RFID Manages Assets for U.S. Air Forces in Europe and Africa

RFID Manages Assets for U.S. Air Forces in Europe and Africa

Computer Assisted Design building the RFID Key Cyber Terrain

    Multiple industries that have deployed RFID in the past suggest that more than 98% of RFID design activity comprises recurring redundant configuration changes. However, despite the importance of RFID deployment, implementation, and change management design activities, researchers have estimated that 95% of companies have no systematic approaches to preventing RFID deployment procedures that facilitate indefinite repeat. 

Problem Domain 

    Many companies have no idea where to start after deciding that an RFID solution is a technology they want to employ. Even after companies accomplish comprehensive requirements assessments, many find out that their vendor's technology or product has limits. Requiring the vendor to accommodate your dynamic needs increases TCO (total cost of ownership), not just the initial buying price. But you are most likely thinking, "I can seek guidance from independent experts to support my efforts in areas where I need assistance." But even acquiring experts will still cost you money and time (and possibly a few headaches) if you or your company is lacking knowledge of RFID Unfortunately, with many RFID solutions on the market today, it's rare to find any with the ability to negotiate change. Whether it is changing infrastructure location and size, paradigms, tracking area footprint, transitory asset trends, employees (think knowledge management), and network topologies, to name a few. Sound predictions of RFID performance are now a necessity if companies are to cope with unforeseen changes. Still, they must understand the range of forecasting possibilities available from the RFID vendor or their products. ProxiTrak has a unique functionality that can organize RFID data to align with a predictive model so companies can forecast RFID coverage performance and outputs for hypothesis tests (Motamedi, Setayeshgar, Soltani, & Hammad, 2013). The prediction designer is based upon the availability of historical data and the degree of zone accuracy desirable, among other factors like antenna/tag RSSI strength. Predictive Analytics ProxiTrak uses historical data to predict future RFID events that affect health. Plan, Predict, Act on Change ProxiTrak utilizes historical data in concert with the CAD model that captures essential trends. That predictive model is then used to adjust the correlation of hardware functionality between the virtual and real world.

References

Costin, A., Pradhananga, N., & Teizer, J. (2012). Integration of passive RFID location tracking in                 building information models (BIM). Paper presented at

the EG-ICE, Int. Workshop, Herrsching, Germany.

Costin, A., Pradhananga, N., & Teizer, J. (2014). Passive RFID and BIM for real-time visualization and        location tracking. Paper presented at the

Construction Research Congress 2014: Construction in a Global Network.

Costin, A. M., & Teizer, J. (2015). Fusing passive RFID and BIM for increased accuracy in indoor             localization. Visualization in Engineering, 3(1), 17.

Costin, A. M., Teizer, J., & Schoner, B. (2015). RFID and BIM-enabled worker location tracking to             support real-time building protocol and data

visualization. Journal of Information Technology in Construction (ITcon), 20(29), 495-517.

Li, C. Z., Zhong, R. Y., Xue, F., Xu, G., Chen, K., Huang, G. G., & Shen, G. Q. (2017). Integrating             RFID and BIM technologies for mitigating risks and

improving schedule performance of prefabricated house construction. Journal of Cleaner Production, 1           65, 1048-1062.

Motamedi, A., Setayeshgar, S., Soltani, M., & Hammad, A. (2013). Extending BIM to incorporate             information of RFID tags attached to building assets.

Paper presented at the International Conference on Computing in Civil and Building Engineering,             Montreal, Canada.

Xie, H., Shi, W., & Issa, R. R. (2010). Implementation of BIM/RFID in computer-aided design-            manufacturing-installation process. Paper presented at the

2010 3rd International Conference on Computer Science and Information Technology.

Proxigroup's Socio-Technical Strategy

       The socio-technical aims to coalesce a full-scale version of CAD-Design RFID and the end-users in the operational environment of multiple verticals provided by a company named Proxigroup. Proxigroup aims to improve its inventory tracking, asset surveillance and processing management technology, and other areas. The innovation will create, manage, and provide real-time surveillance to tagged assets to improve the systematic process of deploying, operating, maintaining, shipping, and delivering retail packages and assets cost-effectively (Shull & Proxigroup). Though the current supply chain system is mainly centralized, few techno-economic and socio-institutional alternatives require socio-technical innovation. CAD-based RFID technologies' technical, demand, and social articulations will be examined in this strategy to get insight into these problems (Reich & Benbasat, 2000).

Proxigroup's Socio-Technical Overview Video

References

 

Avgerou, C. (2003). New socio-technical perspectives of IS innovation in organizations.

Domlyn, A. M., & Wandersman, A. (2019). Community coalition readiness for implementing something new: using a Delphi methodology. Journal of community psychology, 47(4), 882-897.

Flichy, P. (2008). Understanding technological innovation: a socio-technical approach: Edward Elgar Publishing.

Reich, B. H., & Benbasat, I. (2000). Factors that influence the social dimension of alignment between business and information technology objectives. MIS quarterly, 81-113.

Shull, C., & Proxigroup, O. CAD Design RFID-Why?

Innovation and Discoveries

Some features of innovation are that many discoveries occur by chance instead of design. This trait is significantly more essential than a sudden change. Because of this "contingency is significantly higher than planned" aspect, it is harder to trace the genesis of an innovative idea (Roberts, 1989). 

  Usefulness through Discovery 

 Pfizer's Viagra, which cures erectile dysfunction in males, originally aimed to create medications to treat cardiovascular disease. Marletta (2017) discovered that not only did Pfizer earn financially from creating the globe's most well-known medicine for treating sexual dysfunction, but the condition itself was left untreated as a result. 

  Discovery through Error 

 The founders of Google and Instagram (a photo-editing app) both wanted to start a mobile gaming company instead of a search service. In contrast, the founders of Twitter (a prominent social media platform) wanted to allow users to recognize podcasts instead of a social network. (Schroeder et al., 1989) state that to put it another way, innovation has emerged in a way that these basic assumptions could never have predicted. Percy Spencer, a senior engineer at the American armaments firm Raytheon, discovered the temperature gradient of microwaves by mistake (GANESH & MITTAL, 2020). Paternity testing and crime detection now employ gene recognition technology created originally to examine human Genetic mutation. This technique was originally designed to analyze human Gene mutation. And during the process of being developed, it was determined that everyone's DNA is unique. Currently, the societal impact of this innovation is on par with that of medical advances, if not superior.

  Progressive more than sudden 

 (Larson et al., 2013) assert that insights left by several developments in our lives can be found if we pay attention. Electric kettles are now in nearly every home, allowing people to make and consume boiling, germ-free drinkable water whenever they choose. Attempting this in a time without electricity is impossible. Boiling water over a wood-burning stove is a laborious process. What in the world are you thinking? Even the commonplace flush toilets of today's dwellings are a valuable invention. Most people's lives would be unlivable if they didn't have access to these water tanks, toilets, and S-shaped curves. According to ancient Chinese medicine theory, toilets at that time had a cross-flow of feces and bugs that flew indiscriminately as far away from the toilet as possible. Due to the widespread usage of flush toilets nowadays, it is not an exaggeration to suggest that modern toilets can be cleaner than old kitchens, where bugs and flies are always buzzing around. (Dew et al., 2004) contend that as a result, the invention is fraught with chance and uncertainty. Investing in new enterprises in the capital market is extremely tough, as this information demonstrates in great detail. It is difficult for investors who do not work in the industry to make sound decisions when scientists and problem solvers themselves cannot predict what their ideas will be or how they will be used in society. 

 References 

 Dew, N., Sarasvathy, S. D., & Venkataraman, S. (2004). The economic implications of exaptation.                  Journal of Evolutionary Economics, 14(1), 69-84. 

 GANESH, M., & MITTAL, P. (2020). CASE STUDY: MICROWAVE OVENS. 

 Larson, G., Stephens, P. A., Tehrani, J. J., & Layton, R. H. (2013). Exapting exaptation. Trends in 

           ecology & evolution, 28(9), 497-498. 

 Marletta, M. A. (2017). Serendipity in discovery: from nitric oxide to Viagra. Proceedings of the 

            American Philosophical Society, 161(3), 189-201. 

 Roberts, R. M. (1989). Serendipity: Accidental discoveries in science. 

 Schroeder, R. G., Van de Ven, A. H., Scudder, G. D., & Polley, D. (1989). The development of 

           innovative ideas. Research on the management of innovation: The Minnesota studies, 107-134.

 

      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.

 

 

Blogger's BIO

I have been building, researching developing RFID and Io Technology since 2009. Since the end of 2009, I've engineered Computer-Aided Design (CAD) based RFID technology. I wrote my first technical article on Code Project at the beginning of 2001 about an approach to Inversion of Control and Dependency Injection. From 2010 to 2016 I single-handedly wrote the software code for CAD-First RFID with Machine Learning Predictive Algorithm named ProxiTrak. It’s true if you assume I had very little social life at this time but I was extremely obsessed with this project during these years. Then I released several more articles on RFID Journal with the latest title “ProxiTrak Design Your ROI”. At the beginning of 2016, I created a new company called ProxiTrak that received 1.1 million dollars in Research and Development Funding under the EU Framework Program for Research and Development. During this time I accomplished research and development of R & D Mesh logic behind 2D/3D model Virtual-Live synchronization.                                    

In the newly created R & D Laboratory in Wroclaw Poland, we created a 3D perennial computer-aided design code to determine how many views it takes to fully define a zone part. The conclusion of the research found that with the ProxiTrak algorithm, due to zone level accuracy, machine learning was needed the Radio Signal Strength (RSSI) to drive RFID/IoT infrastructure accuracy visually in ProxiTrak.

In this Blog, you are introduced to different paths regarding the development and evolution of RFID and the Internet of Things that connect, people, organizations, and assets globally. - from ideas to scholarly publications.

I've amassed a lot of different types of stuff over the years of researching, developing, and writing content over a vast period. Naturally, I have achieved what many other academics and developers have attempted, and with so much content, I intend to disperse it over a variety of topics. From the user's perspective, I envision embarking on a journey to learn more about a specific RFID or IoT evolutionary event, discovery, or literature. The concept is to give a single spot where a user may view everything related to a specific domain.

Currently, as a DCS student, we are learning about the future, prosperity, discovery, and expansion of technology and the impact innovation may bring in different facets of the world.