Technology has continued to shape contemporary trends. Advances in communication technology have impacted almost all sectors ranging from education, engineering, healthcare, and many other aspects of our daily lives. Specifically, the impact and importance of technology in healthcare have been well documented (e.g., Rahmani et al. 2015; Khan 2017; Alotaibi and Federico 2017). For instance, in their article that reviewed the impact of health information technology on patient safety Alotaibi and Federico (2017) observed that there should be no doubt that health information technology is an important tool for improving healthcare quality and safety. It was observed that the use of technology in its various forms, including the use of sensors, the internet, and monitoring systems, helped avoid medication errors, reduce adverse drug reactions, and improve compliance. These views have extensively been addressed in the literature and have even been adopted in healthcare policy (Storey 2013 and Frantzidis, et al. 2010).
While the application of technology in healthcare is commonplace, changing technologies continue to improve and stretch available possibilities. The current literature review supposes that the internet of things (IoT) is a somewhat new technology with considerable applications. According to Kulkarni and Sathe (2014), it was observed that the fields of computer science and electronics continue to interface to produce revolutionary technologies. The IoT is viewed as having significant potential in a myriad of themes. Fundamentally, IoT facilitates communication between various electronic systems, especially sensors, to facilitate applications of different systems. IoT has been described as having the ability to connect everyday objects such as household electronics, sensors, and actuators through a network. The way such objects connect and intelligently communicate with each other has been otherwise illustrated as a web of objects with inimitable identifiers that can converse with each other with or without the assistance of a computer or the internet (Darshan & Anandakumar 2015 and Kulkarni & Sathe 2014).
Importantly, IoT systems enable new forms of intelligent communication between devices and people, the environment and devices through sensors, and between things themselves (Kulkarni & Sathe 2014). Enabled by a range of sensors and actuators, and linked through a network, IoT facilitates device systems that can gather and share information directly with each other and the cloud. Such a network makes it possible for supported systems to collect and record significant amounts of data. Gelogo, Hwang, and Kim (2015) even mentioned that such a capability for systems to communicate remotely over the internet allows devices to record and analyze new data streams faster and more accurately. Overall, and as collaborated on by Darshan and Anandakumar (2015), IoT facilitates the creation of smart objects; in a sense, they can gather data and give meaningful information or even facilitate various actions. The illustration in Figure 1 provides a typical process diagram of the components of IoT. Notably, the ecosystem leads to a decision-making tool or an information system: a conceptual and logical flow of IoT systems. Essentially, the illustration provides components that communicate to provide meaningful information. (Alotaibi and Federico 2017; Kulkarni & Sathe 2014; Gelogo, Hwang, and Kim 2015).
The current review supposes that data mining is at the core of IoT – artificial intelligence (AI), machine learning, and data extraction. IoT facilitates convenient and autonomous data mining making it ubiquitous and extensively applicable in many disciplines including manufacturing and supply chain management. For example, He, Xue, and Gu (2020) noted that the increasing adoption of IoT is triggering what is described as Industry 4.0. According to MIIT (2015) (As cited in He, Xue, and Gu 2020), Industry 4.0 entails the integration of next-generation information technology to form new production modes, new industrial forms, and business models. Fundamentally, it is claimed that IoT has changed the environment in which manufacturing, supply chains, resource management, social models, and networks operate (He, Xue, and Gu 2020; López, Ranasinghe, Harrison, and Mcfarlane, 2020).
In a review of the potential of IoT in smart city development, Simmhan et al. (2017) noted that smart cities and homes are a practical manifestation of IoT. IoT systems allow smart cities to achieve intelligent management of operations, efficient transportation, better security, and better access to information. Furthermore, the sensor network enabled through internet IoT continues to play a critical role in education and research by facilitating more accurate and low-cost data collection options and enabling data analytics. Notably, the data mining ability of IoT and the ability of IoT systems to communicate remotely provide significant potential to explore trends (Rainie & Anderson, 2017). Additionally, the ability of IoT to use a variety of sensors and complex algorithms to analyze data and share it through a network has facilitated various engineering, transportation, and research activities (Kimsey et al. 2015; Simmhan et al. 2017). For instance, Kimsey et al. (2015) mentioned that students continue to use low-cost, open-source IoT platforms to create their applications and conduct research.
IoT-based systems have also facilitated security and law enforcement applications. According to Tundis, Kaleem, and Mühlhäuser (2020), IoT systems — a combination of various monitoring systems that range from mobile applications with the capability to provide critical data, to body cameras for law enforcement officers — contribute to safer spaces. IoT-driven, remote, and non-invasive monitoring systems have been applied in specialized healthcare, research, supply chain management, and even sports (Kulkarni and Sathe 2014).
Notably, there has been a growing body of knowledge regarding the use of IoT in personalized healthcare (Deng et al. 2017; Darshan and Anandakumar 2015). For example, Darshan and Anandakumar (2015) observed that IoT-facilitated remote health monitoring systems have enormous benefits over customary health monitoring systems. Acquiring healthcare data, especially for personalized healthcare, can be challenging. While commenting on the matter, a study by Gelogo, Hwang, and Kim (2015) mentioned that IoT supports a variety of sensors and complex algorithms that can be used to analyze the data and share it remotely over the internet. Such an ability may mean that individuals can access medical care remotely. That is, they can be monitored from their respective homes, as an example.
Another example of how IoT can facilitate personalized care is through the use of body sensor networks (BSN). In their analysis, Deng et al. (2017) noted that personalized care can be availed to elderly people through wearable body sensors. Such sensors collect physiological data, as coded for different patients, and relay the date to a healthcare provider. These features have facilitated improved access to care, increased quality of care, and most importantly, reduced the cost of healthcare (Kulkarni and Sathe 2014).
While it is possible to appreciate the significant opportunities for ubiquitous IoT, personalized healthcare (PHC) is amongst the most notable successes of the technology. Advances in IoT-enabled PHC range from applications in personal health management by individual cases through the use of smart sensors to diagnose where body sensors are used to relay data to healthcare providers. For instance, in diabetes therapy management, Jara, Zamora, and Skarmeta (2011) demonstrate a case where IoT personal care devices are used to facilitate getting the right insulin infusion calculation needs for individual patients. It is observed that sometimes many individual factors and conditions can complicate getting the right dosage for patients. However, personal IoT devices (such as radio-frequency identification (RFID) tags) can be developed to assist and consider more individual factors in the insulin therapy dosage calculation.
In similar cases, Serna, Pigot, and Rialle (2007) and Datta et al. (2015) discussed how IoT-enabled smart homes facilitated personalized healthcare. Serna, Pigot, and Rialle (2007) noted that smart homes, which are fundamentally enabled through IoT and data mining, provide support to cognitively impaired people. Giving a case of patients with Alzheimer’s disease, the article presents findings on how IoT-based data mining models were used to simulate the progression of dementia of the Alzheimer’s type by evaluating performance in the execution of an activity of daily living (ADL) in a smart home environment.
The foregoing cases demonstrate some of the capabilities of IoT-enabled data mining in developing personalized healthcare models and interventions. From near body sensors that collect physiological data to smart homes complete with networked smart devices, it is possible to argue that the potential of IoT in facilitating PHC will continue to grow. Most of the literature reviewed seemed to suggest that the integration of IoT in healthcare will continue to evolve. The documentation appears to emphasize the need to expand the knowledge in this area especially following the sensitivity of healthcare data. That is, increased use of interconnected devices in healthcare touches on the integrity of otherwise confidential health data.
Trends in IoT-enabled personalized healthcare appears to indicate that going forward, just like the way other sectors continue to limit personal interaction in service provision, IoT may enable remote diagnosis and other personalized healthcare services. For example, Amendola et al. (2014) demonstrated the use of IoT in data collection including such data as personal movements and gestures, and how such data can be used for human behavior analysis. The article also showcases the use of wearable and smart implants developed from RFID technology and supported through an IoT system, with the capability to provide services and enough read ranges to implement a network of sensors for tracking personalized human wellness and monitoring the quality of the local environment.
Despite considerable success in IoT development, there have been growing data security concerns (Chacko and Hayajneh 2018; Eken and Eken 2016). For example, Deng et al. (2017) explain that a malicious attacker could send incorrect sensing data to a medical reader causing an incorrect diagnosis. The authors highlight some of the security requirements for implementing secure IoT. Nonetheless, it is contended that IoT-based systems will continue to play a critical role in society and will also continue to face several challenges especially regarding data security (Deng et al. 2017; Chacko and Hayajneh 2018). Sensor technologies are continuously improving, indicating the increased potential for IoT- based systems. Also, developments in connectivity, especially the 5G network, is another indication of the potentials in IoT-based systems.
The general objective of this paper is to conduct a thematic analysis of the IoT dynamics including trends and rankings of the most number of apps over four years; descriptive statistics are also part of the research in addition to modeling highlights of an illustrative life digital personalized healthcare (PHC) study. A generic IoT ecosystem is also presented.