Energy Optimization for Green Communication in IoT Using Harris Hawks Optimization

Dev, Kapal; Maddikunta, Praveen Kumar Reddy; Gadekallu, Thippa Reddy; Bhattacharya, Sweta; Hegde, Pawan; Singh, Saurabh

doi:10.1109/tgcn.2022.3143991

Cited by 58 publications

(31 citation statements)

References 26 publications

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“…The main advantages of the HHO algorithm are the possibility of getting a global optimal solution, high convergence speed, high accuracy, and better quality. Consequently, the HHO algorithm can be applied to solve various optimization problems in the engineering domain such as feature extraction, design and development of a model, pattern recognition, and electrical and electronics optimal design applications [ 33 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The proposed model could aid physicians in creating a customised follow-up program for patients depending on their disease stage and risk factors. Reference [ 33 ] attempted to optimize the energy utilization in the IoT networks through an optimal CH selection using a nature-inspired algorithm, HHO. The performance of the HHO-based CH model was analysed through several parameters such as load, Temperature, number of alive nodes, delay, and residual energy.…”

Section: Literature Reviewmentioning

confidence: 99%

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Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model

Gundluru

Rajput

Lakshmanna

et al. 2022

Computational Intelligence and Neuroscience

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In today’s world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with principal component analysis (PCA) for dimensionality reduction, and to extract the most important features, Harris hawks optimization algorithm is used further to optimize the classification and feature extraction process. The results shown by the deep learning model with respect to specificity, precision, accuracy, and recall are very much satisfactory compared to the existing systems.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model

Gundluru

Rajput

Lakshmanna

et al. 2022

Computational Intelligence and Neuroscience

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“…Update the P with equation (3) ( 11) end (12) if |P| ≥ 1 then (13) Update the position vector with equation (1) ( 14) end (15) if |P| < 1 then (16) if a ≥ 0.5 and |P| ≥ 0.5 then (17) Update the position vector with equation (4) (18) else if a ≥ 0.5 and |P| < 0.5 then (19) Update the position vector with equation ( 6) (20) else if a < 0.5 and |P| ≥ 0.5 then (21) Update the position vector with equation ( 10) (22) else if a < 0.5 and |P| < 0.5 then (23) Update the position vector with equation ( 11 To improve the extreme learning machine parameter selection [20] Gaussian barebone HHO HHO is integrated with Gaussian barebone To optimize kernel extreme learning machines for the prediction of entrepreneurial intention [21] Chaotic sequenceguided HHO HHO is integrated with chaotic sequences For data clustering [22] Dynamic HHO with mutation HHO used mutation and dynamic control strategy to balance the exploitation and exploration phases in the HHO method…”

Section: Fuzzy Harris Hawk Algorithmmentioning

confidence: 99%

“…The performance of the HHO optimization algorithm is evaluated by comparing it with other existing metaheuristic techniques, 29 benchmark challenges, and many real-world engineering issues. Experimental findings and comparative results have shown that the HHO algorithm delivers better results than other existing metaheuristic techniques [ 8 , 18 ].…”

Section: Harris Hawk Optimizationmentioning

confidence: 99%

Harris Hawk Optimization: A Survey onVariants and Applications

Tripathy

Maddikunta

Pham

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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In this review, we intend to present a complete literature survey on the conception and variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set of applications in well-established works. For this purpose, we first present an overview of HHO, including its logic of equations and mathematical model. Next, we focus on reviewing different variants of HHO from the available well-established literature. To provide readers a deep vision and foster the application of the HHO, we review the state-of-the-art improvements of HHO, focusing mainly on fuzzy HHO and a new intuitionistic fuzzy HHO algorithm. We also review the applications of HHO in enhancing machine learning operations and in tackling engineering optimization problems. This survey can cover different aspects of HHO and its future applications to provide a basis for future research in the development of swarm intelligence paths and the use of HHO for real-world problems.

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“…So, energy management is a critical issue in designing IoT networks, since many IoT devices can rely only on limited battery power and it is often unfeasible to replace or recharge their batteries. Therefore, efficient energy management strategies should be implemented in IoT devices to prolong their lifetime; for example, in [15] Naeem et al propose an energy-efficient routing protocol to enhance network lifespan, or in [16] Dev et al optimize energy utilization through an optimal cluster head selection. In general, IoT devices consume energy, especially in sensing the environment and processing the acquired data, and transmitting their updates.…”

Section: Related Workmentioning

confidence: 99%

Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems

2022

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The Internet of Things (IoT) brings internet connectivity to everyday devices. These devices generate a large volume of information that needs to be transmitted to the nodes running the IoT applications, where they are processed and used to make some output decisions. On the one hand, the quality of these decisions is typically affected by the freshness of the received information, thus requesting frequent updates from the IoT devices. On the other hand, the severe energy, memory, processing, and communication constraints of IoT devices and networks pose limitations in the frequency of sensing and reporting. So, it is crucial to minimize the energy consumed by the device for sensing the environment and for transmitting the update messages, while taking into account the requirements for information freshness. Edge-caching can be effective in reducing the sensing and the transmission frequency; however, it requires a proper refreshing scheme to avoid staleness of information, as IoT applications need timeliness of status updates. Recently, the Age of Information (AoI) metric has been introduced: it is the time elapsed since the generation of the last received update, hence it can describe the timeliness of the IoT application’s knowledge of the process sampled by the IoT device. In this work, we propose a model-driven and AoI-aware optimization scheme for information caching at the network edge. To configure the cache parameters, we formulate an optimization problem that minimizes the energy consumption, considering both the sampling frequency and the average frequency of the requests sent to the device for refreshing the cache, while satisfying an AoI requirement expressed by the IoT application. We apply our caching scheme in an emulated IoT network, and we show that it minimizes the energy cost while satisfying the AoI requirement. We also compare the case in which the proposed caching scheme is implemented at the network edge against the case in which there is not a cache at the network edge. We show that the optimized cache can significantly lower the energy cost of devices that have a high transmission cost because it can reduce the number of transmissions. Moreover, the cache makes the system less sensitive to higher application-request rates, as the number of messages forwarded to the devices depends on the cache parameters.

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Energy Optimization for Green Communication in IoT Using Harris Hawks Optimization

Cited by 58 publications

References 26 publications

Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model

Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model

Harris Hawk Optimization: A Survey onVariants and Applications

Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems

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