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Edge Computing Builds Privacy and Security

In the recent era, new technologies are emerging day by day causing multiple issues to pop up. Once they get resolved, many new problems arise. This is especially true when considering edge computing, where multiple IoT devices are deployed which are not very secure as they enable a centralized cloud platform. In advanced technological infrastructure, IoT devices are increasing enormously where security raises a potential concern and makes the devices to operate in a secured manner. To make the devices secure, an encryption strategy and authentication method must be adopted. The impact of data reliability also plays a significant role where different devices, which have different behaviors in terms of connectivity, processing time, etc. are needed. While processing these devices, data redundancy and failure management need to be ensured so that data can be delivered properly.

Generally, edge-based cloud computing is developing rapidly and proving its demand in the field of computing. Even though it is proving its ability to make edge-based devices powerful, there is a lack in device performance and need for reduced latency while it is integrated with machine learning, data analytics, etc. To compromise the above challenges, security and privacy play an important role while driving toward edge computing. While exposing millions of edge computing devices and IoT-based devices, it is clear that security plays a vital role in edge-based IoT computing.

Benefits of Edge Computing

Organizations using edge computing to power their IoT systems can minimize the latency of their network, i.e., they can minimize the time for response between client and server devices. Since the data centers are closer to the endpoints, there is no need for data to travel to and from the distant centralized systems. And as the edge storage and control systems are only required to handle the data from the few endpoints they are linked to, bandwidth issues seldom slow down the flow of data. Since IoT systems require high-speed information transfer to function with maximum efficacy, edge computing can significantly boost organizational performance.

Another benefit of decentralizing IoT with edge computing is providing data security. A centralized data repository is prone to hacks that aim to destroy, steal, or leak sensitive data, and such attacks can lead to the loss of valuable data. Conversely, distributing critical data across an IoT network and sitting on edge devices can limit the loss of data. Additionally, it can also help in compliance with data privacy rules, such as the General Data Protection Regulation (GDPR), since data is only stored in devices or subsystems that would use that data. For instance, a multinational corporation can use edge devices to store customer data on local devices that are closer to where the customers are, instead of storing the data in an overseas repository. The data need not be stored in locations where irrelevant personnel may have access to it. Cloud costs will also be minimized as most data will be stored on the edge devices, instead of on centralized cloud servers. Additionally, the cost of maintaining high-capacity, long-range networks will be reduced as bandwidth requirements continue to diminish. It is easy to see now why any discussion on IoT should always include the exploration of edge computing as a key enabler. Edge computing, more than a technology, is a design framework of sorts that would redefine the way IoT systems are built and the way they function. Although the combination of other solutions will also be needed to expedite the widespread adoption of IoT, edge computing might just prove to be the chief catalyst in the process.


Aul can be applied to many processes (e.g., business sales, decision making, identification verification, logistics planning, and remote assistance) and many industries (e.g., health care, journalism, insurance underwriting, manufacturing, finance, real estate, military, and legal).

  • Business sales: The merger of humans and machines is critical to advancing modern business enterprise. Departmental heads, team leads, and vice presidents often identify business opportunities to apply Aul. Although Aul systems can be placed anywhere in a business organization, it is mostly used for business sales. Integrating intelligent capabilities into your sales department is a smart investment in your salespeople. Aul takes the complex sales tasks off the salespersons, enabling them to focus on meeting the needs of their customers. AI is capable of analyzing sales data and translate the data into action [9]. The range of business problems to which Aul applies continues to expand at a rapid pace.
  • Decision making: If properly applied, Aul can provide feedback and insights that enhance decision-making. It is the ability of a manager to leverage AI and collective intelligence for every decision.
  • Logistics planning: A logistics planner requires a lot of knowledge and experience about what works and what does not work in the industry. Aul comes in when AI is used to deal with high-skilled logistics planners. To improve logistics planning, companies should use Aul, which combines inputs from human planners with AI technology [12].
  • Health care: Patients and health care practitioners face enormous challenges, such as rapidly aging populations, shortage of physicians, and high costs of care. This is partly illustrated by the quadruple aims of health care shown in Figure 1.4 [10]. The primary role for AI is augmentation of the intelligence and skills of care givers. Aul is not designed to replace health care practitioners but to enhance human intelligence and the physician-patient relationship [11].
  • Militan/: The military deems a combination of humans and machines as necessary to cope with the complexity of information decoding and maximize the military ability to create, exploit, and adapt. Aul will harness the best of our soldiers and technology to meet future challenges [13-14].


The quadruple aims of health care.

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