Internet of Things and Energy Consumption: Examination and Analysis of Optimization Models

Subject Areas : Computational economics

Amir Abbass Farahmand ¹ , Mehdi Jafari ² , Amir Hossein Kasraee ³

1 - Assistant Professor, Department of Technology Management, Central of Tehran Branch, Islamic Azad University, Tehran, Iran.
2 - Assistant Professor, Department of Technology Economy, Central of Tehran Branch, Islamic Azad University, Tehran, Iran.
3 - P.H.D candidate, Department of Industrial Management, Science & Research Branch, Islamic Azad University. Tehran, Iran.

Received: 2024-02-04 Accepted : 2024-09-22 Published : 2024-10-23

Keywords: Internet of Things, energy consumption, optimization, energy consumption modeling, optimization algorithm,

Abstract :

Extended Abstract

Purpose

The Internet of Things is an extensive network of physical objects such as devices, machines, buildings, and other tools in which sensors, network connections, and software are embedded as a small system. The method of clustering things is one of the efficient methods to reduce energy consumption in the data transfer phase in the Internet of Things. In clustering, each cluster has a node called the cluster head, which is responsible for coordinating network operations and collecting data from sensor nodes. The most important objective of this study is to improve the amount of energy consumption in the data transfer cycle. Most of the communication and computing tasks should be completed within a limited period to avoid unfavorable consequences since IoT applications are very time-sensitive and critical. Thus, ensuring real-time support in large-scale IoT networks is one of the most important and challenging research issues.

Methodology

PSO, CPSO, and FCPSO algorithms were used in this study. Finally, the FCPSO algorithm was formulated using the theory of fractional order derivatives. MATLAB was used to simulate and evaluate IoT information security and PSO, CPSO, and FCPSO in smart buildings. Also, the results of the proposed method were compared with similar methods. Several parameters are affected. The values of these parameters strongly depend on the convergence improvement. In this study, the following parameters are considered when transferring data to the center or between things:

-Etx and Erx: energy used to transfer and receive data, respectively, in the nodes.

-Dij: distance between node i and node j.

-Eelec: energy available in each node.

-Fij: data transfer rate between two nodes.

-CS, CR, and CB: base station node cost, sensor node cost, and reinforcing node cost, respectively.

All simulations were performed in the Windows 10 operating system using quad-core 2.5 GHz processors, working frequency 2, and a memory capacity of 8 GB. The simulation was done in MATLAB 2020 software. Ten bench test functions were used to examine the effectiveness of the proposed algorithm.

Finding

In the scenario where the FCPSO method is used for information transfer, the energy consumption can be reduced by using the FCPSO method for optimization. The second test method uses the same described method to evaluate Etx and Erx parameters. These two parameters can be viewed as a single parameter whose value can be set arbitrarily for each thing. The FCPSO method performs better than the standard transfer mode in this test condition.

In this measurement, the amount of memory consumption (in megabytes) to complete authentication requests in smart buildings based on the Internet of Things has been investigated and compared based on the number of sent packages. The results indicate that the third method has the lowest memory consumption for more packets, while the proposed method could reduce the memory consumption for a high number of packets.

In the second test, the amount of energy required to transfer data from the source to the destination is evaluated. The number of data packets increased in each successive step. Results shows that the proposed method uses less energy than comparable methods.

The accuracy criterion shows how well the proposed technique can accurately display the identity in smart buildings based on the Internet of Things. Results compares the accuracy rate of the proposed technique for 188 unique identity effects with the accuracy of the second and third methods for different packets.

The simulation results indicate that the proposed method is more accurate than the second and third methods for identifying distinct packets in IoT-based smart buildings.

Conclusion

In this study, a model based on the cycle and transfer of information to the base station and between items was used for energy consumption and the Internet of Things network. The most crucial challenges in IoT routing are the energy consumption of each node, comparability, error tolerance, and network dynamics. It is necessary to evaluate the quality of a node using appropriate criteria for effective communication and information transfer. In this study, the energy used to transfer and receive data in nodes, the energy available in each node, the distance between two nodes, and the data transfer rate between two nodes were evaluated. Given the objective of this study, which is to optimize energy consumption in the Internet of Things, the FCPSO algorithm was used to reduce energy consumption during information transfer. Using the FCPSO algorithm to reduce energy consumption in the Internet of Things is possible due to the capability of the algorithm to optimize the problem by considering the number of parameters that can significantly affect the performance of the problem, which is the goal of many optimization problems. The simulation results revealed that using the FCPSO method improves and reduces energy consumption during the program execution and information transfer cycle. The most important limitation of this study is that there is not enough data to build an optimal model. Thus, the possibility for model non-convergence increases. Another crucial issue in this regard is the degree of computational complexity that new data transfer models require.

 

 

 

References:

Al-Ali, A. R., El-Hag, A., Bahadiri, M., Harbaji, M., & El Haj, Y. A. (2011). Smart home renewable energy management system. Energy Procedia, 12, 120-126.
Al-Ali, A. R., Zualkernan, I. A., Rashid, M., Gupta, R., & AliKarar, M. (2017). A smart home energy management system using IoT and big data analytics approach. IEEE Transactions on Consumer Electronics, 63(4), 426-434.
Al-Sodairi, S., & Ouni, R. (2018). Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks. Sustainable computing: informatics and systems, 20, 1-13.
Arjunan, S., & Pothula, S. (2019). A survey on unequal clustering protocols in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 31(3), 304-317.
Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., & Gandomi, A. H. (2019). Residual energy-based cluster-head selection in WSNs for IoT application. IEEE Internet of Things Journal, 6(3), 5132-5139.
Braham, M., Miller, T., Duerr, A. E., Lanzone, M., Fesnock, A., LaPre, L., ... & Katzner, T. (2015). Home in the heat: dramatic seasonal variation in home range of desert golden eagles informs management for renewable energy development. Biological Conservation, 186, 225-232.
Brown, J., Abate, A., & Rogers, A. (2021). Disaggregation of household solar energy generation using censored smart meter data. Energy and Buildings, 231, 110617.
Chakchouk, N. (2015). A survey on opportunistic routing in wireless communication networks. IEEE Communications Surveys & Tutorials, 17(4), 2214-2241.
Ever, E., Shah, P., Mostarda, L., Omondi, F., & Gemikonakli, O. (2019). On the performance, availability and energy consumption modelling of clustered IoT systems. Computing, 101, 1935-1970.
Fanian, F., & Rafsanjani, M. K. (2019). Cluster-based routing protocols in wireless sensor networks: A survey based on methodology. Journal of Network and Computer Applications, 142, 111-142.
Gajjar, S., Sarkar, M., & Dasgupta, K. (2016). FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks. Applied Soft Computing, 43, 235-247.
Huang, X. (2020). Quality of service optimization in wireless transmission of industrial Internet of Things for intelligent manufacturing. The International Journal of Advanced Manufacturing Technology, 107, 1007-1016.
Jesudurai, S. A., & Senthilkumar, A. (2019). An improved energy efficient cluster head selection protocol using the double cluster heads and data fusion methods for IoT applications. Cognitive systems research, 57, 101-106.
Karunanithy, K., & Velusamy, B. (2019). CSDGP: cluster switched data gathering protocol for mobile wireless sensor networks. IET Communications, 13(18), 2973-2985.
Kashfi Nishaburi, M., Malekiniya, M., Ghasemzadeh, M., Abbasgholi, A. (2022). Optimization of Energy Consumption in Smart Cities Based on the Internet of Things. 2nd International Conference on Electrical, Computer and Mechanical Engineering, Tehran. [In Persian]
Kavand, H., Ahmadi, S. T. (2020). Green Internet of Things: Reducing Energy Consumption in the Internet of Things, Challenges and Methods. 2nd National Conference on Creative Ideas in Sustainable Energies, Kermanshah. [In Persian]
Kumar, A., Sharma, S., Goyal, N., Singh, A., Cheng, X., & Singh, P. (2021). Secure and energy-efficient smart building architecture with emerging technology IoT. Computer Communications, 176, 207-217.
Kumar, V., & Kumar, S. (2016). Energy balanced position-based routing for lifetime maximization of wireless sensor networks. Ad Hoc Networks, 52, 117-129.
Li, H., & Liu, J. (2016). Double cluster based energy efficient routing protocol for wireless sensor network. International Journal of Wireless Information Networks, 23, 40-48.
Malathi, L., Gnanamurthy, R. K., & Chandrasekaran, K. (2015). Energy efficient data collection through hybrid unequal clustering for wireless sensor networks. Computers & Electrical Engineering, 48, 358-370.
Mazinani, A., Mazinani, S. M., & Mirzaie, M. (2019). FMCR-CT: An energy-efficient fuzzy multi cluster-based routing with a constant threshold in wireless sensor network. Alexandria Engineering Journal, 58(1), 127-141.
Mba, L., Meukam, P., & Kemajou, A. (2016). Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy and Buildings, 121, 32-42.
Miles, B., Bourennane, E. B., Boucherkha, S., & Chikhi, S. (2020). A study of LoRaWAN protocol performance for IoT applications in smart agriculture. Computer Communications, 164, 148-157.
Misra, S., Chatterjee, S., & Obaidat, M. S. (2014). On theoretical modeling of sensor cloud: A paradigm shift from wireless sensor network. IEEE Systems journal, 11(2), 1084-1093.
Neamatollahi, P., Abrishami, S., Naghibzadeh, M., Moghaddam, M. H. Y., & Younis, O. (2017). Hierarchical clustering-task scheduling policy in cluster-based wireless sensor networks. IEEE Transactions on Industrial Informatics, 14(5), 1876-1886.
Padidegaran Moghaddam, F., & Asadi, H. (2017). Review of Methods and Equipment for Smart Building. 4th International Conference on New Findings in Science and Technology, Qom, Soroush Hekmat Mortazavi Islamic Studies and Research Center. [In Persian]
Panag, T. S., & Dhillon, J. S. (2018). Dual head static clustering algorithm for wireless sensor networks. AEU-International Journal of Electronics and Communications, 88, 148-156.
Salehi, S., Farbeh, H. (2021). Optimization of Energy Consumption in the Internet of Things Based on Software-Defined Networking. Journal of Soft Computing and Information Technology, 10(2), 27-38. [In Persian]
Sangwan, A., & Singh, R. P. (2015). Survey on coverage problems in wireless sensor networks. Wireless Personal Communications, 80, 1475-1500.
Sedik, A., Hammad, M., Abd El-Samie, F. E., Gupta, B. B., & Abd El-Latif, A. A. (2021). Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Computing and Applications, 1-18.
Shin, J. H., & Hwang, J. (2012). Intelligent energy information service based on a multi-home environment. Procedia Computer Science, 10, 197-204.
Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2020). IoT-based big data secure management in the fog over a 6G wireless network. IEEE Internet of Things Journal, 8(7), 5164-5171.
Sun, H. C., & Huang, Y. C. (2012). Optimization of power scheduling for energy management in smart homes. Procedia engineering, 38, 1822-1827.
Taherkhani, A. (2022). Using Artificial Intelligence to Optimize Energy Consumption in the Internet of Things. 9th National Congress on Recent Advances in Electrical and Computer Engineering in Iran, Tehran. [In Persian]
Tartibion, Z., Haghparast, M. (2021). Optimization of Energy Consumption in Internet of Things Using Fuzzy System in Smart Buildings. Journal of Elite in Science and Engineering, 6(6), 80-94.[In Persian]
Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211-223.
Wang, J., Cao, J., Li, B., Lee, S., & Sherratt, R. S. (2015). Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Transactions on Consumer Electronics, 61(4), 438-444.
Xiuwu, Y., Qin, L., Yong, L., Mufang, H., Ke, Z., & Renrong, X. (2019). Uneven clustering routing algorithm based on glowworm swarm optimization. Ad Hoc Networks, 93, 101923.
Xu, L., Collier, R., & O’Hare, G. M. (2017). A survey of clustering techniques in WSNs and consideration of the challenges of applying such to 5G IoT scenarios. IEEE Internet of Things Journal, 4(5), 1229-1249.
Yan, R. (2017). Optimization approach for increasing revenue of perishable product supply chain with the Internet of Things. Industrial Management & Data Systems, 117(4), 729-741.
Younis, O., & Fahmy, S. (2004). HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on mobile computing, 3(4), 366-379.