Browsing by Author "Nakip, Mert"

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Citation - WoS: 16
Citation - Scopus: 26
A Multiscale Algorithm for Joint Forecasting-Scheduling to Solve the Massive Access Problem of IoT
(Institute of Electrical and Electronics Engineers Inc., 2020) Volkan Rodoplu; Mert Nakıp; D. T. Eliiyi; Cüneyt Güzeliş; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert; Eliiyi, Deniz Tursel
The massive access problem of the Internet of Things (IoT) is the problem of enabling the wireless access of a massive number of IoT devices to the wired infrastructure. In this article we describe a multiscale algorithm (MSA) for joint forecasting-scheduling at a dedicated IoT gateway to solve the massive access problem at the medium access control (MAC) layer. Our algorithm operates at multiple time scales that are determined by the delay constraints of IoT applications as well as the minimum traffic generation periods of IoT devices. In contrast with the current approaches to the massive access problem that assume random arrivals for IoT data our algorithm forecasts the upcoming traffic of IoT devices using a multilayer perceptron architecture and preallocates the uplink wireless channel based on these forecasts. The multiscale nature of our algorithm ensures scalable time and space complexity to support up to 6650 IoT devices in our simulations. We compare the throughput and energy consumption of MSA with those of reservation-based access barring (RAB) priority based on average load (PAL) and enhanced predictive version burst-oriented (E-PRV-BO) protocols and show that MSA significantly outperforms these beyond 3000 devices. Furthermore we show that the percentage control overhead of MSA remains less than 1.5%. Our results pave the way to building scalable joint forecasting-scheduling engines to handle a massive number of IoT devices at IoT gateways. © 2020 Elsevier B.V. All rights reserved.
Citation - Scopus: 3
A Smart Home Demand Response System based on Artificial Neural Networks Augmented with Constraint Satisfaction Heuristic
(Institute of Electrical and Electronics Engineers Inc., 2021) Mert Nakıp; Arda Asut; Cennet Kocabiyik; Cüneyt Güzeliş; Kocabiyik, Cennet; Güzelis, Cuneyt; Asut, Arda; Nakip, Mert
Distributing the peak load and alleviating grid stress by considering hourly electricity prices are some of the main research problems for current smart grid systems. This paper deals with the scheduling problem of home appliances' operating hours in smart grids which aims to achieve minimum cost in user-defined operation intervals. To this end scheduling via Artificial Neural Networks Augmented with Constraint Satisfaction Heuristic (ANN-AH) method that emulates the operation of the optimization for smart home demand response is developed. Our results show that a home demand response via ANN-AH achieves close to optimal performance with 10 times lower execution time than the optimal scheduling. These results suggest that the ANN-AH based demand response is highly successful and practical and it is promising for future applications in micro-grid and decentralized renewable energy systems. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
An Associated Random Neural Network Detects Intrusions and Estimates Attack Graphs
(IEEE Computer Society, 2024) Mert Nakıp; Erol Gelenbe; Nalip, Mert; Nakip, Mert; Gelenbe, Erol
Cyberattacks especially Botnet Distributed Denial of Service (DDoS) increasingly target networked systems compromise interconnected nodes by constantly spreading malware. In order to prevent these attacks in their early stages which includes stopping the spread of malware it is vital to identify compromised nodes and successfully predict potential attack paths. To this end this paper proposes a novel system based on an Associated Random Neural Network (ARNN) that simultaneously detects intrusion at the network-level and estimates the network attack graph. In this system ARNN is trained online to minimize problem-specific multi-Task loss so that it identifies compromised network nodes while the neural network connection weights also estimate the attack path. The performance of the method is calculated using the Kitsune attack dataset showing that the method achieves a recall rate above 0.95 in estimating the network attack graph and provides a near-perfect classification of compromised nodes. The ARNN-based system for dynamic and continuous estimation of compromised nodes and network attack graphs can pave the way for enhancing security measures and stopping Botnet DDoS attacks from spreading in networked systems. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 15
An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Mert Nakip; Kubilay Karakayali; Cuneyt Guzelis; Volkan Rodoplu; Karakayali, Kubilay; Guzelis, Cuneyt; Rodoplu, Volkan; Nakip, Mert
We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based wrapper-based and embedded feature selection methods our architecture enables the automatic selection of features dynamically based on feature importance score calculation and gamma-gated feature selection units that are trained jointly and end-to-end with the forecaster. We compare the performance of our FSF architecture on the problem of forecasting IoT device traffic against the following existing (feature selection forecasting) technique pairs: Autocorrelation Function (ACF) Analysis of Variance (ANOVA) Recurrent Feature Elimination (RFE) and Ridge Regression methods for feature selection and Linear Regression Multi-Layer Perceptron (MLP) Long Short Term Memory (LSTM) 1 Dimensional Convolutional Neural Network (1D CNN) Autoregressive Integrated Moving Average (ARIMA) and Logistic Regression for forecasting. We show that our FSF architecture achieves either the best or close to the best performance among all of the competing techniques by virtue of its dynamic automatic feature selection capability. In addition we demonstrate that both the training time and the execution time of FSF are reasonable for IoT applications. This work represents a milestone for the development of predictive networks for IoT in smart cities of the near future.
Citation - Scopus: 11
Botnet Attack Detection with Incremental Online Learning
(Springer Science and Business Media Deutschland GmbH, 2022) Mert Nakıp; Erol Gelenbe; Nakip, Mert; Gelenbe, Erol; E. Gelenbe , M. Jankovic , D. Kehagias , A. Marton , A. Vilmos
In recent years IoT devices have often been the target of Mirai Botnet attacks. This paper develops an intrusion detection method based on Auto-Associated Dense Random Neural Network with incremental online learning targeting the detection of Mirai Botnet attacks. The proposed method is trained only on benign IoT traffic while the IoT network is online, therefore it does not require any data collection on benign or attack traffic. Experimental results on a publicly available dataset have shown that the performance of this method is considerably high and very close to that of the same neural network model with offline training. In addition both the training and execution times of the proposed method are highly acceptable for real-time attack detection. © 2025 Elsevier B.V. All rights reserved.
Comparative Study of Forecasting Models for COVID-19 Outbreak in Turkey
(Institute of Electrical and Electronics Engineers Inc., 2021) Mert Nakıp; Onur Çopur; Cüneyt Güzeliş; Guzelis, Cuneyt; Nakip, Mert; Copur, Onur
This paper gives an explanation for the failure of machine learning models for the prediction of the cases and the other future trends of Covid-19 pandemic. The paper shows that simple Linear Regression models provide high prediction accuracy values reliably but only for a 2-weeks period and that relatively complex machine learning models which have the potential of learning long-term predictions with low errors cannot achieve to obtain good predictions with possessing a high generalization ability. It is suggested in the paper that the lack of a sufficient number of samples is the source of the low prediction performance of the forecasting models. To exploit the information which is of most relevant with the active cases we perform feature selection over a variety of variables such as the numbers of active cases deaths recoveries and population. Furthermore we compare Linear Regression Multi-Layer Perceptron and Long-Short Term Memory models each of which is used for prediction of active cases together with various feature selection methods. Our results show that the accurate forecasting of the active cases with high generalization ability is possible up to 3 days because of the small sample size of COVID-19 data. We observe that the Linear Regression model has much better prediction performance with high generalization ability as compared to the complex models but as expected its performance decays sharply for more than 14-days prediction horizons. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 15
Citation - Scopus: 21
Comparative Study of Forecasting Schemes for IoT Device Traffic in Machine-to-Machine Communication
(ASSOC COMPUTING MACHINERY, 2019) Mert Nakip; Baran Can Gul; Volkan Rodoplu; Cuneyt Guzelis; Gul, Baran Can; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert
We present a comparative study of Autoregressive Integrated Moving Average (ARIMA) Multi-Layer Perceptron (MLP) 1-Dimensional Convolutional Neural Network (1-D CNN) and Long-Short Term Memory (LSTM) models on the problem of forecasting the traffic generation patterns of individual Internet of Things (IoT) devices in Machine-to-Machine (M2M) communication. We classify IoT traffic into four classes: Fixed-Bit Periodic (FBP) Variable-Bit Periodic (VBP) Fixed-Bit Aperiodic (FBA) and Variable-Bit Aperiodic (VBA). We show that LSTM outperforms all of the other models significantly in the symmetric Mean Absolute Percentage Error (sMAPE) measure for devices in the VBP class in our simulations. Furthermore we show that LSTM has almost the same performance in this metric for the FBA class as MLP and 1-D CNN. While the training time per IoT device is the highest for LSTM all of the forecasting models have reasonable training times for practical implementation. Our results suggest an architecture in which an IoT Gateway predicts the future traffic of IoT devices in the FBP VBP and FBA classes and pre-allocates the uplink wireless channel for these classes in advance in order to alleviate the Massive Access Problem of M2M communication.
Citation - Scopus: 4
Converting Utility Meters from Analogue to Smart based on Deep Learning Models
(Institute of Electrical and Electronics Engineers Inc., 2020) Humberto J.Cabeza Barreto; Ilker Kurtulan; Suleyman Inci; Mert Nakıp; Cüneyt Güzeliş; Barreto, Humberto J Cabeza; Kurtulan, Ilker; Guzelis, Cuneyt; Inci, Suleyman; Nakip, Mert
In this paper we proposed a system that automatically interprets the data of the utility meters by analyzing the photo of an analogue meter. In addition it sends the meter data to the consumers and the providers. We based the system on Convolutional Neural Networks (CNN) where we compared the You Only Look Once (YOLO) and a LeNet as CNN models. We collected the data for the training of each CNN model from the demonstration set of the project. Our results show that the YOLO model is reliable and fast. The model has a 99% accuracy for the gas meter and 98% accuracy for the water meter. © 2020 Elsevier B.V. All rights reserved.
Citation - Scopus: 13
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection
(IEEE Computer Society, 2023) Mert Nakıp; Baran Can Gul; Erol Gelenbe; Gül, Baran Can; Nakip, Mert; Gelenbe, Erol
Cyberattacks are increasingly threatening net-worked systems often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. uch attacks can also target multiple components of a Supply Chain which can be protected via Machine Learning (ML)-based Intrusion Detection Systems (IDSs). However the need to learn large amounts of labelled data often limits the applicability of ML-based IDSs to cybersystems that only have access to private local data while distributed systems such as Supply Chains have multiple components each of which must preserve its private data while being targeted by the same attack To address this issue this paper proposes a novel Decentralized and Online Federated Learning Intrusion Detection (DOF-ID) architecture based on the G-Network model with collaborative learning that allows each IDS used by a specific component to learn from the experience gained in other components in addition to its own local data without violating the data privacy of other components. The performance evaluation results using public Kitsune and Bot-loT datasets show that DOF -ID significantly improves the intrusion detection performance in all of the collaborating components with acceptable computation time for online learning. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 5
Development of a Multi-Sensor Fire Detector Based On Machine Learning Models
(IEEE, 2019) Mert Nakip; Cuneyt Guzelis; Guzelis, Cuneyt; Nakip, Mert
This paper proposes a method to reduce false positive fire alarms by fusing data from different sensors using a specific machine learning model. We design an electronic circuit with 6 sensors to detect 7 physical sensory inputs. We experimentally collect dataset for training and testing of machine learning models which are used for the implementation of fusing and classifying sensor data. An algorithm which employs the trained machine learning model for the classification of sensor data and then the thresholding is designed. Machine learning models are selected based on the results of comparisons among multi-layer perceptron support vector machine and radial basis function network. We use classification accuracy percentage false negative error and false positive error as measures for comparison. Multi-layer perceptron is observed as the best model according to its 96.875% classification accuracy.
Citation - WoS: 4
Citation - Scopus: 17
Diffusion Analysis Improves Scalability of IoT Networks to Mitigate the Massive Access Problem
(IEEE Computer Society, 2021) Erol Gelenbe; Mert Nakıp; Dariusz Marek; Tadeusz Czachórskí; Czachorski, Tadeusz; Marek, Dariusz; Nakip, Mert; Gelenbe, Erol
A significant challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from a massive number of IoT devices are forwarded to an IoT gateway. Many IoT devices tend to report their data to their wired or wireless network gateways at closely correlated instants of time leading to congestion known as the Massive Access Problem (MAP) which increases the probability that the IoT data will not meet its required deadlines. Since IoT data loses much of its value if it arrives to destination beyond a required deadline MAP has been extensively studied in the literature. Thus we first take a queueing theoretic view of the problem and also use a Diffusion Approximation to gain insight into the IoT traffic statistics that affect MAP. Then we introduce the Quasi-Deterministic Transmission Policy (QDTP) which significantly alleviates MAP when the average traffic rate grows beyond a given level and substantially reduces the probability that IoT data deadlines are missed. The results are validated using real IoT data which has been placed in IP packets for transmission. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 3
Dynamic Positioning Interval Based on Reciprocal Forecasting Error (DPI-RFE) Algorithm for Energy-Efficient Mobile IoT Indoor Positioning
(Institute of Electrical and Electronics Engineers Inc., 2021) Alper Saylam; Nur Kelesoglu; Rifat Orhan Cikmazel; Mert Nakıp; Volkan Rodoplu; Saylam, Alper; Cikmazel, Rifat Orhan; Rodoplu, Volkan; Kelesoglu, Nur; Nakip, Mert; M.S. Obaidat , S. Bilgen , K.-F. Hsiao , P. Nicopolitidis , S. Oktug , Y. Guo
We develop an algorithm called "Dynamic Positioning Interval based on Reciprocal Forecasting Error (DPIRFE)"for energy-efficient mobile Internet of Things (IoT) Indoor Positioning (IP). In contrast with existing IP algorithms DPIRFE forecasts the future trajectory of a mobile IoT device by using machine learning and dynamically adjusts the positioning interval based on the reciprocal instantaneous forecasting error thereby dynamically trading off transmit energy consumption against forecasting error. We compare the performance of DPIRFE with respect to the total transmit energy consumption and the average forecasting error against Constant Positioning Interval (CPI) and Positioning Interval based on Displacement (PID) algorithms. Our results show that DPI-RFE significantly outperforms both of these benchmark algorithms with respect to transmit energy consumption while achieving a competitive average forecasting error performance. These results open the way to the design of machine learning based trajectory forecasting algorithms that can be utilized for energy-efficient positioning in next-generation wireless networks. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 4
Energy-Efficient Indoor Positioning for Mobile Internet of Things Based on Artificial Intelligence
(Institute of Electrical and Electronics Engineers Inc., 2021) Alper Saylam; Rifat Orhan Cikmazel; Nur Kelesoglu; Mert Nakıp; Volkan Rodoplu; Saylam, Alper; Cikmazel, Rifat Orhan; Rodoplu, Volkan; Kelesoglu, Nur; Nakip, Mert
We develop an energy-efficient indoor positioning system based on Artificial Intelligence (AI). In our system first at the positioning layer a Multi-Layer Perceptron (MLP) estimates the current indoor position of an IoT device based on positioning indicators obtained from the anchors. Second at the forecasting layer a pair of MLPs estimate the future positions of the device based on the past position estimates obtained when the device woke up as well as the forecast positions of the device during the sleep periods. Third the device is awakened to send a positioning beacon at intervals over which a significant displacement is predicted to occur by the forecasting layer. Our results demonstrate that our indoor positioning system saves significant energy via adaptive sleep cycles whose duration is determined by the prediction of a significant displacement. This work establishes a foundation for indoor positioning that utilizes AI-based positioning and trajectory forecasting. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 6
Citation - Scopus: 6
Fire detection and risk assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization
(Elsevier Ltd, 2024) Mert Nakıp; Nur Kelesoglu; Cüneyt Güzeliş; Guzelis, Cueneyt; Kelesoglu, Nur; Nakip, Mert
We propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments l2 norm based smoothing term into an l1-l2 combination facilitating the integration of l1 regularization into the SVR method thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires benchmarking against 15 machine learning models including state-of-the-art techniques such as Recurrent Trend Predictive Neural Network (rTPNN) Long-Short Term Memory (LSTM) Multi-Layer Perceptron (MLP) Gated Recurrent Unit (GRU) and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire enabling early preventive measures. Notably it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly it exhibits computation times significantly lower – by 1 to 2 orders of magnitude – than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR facilitated by flattening-samples based augmented regularization allows for high detection performance even with smaller training sets. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 10
G-Networks Can Detect Different Types of Cyberattacks
(IEEE COMPUTER SOC, 2022) Erol Gelenbe; Mert Nakilp; Nakilp, Mert; Nakip, Mert; Gelenbe, Erol
Malicious network attacks are a serious source of concern and machine learning techniques are widely used to build Attack Detectors with off-line training with real attack and non-attack data and used online to monitor system entry points connected to networks. Many machine learning based Attack Detectors are typically trained to identify specific types attacks and the training of such algorithms to cover several types of attacks may be excessively time consuming. This paper shows that G-Networks which are queueing networks with product form solution and special customers such as negative customers and triggers can be trained just with non-attack traffic can accurately detect several different attack types. This is established with a special case of G-Networks with triggerred customer movement. A DARPA attack and non-attack traffic repository is used to train and test the the G-Network yielding comparable or clearly better accuracy than most known attack detection techniques.
Citation - WoS: 8
Improving Massive Access to IoT Gateways
(ELSEVIER, 2022) Erol Gelenbe; Mert Nakip; Tadeusz Czachorski; Czachorski, Tadeusz; Nakip, Mert; Gelenbe, Erol
IoT networks handle incoming packets from large numbers of IoT Devices (IoTDs) to IoT Gateways. This can lead to the IoT Massive Access Problem that causes buffer overflow large end-to-end delays and missed deadlines. This paper analyzes a novel traffic shaping method named the Quasi-Deterministic Traffic Policy (QDTP) that mitigates this problem by shaping the incoming traffic without increasing the end-to-end delay or dropping packets. Using queueing theoretic techniques and extensive data driven simulations with real IoT datasets the value of QDTP is shown as a means to considerably reduce congestion at the Gateway and significantly improve the IoT network's overall performance.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).
Citation - Scopus: 16
Joint Forecasting-Scheduling for the Internet of Things
(Institute of Electrical and Electronics Engineers Inc., 2019) Mert Nakıp; Volkan Rodoplu; Cüneyt Güzeliş; D. T. Eliiyi; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert; Eliiyi, Deniz Tursel
We present a joint forecasting-scheduling (JFS) system to be implemented at an IoT Gateway in order to alleviate the Massive Access Problem of the Internet of Things. The existing proposals to solve the Massive Access Problem model the traffic generation pattern of each IoT device via random arrivals. In contrast our JFS system forecasts the traffic generation pattern of each IoT device and schedules the transmissions of these devices in advance. The comparison of the network throughput of Autoregressive Integrated Moving Average (ARIMA) Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) forecasting models reveals that the optimal choice of the forecasting model for JFS depends heavily on the proportions of distinct IoT device classes that are present in the network. Simulations show that our JFS system scales up to 1000 devices while achieving a total execution time under 1 second. This work opens the way to the design of scalable joint forecasting-scheduling solutions at IoT Gateways. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 25
MIRAI Botnet Attack Detection with Auto-Associative Dense Random Neural Network
(IEEE, 2021) Mert Nakip; Erol Gelenbe; Nakip, Mert; Gelenbe, Erol
Internet connected IoT devices have often been particularly vulnerable to Botnet attacks of the Mirai family in recent years. Thus we develop an attack detection scheme for Mirai Botnets using the Auto-Associative Dense Random Neural Network that has recently been successful for other attacks such as the SYN attack. The resulting method is trained with normal traffic and tested with attack traffic and shown to result in high accuracy detection of attacks with low false alarms. The approach is compared on the same data set with two other common Machine learning methods (Lasso and KNN) and shown to have higher accuracy and much lower computation times than KNN and slightly higher (but comparable) computation times with respect to Lasso.
MOSAL: A Subspace-Based Forecasting Algorithm for Throughput Maximization in IoT Networks
(Institute of Electrical and Electronics Engineers Inc., 2022) Mert Nakıp; Alperen Helva; Cüneyt Güzeliş; Volkan Rodoplu; Guzelis, Cneyt; Rodoplu, Volkan; Helva, Alperen; Nakip, Mert
Predictive solution techniques have been developed recently to solve the massive access problem of the Internet of Things (IoT). These techniques forecast the traffic generation patterns of individual IoT devices in the coverage area of an IoT gateway and schedule the Medium Access Control (MAC)-layer resources at the gateway in advance based on these forecasts. Although predictive solutions have achieved high network performance a key problem is that their performance depends highly on the performance of forecasters. In this article to minimize the effects of forecasting errors on the performance of predictive networks we develop a subspace-based forecasting algorithm called 'Motion On a Subspace under Adaptive Learning rate (MOSAL).' First our algorithm trains a forecaster by minimizing the performance loss of an IoT network based on the emulation of an Application-Specific Error Function (ASEF) by an Artificial Neural Network (ANN). Second the algorithm moves close to a subspace of the forecasting errors while aiming to maximize network throughput. Our results show that MOSAL achieves a throughput performance that surpasses the performance of commonly used standard gradient descent training algorithms at a reasonable execution time. These results open the way to the deployment of predictive solutions at IoT gateways in practice in the near future. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 12
Citation - Scopus: 18
Multi-Channel Joint Forecasting-Scheduling for the Internet of Things
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020) Volkan Rodoplu; Mert Nakip; Roozbeh Qorbanian; Deniz Tursel Eliiyi; Rodoplu, Volkan; Qorbanian, Roozbeh; Nakip, Mert; Eliiyi, Deniz Tursel
We develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance thus obviating contention collision and handshaking which are found in reactive protocols. In this paper we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system first we design a heuristic called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL) that solves the general form of the scheduling problem. Second for the special case of identical channels we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB) both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.