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Monitoring and resource management taxonomy in interconnected cloud infrastructures: a survey Vingi Patrick Nzanzu; Emmanuel Adetiba; Joke Atinuke Badejo; Mbasa Joaquim Molo; Claude Takenga; Etinosa Noma-Osaghae; Victoria Oguntosin; Sadeeq Suraju
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.20503

Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management.

Compact automatic modulation recognition using over-the-air signals and FOS features Emmanuel Adetiba; Folarin Joseph Olaloye; Abdultaofeek Abayomi; Nasir Faruk; Sibusiso Moyo; Obiseye Obiyemi; Surendra Thakur
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.4119

The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. The utilization of real-time over-the-air digital radio frequency (RF) data for the development of a digital spectrum sensing model based on the automatic modulation classification (AMC) is presented in this study as a step for incorporating opportunistic spectrum sensing onto the NomadicBTS architecture. Some digital modulation techniques were studied for second-generation (2G) through fourth-generation (4G) technology. The raw RF signal dataset was digitized and curated, while non-complex first-order statistical (FOS) features were used with algorithms based on the Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) to find the best learning algorithm for the generated AMR model. The results show that the developed AMR model has a very high likelihood of correctly classifying signals, with distinct patterns for each of the features of FOS. The results are compared to reveal a least mean square error (MSE) of 0.0131 with a maximum accuracy of 93.5 percent when the model was trained with seventy (70) neurons in the hidden layer using the LM method. The best model's accuracy will allow for the most precise identification of spectrum holes in the bands under consideration.

Monitoring and resource management taxonomy in interconnected cloud infrastructures: a survey Vingi Patrick Nzanzu; Emmanuel Adetiba; Joke Atinuke Badejo; Mbasa Joaquim Molo; Claude Takenga; Etinosa Noma-Osaghae; Victoria Oguntosin; Sadeeq Suraju
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.20503

Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management.

Ethylene gas-based fruit expiry predictor: a sustainable solution to fruit wastage Olabode Idowu-Bismark; Anyiam Chinemere Eberchukwu; Oluwadamilola Oshin; Ibiang Emmanuel; Tonwe Mary; Emmanuel Adetiba
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9809

Globally, environmental and economic issues are very apparent regarding food wastage. Fruits play a significant role since they can be damaged easily. Ethylene gas is one of the significant gases produced by fruits when they begin to ripen; it tests whether the fruit is ripe or else spoilable. This work presents a sustainable solution for fruit wastage: an ethylene gas-based fruit expiry predictor (FEP). The system is designed and developed around advanced sensing and artificial intelligence (AI) models for real-time monitoring of ethylene levels and temperature in forecasting the spoilage of fruits. The system consists of non-invasive sensors for detecting ethylene and temperature, a data processing microcontroller, and an AI model trained on a large dataset to make accurate predictions regarding the expiry of fruit. The AI model processes the information collected by the sensors and then displays the grade (level of ripeness of the fruit) on a liquid crystal display (LCD) screen. This solution improves fruit management with reduced wastage in line with international sustainability targets. These would enable real-time and highly accurate predictions of fruit spoilage, allowing end-users informed choices that will eventually lead to reducing the carbon footprint from food waste and increasing food security.

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