Cloud computing is one revolution in information technology (IT) that can share resources, services and data through a network among users. Because users have same rights on the network to transfer data, data are vulnerable to be attacked by unauthorized person. Lately, data security in a system only concentrates on data storage on cloud by utilizing internet security, but a little concentration is found during data transfer. By considering security as a serious problem, an encryption-based proposed system is presented to secure during data transfer. Authors propose an approach to boost system security during data transfer in order to prevent data theft by unauthorized person. To prevent an attack by unauthorized person, Advanced Encryption Standard (AES) will be proposed to secure data transfer and storage in cloud computing. For better future, authors will propose Systematic Literature Review (SLR) to generate suggestions and opportunities in AES cloud computing.

Lately, utilization of Wireless Sensor Network (WSN) technology is increasing and widely applied in various fields. WSN is currently applied for Internet of Things (IoT)-based agriculture. An example of WSN technology is ZigBee. ZigBee provides irrigation control, climate monitoring and food chain control systems. To support all mentioned systems, ZigBee’s IoT supports communication among available sensors. By having communication, ZigBee is expected to improve productivity and predict agriculture problem. The paper will use Systematic Literature Review (SLR) to give an overview that IoT has potential for agriculture. This is because IoT can help every farmer during operational on food and livestock production.

The use of load balance on a network will be very much needed if the network is an active network and is widely accessed by users. A reason is that it allows network imbalances to occur. Round Robin (RR) algorithm can be applied for network load balancing because it is a simple algorithm to schedule processes so that it can provide work process efficiency. Authors use the Systematic Literature Review (SLR) method in which it can be applied for criteria selection during papers search to match the title being raised. SLR is divided into five stages, namely formalization of questions, criteria selection, selection of sources, selection of search results, and quality assessment. By using SLR, it is expected that papers according to criteria and quality can be found.

Current mobile telecommunications deployment in Indonesia, based on 2G, 3G and 4G technologies, lags behind many other developing countries because of Indonesia’s larger territory. This paper presents recent data on revenue growth (%) and the number of Base Transceiver Stations (BTSs) in Indonesia, divided among 2G, 3G and 4G technologies, and forecasts future revenue growth and numbers of BTSs for the next few years. The results show that, while revenue growth from 2G operation is decreasing and 4G deployment is significantly increasing, there are still significant revenues from 2G services and many 2G BTSs in operation at the end of the forecast period, making it difficult to shut down the 2G networks in the near future.

Blockchain is used as encryption algorithm in cryptocurrency, but less researches are found to study blockchain for data encryption. Data encryption is needed to protect the data from data theft. We know about data encryption, there are RSA, LEAP, AES, and other algorithms. This research proposed a review in AES algorithm for data encryption within blockchain technology. The research process is followed by determining library, then creating relevant questions and criteria. For good opportunity in the future, this paper generated suggestions and opportunities so that better research can be established in data encryption.

Currently, utilizing virtualization technology in data centers often imposes an increasing burden on the host machine (HM), leading to a decline in VM performance. To address this issue, live virtual migration (LVM) is employed to alleviate the load on the VM. This study introduces a hybrid machine learning model designed to estimate the direct migration of pre-copied migration virtual machines within the data center. The proposed model integrates Markov Decision Process (MDP), genetic algorithm (GA), and random forest (RF) algorithms to forecast the prioritized movement of virtual machines and identify the optimal host machine target. The hybrid models achieve a 99% accuracy rate with quicker training times compared to the previous studies that utilized K-nearest neighbor, decision tree classification, support vector machines, logistic regression, and neural networks. The authors recommend further exploration of a deep learning approach (DL) to address other data center performance issues. This paper outlines promising strategies for enhancing virtual machine migration in data centers. The hybrid models demonstrate high accuracy and faster training times than previous research, indicating the potential for optimizing virtual machine placement and minimizing downtime. The authors emphasize the significance of considering data center performance and propose further investigation. Moreover, it would be beneficial to delve into the practical implementation and dissemination of the proposed model in real-world data centers.