# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT on the Ground  
#### 3.2.1 Extensions with Applications in Medicine  
Internet of Medical Things  
The recent advancements in mini-hardware manufacturing, microcomputing, and Machine-to-Machine (M2M) communications have made it possible for IoT platforms to reshape many existing networking applications. Healthcare systems are among these applications. Their evolution under the impact of IoT has led to an IoT extension referred to as the Internet of Medical Things. The application areas of the Internet of Medical Things (IoMT) vary from heart [56] and skin [57] disease detection, as well as C-reactive protein and serum Amyloid detection to healthcare monitoring [58], emotion recognition [59], tumor prediction [60], Colonoscopy [61], remote patient diagnosis [62] and surgery [63]. As suggested in recent research, the most important security challenges and mechanisms of this extension can be listed below:  
* Security Challenges
* Privacy The authors of the paper [64] suggest the use of a blockchain-based record to have decentralized electronic health records. They propose that the blockchain model be used in part with smart-contract-based service automation. Some of the major drawbacks of such a system include high latency, large storage costs, and single-point failure. The proposed solution is the use of a Distributed Data Storage System. Device authentication is handled by a decentralized selective ring-based access control. The anonymity of the patient information is protected through the use of different patient anonymity algorithms. Similarly, the paper [65] uses the blockchain-based model. The proposed system uses a triple subject purpose-based access control model. The model would work in part with a transactional blockchain network to also allow for a more decentralized approach. Access to the system will be limited to users with certain privileges following a Local Differential Privacy-based policy. This will prevent malicious users from having access to the entire system. The proposed blockchain-enabled method has been tested in a live setting with more than 100,000 patient records and has shown great improvements over current systems. To compare the decentralized blockchain methods proposed above, ref. [66] uses two non-colluding severs and a privacy-preserving cloud-aided diagnosis scheme to create a secure way to outsource diagnoses. This diagnosis scheme uses a combination of AES and homomorphic encryption to make user requests more efficient. The analysis in the paper illustrates that the proposed scheme is better than prior works when looking at the system in terms of security and usability. The paper [67] goes further and talks about how most new solutions focus on data encryption, increasing the cost of sending and computing information to patients and users. The scheme these authors propose focuses on three main points: a guarantee of privacy, efficient integrity verification to prevent incorrect query and computation, and lastly, lightweight operations of the patient and the user. This aims to take full advantage of cloud-aided systems while limiting resource costs. Another area that has gained a lot of attention is the future of 5G networks. Ref. [68] suggests a method of device-to-device communication for medical services that uses future 5G networks. The method is the use of an intelligent trust cloud management system. The authors propose an update to the current system to make it more adaptive and intelligent in a wireless medium. Tests performed by the authors demonstrate that the updates will address trust uncertainty and improve the detection accuracy of malicious devices. The following paper [69] was inspired by the current pandemic and proposes a new privacy-enhanced data fusion strategy. The design of the system is proven to successfully demonstrate an improvement in the protection of data fusion during the outbreak of COVID-19. The authors take into account task completion, classification accuracy, reliability, and a low rate of errors based on its use in IoMT.