Everything that can be calculated must be calculated. This is the key to avoiding failure in enterprise Internet of Things (EIoT) projects. However, how achievable is it to create the most accessible IoT ecosystem?
This may sound paradoxical, and while the IoT approach aims to increase transparency in business operations, there can still be uncertainty about its technical implementation. While the practical benefits of IoT-based applications, such as IoT** maintenance and industrial automation, have been demonstrated, it remains challenging to be absolutely accurate** and manage benefit parameters. This naturally raises the question of calculating investment and returns.
Poor initial strategy and lack of calculations are the main reasons why most EIOT initiatives fail.
Uncertainty is an important aspect to consider in enterprise IoT solutions. It refers to an event that can cause the system to behave differently than expected, resulting in an erroneous outcome. Put simply, it means that we can't be sure if the IoT ecosystem will function as effectively as theoretically thought. By understanding and addressing these uncertainties, we can work towards creating more reliable and efficient IoT systems.
Events that cause measurements to deviate from expectations include:
The data is inaccurate.
Network failures and latency issues.
Security breaches. Human alerts.
These seemingly small and rare events don't allow us to determine when the investment in an enterprise IoT solution will pay off. This fact not only exacerbates business operations, but also creates untrustworthy patterns that damage the reputation of IoT-based solutions. The higher the uncertainty metric, the more likely it is that the IoT solution will fail to meet performance targets, resulting in false, false-positive, and false-negative results. In general, any event that is difficult to ** increases the uncertainty of the system.
Reasons for uncertainty often include incomplete information, inadequate understanding, and unclear alternatives. The following factors put any mature IoT ecosystem at high risk:
1.The Internet of Things is a fairly flexible new conceptThe Internet of Things is a fast-growing field, which leaves room for unexpected new technologies. Due to its modular nature, there is no limit to the technologies that can be exploited in the IoT ecosystem. In addition to the uncertainties associated with technology, we should also consider social, economic, regulatory, and other uncertainties. All this uncertainty is due to a lack of information and pairs"A connected world"caused by ignorance.
2.Interaction of IoT systems with the real world
The real physical environment cannot be 100% available, that is, we can't know its future state by having data on its initial state. Of course, it is not possible to develop a scenario for all scenarios. This is especially true for the analysis of real-time events, such as road traffic or people's behavior in certain locations.
3.The system is primarily based on Complex Event Processing (CEP).
The IoT ecosystem processes events from different ** to determine patterns that allow the system to make decisions. Uncertainty arises at the stage where data is collected through ineffective sensing. It then influences the end result by processing the rules. In this way, a cascade of uncertainties is created.
Uncertainty, as well as complexity and dynamics, is one of the main characteristics of modern systems. When it comes to enterprise IoT solutions, these factors are highly correlated: the more complex and dynamic the IoT-enabled system, the more important the uncertainty factor is to the overall evaluation of the IoT solution. In general, complexity is determined by the challenges that an IoT system must solve, while dynamics are determined by the environment in which an IoT system operates.
There may be no limits to what the IoT ecosystem can do – it should work with any object and person in any environment. As a result, uncertainty increases with the complexity of the system and the connection to a large number of networks. This is the case of smart factories, power grids, smart buildings, and especially smart cities.
The main factors that reduce the effectiveness of an IoT solution include:
A large number of devices of different natures.
Rapid scalability.
Greater use of wireless data transmission.
Human involvement in the operation process.
Redundant and diverse data.
Lack of cybersecurity measures.
Sophisticated analytical models designed to find problems.
In addition, there are uncertainties about specific applications in the IoT ecosystem. For example, sophisticated artificial intelligence. Often, it processes inputs from a real-world physical environment outside of the lab or shop floor that inherently cannot**. As a result, IoT and AI-based applications have conflicting and unreliable analysis results due to a lack of information.
Another example is the intelligent reactive system developed in an IoT solution. A complex application processes multiple signals to decide what to do. It may require adaptive software that evaluates probabilities, which introduces considerable uncertainty issues. For example, a logistics IoT solution can analyze data from transportation sensors as well as third-party information (weather, congestion, road conditions) to decide whether or not to allow a vehicle to drive.
In order to improve the availability of EIOT systems, we make the following recommendations:
1.Minimize uncertainty at the data collection level。Poor data quality is the root cause of inaccurate outcomes and behavioral flaws in the IoT ecosystem. Misjudgments made from poor initial data can significantly reduce the business efficiency of the solution. Faulty, fail-prone, poorly calibrated sensors, RFID issues, and external factors can all produce noisy data. First and foremost, sensor-related issues must be avoided;Second, all data must be filtered before it is sent to the analytics department.
2.Interval assessment effectiveness indicators。This approach helps to refine the estimate of the required investment and the expected return on investment. Once uncertainty is accounted for, it's easier to calculate the best- and worst-case scenarios to evaluate ROI and BEP at intervals. While enhancing risk assessment, this approach also increases the credibility of IoT solutions. Basically, for enterprise IoT solutions"Precise"Often, it doesn't come true, it does. You can use scenario simulation to detect unprecedented events before they occur and protect management from the consequences of uncertainty.
3.Decentralized IoT ecosystem。In centralized systems, compatibility challenges, outages, and overloads are common. To minimize these"Accidental"to simplify the scalability of IoT-based systems, preferably with decentralized edge technologies. Edge technology allows for the creation of additional processing nodes close to the data source (on-site), offloading the cloud and freeing up bottlenecks associated with speed limitations. This is especially important for complex IoT applications, including real-time operations and advanced analytics.
4.Adopt the scientific method (R&D).。If you decide to invest in an innovative IoT project for the long term, you can't afford to turn a blind eye to some uncertainties. Ready-made software is obviously not for you, and you need to create a custom one, which should be quite complicated. Applying a variety of mathematical models for valuable data analysis can be valuable. In addition, simulation can also help. If you're creating a complex system with multiple data sources, it's best to simulate it first.
5.Reassess cyber risk frequently。Cybersecurity is a separate vulnerability associated with IoT solutions. Multiple data sources naturally increase the attack surface, and the connectivity of the entire system poses a danger to devices or data stores even when compromised into IoT devices. As new devices are connected and new technologies are applied in the IoT ecosystem, enterprise cybersecurity measures must be updated as well.
Rapid development of IoT solutions for enterprises requires minimal uncertainty in order to gain a foothold in the market. Whoever can navigate it faster will have greater success in the long run.
Uncertainty arises from complex and dynamic systems and is increased by their complexity, human factors, neglect of security measures, and the use of unreliable networks.
When calculating the investment and return on an IoT solution, the uncertainty must be fully assessed to account for all possible scenarios.
In order to improve the availability of the system, problems with sensors should be minimized, the burden on the network should be reduced, and the updates of network security should be kept in mind.
When maximum accuracy is critical to an EIOT solution, complex mathematical models can be applied in the software for data processing and analysis.