As a species, the rate of growth of the human race over the past 200 to 250 years has increased exponentially with each industrial revolution. Through technological advances, society has been able to improve upon its collective standard of living with innovative machinery and processes. Firstly, with the use of water- and steam-powered manufacturing in the 18th century, to the division of labor in the 20th century and thirdly the use of programmable logic controllers in the 1970s (Brettel et al., 2014), humans are adept at improving quality of life and increasing efficiency in the workplace. It is now thought that humans are on the cusp of the fourth industrial revolution- one that will achieve greater interconnectivity among humans and systems.
The term Industry 4.0, a term originally coined by German industry in reference to the fourth industrial revolution, refers to the digitization of industries through the use of internet-based technologies, automation and communication between humans and machines (BMBF-Internetredaktion, 2016). By capitalizing on the information technology of the third industrial revolution, Industry 4.0 builds upon these concepts by adding another dimension. It does this through the use of cyber technology to connect smart devices (the concept known as the Internet of Things) while also benefitting from access to vast amounts of data stored in the Cloud, thus integrating all digital systems (Pereira et al., 2017).
The Internet of Things (IoT) will allow the integration of smart technology into everyday life by using “the cloud” to provide interconnectivity as opposed to traditional standalone central processing systems (Henze et al., 2014). By using cloud computing, users can overcome the issues of CPS storage because cloud computing is capable of storing massive amounts of data. This data can then provide enhanced services through statistical analysis and data computation (Singh et al., 2016). Current and predicted uses for IoT technology include assisted living, healthcare, creating smart homes and cities as well as industrial applications in chemical and manufacturing plants to improve efficiency and increase production in a safe manner. Other uses include logistics, environmental monitoring and automotive applications. The applications are endless and there is no singular strategy for applying IoT technology (Roman et al., 2013).
There are many benefits associated with increased connectivity, some of which include: achieving embedded security into a system, reducing costs in industry applications, improved production time and increasing redundancy and flexibility in a system through decentralized data storage systems (Sajid et al., 2016). The Internet of Things will also enable pervasive health care, particularly for the elderly through the use of smart devices that are no longer constrained by limited processing and storage resources and a finite energy budget (Henze et al., 2016). Additionally, users benefit from systems that are engineered to allow for increased user mobility (Henze et al., 2016).
It is evident that with the development of Industry 4.0, users can expect immense growth as computers become more autonomous. These machines can utilize large volumes of data in order to make decisions without human intervention (Singh et al., 2016). Industry 4.0 can enable horizontal integration of organizations desirous of integrating with other organizations, vertical integration within organizations through actuator and sensor signals, and end-to-end integration for companies aiming to produce continual positive improvement on goods and services provided (Gunasekaran et al., 2017). However, major concerns surrounding data security and privacy have eroded user trust in these systems, especially in light of reported data hacks and breaches in data storage by companies. These breaches have resulted in confidential user information being accessed without user consent. While Industry 4.0 will result in the majority of smart systems being connected in the global future, companies providing these services must simultaneously develop the corresponding infrastructure to prevent unauthorized identification, tracking and breaches of security (Mayer, 2009).
One method of applying these systems in order to protect user privacy is to focus on User-Driven Privacy Enforcement for Cloud Based Services (Henze et al., 2016). This would require companies designing these systems to envision a user-friendly method of allowing end users to choose their privacy preferences to maintain confidentiality. Examples of this flexibility in user-driven privacy include health care systems, where some users may or may not place more importance on privacy of health information over access in the event of an emergency (Henze et al., 2016).
Other concerns with respect to security in IoT cloud environments include the susceptibility of these systems to cybersecurity attacks. One example of this is the Siberian Pipeline explosion in 1982 through a data security hack of the SCADA system. By using cloud-based systems, industries are at risk of losing data or allowing it to be subjected to modification and attack by cyber terrorists. These systems may be subjected to all of the risks associated with typical cloud infrastructure and may not be utilizing proper or enhanced security controls beyond standard commercial “off-the-shelf” solutions (Sajid et al., 2016).
Companies employing IoT cloud architecture for industry applications should look to using cloud systems that have been specially designed with reinforced cloud security controls. Other possible security protocols to employ include network segregation of each network and increasing monitoring for security violations. The use of continuous monitoring should be employed, along with continuous-, log-, network traffic- and memory dump- analyses in order to detect any network intrusions or sophisticated cyber-security hacks (Sajid et al., 2016).
An emerging trend that may also be applied to IoT architecture in order to secure data and protect user privacy is the use of blockchain technology, which is currently associated with cryptocurrencies. Blockchain technology uses decentralized systems with computer (nodes) that maintain a distributed ledger of information. It also employs algorithms such as Proof-of-Work or Proof-of-Stake functions in order to verify and secure user information so that cyber attackers are unable to corrupt an entire chain or information through data modification. Through the mining of each additional block of data, it is difficult to subsequently modify older blocks of information that have been downloaded to multiple nodes on its distributed ledger. It is therefore possible to secure information and user privacy.
The Internet of Things will connect people and machines in a wide variety of environments and can therefore provide valuable insights through the collection and analysis of user data. Industry 4.0 has provided a number of solutions to industry, business and daily activities whose growth thus far has been limited by cost as well as energy and data storage constraints. However, with the rapid and often unregulated expansion of cyber technology, organizations must also look to designing inherently secure systems that are capable of robust authentication and data security in order instill trust and allay privacy concerns of end users. Greater security risks are posed as systems become more interconnected, therefore each system must be equally as robust as the system it is connected with or risk compromising the security of the entire system.
Author Bio:
Gregory Miller is a writer with DO Supply (https://www.dosupply.com) who covers Robotics, Artificial Intelligence and Automation. When not writing, he enjoys hiking, rock climbing and opining about the virtues of coffee.
BMBF-Internetredaktion (21 January 2016). "Zukunftsprojekt Industrie 4.0 - BMBF". Bmbf.de. Retrieved 17 June, 2019: https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html
Brettel, M., Friederichsen, N., Keller, M. and Rosenberg, M. 2014. How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. World Academy of Science, Engineering and Technology International Journal of Information and Communication Engineering Vol:8, No:1. Retrieved 17 June, 2019: http://scholar.waset.org
Gunasekaran, M., Thota, C., Lopez, D. and Sundarasekar. 2017. Big Data Security Intelligence for Healthcare Industry 4.0. L. Thames and D. Schaefer (eds.), Cybersecurity for Industry 4.0, Springer Series in Advanced Manufacturing. Retrieved 17 June, 2019: https://doi.org/10.1007/978-3-319-50660-9_5
Henze, M., Hermerschmidt, L., Kerpen, D., Häußling, R., Rumpe, B. and Wehrle, K. 2014. User-driven Privacy Enforcement for Cloud-based Services in the Internet of Things. The 2nd International Conference on Future Internet of Things and Cloud. 6 pps. Retrieved 17 June, 2019: https://doi.org/10.1109/FiCloud.2014.38
Henze, M., Hermerschmidt, L., Kerpen, D., Häußling, R., Rumpe, B. and Wehrle, K. 2016. A comprehensive approach to privacy in the cloud-based Internet of Things. Future Generation Computer Systems: Volume 56, Pages 701-718. Retrieved 17 June, 2019: https://doi.org/10.1016/j.future.2015.09.016
Mayer, C. 2009. Security and Privacy Challenges in the Internet of Things. Electronic Communications of the EASST; Volume 17; 13 pps. Retrieved 17 June, 2019: https://doi.org/10.14279/tuj.eceasst.17.208 ·
Pereira, T., Barreto, L. and Amaral, A. 2017. Networking and information security challenges within Industry 4.0 paradigm. Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30. Retrieved 17 June, 2019: https://doi.org/10.1016/j.promfg.2017.09.047
Roman, R., Zhou, J. and Lopez, J. 2013. On the features and challenges of security and privacy in distributed internet of things, Comput. Netw. 14 pps. Retrieved 17 June, 2019: https://doi.org/10.1016/j.comnet.2012.12.018
Sajid, A., Abbas, H. and Saleem, K. 2016. Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges. IEEE Access (Volume: 4); pgs 1375-1384. Retrieved 17 June, 2019: https://ieeexplore.ieee.org/abstract/document/7445139
Singh, J., Pasquier, T., Bacon, J., Ko, H. and Eyers. D. 2016. “Twenty Security Considerations for Cloud-Supported Internet of Things.” IEEE Internet of Things Journal 3 (3) (June): 269–284. Retrieved 17 June, 2019: https://doi.org/10.1109/JIOT.2015.2460333
