service. The largest providers of these cloud services are Amazon, Microsoft, IBM and Google. The cloud has replaced the need for companies to run expensive physical servers on-site and offers server-like services, with users paying when the services are used. Large amounts of data can be stored inexpensively in the cloud. Analysis and post processing When data arrives in the cloud, a typical task would be for a software application to • Unpack the data • Extract the values from each sensor (for example, temperature, humidity) • Check that these values are within acceptable ranges. Processing in the cloud IoT devices normally send data to the cloud for processing. Its huge processing power enables the execution of complex algorithms, machine learning and artificial intelligence to extract maximum value from the data. Benefits of cloud processing • Huge processing power can perform complex tasks • Data analytics can be performed on incoming data to detect trends or abnormalities Analysis of the data Cost of storage Ongoing costs Privacy of data Security of data Analysis of the data is where the real value is unlocked and many IoT companies build their value proposition around this. For example, a business manufactures and sells hardware for sensing the movement of people or traffic through an area. Analytics are performed on the captured data. These analytics can be used to detect trends or anomalies in the movement of people or traffic, which becomes a “service” they can sell to improve the efficiency of other systems. Scalability Considerations when choosing between local data storage options and the cloud Data storage location Ease of access Tools for development 6 Visualisation and presentation The last stage of the process is to present the information in a meaningful way. Depending on the requirements of the user, this could be as simple an action as ringing a buzzer or sending an alert by SMS that there is an abnormality. More frequently, it is a web page, or dashboard, with a series of graphs showing real-time information from the network. Many cloud platforms now include tools to visualise data instead of creating separate traditional web pages to display it. This may also include an automated feedback loop or manual two-way communication - the user may wish to input into the system to control the sensors or an actuator based on the information they have received. 19
Implementing IoT The most successful solutions begin by focusing on the problem to be solved or opportunity to be realised, rather than on technology. Look for areas in an organisation where an IoT solution will provide a benefit over the existing process. Perhaps manual monitoring could be automated? If maintenance needs could be predicted, costly down time could be prevented. More information in a specific area of a business could improve a process or service. With the right information, processes can often be improved, efficiencies implemented, and business decisions made easier. Questions to ask when exploring your IoT project There are three parts to developing an IoT system. The more bespoke a system is, the more complex and expensive the development. Hardware 1 Off the shelf hardware - dedicated solutions. Buy it, install it 2 Development boards, giving you flexibility to adapt interfaces and functionality to your needs 3 Custom design - tailored solutions requiring engineering development Networks What information would be useful to measure? Is there a business case for the application? Who/what does it solve a problem for? Is data already being measured in this application? 1 Use existing networks - Wi-Fi, Cellular, LPWAN, Hardwired (ethernet) 2 Setup own network - manage network server and network hardware Software 1 Database storage - Can be as simple as viewing data collected in a spreadsheet 2 Dashboard information - web app to display data 3 Custom dashboard development - customised web application or software interface Are there existing systems to integrate with? What would make an effective proof of concept trial? Is senior and cross functional organisational support available? 20
Page 1 and 2: Getting started with IoT Exploring
Page 3 and 4: Integrating IoT IoT is here and it
Page 5 and 6: The road to IoT From M2M to IoT Whi
Page 7 and 8: IoT in action Since 2016, multiple
Page 9 and 10: People flow Challenge: Counting and
Page 11 and 12: Industrial IoT (IIoT) You will also
Page 13 and 14: Emerging business models Data optim
Page 15 and 16: 1 Sensors As the ‘data gatherers
Page 17 and 18: Edge computing Edge computing moves
Page 19: Protocols LoRaWAN: LoRaWAN is desig
Page 23 and 24: Glossary TERM MEANING Actuator Appl

Getting started with IoT

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