This article is a continuation of the series of articles for disruptive technologies for smart cities we started publishing back in April, 2020. It is result of the ongoing Erasmus+ project Smart technologies by design (Smart by Design) and is based on the outputs produced by the project partners led GAIA & DEUSTO.

It is expected that Internet of Things may be considered as the arrival of new disruption in the digital realm. The term is quite recent. It was in 2009 when Kevin Ashton, a professor at MIT at that time, used the expression Internet of Things. In a summary it can be described that it is based on interconnection of any product with any other around it. Its significance is brutal and, according to a report by McKinsey Global Institute (Manyika, J., 2015), IoT is one of the 12 most important technological trends for the future.

It is difficult to make estimates in this area and vary based on the source. In terms of the number of embedded devices, taking into account that every human being is surrounded by at least about 1,000 to 5,000 objects, it is not unreasonable to expect that Internet of Things could grow to over 30 million devices in 2020 and 75 million in 2025, although there are forecasts, such as Gartner which are much higher. The business turnover of IoT platforms market is expected to reach a market value of one billion dollars by 2019 and 1.6 billion dollars by 2021, although there are estimates that far outweigh these forecasts.
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The IoT platforms are the superbase for interconnecting devices and for generating an ecosystem of their own (Ashton, K. 2011). At present the issue is not the number of platforms, over 300 have been identified, but what this high number shows. It is an immature “space”, which is being accessed by large number of potential service providers.

Some of the important platforms are:

• Thingspeak
• Carriots
• Adafruit IO
• Sentilo 
• Devicehive (open source)
• Smart Cities as a Service 
• Pubnub
• Thingworx
• Temboo
• Thethings
• Thinger
• Ubidots
• Bought by Amazon
• Onion Cloud
• IBM Bluemix
• B-scada      
• Amazon      
• An interesting open source project that allows different IoT devices to speak among themselves: http://thethingsystem.com/index.html

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The fact that there are no consolidated standards regarding connection, protocols and, in particular, security is a decisive factor for the expansion of IoT. Since the past few years there are a number of associations of players in this field working to define these standards (AIISeen Alliance, ZigBee, Open Interconnect Consortium…), as well as global leaders such as Google or Apple are positioning themselves on elements to standardise, such as connection.

​In fact, there is still a long way to go, although currently progress is being made. Concentration movements, such as the Open Connectivity Foundation are happening, which bring together several of the existing consortia, giving rise to standards such as IEEE P2413, for internet architecture.
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IoT has application in virtually all sectors and environments, some of them include (CeArley, D et al,. 2012):·      

• Transport: connected cars are already a reality and it is estimated that in 2020 there will be more than 220M.
• Agriculture: sensors that measure acidity levels, temperature and other variables that help to improve crops.
• Retail: study customer behaviour, offer them personalised ads and improve the distribution and location of products.
• Power: ranging from sensors that record metrics in power generation and distribution systems to smart meters in the homes of end customers.
• Connected home: it is estimated that by 2030, the majority of home devices will be connected, this opens up a wide range of new applications.
• Health: devices in the healthcare field can collect data and automate processes that allow a better diagnosis and improve treatment of patients.   
• Tourism: smart doors in rooms, sensors, beacons and other devices that improve the comfort and experience of customers.
• Smart cities: Smart cities are already developed, IoT is one of the key elements for information gathering and knowledge management.

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It is expected that the evolution of the importance of IoT in the field of interconnectivity will be a determining factor in the near future. The estimated evolution of basic elements in this area are expected as follows:
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It is expected that not more than 10% of the platforms currently running or being advertised in the market are going to survive in the short term (Chatelain, J. et al,. 2017).

The features of the ones that will survive will be:·      

• Credibility , based on relevant use cases relating to consumers, to develop solid business. 
• Experience of owning, managing and monetising a platform.
• A strong community of developers who can amplify and diversify the IoT solutions to end customers.
• Experience in the handling of large infrastructure and its management, to achieve economies of scale.
• A scalable infrastructure and capacities in the information value chain for the integration, storage, processing and presentation of data.

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KFIWARE is an open source initiative that aims to promote the creation of standards needed to develop Smart applications in different domains. There are also agreements between companies to create common standards: Open Connectivity Foundation (OCF), born from the Alliance between AllSeen and Open Interconnect Consortium, backed by Intel (IEEE P2413 by end 2017) is to serve all kinds of industries, as well as for consumer devices, and although it will not replace the existing data formats it will serve to reduce the effort these devices need to share data.

​There are technologies to function as common interoperability layers, such as: Dotdot, presented at CES by the ZigBee Alliance, which aspires to become the universal language of the Internet of Things. On its behalf, Sigma Designs has presented the Z-Wave language, which aspires to become the layer with which the developers can integrate services and applications in IoT networks through cloud platforms, such as Apple HomeKit.

​Although Internet of Things is a reality now, trends to which they will be applied are as follows:

• Connected vehicles: Fleet management, pay-as-you-drive, smart parking, electric vehicles. 
• Industry 4.0: smart products, adaptive plants, agile logistics, optimised value chains.
• Monitoring and tracking: location information, cold chain tracking, security and fraud detection.·
• Remote service management: real time monitoring, remote diagnostics, guarantees, consumption management, marketing and usage-based billing.

Author
KISMC

This article is a continuation of an article we published in August 2020 - “Disruptive Technologies for Smart Cities – Cloud Computing” for the presentation of the interim results of the ongoing Erasmus+ project Smart technologies by design (Smart by Design). The article is based on the materials produced by the project partners GAIA & DEUSTO.
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Data Analytics is the approach that allows companies to analyse the data they generate in their activity enabling them to draw conclusions that affect their business. Better known as Big Data, companies manage this information in order to adopt strategies that will help them to improve their business turnover. Thus, it helps them improve operational efficiency, customer user experience and also allows them to improve their business models. All these data generated by companies in their activity is one of the concerns they have to face today. They should evaluate the importance of this information, what information they will have to store or even what part of all these data they can sell.
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Data analysis means the translation of information into opportunities for companies to take advantage of all these data (Schneider. 2017). This is why, “Data Analytics” is also called as a translator or business generator, because it allows to explore personalised solutions to carry out your projects. At present, information as services is a business model that is expanding wherein increasingly more businesses are seeking to monetise the information they obtain. According to the International Statistical Institute, businesses that use information will see their productivity increase by 430 billion dollars by 2020 in contrast with those that do not use it.
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Services offered by platforms related to information analysis is growing along with new solutions in terms of storage capacities as well as processing. Some of the platforms that currently exist are as follows:

• Hadoop
• Gridgain
• HPCC
• Storm
• Spark
• Hive
• Kafka
• Flume

​The first standard on big data was published in the end of 2015 by the International Telecommunication Union (ITU), hence, there are already international rules and standards. ITU-T Y.3600: provides requisites, capabilities and use cases of cloud computing based big data (Y.BigDatareqts, 2015).

Big Data when merged with Cloud Computing offers the ability to collect, store, analyse, visualise and handle large amounts of data, which cannot be analysed with traditional technologies (Iglesias. A, 2015).
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When we refer to data analysis, we can differentiate five key applications of such technologies: ​

1. Explore massive data or Big Data management. Information management is assumed to be one of the biggest challenges that the companies will be facing for best decision-making, operations improvement and risk reduction. To obtain a more complete view of customers. The companies have a greater number of information sources about their customers, which they manage to provide better and more personalised services, as well as to predict customer behaviour.

• Increase in security. Such technologies are used in order to prevent attacks by locating anomalies that may occur, by analysing patterns and threats. In this usage type, we can distinguish three applications:

• Improve intelligence and surveillance: with continued real time analysis to find patterns.
• Prevention of attacks: with network traffic analysis to deal with espionage, intrusiveness, cyber attacks…
• Prediction and prevention of cybercrime: by analysing telecommunications and social network data to analyse threats and to act before the criminals.

• Operations Analysis. Helps companies to make operational decisions, increasing their intelligence and efficiency. To do so, they can check the updated information with the different possible systems.
• Increase in data storage. Creation of new data storage structures.

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​The expected evolution is that the data volumes will continue to grow due to the expected increase in the number of networked devices. The future platforms will improve the ways in which data is analysed, while SQL will continue to be the standard, Spark is emerging as a complementary tool which will continue to grow.

New tools will be created to analyse without an analyst, companies such as Microsoft and Salesforce have announced such type of solutions. Programmes such as Kafka and Spark that allow to use these data in real time will also continue to be developed. According to many experts, it is thought that “fast data” and “actionable data” will replace big data. It is also expected that algorithm markets will emerge. Companies will begin to buy algorithms instead of programming them and add their own information (Logicalis, 2016). Although such type of solutions already exist, it is assumed that these will grow multi-fold.
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On the other hand, one of the challenges data analytics platforms will face is privacy, especially since the latest regulations made by the European Commission.
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With regard to standards, The Big Data Value Association (BDVA) is working to define standards of Big Data priorities and interoperability. The association has a team dedicated to this matter (Task Force 6) that, as of today, has already defined a reference model for Big Data.

​A workshop was held in Brussels in June 2017 to collaborate with other standardisation communities to create a roadmap for the harmonisation of Big Data standards. Representations from ETSI, AIOTI WG3, CEN/CENELEC, OASC, ISO/JTC1/WG9, W3C, OneM2M, Industry 4.0, European Commission, PPP based important Big Data projects among others, participated in the event. Follow-up activities took place in 2017 on the side-lines of the ISO IEC JTC1 WG9 Data Reference Architecture meeting held in Dublin.
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Information analysis has a large number of potential applications and areas of use (Marr.B, 2016):

• Continue working in customer segmentation
• Optimisation and understanding of business processes
• Monitoring and optimisation of business processes
• Improve public health systems
• Improve sport yields of citizens
• Improvements in science and innovation
• Optimise the performance of machinery of companies
• Improvement in security and support for the fulfilment of law
• Applications in Smart Cities related solutions
• Finance

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