Artificial intelligence-based load optimization in cognitive Internet of Things
Wei Yao, Fazlullah Khan, Mian Ahmad Jan, Nadir Shah, Izaz ur Rahman, Abid Yahya, Ateeq ur Rehman
Electrical, Computer & Telecommunications Engineering
Botswana International University of Science & Technology
The Internet of Things (IoT) comprises smart objects capable of sensing, processing, and transmitting application-specific data. These objects collect and transmit a huge amount of correlated and redundant data due to overlapped sensing regions, causing unnecessary exploitation of spectral bands and load balancing issues in the network. As a result, time-critical and delay-sensitive data experience a higher delay, lower throughput, and quality of service degradation. To circumvent these issues, in this paper, we propose a model that is energy efficient and is capable of maximizing the spectrum utilization with optimal Device-to-Gateway configuration. Initially, the network gateways perform spectrum sensing for available channels using an energy detection technique and forward them to a cognitive engine (CE). The CE assigns the best available channels in the licensed band to the network devices for communication. Each channel is divided into equal-length time slots for the timely delivery of critical data. In addition, the CE calculates the load on each gateway and uses particle swarm optimization algorithm for optimal load distribution among the network gateways. Our experimental results show that the proposed model is efficient for the resource-constrained IoT devices in terms of packet drop ratio, delay, and throughput of the network. Moreover, the proposed scheme also achieves optimal Device-to-Gateway configuration with efficient spectrum utilization in the licensed band.
Neural Computing and Applications
https://doi.org/10.1007/s00521-020-04814-w
10.1007/s00521-020-04814-w
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Artificial intelligence Engineering & Materials Science 100%
Internet of things Engineering & Materials Science 95%
Engines Engineering & Materials Science 54%
Throughput Engineering & Materials Science 39%
Resource allocation Engineering & Materials Science 24%
Particle swarm optimization (PSO) Engineering & Materials Science 23%
Quality of service Engineering & Materials Science 22%
Degradation Engineering & Materials Science 19%
Yao, W., Khan, F., Jan, M. A., Shah, N., ur Rahman, I., Yahya, A., & ur Rehman, A. (2020). Artificial intelligence-based load optimization in cognitive Internet of Things. Neural Computing and Applications, 32, 16179–16189. https://doi.org/10.1007/s00521-020-04814-w
Yao, Wei ; Khan, Fazlullah ; Jan, Mian Ahmad ; Shah, Nadir ; ur Rahman, Izaz ; Yahya, Abid ; ur Rehman, Ateeq. / Artificial intelligence-based load optimization in cognitive Internet of Things. In: Neural Computing and Applications. 2020 ; Vol. 32. pp. 16179–16189.
@article{27b0b4ca5d0941768e346e8fb6850418,
title = "Artificial intelligence-based load optimization in cognitive Internet of Things",
abstract = "The Internet of Things (IoT) comprises smart objects capable of sensing, processing, and transmitting application-specific data. These objects collect and transmit a huge amount of correlated and redundant data due to overlapped sensing regions, causing unnecessary exploitation of spectral bands and load balancing issues in the network. As a result, time-critical and delay-sensitive data experience a higher delay, lower throughput, and quality of service degradation. To circumvent these issues, in this paper, we propose a model that is energy efficient and is capable of maximizing the spectrum utilization with optimal Device-to-Gateway configuration. Initially, the network gateways perform spectrum sensing for available channels using an energy detection technique and forward them to a cognitive engine (CE). The CE assigns the best available channels in the licensed band to the network devices for communication. Each channel is divided into equal-length time slots for the timely delivery of critical data. In addition, the CE calculates the load on each gateway and uses particle swarm optimization algorithm for optimal load distribution among the network gateways. Our experimental results show that the proposed model is efficient for the resource-constrained IoT devices in terms of packet drop ratio, delay, and throughput of the network. Moreover, the proposed scheme also achieves optimal Device-to-Gateway configuration with efficient spectrum utilization in the licensed band.",
author = "Wei Yao and Fazlullah Khan and Jan, {Mian Ahmad} and Nadir Shah and {ur Rahman}, Izaz and Abid Yahya and {ur Rehman}, Ateeq",
doi = "10.1007/s00521-020-04814-w",
pages = "16179–16189",
journal = "Neural Computing and Applications",
Yao, W, Khan, F, Jan, MA, Shah, N, ur Rahman, I, Yahya, A & ur Rehman, A 2020, 'Artificial intelligence-based load optimization in cognitive Internet of Things', Neural Computing and Applications, vol. 32, pp. 16179–16189. https://doi.org/10.1007/s00521-020-04814-w
Artificial intelligence-based load optimization in cognitive Internet of Things. / Yao, Wei; Khan, Fazlullah; Jan, Mian Ahmad; Shah, Nadir; ur Rahman, Izaz; Yahya, Abid; ur Rehman, Ateeq.
In: Neural Computing and Applications, Vol. 32, 16.03.2020, p. 16179–16189.
T1 - Artificial intelligence-based load optimization in cognitive Internet of Things
AU - Yao, Wei
AU - Khan, Fazlullah
AU - Jan, Mian Ahmad
AU - Shah, Nadir
AU - ur Rahman, Izaz
AU - Yahya, Abid
AU - ur Rehman, Ateeq
N2 - The Internet of Things (IoT) comprises smart objects capable of sensing, processing, and transmitting application-specific data. These objects collect and transmit a huge amount of correlated and redundant data due to overlapped sensing regions, causing unnecessary exploitation of spectral bands and load balancing issues in the network. As a result, time-critical and delay-sensitive data experience a higher delay, lower throughput, and quality of service degradation. To circumvent these issues, in this paper, we propose a model that is energy efficient and is capable of maximizing the spectrum utilization with optimal Device-to-Gateway configuration. Initially, the network gateways perform spectrum sensing for available channels using an energy detection technique and forward them to a cognitive engine (CE). The CE assigns the best available channels in the licensed band to the network devices for communication. Each channel is divided into equal-length time slots for the timely delivery of critical data. In addition, the CE calculates the load on each gateway and uses particle swarm optimization algorithm for optimal load distribution among the network gateways. Our experimental results show that the proposed model is efficient for the resource-constrained IoT devices in terms of packet drop ratio, delay, and throughput of the network. Moreover, the proposed scheme also achieves optimal Device-to-Gateway configuration with efficient spectrum utilization in the licensed band.
AB - The Internet of Things (IoT) comprises smart objects capable of sensing, processing, and transmitting application-specific data. These objects collect and transmit a huge amount of correlated and redundant data due to overlapped sensing regions, causing unnecessary exploitation of spectral bands and load balancing issues in the network. As a result, time-critical and delay-sensitive data experience a higher delay, lower throughput, and quality of service degradation. To circumvent these issues, in this paper, we propose a model that is energy efficient and is capable of maximizing the spectrum utilization with optimal Device-to-Gateway configuration. Initially, the network gateways perform spectrum sensing for available channels using an energy detection technique and forward them to a cognitive engine (CE). The CE assigns the best available channels in the licensed band to the network devices for communication. Each channel is divided into equal-length time slots for the timely delivery of critical data. In addition, the CE calculates the load on each gateway and uses particle swarm optimization algorithm for optimal load distribution among the network gateways. Our experimental results show that the proposed model is efficient for the resource-constrained IoT devices in terms of packet drop ratio, delay, and throughput of the network. Moreover, the proposed scheme also achieves optimal Device-to-Gateway configuration with efficient spectrum utilization in the licensed band.
U2 - 10.1007/s00521-020-04814-w
DO - 10.1007/s00521-020-04814-w
JO - Neural Computing and Applications
JF - Neural Computing and Applications
Yao W, Khan F, Jan MA, Shah N, ur Rahman I, Yahya A et al. Artificial intelligence-based load optimization in cognitive Internet of Things. Neural Computing and Applications. 2020 Mar 16;32:16179–16189. https://doi.org/10.1007/s00521-020-04814-w