IoT is defined as the interconnection of machines and devices through the internet, enabling the creation of data that can yield analytical insights and support new operations.

Typical IoT communication architectures enable IoT devices to not only connect to the communication backbone - the Internet - using an infrastructure-based wireless network paradigm but also to communicate with one another autonomously.

Although there is no proposed standard for IoT architecture below table presented five architects or models developed by researchers, authors, and practitioners. Components included in each architecture model are listed in Table below.

Although there are slight differences in the architectural models, an IoT system generally contains three layers: a physical perception layer that perceives physical environment and human social life; a network layer that transforms and processes perceived environment data; and an application layer that offers context-aware intelligent services in a pervasive manner.

Some also describe the combination of multiple software and hardware components in a multilayer stack of IoT technologies composed of three core layers, i.e., the thing or device layer, the connectivity layer, and the IoT cloud layer.

An explanation is offered stating that architecturally, IoT consists of three layers: the device layer which is the basic infrastructure of IoT that uses technologies such Radio Frequency Identification (RFID), Near Field Communication (NFC), wireless sensor networks, and embedded intelligence; the connection layer which includes gateways and the core network; and the application layer which is comprised of objects that are sensor equipped.

Sensors measure, evaluate, and gather data. IoT comes together with the connection of sensors and machines.

Furthermore, cloud-based applications are the key to using leveraged data. The IoT doesn't function without cloud-based applications to interpret and transmit the data coming from multiple sensors. Drawing from this research, a simplified IoT architectural model was developed (Figure below).

While the architecture is important for a complete understanding of IoT and to IoT development teams for the implementation and maintenance of IoT, researchers and practitioners are likely to have a greater interest in IoT applications.

Although the IoT is an emerging technology, it covers a broad spectrum of application areas and impacts a significant percentage of the population.

Wortmann and Flüchter (2015) confirm this, indicating that the fields of application for IoT technologies are as numerous as they are diverse, as IoT technologies are increasingly extending to virtually all areas of everyday life.

As noted, IoT impacts almost every area of our lives, however, Bian et al. (2016) analyzed Twitter posts on the IoT for the period 2009–2015 and found that business and technology were the main areas of interest as they relate to IoT.

Wortmann and Flüchter (2015) support this finding indicating that the most prominent areas of application are the smart industry with the development of intelligent production systems and connected production sites. Based on these findings, the focus from this point onward will be on IoT in a business environment.

Findings revealed that IoT is affecting many parts of organizations, but the top three priority areas are customer experience, finance, and asset management (Insights Team, 2017e).

First on the list of priority areas and one of the most broadly applicable manifestations of the IoT is in its potential to help organizations improve the customer experience. Four ways that IoT can help improve the customer experience:

The second priority area is financial decision making. Jay (2018) suggests that the IoT will make it significantly easier for CFOs to measure and monitor business performance in a timely manner to ensure that their organization can respond to events in an agile fashion with data flowing into billing, enterprise resource planning, and accounting systems in real time.

“The IoT can play an important role in financial decision making by providing real-time visibility that complements data from enterprise resource planning (ERP) and accounting systems and allows for a more holistic view of the enterprise (Insights Team, 2017a, para. 13)”.

The third area identified as being a high priority area for executives is asset tracking and management. IoT is changing asset management. Junnila (2018) explains asset management as the process of keeping track of a company's physical assets.

Depending on the business, this could be equipment, computer devices, tools, or vehicles, for example. Utilizing IoT sensors attached to assets, companies can actively track information about their assets without human involvement.

Further, wireless sensor networks (WSN) can cooperate with RFID systems to better track the status of things such as asset location, temperature, movements, and efficiency of equipment (Lee and Lee, 2015, Team, 2017a, Atzori et al., 2010).

IoT based solutions are typically made up of several technologies, creating an environment that is complex and rapidly changing. The top five challenges identified by Insight Team, 2017c, p. 5) with building out IoT capabilities are “… investment, keeping the IoT secure, cross-department cooperation, integration of disparate data, and availability of skilled staff.”

Insight Team (2017d) identified four challenges often encountered when managing IoT technologies. These are:

Building out and managing IoT does not happen without challenges, but of significant concern and recurring throughout the IoT literature, are issues of privacy, security, and trust.

IoT is seen as part of the future of virtually every industry from healthcare to financial services to transportation. The IoT is dependent upon sensors, wireless communications, networks, cloud, storage, software, etc. (Insights Team, 2017c) escalating concerns about privacy, security, and trust.

The top hindrances to IoT growth are security and privacy concerns (Lund et al., 2014). A study listed the following as the main security challenges in IoT: access control, privacy, policy enforcement, trust, mobile security, secure middleware, confidentiality, and authentication.

Extensive literature was found on IoT privacy and security as related topics. Lee and Lee (2015) conducted a survey of IoT practices and identified multiple challenges of IoT adoption including data management, data mining, privacy, and security.

Supporting this finding, Insights Team (2017d) indicates that security is critical, as IoT devices are more frequently becoming a target for hackers and cyber terrorists.

Peppet (2014) contends that the IoT is difficult to anonymize and secure, thus creating privacy issues.

Many of the vulnerabilities in IoT could be mitigated through recognized security best practices, but too many products today do not incorporate even basic security measures. There are many contributing factors to this security shortfall.

One is that it can be unclear who is responsible for security decisions in a world in which one company may design a device, another supplies component software, another operates the network in which the device is embedded, and another deploys the device. This challenge is magnified by a lack of comprehensive, widely adopted international norms and standards for IoT security (p. 3).

The following principles offer stakeholders a way to organize their thinking about how to address these IoT security challenges:

IoT privacy and security concerns must be addressed before user trust in IoT applications can be established.

Trust is a complicated concept regarding confidence, beliefs, and expectations (Yan et al., 2014).

Miorandi et al. (2012) agree, indicating that although it is widely recognized as being important, trust is a complex notion about which no consensus exists in the information systems literature.

Sicari et al. (2016) describes the concept of trust as being associated with source reputation and thus reliability.

Mišura and Žagar (2016) suggest that application trustworthiness can be quantitatively evaluated by the similarity between the application's behavior and the behavior expected by the user.

In this same context, Roman et al. (2013) point out that there are two dimensions of trust as related to IoT, i.e., (1) trust in the interaction between entities, and (2) trust in the system from the point of view of the user.

Trust is essential when adopting and implementing IoT. It encompasses how users feel while interacting in the IoT (Roman, Najera, & Lopez, 2011).

“Trust management plays an important role in IoT for reliable data fusion and mining, qualified services with context-aware intelligence, and enhanced user privacy and information security” (Yan et al., 2014, p. 120).

Fernandez-Gago, Moyano, and Lopez (2017) established a framework for developers to include trust concerns in IoT systems. The framework suggests that trust should be included in all phases of the development of IoT systems following a proactive approach.

Guo et al. (2017) conducted a survey of trust computation models for IoT concluding with directions for trust computation research. While privacy, security, and trust are all critical to the success of IoT, privacy and security are precursors to trust and must be an ongoing consideration.