Effects of Novel Material Field Effect Transistor for Heterogeneous Energy and Traffic-Aware Secure Applications

Abstract

The advent of the automated technological revolution has enabled the Internet of Things to rejuvenate, revolutionize, and redeem the services of sensors. The recent development of microsensor devices is distributed in a real-world terrestrial environment to sense various environmental changes. The energy consumption of the remotely deployed microsystems depends on its utilization efficiency. Improper utilization of sensor nodes’ heterogeneity could lead to uneven energy consumption and load imbalance across the network, which will degrade the performance of the network. The proposed heterogeneous energy and traffic aware (HETA) considers the key parameters such as delay, throughput, traffic load, energy consumption, and life span. The residual energy and a minimum distance between the base station and cluster members are taken into consideration for the cluster head selection. The probability of hitting data traffic has been utilized to analyse energy and traffic towards the base station. The role of the sensor node has been realized and priority-based data forwarding are also proposed. As a result, the heterogeneous energy and traffic aware perform well in balancing traffic towards the base station, which is analysed in terms of maximum throughput and increase in a lifetime of heterogeneous energy networks more than 5000 rounds, and the algorithm outperforms 34.5% of nodes are alive with transmissible energy. The proposed research also endorses unequal clustering and minimum energy consumption. We have modeled our proposed research using various p-type junctionless nanowire FET without doping injunctions. The materials used in this analysis were silicon (Si), germanium (Ge), indium phosphide (InP), gallium arsenide (GaAs), and Al(x)Ga(1−x)As. The dimensions of the p-type cylindrical nanowire channel were 25 nm long and 10 nm in diameter.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The pandemic monster, COVID-19, has become a huge challenge to present-day trauma-free automation technology. Now, the technology is moving in such a mode. So, beyond the specific application, the need for microsystems has been developed. Towards the smart living environment, wireless sensor network with multisourced Internet of things (IoT) has changed in many applications such as monitoring, surveillance, healthcare, automation, entertainment, and industry. The only major challenge of the world for the last year has been the hazardous increase of COVID-19 positive test cases. The impending doom of the situation is the emergent need of the hour, and it has forced us to undergo basic health monitoring for the nonsymptom cases too. The technology in engineering always concerns with providing solutions to the medical field. Wireless sensor networks, artificial intelligence, robotics, and pervasive computing all as a whole have built an interdisciplinary concept to overcome day-to-day challenges in a smart way.

An environmental sensor such as RFID readers, video cameras, sound, pressure, temperature, humidity, and luminosity are some of the devices that provide information about the people to be monitored. The key problem for planning and managing the network for continuous monitoring will lead to maximum energy consumption. The types of multisources have been classified as link heterogeneity, energy heterogeneity, and computation heterogeneity which progresses the routing algorithm performance, network lifetime, network stability, reliability, etc., [1]. In the previous work, energy heterogeneity has been considered and topology has been designed to manage the energy by a centralized clustering scheme. Together with energy, network traffic is exponentially growing.

The development of micro-electro-mechanical system technology provides sophisticated applications that make the sensors relatively better and complex in technology advancement [2]. The cost of servicing and maintaining the IoT and handling with a larger number of sensors deployment play the major role [3], and replacing batteries in already deployed location is difficult based on the specific application.

This paper aims at balancing the traffic load towards the base station from the dual communication environment. With regards to direct communication and cluster-based communication, more traffic can be experienced in direct communication. The probability of traffic has been analysed concerning the data priority using finite-state mechanism, and from that, the sleep and awake schedule are provided for nonpriority and priority nodes. This would help in preventing the NP-hard problem and enhance the network lifetime with an increase in the number of heterogeneous energy nodes.

This paper is organized as follows. In Section 2, related works and required background are provided. Our proposed system model and algorithm are explained in Section 3. From the evaluation, the simulation results and analysis are discussed in Section 4. Finally, the article is concluded in Section 5.

2. Related Works

In [4], the author has concentrated on the scheduling of clustering through polling technique instead of traditional TDMA and CDMA, in cluster head election and routing algorithm have been developed under ABC and ant colony optimization. Fuzzy C means that clustering is taken for the artificial bee colony algorithm to find the optimal cluster head and to avoid the long-distance intracluster communication and the ant colony optimization has been applied for the best routing technique. The uneven clustering is balanced in [5]; the algorithm divides the network into different sizes such as near the base station clusters are smaller in size and the farther cluster is larger to balance the cost of the network and the increase of life too. Many energy-oriented algorithms are available using the Markov decision process formulated to balance the transmission energy. In [6], a centralized distributed algorithm is proposed to minimize the node’s intracluster transmission energy. The author concludes that using the decision policy, the power of transmission remains constant for the 40-time slots. Another work for centralized energy, proposed for the energy efficiency [7, 8], measured MDP for the transmission power selection, from which the state has been performed based on the fading channel and the reception error. Processing gain of the system is considered in modulation and coding schemes [9]. The simultaneous transmissions from different sensors are on different spreading codes. The interference of the base station concerns the priority frame selection-based CDMA [10]; from that, optimal selection of sensing groups has been given. In [11], the author has proposed the two priority schemes such as energy efficiency and data gathering. It mainly focuses on event-related data to be transmitted in the system. In [12], the matrix geometric method evaluates the performance of each traffic class by dynamic priority adjustment. Advanced zonal selection [13] is a heterogeneous routing protocol. In the middle zone, the nodes make direct communication with the sink node and they follow cluster-based communication at the boundary region. The cluster head selection is based on maximum residual energy and the minimum distance from the sink node. The unbalanced energy consumption due to the dynamic change of the topology is carried out using distance similarity index, and CH load is reduced using dual cluster head [14]. In [15], the author proposes an organized multipath and balanced load algorithm that ensures awareness of energy consumption. In [16], the author discusses the Markov decision process for adaptive intelligent dynamic water resource planning for urban areas to supply water on a sensitivity-driven method. The unequal clustering is addressed using a single path and mobile sink’s multipath routing [17] and the HEESR is proposed. In [18–20], the swarm intelligence maximum coverage of the node has been discussed and compared with designed p-type junctionless nanowire FET without doping injunctions. In [21–26], sensed heterogeneous data processing is carried out for different applications such as agriculture, weather information, and health monitoring for both live information and stored information. In [27–32], results have represented the feasibility that the sensor could be used to distinguish the different harnesses of the materials. To investigate the electrical transfer studies, various semiconductor materials such as silicon (Si), germanium (Ge), indium phosphide (InP), gallium arsenide (GaAs), and Al(x)Ga(1−x)As are used. Additionally, surface charge and potential analysis are also studied.

3. Proposed Methodology

The HETA proposes a novel priority-based traffic-aware algorithm that the lifetime and stability of the network depends on the residual energy, the distance between the nodes, and the sleep and awake schedule is based on the coverage area-based node selection from the priority table to maintain the stability of the network even during the increase in the number of heterogeneous energy nodes.

3.1. HETA Method

The proposed research is the priority-based balancing of the load over the network. The energy consumption of each node depends on the total number of packets and distance towards the receiving node. Here, the sink node performs the centralized clustering algorithm as explained in the previous work. The cluster head selection has been carried out in two important parameter considerations. Firstly, in each zone, the nodes which have maximum energy are selected as CH, and while increasing the number of transmission rounds, it should not become less than minimum energy (ETX + EDA). Secondly, the node has a minimum intracluster distance and less threshold distance to the sink to which the cluster member can be added.

The increase in scalable leads to a decrease in throughput. Due to the simultaneous transmission by the different sensors, whose transmission is on different spreading codes, interference could occur at the base station. Hence, the priority-based data processing is carried out using Moore’s finite state machine. Since the state of the node always depends on the present situation and also this data information is controlled by the state, this method has been followed. The prioritized code division multiple access is used for the data transmission between the zone1 active nodes and the CHs from the other zones. Active sensors are classified into different priorities that are controlled by the base station. In Figure 1, HETA is explained via flow diagram to differentiate the low-priority nodes from high-priority nodes, and we preassign the priority level at the system setup. From Figure 2(b), the CH has a high priority, and other active nodes (CM) from zone1 have low priority of the nodes’ state diagram. The gate contact work function is achieved by applying gate terminal input potential. The SiO₂ gate has 4.1 eV work function and relative permittivity value of 4.2. On both ends of the wire, perfect ohmic contacts were established. Figure 2(a) shows the structure and electrical connection of the junctionless nanowire FET. Because of p-type nanowire FET, the gate signal, which has the negative bias and drain, also has negative bias with respect to the source.

[figure omitted; refer to PDF]

Figure 7 illustrates the remaining energy of the network having 200 nodes with 20% of heterogeneous energy. In 80% of normal nodes, applying the HETA algorithm helps cluster head selection from higher energy nodes such as advanced or intermediate nodes; also, the cluster members are managed within the threshold distance, so the energy consumption of the normal nodes is well balanced. Near the BS, load balancing is achieved based on the traffic-aware sleep-and-awake schedule; the energy consumption has been reduced; 38.5% of nodes are still alive after 5000 rounds even with an increase in the number of nodes.

[figure omitted; refer to PDF]

Similarly, Figure 8 illustrates the remaining energy of the network that has 300 nodes with 20% of heterogeneous energy. In 80% of normal nodes, applying the HETA algorithm moderately helps in increase of the number of nodes, and load balancing achieved comparatively more than 5% from 200 nodes and more than 15% from 100 nodes scenarios. Figure 9 illustrates the throughput, comparing HETA algorithm, performs 92% better than SEP and 40% better than EECCP. In more than 300 nodes, the algorithm results are stable due to the increase in traffic near the base station.

[figure omitted; refer to PDF][figure omitted; refer to PDF]

5. Conclusion

The proposed heterogeneous energy and traffic-aware algorithm serves as a better solution for scalable and priority-based packet forwarding. From the quality-of-service analysis, the proposed algorithm outperforms well in network stability. The network stability parameter of FND is compared with SEP, and it provides better stability up to 1097 rounds and 65% more energy efficient in heterogeneous network. Further incorporating the priority-based traffic-aware packet forwarding technique, the designed HETA outperforms the existing in terms of throughput by 40%, ultimately increasing the performance of the network. The proposed method includes an improvement of cluster head selection to reduce the data congestion with minimized energy consumption. To conclude, the designed algorithm gives a better performance for a heterogeneous network, leaving a track of additional delay which will be dealt in the future work. The indirect bandgap materials have higher response over the direct band gap material. InP has the highest surface potential values. This high change of surface potential in nanowire FETs can be used in nanostructured sensor applications.

References

[1] S. Tanwar, N. Kumar, J. J. P. C. Rodrigues, "A systematic review on heterogeneous routing protocols for wireless sensor network," Journal of Network and Computer Applications, vol. 53, pp. 39-56, DOI: 10.1016/j.jnca.2015.03.004, 2015.

[2] M. Barceló, A. Correa, J. Vicario, A. Morell, "Multi-tree routing for heterogeneous data traffic in wireless sensor networks," Proceedings of the 2013 IEEE International Conference on Communications (ICC). IEEE, pp. 1899-1903, DOI: 10.1109/icc.2013.6654799, .

[3] Y.-K. Chen, "Challenges and opportunities of internet of things," Proceedings of the 17th Asia and South Pacific design automation conference. IEEE, pp. 383-388, .

[4] Z. Wang, H. Ding, B. Li, L. Bao, Z. Yang, "An energy efficient routing protocol based on improved artificial bee colony algorithm for wireless sensor networks," IEEE Access, vol. 8, pp. 133577-133596, DOI: 10.1109/access.2020.3010313, 2020.

[5] X. Fan, H. Jia, L. Wang, P. Xu, "Energy balance based uneven cluster routing protocol using ant colony taboo for wireless sensor networks," Wireless Personal Communications, vol. 97 no. 1, pp. 1305-1321, DOI: 10.1007/s11277-017-4567-7, 2017.

[6] A. Kobbane, M.-A. Koulali, H. Tembine, M. El Koutbi, J. Ben-Othman, "Dynamic power control with energy constraint for multimedia wireless sensor networks," pp. 518-522, DOI: 10.1109/icc.2012.6363971, .

[7] K. Gatsis, A. Ribeiro, G. J. Pappas, "Optimal power management in wireless control systems," IEEE Transactions on Automatic Control, vol. 59 no. 6, pp. 1495-1510, DOI: 10.1109/tac.2014.2305951, 2014.

[8] C. A. Bhuvaneswari, E. D. K. Ruby, "HETA: end-to-end delay analysis of enhanced centralized clustering protocol for wireless sensor networks," International Journal of System Assurance Engineering and Management, vol. 11,DOI: 10.1007/s13198-021-01214-8, 2021.

[9] A. A. Ajibesin, H. A. Chan, M. E. Dlodlo, "A priority-based adaptive CDMA scheme formultimedia wireless systems," Proceedings of the MILCOM2008-2008 IEEEMilitary Communications Conference. IEEE, .

[10] T. D. Ho, J. Park, S. Shimamoto, "QoS constraint with prioritized frame selection CDMA MAC protocol forWSN employing UAV," Proceedings of the 2010 IEEE Globecom Workshops. IEEE, pp. 1826-1830, DOI: 10.1109/glocomw.2010.5700257, .

[11] S. M. Martınez Chávez, M. E. Rivero-Angeles, L. I. Garay-Jiménez, I. C. Romero Ibarra, "Priority schemes for life extension and data delivery in body area wireless sensor networks with cognitive radio capabilities," Wireless Communications and Mobile Computing, vol. 2019,DOI: 10.1155/2019/2637830, 2019.

[12] M.-S. Mi-Sun Do, Y. Youngjun Park, J.-Y. Jai-Yong Lee, "Channel assignment with QoS guarantees for a multiclass multicode CDMA system," IEEE Transactions on Vehicular Technology, vol. 51 no. 5, pp. 935-948, DOI: 10.1109/tvt.2002.801766, 2002.

[13] F. A. Khan, M. Khan, M. Asif, A. Khalid, I. U. Haq, "Hybrid and multi-hop advanced zonal-stable election protocol for wireless sensor networks," IEEE Access, vol. 7, pp. 25334-25346, DOI: 10.1109/access.2019.2899752, 2019.

[14] M. Aslam, E. U. Munir, M. Bilal, M. Asad, A. Ali, T. Shah, S. Bilal, "Hybrid advanced distributed and centralized clustering path planning algorithm for WSNs," pp. 657-664, .

[15] A. F. Subahi, Y. Alotaibi, O. I. Khalaf, F. Ajesh, "Packet drop battling mechanism for energy aware detection in wireless networks," Computers, Materials & Continua, vol. 66 no. 2, pp. 2077-2086, 2020.

[16] X. Xiang, Q. Li, S. Khan, O. I. Khalaf, "Urban water resource management for sustainable environment planning using artificial intelligence techniques," Environmental Impact Assessment Review, vol. 86,DOI: 10.1016/j.eiar.2020.106515, 2021.

[17] O. I. Khalaf, G. M. Abdulsahib, "Energy efficient routing and reliable data transmission protocol in WSN," International Journal Advance Software Computation Applied, vol. 12 no. 3, 2020.

[18] O. I. Khalaf, G. M. Abdulsahib, B. M. Sabbar, "Optimization of wireless sensor network coverage using the bee algorithm," Journal of Information Science and Engineering, vol. 36 no. 2, pp. 377-386, 2020.

[19] C. A. Bhuvaneswari, G. Vairavel, "Optimized energy using centralized clustering protocol in heterogeneous wireless sensor networks," ARPN Journal of Engineering and Applied Sciences, vol. 16 no. 2, pp. 215-223, 2021.

[20] Y. Haddara, M. Howlader, "Integration of heterogeneous materials for wearable sensors," Polymers, vol. 10 no. 1,DOI: 10.3390/polym10010060, 2018.

[21] S.-N. Zhao, G. Wang, D. Poelman, P. Van Der Voort, "Metal organic frameworks based materials for heterogeneous photocatalysis," Molecules, vol. 23 no. 11,DOI: 10.3390/molecules23112947, 2018.

[22] P. R. Karthikeyan, G. Chandrasekaran, N. S. Kumar, E. Sengottaiyan, P. Mani, D. T. Kalavathi, V. Gowrishankar, "IoT based moisture control and temperature monitoring in smart farming," Journal of Physics: Conference Series, vol. 1964 no. 6, 2021.

[23] N. S. Kumar, G. Chandrasekaran, K. P. Rajamanickam, "An integrated system for smart industrial monitoring system in the context of hazards based on the internet of things," International Journal of Safety and Security Engineering, vol. 11 no. 1, pp. 123-127, 2021.

[24] G. Chandrasekaran, S. Periyasamy, P. R. Karthikeyan, V. Kumarasamy, "Whale and grey wolf optimization algorithms for test scheduling of system-on-chip," Solid State Technology, vol. 63 no. 5, pp. 3931-3944, 2020.

[25] G. Chandrasekaran, P. R. Karthikeyan, N. S. Kumar, V. Kumarasamy, "Test scheduling of system-on-chip using dragonfly and ant lion optimization algorithms," Journal of Intelligent and Fuzzy Systems, vol. 40,DOI: 10.3233/jifs-201691, 2021.

[26] N. S. Kumar, P. Nirmalkumar, "A robust decision support system for wireless healthcare based on hybrid prediction algorithm," Journal of Medical Systems, vol. 43 no. 6, pp. 170-179, DOI: 10.1007/s10916-019-1304-7, 2019.

[27] N. S. Kumar, P. Nirmalkumar, "A novel architecture of smart healthcare system on integration of cloud computing and iot," pp. 0940-0944, DOI: 10.1109/iccsp.2019.8698048, .

[28] A. Muneer, S. M. Fati, S. Fuddah, "Smart health monitoring system using IoT based smart fitness mirror," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 18 no. 1, pp. 317-331, DOI: 10.12928/telkomnika.v18i1.12434, 2020.

[29] H. Pandey, S. Prabha, "Smart health monitoring system using IOT and machine learning techniques," ,DOI: 10.1109/icbsii49132.2020.9167660, .

[30] N. M. Shagari, M. Y. I. Idris, R. B. Salleh, I. Ahmedy, G. Murtaza, H. A. Shehadeh, "Heterogeneous energy and traffic aware sleep-awake cluster-based routing protocol for wireless sensor network," IEEE Access, vol. 8, pp. 12232-12252, DOI: 10.1109/access.2020.2965206, 2020.

[31] Y. M. Abdelradi, A. A. El-Sherif, L. H. Afify, "A queueing theory approach to traffic offloading in heterogeneous cellular networks," AEU - International Journal of Electronics and Communications, vol. 139,DOI: 10.1016/j.aeue.2021.153910, 2021.

[32] D. Bhattacharjee, T. Acharya, S. Chakravarty, "Energy efficient data gathering in IoT networks with heterogeneous traffic for remote area surveillance applications: a cross layer approach," IEEE Transactions on Green Communications and Networking, vol. 5,DOI: 10.1109/tgcn.2021.3092765, 2021.