WF-IOT-2014, Seoul, Korea, 06 March 2014
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This document summarizes a presentation on improving the sustainability of Internet of Things (IoT) solutions through a trading-based value creation model. The presentation introduces a sensing as a service model where sensor owners can publish sensor data and consumers such as companies can access this data in exchange for offers like discounts or fees. Two surveys found that most respondents viewed this model positively and would be motivated to purchase smart devices. However, open challenges remain regarding technology, economics, social and legal issues. The model aims to increase adoption of IoT solutions in a sustainable way.
WF-IOT-2014, Seoul, Korea, 06 March 2014
- 1. Improve the Sustainability of Internet of Things Through Trading-based Value Creation Charith Perera and Arkady Zaslavsky IEEE WORLD FORUM ON INTERNET OF THINGS 2014, SEOUL, KOREA 6-8 MARCH 2014.
- 2. Agenda • Introduction and Background • Smart Cities and Internet of Things • Trading-based Sensing as a Service Model • Sensing as a Service in Action • Value Creation And Social Impact • Community View and Discussion • Open Challenges and Conclusions Slide 2 of 20
- 3. Introduction and Background • Internet of Things (IoT) envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. • Despite the strong interest, the adoption of IoT solutions among the general public (i.e. non-technical community and typical households) is still remaining low. • Our objective is to address this issue of low adoption. 1. How to increase the adoption of IoT solutions among non-technical community? 2. How to make the adoption more sustainable by motivating the consumers in typical households? • We propose an trading-based value creation business model to motivate the different people to deploy sensors and IoT solutions. • Sensing as a service business model help to create a sustainable IoT Slide 3 of 20
- 4. Smart Cities and Internet of Things • Smart Cities and IoT, which have different origins, are moving towards each other to achieve a common goal. • Smart Cities started as a need • Internet of Things started as an advance in technology Slide 4 of 20
- 5. Trading-based Sensing as a Service Model Slide 5 of 20
- 6. Trading-based Sensing as a Service Model • Sensors and Sensor Owners Layer: consists of sensors and sensor owners. A sensor is a device that detects, measures or sense a physical phenomenon such as humidity, temperature. • Sensor Publishers Layer: consists of sensor publishers (SP). The main responsibility of a sensor publisher is to detect available sensors, communicate with the sensor owners, and get permission to publish the sensors in the cloud. • Extended Service Providers Layer: consists of extended service providers (ESP). To become an ESP, they have to provide value added services to the sensor data consumers. • Sensor Data Consumers Layer: consists of sensor data consumers. They can request data or service needed from the online platforms Slide 6 of 20
- 7. Trading-based Sensing as a Service Model Slide 7 of 20
- 8. Trading-based Sensing as a Service Model Personal and Household: All personal items, such as mobile phones, wrist watches, spectacles, laptops, soft drinks, food items and household items, such as televisions, cameras, microwaves, washing machines belong to the personal and household category. Private Organizations / Places: This category consists of all items own by private organizations. The same items we listed under personal and household category can be listed under here as well depending on the ownership. Public Organizations / Places: This category is similar to the private organizations and places category we discussed above. However, this category also includes public infrastructure such as bridges, roads, parks, etc. Commercial Sensor Data Providers: They are business entities who deploy and manage sensors by themselves by keeping ownership. They earn by publishing the sensors and sensor data they own through sensor publishers. Slide 8 of 20
- 9. Sensing as a Service in Action: Technical Perspective Slide 9 of 20
- 10. • • • • • • • • • Mike bought a new refrigerator for his new home. Fridge automatically identifies the availability of Wi-Fi in the house in step (1). Fridge communicates with a sensor publisher (SP) and informs about its presence (2). In step (3), the SP communicates with Mike to check whether he likes to publish the sensors attached to the refrigerator in the cloud. Mike is allowed to define which sensors to publish, what kind of consumers are allowed to bid, and what kind of return (fee or any other offer) is expected. Later, Mike receives an email from a company called DairyIceCream (via a SP called EasySensing), an ice cream manufacturer, with an offer in step (4). DairyIceCream is interested to have access to the RFID reader and the door sensor attached to the freezer in Mike’s refrigerator. As a return, DairyIceCream is willing to offer either 3% discount on every product purchased from DairyIceCream or a monthly fee of $2. As Mike likes DairyIceCream products, he agrees to the 3% discount offer instead of the monthly fee in step (5). A week later, Mike receives an email from a company called ProductiveAnalytics which has been sent on behalf of the GoldenCheese company, a cheese manufacturer, with an similar offer. However, the offer is either 4% discount on every product purchase by GoldenCheese or a monthly fee of $1. As Mike does not like GoldenCheese products, he decides to accept the monthly fee option. Slide 10 of 20
- 11. Sensing as a Service in Action • In the scenario, Mike is the sensor owner. Therefore, he and his sensors represent the sensors and sensor owners layer. Further, in ownership categorization, Mike represents the Personal and households scheme. • Both the DairyIceCream and GoldenCheese companies represent the sensor data consumers layer. • EasySensing is a SP who enables the communication and transactions between Mike and the DairyIceCream. EasySensing is responsible for matching the sensor owners expectations with the requirements of sensor data consumers. • DairyIceCream retrieves the data from EasySensing directly and conducts the data analysis with the help of in-house experts. • ProductiveAnalytics is an ESP who works on behalf of GoldenCheese. GoldenCheese has hired ProductiveAnalytics to perform the data analysis as they do not have the required technical skills within the company. • ProductiveAnalytics collects the data by handling all the deals and transaction with the sensor owners though their partner SPs. Slide 11 of 20
- 12. Futuristic Scenarios Waste Management Supply chain Management Environment Monitoring Smart Infrastructure Slide 12 of 20 Smart Home Smart Agriculture
- 13. Value Creation And Social Impact • Win-Win situation: Cost of IoT solutions have put many people away from adopting these solutions. Sharing such cost can address this issue. The proposed model can help the consumer to earn back the additional costs that they may need incur due to adoption of smart IoT solutions • Sharing and reusing: In traditional methods, each party (group or person) who wants to collect sensor data needs to visit the field and deploy the sensors manually by themselves. Sharing data (i.e. sense once, use by many) also reduces the cost involved • Collect data previously unavailable: This model allows to collect sensor data which is impossible to collect using traditional non-collaborative methods. This business model promotes and stimulates the sensor deployments by companies at commercial level. Slide 13 of 20
- 14. Community View and Discussion – Survey 1 21% Male Positive > Female More Positive Younger > Older More Positive Survey 1 conducted through Google Consumer Surveys platform where we collected 1000 responses. The Google Consumer Surveys is a single question survey. United States general population Slide 14 of 20
- 15. Community View and Discussion - Survey 2 64% Positive Survey 2 has been administrated using SurveyMonkey platform. Contained total of nine questions (including 5 demographics questions i.e. country, gender, age, household income, IT knowledge familiarity). We collected total of 137 responses. Slide 15 of 20
- 16. Community View and Discussion - Survey 2 Male > Female More Positive Younger > Older More Positive Male > Female Motivates More Younger > Older Motivates More Slide 16 of 20
- 17. Community View and Discussion - Survey 2 • 67% of the respondents expect less than US$500 per year. • 66% respondents are happy to make additional investments as long as the additional cost can be covered within 3 to 24 months. • The proposed model motivates respondents (65%) to purchase smart devices and adopt IoT solutions even at higher prices. Slide 17 of 20
- 18. Open Challenges and Conclusions Technological • Architectural designs, sensor configuration, data processing storage, infrastructure and energy consumption • Standardisation, accuracy, and security and privacy Economical • Innovation, entrepreneurship and entry barriers • Sustainability, licensing, business practices and credibility Social • Trust, social acceptance, change management, Awareness • Security and privacy, safety, accessibility, usability & legal terms Slide 18 of 20
- 19. References Charith Perera, Prem Prakash Jayaraman, Arkady Zaslavsky, Peter Christen, Dimitrios Georgakopoulos, Sensor Discovery and Configuration Framework for the Internet of Things Paradigm, Proceedings of the IEEE World Forum on Internet of Things (WF-IoT), Seoul, Korea, March, 2014 (6 pages) Charith Perera, Arkady Zaslavsky, Peter Christen, Dimitrios Georgakopoulos, Sensing as a Service Model for Smart Cities Supported by Internet of Things, Transactions on Emerging Telecommunications Technologies (ETT) Volume 25, Issue 1, 2014, Pages 81-93 (13 pages) Please note that this slides set is available on http://www.slideshare.net/ngcharithperera Slide 19 of 20
- 20. Thank You! Charith Perera The Australian National University CSIRO Computational Informatics t +61 2 6216 7135 e [email protected] w www.charithperera.net DECISION AND USER SCIENCE / INFORMATION ENGINEERING LAB