## 4 IoT Contribution to Monitoring Systems
### Security Aspects of Data for IoT-Based Big Data Transmission & Management over the Cloud
To identify potential security breaches or assaults on IoT devices or cloud infrastructure, advanced anomaly detection algorithms and machine learning approaches can be deployed. Real-time response to security incidents is made possible by these tools [3; 5; 10; 11]. Regular Security Updates and PatchesThe most recent security patches and upgrades should be applied to IoT devices, cloud systems, and related software to maintain a secure environment. This lessens the chance of exploitation and addresses any known vulnerabilities [3; 5; 10; 11]. Based on the aforementioned factors, effort was conducted in the first stage to build an algorithm that would harmonize the logic and operation of the most popular encryption algorithms. The technique that shows how to combine encryption algorithms for secure data management and transmission is described below. The encryption and decryption operations of the first encryption algorithm are denoted in Algorithm 1 by the functions _encryptWithAlgorithm1()_ and _decryptWithAlgorithm1()_, respectively. The encryption and decryption functions of the second encryption algorithm are represented, respectively, by the functions _encryptWithAlgorithm2()_ and _decryptWithAlgorithm2()_. The actual encryption and decryption methods we want to employ each time, such as AES, RSA, or any other encryption algorithms of our choice, can be substituted for the aforementioned function names in the suggested scenario. The input of Algorithm 1 obtains the transmitted data of video/image before encryption and the output is the transmitted data of video/image after encryption and ready to be transmitted. Also, in the reverse procedure, Algorithm 1 obtains the encrypted transmitted data of video/image and the output is the decrypted transmitted data of video/image ready to be used. ```
1. Initialize variables: -plaintext: the original data to be transmitted -key1: encryption key for the first encryption algorithm -key2: encryption key for the second encryption algorithm - ciphertext: the encrypted data 2. Encryption the data using the first encryption algorithm: encryptedData1 =encryptWithAlgorithm1(plaintext, key1) 3. Encryption the output of the first encryption using the second encryption algorithm: encryptedData2 =encryptWithAlgorithm2(encryptedData1, key2) 4. Transmit the encrypted data (encryptedData2) over the network. 5. On the receiving end, receive the encrypted data. 6. Decrypt the encrypted data using the second encryption algorithm: decryptedData1 =decryptWithAlgorithm2(encryptedData2, key2) 7. Decrypt the output of the second encryption using the first encryption algorithm: decryptedData2 =decryptWithAlgorithm1(decryptedData1, key1) 8. The original plaintext data is now recovered and stored in decryptedData2. 9. Perform further processing or utilize the decrypted data as needed. ### Video/Image Transmission and Processing Algorithm

Additionally, to the discussion about the contributions of MCC, IoT, and Monitoring, in this subsection, we will try to correlate the available connections that provide the possibilities for accessing video/images that are transmitted and processed through a Cloud-based Monitoring system. Based on the related review and all the contributions that progressed previously, we sum up to the conclusion that the better way to contact video/images that are transmitted and processed through a Cloud-based Monitoring system will be through a new algorithm approach. Relying on the previous works of [PERSON] et al. [32] and [PERSON] et al. [72], and the whole related works we studied, we can approach the video/image transmission and processing with the following equation:

\[ \begin{array}{l}b_{k+1}^{next}=b_{k}^{first}*[x*(f(T_{k}+\ldots+T_{k-1})\\ +g(PL_{k},PL_{k-1})+h(S_{k}+\ldots+S_{k-1}))]+b_{k-1}^{first-1}\end{array}  \tag{1}\]

In the above Equation (1) \(x\) represents a stable that highlights the significance of each component, \(f\), \(g\), and \(h\) are three functions that compare the modifications in each factor's value to the _first time window_.