Browsing by Author "Gungor, Vehbi Cagri"

Now showing 1 - 20 of 48

Citation - WoS: 22
Citation - Scopus: 26
On the Lifetime of Compressive Sensing Based Energy Harvesting in Underwater Sensor Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2019) Erdem, Huseyin Emre; Yildiz, Huseyin Ugur; Gungor, Vehbi Cagri
Recently, there has been a growing interest in academia and industry on the development of underwater acoustic sensor networks (UASNs) for scientific, commercial, and military purposes. Severe underwater channel conditions and limited battery energy of underwater nodes pose great challenges to prolong UASNs lifetime. Compressive sensing (CS), energy harvesting (EH), and transmission power control (TPC) are three promising solutions to improve UASNs lifetime. This paper aims to quantitatively investigate the joint impact of CS, EH, and TPC methods on the lifetime of UASNs. A novel Mixed Integer Programming framework is developed to maximize the network lifetime by joint consideration of CS, EH, and TPC. The performance results show that the impact of CS on the network lifetime is higher than that of EH when both methods are combined with TPC. Moreover, when all three methods are combined, the network lifetime can be extended up to three times as compared to the case when all three methods are not utilized.
Citation - WoS: 11
Citation - Scopus: 17
The Impact of Error Control Schemes on Lifetime of Energy Harvesting Wireless Sensor Networks in Industrial Environments
(Elsevier, 2020) Tekin, Nazli; Gungor, Vehbi Cagri
Due to the harsh channel conditions of the industrial environments, the data transmission over the wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of great importance for battery-limited Wireless Sensor Networks (WSNs) in industrial environments. In this paper, the lifetime analysis of error control schemes, i.e., Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ), is presented under different industrial environment channel conditions. Furthermore, the impact of energy harvesting methods on the network lifetime is investigated. A novel Mixed Integer Programming (MIP) framework is developed to maximize the network lifetime while meeting application reliability. Performance results show that utilizing HARQ-II error control scheme for Mica2 and BCH(31,21,5) for Telos improves the network lifetime while meeting the desired application reliability rate.
Citation - WoS: 5
Citation - Scopus: 5
Performance Analysis of Hamming Code for WSN-Based Smart Grid Applications
(Tubitak Scientific & Technological Research Council Turkey, 2018) Yigit, Melike; Gungor, Vehbi Cagri; Boluk, Pinar
Many methods have been employed to detect, compare, and correct errors to increase communication reliability and efficiency in wireless sensor networks (WSNs). However, to the best of our knowledge, no existing study has compared the performance of error control codes by using different modulation techniques in a smart grid communication environment when multichannel scheduling is used. This paper presents a detailed performance evaluation and makes a comparison of different modulation techniques, such as frequency shift keying (FSK), differential phase shift keying (DPSK), binary phase shift keying (BPSK), and offset quadrature phase-shift keying (OQPSK), using Hamming codes in a 500-kV line-of-sight substation smart grid environment with multichannel scheduling. A link-quality-aware routing algorithm is used as a routing protocol and a log-normal shadowing channel is employed as a channel model. Simulations are performed in MATLAB and the performance of the Hamming code with various modulation techniques is compared with the results obtained without using any error correction codes for throughput, delay, and bit error rate. The results show that the performance of the Hamming code with OQPSK modulation is better than its performance with other modulation techniques. Moreover, the results show that the performance of Hamming code improves with multichannel scheduling for all modulation techniques.
Citation - WoS: 8
Citation - Scopus: 8
Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission
(Wiley-VCH Verlag GmbH, 2024) Gulsen, Abdulkadir; Kolukisa, Burak; Caliskan, Umut; Bakir-Gungor, Burcu; Gungor, Vehbi Cagri
Acoustic emission (AE) serves as a noninvasive technique for real-time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb-core carbon fiber-reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble-supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes. This article presents a novel ensemble feature selection methodology to rank features relevant to damage modes on acoustic emission signals in carbon fiber-reinforced polymer sandwich composites. Subsequently, ranked features are utilized in unsupervised clustering models to identify damage modes. The comparative results demonstrate that, along with common features, other features, like partial powers, have a robust correlation with damage modes.image (c) 2024 WILEY-VCH GmbH
Citation - WoS: 8
Citation - Scopus: 15
QoS-Aware LTE-A Downlink Scheduling Algorithm: A Case Study on Edge Users
(Wiley, 2019) Uyan, Osman Gokhan; Gungor, Vehbi Cagri
4G/LTE-A (Long-Term Evolution-Advanced) is the state of the art wireless mobile broadband technology. It allows users to take advantage of high Internet speeds. It makes use of the OFDM technology to offer high speed and provides the system resources both in time and frequency domain. A scheduling algorithm running on the base station holds the allocation of these resources. In this paper, we investigate the performance of existing downlink scheduling algorithms in two ways. First, we look at the performance of the algorithms in terms of throughput and fairness metrics. Second, we suggest a new QoS-aware fairness criterion, which accepts that the system is fair if it can provide the users with the network traffic speeds that they demand and evaluate the performance of the algorithms according to this metric. We also propose a new QoS-aware downlink scheduling algorithm (QuAS) according to these two metrics, which increases the QoS-fairness and overall throughput of the edge users without causing a significant degradation in overall system throughput when compared with other schedulers in the literature.
Citation - WoS: 5
Node-Level Error Control Strategies for Prolonging the Lifetime of Wireless Sensor Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Tekin, Nazli; Yildiz, Huseyin Ugur; Gungor, Vehbi Cagri
In Wireless Sensor Networks (WSNs), energy-efficiency and reliability are two critical requirements for attaining a long-term stable communication performance. Using error control (EC) methods is a promising technique to improve the reliability of WSNs. EC methods are typically utilized at the network-level, where all sensor nodes use the same EC method. However, improper selection of EC methods on some nodes in the network-level strategy can reduce the energy-efficiency, thus the lifetime of WSNs. In this study, a node-level EC strategy is proposed via mixed-integer programming (MIP) formulations. The MIP model determines the optimum EC method (i.e., automatic repeat request (ARQ), forward error correction (FEC), or hybrid ARQ (HARQ)) for each sensor node to maximize the network lifetime while guaranteeing a pre-determined reliability requirement. Five meta-heuristic approaches are developed to overcome the computational complexity of the MIP model. The performances of the MIP model and meta-heuristic approaches are evaluated for a wide range of parameters such as the number of nodes, network area, packet size, minimum desired reliability criterion, transmission power, and data rate. The results show that the node-level EC strategy provides at least 4.4% prolonged lifetimes and 4.0% better energy-efficiency than the network-level EC strategies. Furthermore, one of the developed meta-heuristic approaches (i.e., extended golden section search) provides lifetimes within a 3.9% neighborhood of the optimal solutions, reducing the solution time of the MIP model by 89.6%.
Citation - WoS: 1
Citation - Scopus: 1
Intelligent Traffic Light Systems Using Edge Flow Predictions
(Elsevier, 2024) Thahir, Adam Rizvi; Coskun, Mustafa; Kilic, Sultan Kubra; Gungor, Vehbi Cagri
In this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations.
Ensemble Churn Prediction for Internet Service Provider with Machine Learning Techniques
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2020) Goy, Gokhan; Kolukisa, Burak; Bahcevan, Cenk; Gungor, Vehbi Cagri
With the developing technology in every fields, a competitive marketing environment has been arised In this competitive environment analyzing customer behavior has become vital In particular, the ability to easily change any service provider has become vet) , critical for the company to continue its existence At the same time, the amount of financial resources spent on retaining instituters much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities In this study. we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value In this context the aunt successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936.
Citation - WoS: 77
Citation - Scopus: 89
Packet Size Optimization for Lifetime Maximization in Underwater Acoustic Sensor Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2019) Yildiz, Huseyin Ugur; Gungor, Vehbi Cagri; Tavli, Bulent
Recently, underwater acoustic sensor networks (UASNs) have been proposed to explore underwater environments for scientific, commercial, and military purposes. However, long propagation delays, high transmission losses, packet drops, and limited bandwidth in underwater propagation environments make realization of reliable and energy-efficient communication a challenging task for UASNs. To prolong the lifetime of battery-limited UASNs, two critical factors (i.e., packet size and transmission power) play vital roles. At one hand, larger packets are vulnerable to packet errors, while smaller packets are more resilient to such errors. In general, using smaller packets to avoid bit errors might be a good option. However, when small packets are used, more frames should be transmitted due to the packet fragmentation, and hence, network overhead and energy consumption increases. On the other hand, increasing transmission power reduces frame errors, but this would result in unnecessary energy consumption in the network. To this end, the packet size and transmission power should be jointly considered to improve the network lifetime. In this study, an optimization framework via an integer linear programming (ILP) has been proposed to maximize the network lifetime by joint optimization of the transmission power and packet size. In addition, a realistic link-layer energy consumption model is designed by employing the physical layer characteristics of UASNs. Extensive numerical analysis through the optimization model has been also performed to investigate the tradeoffs caused by the transmission power and packet size quantitatively.
Citation - WoS: 24
Citation - Scopus: 37
An Efficient Network Intrusion Detection Approach Based on Logistic Regression Model and Parallel Artificial Bee Colony Algorithm
(Elsevier, 2024) Kolukisa, Burak; Dedeturk, Bilge Kagan; Hacilar, Hilal; Gungor, Vehbi Cagri
In recent years, the widespread use of the Internet has created many issues, especially in the area of cybersecurity. It is critical to detect intrusions in network traffic, and researchers have developed network intrusion and anomaly detection systems to cope with high numbers of attacks and attack variations. In particular, machine learning and meta-heuristic methods have been widely used for network intrusion detection systems (NIDS). However, existing studies on these systems usually suffer from low performance results such as accuracy, F1-measure, false positive rate, and false negative rate, and generally do not use automatic parameter tuning techniques. To address these challenges, this study proposes a novel approach based on a logistic regression model trained using a parallel artificial bee colony (LR-ABC) algorithm with a hyper-parameter optimization technique. The performance of the proposed model is evaluated against state -of-the-art machine learning and deep learning models on two publicly available NIDS datasets. Comparative performance evaluations show that the proposed method achieved satisfactory results with accuracy of 88.25% on the UNSW-NB15 dataset and 90.11% on the NSL-KDD dataset, and F1-measures of 88.26% and 90.15%, respectively. These findings demonstrate the efficacy of the proposed LR-ABC model in enhancing the accuracy and reliability, while providing a scalable solution to adapt to the dynamic and evolving landscape of cybersecurity threats.
Citation - WoS: 1
Cognitive Radio Networks for Smart Grid Communications Potential Applications, Protocols, and Research Challenges
(CRC Press-Taylor & Francis Group, 2016) Kogias, Dimitris; Tuna, Gurkan; Gungor, Vehbi Cagri
Citation - WoS: 14
Citation - Scopus: 19
Machine Learning Approaches for Underwater Sensor Network Parameter Prediction
(Elsevier, 2023) Uyan, Osman Gokhan; Akbas, Ayhan; Gungor, Vehbi Cagri
Underwater Acoustic Sensor Networks (UASNs) have recently attracted scientists due to its wide range of real -world applications. However, there are design challenges in UASNs, such as limited network lifetime and low communication reliability provoked by the constrained battery supply of sensors and harsh channel conditions in the underwater environments. To meet communication reliability requirements, packet-duplication and multi -path routing algorithms have been recommended in the literature. Furthermore, underwater sensors may convey sensitive data, which must be masked to avoid eavesdropping attempts. To improve network security, cryptographic encryption is the most widely used method. Nevertheless, data encryption needs computations to cipher the data, which consumes extra energy, resulting in a cutback in the life span of the network. To address these challenges, an optimization model has been proposed to evaluate the impacts of multi-path routing, packet duplication, encryption, and data fragmentation on the lifetime of the UASNs. However, the solution time of the proposed optimization model is quite high, and sometimes it cannot come up with feasible solutions. To this end, in this study, different regression and neural network methods have been proposed to predict network param-eters and energy consumptions of underwater nodes as supplementary methods to optimization models. Per-formance evaluations show that the proposed methods yield remarkably accurate predictions and can be used for energy consumption prediction in UASNs.
Makine Öğrenmesi Teknikleri ile İnternet Servis Sağlayicisi için Müşteri Kayip Tahmini
(IEEE, 2020) Goy, Gokhan; Kolukisa, Burak; Bahcevan, Cenk; Gungor, Vehbi Cagri
With the developing technology in every fields, a competitive marketing environment has been arised In this competitive environment analyzing customer behavior has become vital In particular, the ability to easily change any service provider has become vet) , critical for the company to continue its existence At the same time, the amount of financial resources spent on retaining instituters much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities In this study. we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value In this context the aunt successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936.
Citation - WoS: 70
Citation - Scopus: 86
QERP: Quality-Of (QoS) Aware Evolutionary Routing Protocol for Underwater Wireless Sensor Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2018) Faheem, Muhammad; Tuna, Gurkan; Gungor, Vehbi Cagri
Quality-of-service (QoS) aware reliable data delivery is a challenging issue in underwater wireless sensor networks (UWSNs). This is clue to impairments of the acoustic transmission caused by excessive noise, extremely long propagation delays, high bit error rate, low bandwidth capacity, multipath effects, and interference. To address these challenges, meet the commonly used UWSN performance indicators, and overcome the inefficiencies of the existing clustering-based routing schemes, a novel QoS aware evolutionary cluster based routing protocol (QERP) has been proposed for UWSN-based applications. The proposed protocol improves packet delivery ratio, and reduces average end-to-end delay and overall network energy consumption. Our comparative performance evaluations demonstrate that QERP is successful in achieving low network delay, high packet delivery ratio, and low energy consumption.
Citation - WoS: 28
Citation - Scopus: 31
Capacity and Spectrum-Aware Communication Framework for Wireless Sensor Network-Based Smart Grid Applications
(Elsevier Science Bv, 2017) Faheem, Muhammad; Gungor, Vehbi Cagri
Recently, wireless sensor networks (WSNs) have been widely recognized as a promising technology for enhancing various aspects of smart grid and realizing the vision of next-generation electric power system in a cost-effective and efficient manner. However, recent field tests show that wireless links in smart grid environments have higher packet error rates and variable link capacity because of dynamic topology changes, obstructions, electromagnetic interference, equipment noise, multipath effects, and fading. To overcome these communication challenges, in this paper, we propose a data capacity-aware channel assignment (DCA) and fish bone routing (FBR) algorithm for WSN-based smart grid applications. The proposed DCA framework deals with the channel scarcities by dynamically switching between different spectrum bands and employs a network for organizing WSN into a highly stable connected hierarchy. In addition, the proposed FBR mechanism provides robust loop free data paths and avoids high transmission cost, excessive end-to-end delay and restricts unnecessary multi-hop data transmission from the source to destination in the network. Thus, it significantly reduces the probability of data packet loss and preserves stable link qualities among sensor nodes for load balancing and prolonging the lifetime of wireless sensor networks in harsh smart grid environments. Comparative performance evaluations show that our proposed schemes outperform the existing communication architectures in terms of data packet delivery, communication delay and energy consumption.
Citation - WoS: 33
Citation - Scopus: 40
Big Data Acquired by Internet of Things-Enabled Industrial Multichannel Wireless Sensors Networks for Active Monitoring and Control in the Smart Grid Industry 4.0
(Elsevier, 2021) Faheem, Muhammad; Fizza, Ghulam; Ashraf, Muhammad Waqar; Butt, Rizwan Aslam; Ngadi, Md. Asri; Gungor, Vehbi Cagri
Smart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyberphysical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) re-quirements in the smart grid. In this context, this paper de-scribes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assign-ment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid. (C) 2021 The Authors. Published by Elsevier Inc.
Citation - WoS: 2
Citation - Scopus: 2
Accelerated Artificial Bee Colony Optimization for Cost-Sensitive Neural Networks in Multi-Class Problems
(Wiley, 2025) Hacilar, Hilal; Dedeturk, Bilge Kagan; Ozmen, Mihrimah; Celik, Mehlika Eraslan; Gungor, Vehbi Cagri
Metaheuristics are advanced problem-solving techniques that develop efficient algorithms to address complex challenges, while neural networks are algorithms inspired by the structure and function of the human brain. Combining these approaches enables the resolution of complex optimization problems that traditional methods struggle to solve. This study presents a novel approach integrating the ABC algorithm with ANNs for weight optimization. The method is further enhanced by vectorization and parallelization techniques on both CPU and GPU to improve computational efficiency. Additionally, this study introduces a cost-sensitive fitness function tailored for multi-class classification to optimize results by considering relationships between target class levels. It validates these advancements in two critical applications: network intrusion detection and earthquake damage estimation. Notably, this study makes a significant contribution to earthquake damage assessment by leveraging machine learning algorithms and metaheuristics to enhance predictive models and decision-making in disaster response. By addressing the dynamic nature of earthquake damage, this research fills a critical gap in existing models and broadens the understanding of how machine learning and metaheuristics can improve disaster response strategies. In both domains, the ABC-ANN implementation yields promising results, particularly in earthquake damage estimation, where the cost-sensitive approach demonstrates satisfactory outcomes in macro-F1 and accuracy. The best results for macro-F1, weighted-F1, and overall accuracy provides best results with the UNSW-NB15 and earthquake datasets, showing values of 64%, 72%, 68%, and 60%, 80%, and 79%, respectively. Comparative performance evaluations reveal that the proposed parallel ABC-ANN model, incorporating the novel cost-sensitive fitness function and enhanced by vectorization and parallelization techniques, significantly reduces training time and outperforms state-of-the-art methods in terms of macro-F1 and accuracy in both network intrusion detection and earthquake damage estimation.
Citation - WoS: 20
Citation - Scopus: 30
A Review of On-Device Machine Learning for IoT: An Energy Perspective
(Elsevier, 2024) Tekin, Nazli; Aris, Ahmet; Acar, Abbas; Uluagac, Selcuk; Gungor, Vehbi Cagri
Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications.
Citation - WoS: 6
Citation - Scopus: 11
Deep Learning Approaches for Vehicle Type Classification With 3D Magnetic Sensor
(Elsevier, 2022) Kolukisa, Burak; Yildirim, Veli Can; Elmas, Bahadir; Ayyildiz, Cem; Gungor, Vehbi Cagri
In the Intelligent Transportation Systems, it is crucial to determine the type of vehicles to improve traffic management, increase human comfort, and enable future development of transport infrastructures. This paper presents a deep learning-based vehicle type classification approach for intermediate road traffic. Specifically, a low-cost, easy-to-install, battery-operated 3-D traffic sensor is designed and developed. In addition, a total of 376 vehicle samples are collected, and the vehicles are identified into three different classes according to their structures: light, medium, and heavy. Firstly, an oversampling method is applied to increase the number of samples in the training set. Then, the signals are converted into time series for LSTM and GRU and 2-D images for transfer learning models. Finally, soft voting is proposed using the LSTM, GRU, and VGG16, which is the best transfer learning method for vehicle type classification. The developed system is portable, power-limited, battery-operated, and reliable. Comparative performance results show that the soft voting ensemble method using a deep learning classifier improves the accuracy and f-measure performances by 92.92% and 93.42%, respectively. Additionally, the battery lifetime of the developed magnetic sensor node can work for up to 2 years.
Citation - WoS: 17
Citation - Scopus: 25
Analysis of Compressive Sensing and Energy Harvesting for Wireless Multimedia Sensor Networks
(Elsevier, 2020) Tekin, Nazli; Gungor, Vehbi Cagri
One of the main concerns of Wireless Multimedia Sensor Networks (WMSNs) is the huge data size causing the higher energy consumption in transmission. The high energy consumption is a critical problem for lifetime of network includes sensor nodes with limited battery. The data size reduction and Energy Harvesting (EH) methods are the promising solutions to improve the network lifetime. The main objective of this paper is to evaluate the impact of the different data size reduction methods, such as image compression and Compressive s Sensing (CS), and EH methods, such as vibration, thermal and indoor solar, on WMSNs lifetime in industrial environments. In addition, a novel Mixed Integer Programming (MIP) framework is proposed to maximize the network lifetime when EH, CS, and Error Control (EC) approaches are utilized together. Comparative performance results show that utilizing Binary Compressive Sensing (BCS) and Indoor Solar Harvester (ISH) extends industrial network lifetime significantly. (C) 2020 Elsevier B.V. All rights reserved.