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DRL-Based IRS-Assisted Secure Hybrid Visible Light and mmWave Communications
Danya A. Saifaldeen;Abdullatif M. Al-Baseer;Bekir S. Ciftler;Mohamed M. Abdallah;Khalid A. Qaraqe
This paper explores a new advancement in physical layer security (PLS) techniques, focusing on the integration of Intelligent Reflecting Surfaces (IRS). This work centers on developing an intelligent hybrid system combining communication lines using millimeter wave (mmWave) and Visible light communication (VLC). The system comprises four VLC access points with light fixtures, reinforced by a mirro...Show More
AUVs' Distributed Mission Planning System for Effective Underwater Chlorophyll Sampling
Somaiyeh MahmoudZadeh;Amirmehdi Yazdani;Yashar Kalantari;Bekir Ciftler
2024 2nd International Conference on Unmanned Vehicle Systems-Oman (UVS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
This paper presents a distributed mission planning system that enables a team of autonomous underwater vehicles (AUVs) for effective underwater chlorophyll sampling. The proposed system is developed based on a distributed algorithm that optimizes the real-time task allocation and mission planning of AUVs in a cooperative fashion while considering environmental and vehicular constraints to ensure e...Show More
Reputation-Aware Multi-Agent DRL for Secure Hierarchical Federated Learning in IoT
Noora Mohammed Al-Maslamani;Mohamed Abdallah;Bekir Sait Ciftler
Aiming at protecting device data privacy, Federated Learning (FL) is a framework of distributed machine learning in which devices’ local model parameters are exchanged with a centralized server without revealing the actual data. Hierarchical Federated Learning (HFL) framework was introduced to improve FL communication efficiency where devices are clustered and seek model consensus with the support...Show More
Secure Federated Learning for IoT using DRL-based Trust Mechanism
2022 International Wireless Communications and Mobile Computing (IWCMC)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Federated learning (FL) has evolved to leverage a distributed dataset from numerous IoT devices to improve the performance of a Machine Learning (ML) model while preserving the privacy of device data. Client devices train a global model jointly and share local model updates with a central entity or a server. However, FL is vulnerable to a variety of adversarial attacks that target its security and...Show More
Toward Secure Federated Learning for IoT Using DRL-Enabled Reputation Mechanism
Noora Mohammed Al-Maslamani;Bekir Sait Ciftler;Mohamed Abdallah;Mohamed M. E. A. Mahmoud
IEEE Internet of Things Journal
Cited by: Papers (6)
Federated learning (FL) has emerged to leverage datasets from multiple devices to improve the performance of a machine learning (ML) model while providing privacy preservation for devices. The training data is collected at the devices, also known as FL workers, which collaboratively train a global learning model and share their local model updates with a central entity or server without sharing th...Show More
DRL-Based IRS-Assisted Secure Visible Light Communications
Danya A. Saifaldeen;Bekir S. Ciftler;Mohamed M. Abdallah;Khalid A. Qaraqe
In this paper, we develop a novel Physical Layer Security (PLS) technique for a Visible Light Communication (VLC) system composed of light fixtures assisted by mirror array sheets serving as Intelligent Reflecting Surfaces (IRS). Our objective is to optimize the Secrecy Capacity (SC) by finding the optimal beamforming (BF) weights equipped at the VLC fixtures and mirror orientations at the mirror ...Show More
DRL-Based Joint RAT Association, Power and Bandwidth Optimization for Future HetNets
Abdulmalik Alwarafy;Bekir Sait Ciftler;Mohamed Abdallah;Mounir Hamdi;Naofal Al-Dhahir
Multi-radio access technologies (RATs) networks, where various heterogeneous networks (HetNets) coexist, are in service nowadays and considered a main enabling technology for future networks. In such networks, managing radio resources is challenge. In this letter, we address the problem of RATs-edge devices (EDs) association and joint power and bandwidth allocation in multi-RAT multi-homing HetNet...Show More
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA
Muhammad Shehab;Bekir S. Ciftler;Tamer Khattab;Mohamed M. Abdallah;Daniele Trinchero
In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario intending to maximize the sum-rate of users. The optimization problem at the IRS is quite complicated, and non-convex since it requires the tuning of the phase shift reflection matrix. Driven by the rising deployment of deep reinforcement learning (DRL) techniques that ...Show More
Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets
Abdulmalik Alwarafy;Bekir Sait Çiftler;Mohamed Abdallah;Mounir Hamdi;Naofal Al-Dhahir
This article considers the problem of cost-aware downlink sum-rate maximization via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation heterogeneous wireless networks (HetNets). We consider a future HetNet comprised of multi-RATs and serving multi-connectivity edge devices (EDs), and we formulate the problem as a mixed-integer non-linear programming (...Show More
The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions
Abdulmalik Alwarafy;Mohamed Abdallah;Bekir Sait Çiftler;Ala Al-Fuqaha;Mounir Hamdi
Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of ...Show More
Hybrid radio frequency (RF) and visible light communication (VLC) networks can provide high throughput and energy efficiency with VLC access points (APs) while ensuring ubiquitous coverage with RF APs. Due to dynamic channel conditions and limited resources, the hybrid RF/VLC networks’ resource allocation problem is complex and challenging. Conventional resource allocation techniques fail to overc...Show More
AI-Based Radio Resource Allocation in Support of the Massive Heterogeneity of 6G Networks
Abdulmalik Alwarafy;Abdullatif Albaseer;Bekir Sait Ciftler;Mohamed Abdallah;Ala Al-Fuqaha
There is a consensus in industry and academia that 6G wireless networks will incorporate massive heterogeneous radio access technologies (RATs) in order to cater to the high quality of service (QoS) demands of next-generation wireless applications. Allocation of radio resources in such large-scale networks will pose extra challenges to the traditional resource allocation techniques. Hence, it beco...Show More
DQN-Based Multi-User Power Allocation for Hybrid RF/VLC Networks
Bekir Sait Ciftler;Mohamed Abdallah;Abdulmalik Alwarafy;Mounir Hamdi
ICC 2021 - IEEE International Conference on Communications
Year: 2021 | Conference Paper |
Cited by: Papers (11)
In this paper, a Deep Q-Network (DQN) based multi-agent multi-user power allocation algorithm is proposed for hybrid networks composed of radio frequency (RF) and visible light communication (VLC) access points (APs). The users are capable of multihoming, which can bridge RF and VLC links for accommodating their bandwidth requirements. By leveraging a non-cooperative multi-agent DQN algorithm, whe...Show More
DeepRAT: A DRL-Based Framework for Multi-RAT Assignment and Power Allocation in HetNets
Abdulmalik Alwarafy;Bekir Sait Ciftler;Mohamed Abdallah;Mounir Hamdi
2021 IEEE International Conference on Communications Workshops (ICC Workshops)
Year: 2021 | Conference Paper |
Cited by: Papers (10)
Wireless heterogeneous networks (HetNets), where several systems with multi-radio access technologies (multi-RATs) coexist for massive multi-connectivity networks, are in service nowadays and are expected to be a main enabling technology in the future wireless networks. The extensive heterogeneity of such networks in terms of RATs architectures and their supported applications poses extra challeng...Show More
Federated Mimic Learning for Privacy Preserving Intrusion Detection
Noor Ali Al-Athba Al-Marri;Bekir S. Ciftler;Mohamed M. Abdallah
2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)
Year: 2020 | Conference Paper |
Cited by: Papers (24)
Internet of things (IoT) devices are prone to attacks due to the limitation of their privacy and security components. These attacks vary from exploiting backdoors to disrupting the communication network of the devices. Intrusion Detection Systems (IDS) play an essential role in ensuring information privacy and security of IoT devices against these attacks. Recently, deep learning-based IDS techniq...Show More
Exploiting Unlabeled Data in Smart Cities using Federated Edge Learning
Abdullatif Albaseer;Bekir Sait Ciftler;Mohamed Abdallah;Ala Al-Fuqaha
2020 International Wireless Communications and Mobile Computing (IWCMC)
Year: 2020 | Conference Paper |
Cited by: Papers (50)
Privacy concerns are considered one of the main challenges in smart cities as sharing sensitive data induces threatening problems in people's lives. Federated learning has emerged as an effective technique to avoid privacy infringement as well as increase the utilization of the data. However, there is a scarcity in the amount of labeled data and an abundance of unlabeled data collected in smart ci...Show More
Federated Learning for RSS Fingerprint-based Localization: A Privacy-Preserving Crowdsourcing Method
Bekir Sait Ciftler;Abdullatif Albaseer;Noureddine Lasla;Mohamed Abdallah
2020 International Wireless Communications and Mobile Computing (IWCMC)
Year: 2020 | Conference Paper |
Cited by: Papers (44)
Received Signal Strength (RSS) fingerprint-based localization has attracted a lot of research effort and cultivated many commercial applications of location-based services due to its low cost and ease of implementation. Many studies are exploring the use of deep learning (DL) algorithms for localization. DL's ability to extract features and to classify autonomously makes it an attractive solution ...Show More
Performance Evaluation of Physical Attacks against E2E Autoencoder over Rayleigh Fading Channel
2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
The use of Deep Learning (DL) in wireless communication systems is becoming very popular. As an example to the use of DL, the end-to-end (E2E) communication system can be implemented as an autoencoder. However, security and robustness are the main challenges due to the weaknesses of the autoencoders against physical adversarial attacks. Some works have been devoted to addressing these issues using...Show More
Occupancy Counting With Burst and Intermittent Signals in Smart Buildings
Bekir Sait Çiftler;Sener Dikmese;İsmail Güvenç;Kemal Akkaya;Abdullah Kadri
IEEE Internet of Things Journal
Cited by: Papers (45)
Zone-level occupancy counting is a critical technology for smart buildings and can be used for applications, such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building and an established wireless network. In this paper, in order to achieve occupancy coun...Show More
The Impact of Incomplete Secure Connectivity on the Lifetime of Wireless Sensor Networks
Huseyin Ugur Yildiz;Bekir Sait Ciftler;Bulent Tavli;Kemal Bicakci;Davut Incebacak
Key predistribution schemes accommodate secure connectivity by establishing pairwise keys between nodes. However, ensuring security for all communication links of a wireless sensor network (WSN) is nontrivial due to the memory limitations of the nodes. If some of the links are not available due to the lack of a primary security association between the transmitter and the receiver, nodes can still ...Show More
IoT Localization for Bistatic Passive UHF RFID Systems With 3-D Radiation Pattern
IEEE Internet of Things Journal
Cited by: Papers (45)
Passive radio-frequency identification (RFID) systems carry critical importance for Internet of Things (IoT) applications due to their energy harvesting capabilities. RFIDbased position estimation, in particular, is expected to facilitate a wide array of location-based services for IoT applications with low-power requirements. In this paper, considering monostatic and bistatic configurations and 3...Show More
Indoor occupancy tracking in smart buildings using passive sniffing of probe requests
Edwin Vattapparamban;Bekir Sait Çiftler;İsmail Güvenç;Kemal Akkaya;Abdullah Kadri
2016 IEEE International Conference on Communications Workshops (ICC)
Year: 2016 | Conference Paper |
Cited by: Papers (43)
Zone-level occupancy tracking is a critical technology for smart buildings and can be used for applications such as building energy management, surveillance, and security. Existing occupancy tracking techniques typically require installation of large number of occupancy monitoring sensors inside a building as well as an established network. In this study, in order to achieve occupancy tracking, we...Show More
Experimental performance evaluation of passive UHF RFID systems under interference
2015 IEEE International Conference on RFID Technology and Applications (RFID-TA)
Year: 2015 | Conference Paper |
Cited by: Papers (3)
Performance of passive ultrahigh frequency radio-frequency identification (UHF RFID) systems is determined by the ability of the RFID reader to precisely decode the RF signal backscattered by the RFID tag. For a successful operation, two conditions must be met: (1) the power level of the impinged signal on the RFID tag must be above its threshold to power up its internal circuitry, and (2) the bac...Show More
Efficient Privacy-Preserving Fingerprint-Based Indoor Localization Using Crowdsourcing
Patrick Armengol;Rachelle Tobkes;Kemal Akkaya;Bekir S. Çiftler;Ismail Güvenç
2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems
Year: 2015 | Conference Paper |
Cited by: Papers (11)
Indoor localization has been widely studied due to the inability of GPS to function indoors. Numerous approaches have been proposed in the past and a number of these approaches are currently being used commercially. However, little attention was paid to the privacy of the users especially in the commercial products. Malicious individuals can determine a client's daily habits and activities by simp...Show More
Fundamental bounds on RSS-based wireless localization in passive UHF RFID systems
2015 IEEE Wireless Communications and Networking Conference (WCNC)
Year: 2015 | Conference Paper |
Cited by: Papers (8)
Radio-Frequency IDentification (RFID) systems carry critical importance for reaping the benefits of a diverse set of applications that will be enabled by the emerging Internet of Things (IoT). In particular, RFID based position estimation and tracking will facilitate a wide array of location based services. Considering a monostatic RFID configuration with a three dimensional antenna radiation patt...Show More
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