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For Safety and Security Reasons: The Cost of Component-Isolation in IoT

R eferences 1. Beckmann, K. & Dedi, O. (2015). sDDS: A portable data distribution service implementation for WSN and IoT platforms. In 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES), 29-30 October 2015 (pp. 115-120). Ancona, Italy: IEEE.J. Clerk Maxwell, A Treatise on Electricity and Magnetism , 3 rd ed., vol. 2. Oxford: Clarendon, 1892, pp. 68–73. 2. Object Management Group. (2015). Data Distribution Service. DDS Version 1.4. Retrieved June 6, 2016, from http://www.omg.org/spec/DDS/1.4/ . 3. Various

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Defining the IoT

will exceed $1 trillion from 2017 to 2021, retrieved at https://cybersecurityventures.com/cybersecurity-market-report/ Quora, (2018), retrieved from https://www.quora.com/What-exactly-is-Internet-of-Things-IoT Reese, H., (2017), Report: IoT attacks exploded by 280% in the first half of 2017, retrieved at https://www.techrepublic.com/article/report-iot-attacks-exploded-by-280-inthe-first-half-of-2017/ Sun, C., (2016), No IoT without IPv6, retrieved at https://www.computerworld.com/article/3071625/internet

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Research On A Cotton Storage Platform And Security Strategy Based On Iot

Algorithm. - Manufacturing Automation, Vol. 02, 2011, 180-182. 8. Wang, Yan. Research on Key Technologies of Information Transmission for IoT Control System. Northeast Forestry University, 2012. 9. Zhang, Zejian. Internet Information Security Research. - Logistics Technology, Vol. 20, 2011, 29-31. 10. Hong, Ren. Introduction to the Internet of Things Security Issues and Measures. - Computer CD Software and Application, Vol. 13, 2012, 54-55. 11. Liu, Junbin, Wang Yong. Application of Multi-Campus Network Based

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Iot Forest Environmental Factors Collection Platform Based On Zigbee

References 1. Karl, H., A. Willig. Protocols and Architectures for Wireless Sensor Networks. London, Wiley, 2005. 2. Al-Harbawi, M., M. Rasid, N. Noordin. Utilizing Neighbours-Table to Improve Tree Touting Protocol in ZigBee Network. - Wireless Pers. Commun., Vol. 65, 2012, 469-488. doi: 10.1007/s11277-011-0268-9. 3. Li, N. et al. A New Methodology to Support Group Decision-Making for IoT-Based Emergency Response Systems. - Inf. Syst. Front., 2013. doi: 10.1007/s10796-013-9407-z. 4. Li, S., L. Xu

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IoT and EU Law – E-Human Security

References DBBestTechnologies (2016) „Database decisions for the Internet of Things” , available on-line at https://www.dbbest.com/blog/database-decisions/ EUGDPR (2017) „ EU General Data Protection Regulation” , available on-line at http://www.eugdpr.org/key-changes.html Kim, I. (2016) „The Internet of Things: A Reality Check for Legal Professionals” , available on-line at http://www.lawpracticetoday.org/article/the-internet-of-things-areality-check-for-legal-professionals/ Olenik, D. (2017) „IoT liability: Legal issues abound

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The Path of Emergent Experience in the Consumer IoT: From Early Adoption to Radical Changes in Consumers’ Lives

Abstract

Up to now, IoT device adoption is happening mainly in the niche segments of technologically sophisticated upscale consumers and technology-focused DIYers. To reach a broader range of users, marketers must do a better job of understanding and offering the inherent value of smart products. Current marketing approaches are fragmented and tend to focus on individual products and single use cases. They may actually be underselling the consumer IoT. The mass-market consumer is not buying a platform or devices controlled by an algorithm, they are buying an experience. We need to ask, in what ways consumers and devices will interact with each other to create the experience they actually seek. Therefore, the main challenge is to implement a bottom-up approach that encourages users to experiment with their devices and their interactions and to integrate their individual experiences into everyday routines.

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IoT Stories: The Good, the Bad and the Freaky

Abstract

Consumers’ perceptions of technology are less matters of product attributes and concrete statistical evidence and more of captivating stories and myths. Managers of IoT can instill consumer trust when they tell highly emotional stories about the technologically empowered self, home, family or society. The key benefit of this approach is that storytelling-based IoT marketing allows consumers to forge strong and enduring emotional bonds with IoT and, in many cases, to develop loyalty beyond belief. However, stories aren’t always positive. Negative stories and meanings about a technology that are circulated in popular culture can be dangerous and harmful to a brand or a new technology. Regardless of its source, marketers need to understand the nature of the doppelgänger images that may be circulating for their technologies. They can be regarded as diagnostic tools to better understand how consumers think about and experience their IoT solutions. Also, doppelgänger narratives are valuable raw ingredients from which marketers can cull new, more captivating IoT stories that nurture consumer adoption.

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SoC as IoT sensor network hub

R eferences [1] Z. Arkady, Ch. Perera, and D. Georgakopoulos. “Sensing as a service and big data.”, Proc. of the International Conf. on Advances in Cloud Computing (ACC), Bangalore, India, July, 2012. [2] F. Sheikh, and X. Li. “Wireless sensor network system design using Raspberry Pi and Arduino for environmental monitoring applications.”, Procedia Computer Science 34, 2014, pp. 103-110. [3] Singh K.J., Kapoor D.S. Create Your Own Internet of Things: A survey of IoT platforms. IEEE Consum. Electron. Mag. 2017;pp. 6:57–68 [4] N.Q. Mehmood

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From the IoT to the IoBT. The Path to Superior Situational Understanding

REFERENCES Ashton, K. (2009). That ‘Internet of Things’ Thing. RFID Journal . Alpharetta, GA, SUA. Gershenfeld, N. (1999). When Things Start to Think . Canada: Fitzhenry & Whiteside Ltd. Keeley, P. (2017). Understanding the Explosion of IoT and Its Impact , available at: https://www.fortinet.com/blog/industry-trends/understanding-the-explosion-of-iot-and-its-impact.html , accessed on 16 September 2019. Manyika, J., Chui, M., Bisson, P., Woetzel, J., Dobbs, R., Bughin, J., & Aharon, D. (2005). The Internet of Things: Mapping The value

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Experimental load test statistics for the selected IPS tools on low-performance IoT devices

. Tseung, K. P. Chow, and X. Zhang, “Anti-DDoS technique using self-learning bloom filter”, Proceedings of the 2017 IEEE International Conference on Intelligence Security Informatics (ISI) , Beijing China, 2017. [4] B. Mukherjee, R. L. Neupane, and P. Calyam, “End-to-End IoT Security Middleware for Cloud-Fog Communication”, Proceedings of the 2017 IEEE 4th International Conference on Cyber Security Cloud Computing (CSCloud) , New York USA, 2017. [5] G. Kalnoor and J. Agarkhed, “Pattern Matching Intrusion Detection Technique for Wireless Sensor Networks

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