## 4 IoT Contribution to Monitoring Systems
### Security Aspects of Data for IoT-Based Big Data Transmission & Management over the Cloud  
The safety and security of the data generated by IoT-based systems, which mostly refers to Big Data, which is transferred and managed over a Cloud-based system, is a key concern for this research. Multiple aspects of data security and privacy are involved in IoT-based Big Data secure transmission and administration through a cloud system. Below is an overview of the main methods and safeguards employed to guarantee the privacy and security of data in this situation. _Encryption:_ A crucial method for preserving data secrecy during transmission and storage is encryption. For secure communication channels with cloud services, IoT devices can use encryption protocols like Transport Layer Security (TLS) or Secure Sockets Layer (SSL). As a result, information sent between IoT devices and the cloud is encrypted and kept out of the hands of unauthorized parties [3; 5; 10; 11]. _Access Control_: Access control technologies are essential for limiting illegal access to cloud-based IoT data. To implement data access limitations based on user roles, privileges, or particular attributes, role-based access control (RBAC) and attribute-based access control (ABAC) are frequently utilized. Mechanisms for access control help prevent unwanted data alteration or leaking [3; 5; 10; 11]. _Data Integrity:_ For IoT data to be accurate and reliable, maintaining data integrity is essential. Data integrity can be checked during transmission and storage using methods such as message authentication codes (MACs) and digital signatures. These methods aid in the detection of unapproved data alterations [3; 5; 10; 11]. _Secure Protocols and Standards_: It is crucial to adhere to secure communication methods and standards. IoT device-specific protocols with security features, like authentication and encryption, include MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol). Data transmission over the cloud is secure when certain procedures are followed [3; 5; 10; 11]. \begin{table}
\begin{tabular}{l l} \hline \hline (a) Storage over the Internet & (d) Energy efficiency \\ (b) Service over the Internet & (e) Computationally capable \\ (c) Applications over the internet & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MCC Features Related to the IoT (with all extensions) and Monitoring in Smart Healthcare. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video Monitoring Characteristics** & **MCC-(a)** & **MCC-(b)** & **MCC-(c)** & **MCC-(d)** & **MCC-(e)** \\ \hline _Computer_ & X & & X & X \\ _Telephones_ & X & X & X & X \\ _Cameras_ & X & X & X & X \\ _Biometric_ & & X & X & \\ _Data mining and profiling_ & X & X & X & X \\ _RFID and geolocation devices_ & & X & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Contributions of Mobile Cloud Computing in Monitoring. Privacy PreservationWhen working with sensitive IoT data, maintaining user privacy is essential. A useful analysis of the aggregated data can still be performed while masking or obscuring personally identifying information (PII), using methods such as data anonymization, aggregation, and differential privacy. While allowing useful insights from IoT big data, privacy-enhancing solutions help protect user privacy [3; 5; 10; 11]. Data Lifecycle ManagementThe security and privacy of IoT data are supported by a thorough approach to data lifecycle management. This includes regular backups to guard against data loss and secure data deletion when it is no longer required. Data lifecycle management requires the implementation of data retention regulations and secure data disposal procedures [3; 5; 10; 11]. Intrusion Detection and PreventionNetwork traffic can be monitored by intrusion detection and prevention systems (IDPS), which can then identify any unusual or malicious activity.