The Delphi method employs a means of anonymously expressing opinions that are based on methodical procedures. Experts are not permitted to talk, have horizontal contact, or have relationships with anybody other than investigators.     Linstone and Turoff (1975) assert that expert perspectives on the questions covered in the questionnaire are gathered through many rounds of questionnaires. The prediction result produces conclusions that are summarized into the consensus views of the experts after numerous consultations, summaries, and revisions. The Delphi method is distinguished by the fact that the participants do not meet face to face and do not conduct face-to-face conversations.

Qualitative decision-making method

      The qualitative decision-making paradigm is used to make decisions dependent on people who make decisions or subject matter experts. This is known as soft technology, where the decision-makers leverage social science ideas gathered from experience, education, and skills and set a structure that fully utilizes these attributes.

                Eldabi et al. (2002) find that it begins with an examination of the critical components of decision-making artifacts, followed by mastery of local collaboration. This strategy is commonly used in complex situations driven by economic or social qualitative elements, has numerous social and psychological variables, and is difficult to explain in precise numbers. This type of "soft technology" method is the most commonly used by businesses in decision-making, and it compensates for the flaws of "hard" methods that are difficult to apply due to human and social aspects. "Hard" and "soft" technologies collaborate and learn to make better decisions.

　　There are numerous approaches for making qualitative decisions. The management decision-making method, expert meeting method, brainstorming method, Delphi method, and other approaches are regularly utilized, with the Delphi technique being the most common. Due to the uncertainty of many variables, the Delphi decision-making paradigm is particularly appropriate, especially under longevity.

 Quantitative decision-making method

　　In this type of decision-making, mathematical instruments are leveraged to devise models that convey different numerous aspects and associations while calculating solutions to solve some decision-making problems. Jaiswal (2012) discovered that the accuracy and speed of everyday decision-making could be improved through quantitative study of decision-making difficulties. Quantitative decision-making methods are another evidence of scientific decision-making approaches.

　　Risk-based decision-making, deterministic decision-making, and nondeterministic decision-making are the most common quantitative decision-making methodologies.

　　　　1. Risk-based decision-making methods

　　　When decision-makers cannot make a favorable judgment on an unlikely circumstance, risk-based decision-making procedures refer to how they can make decisions based on various probabilities by forecasting the occurrence of various situations. There are numerous approaches for making risk-based decisions. The decision tree method is the most widely utilized. Peterman and Anderson (1999) state that a decision tree strategy could illustrate the associations and related probabilities and lead to the best decision-making approach.

                The decision point is one of five elements that make up a decision tree: the others are the plan branch, the natural state point, the probability branch, and the probability branch's end. The decision tree method is widely used in quantitative decision-making analysis and has a number of advantages: first, it can compare the pros and the probability of achieving a plan's goal; fourth, it can calculate the expected benefits and losses of each plan; and fifth, it can be used for multi-level decision-making analysis of a specific problem.

 

　　2. Deterministic decision-making methods

　　　　Problems of deterministic decision-making: there is only one natural state, and decision-makers can use scientific methods to make decisions. Different types of deterministic decision-making methods exist:

　　 Inventory theory, queuing theory, network technology, and other mathematical model methodologies.

·         Differential extreme value method.

·         Break-even analysis method.

　　3. Methods for making nondeterministic decisions

                (Hußmann, 1993) discovers that the probability of various natural states of nondeterministic decision-making is difficult to estimate, modern decision-making theory summarizes a set of practical and feasible methods based on the characteristics of nondeterministic decision-making problems. Problems such as first assuming some criteria, determining the expected value of the plan based on these criteria, and then determining the optimal value, pessimistic, equal probability, decision coefficient, and regret criteria are the most used criteria for nondeterministic decision-making programs.

 

 

References

 

Eldabi, T., Irani, Z., Paul, R. J., & Love, P. E. (2002). Quantitative and qualitative decision‐making methods in simulation modelling. Management Decision.

 

Hußmann, H. (1993). Nondeterministic algebraic specifications. In Nondeterminism in Algebraic Specifications and Algebraic Programs (pp. 17-42). Springer.

 

Jaiswal, N. K. (2012). Military operations research: quantitative decision making (Vol. 5). Springer Science & Business Media.

 

Linstone, H. A., & Turoff, M. (1975). The delphi method. Addison-Wesley Reading, MA.

 

Peterman, R. M., & Anderson, J. L. (1999). Decision analysis: a method for taking uncertainties into account in risk-based decision making. Human and Ecological Risk Assessment: An International Journal, 5(2), 231-244.

 

 

(c) Can Stock Photo / everythingpossible

Internet of Things

                Radiofrequency identification (RFID), multiple types of sensors including laser and GPS devices, and other hardware-based data sensors typically make up the components in the Internet of Things (IoT). The industry standard has set universal protocols where various items make up an ecosystem from an internet-based infrastructure that exchanges data through various communication channels that foster smart object relationships that are used for identifying, locating, monitoring and tracking. Xia et al. (2012) state that If we keep in mind that the "things" in the Internet of Things are not everything in the conventional sense. Then we must infer that the "things" in this situation must satisfy the following requirements:

 

1. a receiver with relevant data

2. a data transmission channel

3. a specific storage function

4. a central processor

5. an operating system

6. A unique application is required

7. be a data transmitter

 8. use the Internet of Things' communication protocol

 9. have a unique identifier (think IP or MAC address)

                 IoT may be classified into three different areas. The Internet of Things is categorized into separate layers: a core and an access layer.  Wortmann and Flüchter (2015) assert that the software (core layer) consists primarily of an application service layer, with a network transmission layer and a perceptual control layer in the hardware layer. Hardware sensors, gateways, nodes, and terminals are all part of the perception control layer. Sensor and mobile networks might be included in the network transmission layer. To administer and maintain IoT operations and particular duties for end-users, the internet, data centers, and software is written in multiple languages are used.

Forces that Affect Internet of Things

                Compliance Governance - When a new technology emerges, radicalism from the industry's early adopters will frequently stand in stark contrast to the lag of regulation and governance. In the early days of new technologies, the industry's technical force was concentrated on innovation, and supervision was minimal. As this type of invention and application spreads, the many risks posed by the new technology will become more apparent.

                Data privacy has become a severe concern in the online world in the past three years, with numerous user data leaks or abuses, most notably the Facebook security breach, which drew worldwide attention. Every Internet company and organization has amassed a vast amount of user information. When phrases like "thousands of people and thousands of faces" and "tailored recommendations" become spokespeople for Internet corporations' technological prowess, it also means that each of us is "naked" in front of their cameras.

                Almeida et al. (2015) discovered that the regulatory/legislative governance proposal claims increased robust data privacy requirements going forward, and users' sensitive data will be exposed to increased scrutiny. IoT regulations imposed two years ago have become a significant force in terms of IoT data compliance management and governance.

                Protective measures - As IoT devices' costs and associated infrastructures continue to plummet, businesses' use of IoT devices will continue to rise. Williams et al. (2016) suggest that businesses and organizations focus more on safeguarding IoT protocols and infrastructures. Security software will become a critical force that will affect IoT's evolution and be a crucial component to help secure sensor data in the future.

                At the same time, sensor-level security paradigms will also become popular, especially for IoT platforms encompassing compassionate data. A fundamental approach is to leverage trusted OS and middleware through the sensors whenever possible to help alleviate unwanted attacks and decrease threats. However, the openness of IoT hardware and software is more vulnerable to cyber-attacks. Fundamental verticals such as health care, aviation and finance, and transportation will need to embrace security-centric IoT methods to ensure that the growth of the technology does not extend the security required to protect its host.

 

References

 

 

Almeida, V. A., Doneda, D., & Monteiro, M. (2015). Governance challenges for the Internet of Things. IEEE Internet Computing, 19(4), 56-59.

 

Williams, M., Nurse, J. R., & Creese, S. (2016). The perfect storm: The privacy paradox and the Internet-of-Things. 2016 11th International Conference on Availability, Reliability, and Security (ARES),

 

Wortmann, F., & Flüchter, K. (2015). Internet of things. Business & Information Systems Engineering, 57(3), 221-224.

 

Xia, F., Yang, L. T., Wang, L., & Vinel, A. (2012). Internet of things. International journal of communication systems, 25(9), 1101.