| 1. |
- Aboelwafa, Mariam M. N., et al.
(author)
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A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
- 2020
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In: IEEE Internet of Things Journal. - Piscataway : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:9, s. 8462-8471
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Journal article (peer-reviewed)abstract
- The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data. © 2014 IEEE.
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| 2. |
- Akhtar, Muhammad Waseem, et al.
(author)
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Exploiting NOMA for Radio Resource Efficient Traffic Steering Use-case in O-RAN
- 2022
-
In: 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings. - : IEEE conference proceedings. - 9781665435406 ; , s. 5771-5776
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Conference paper (peer-reviewed)abstract
- In this work, we consider the design of a radio resource management (RRM) solution for traffic steering (TS) use-case in the open radio access network (O-RAN). The O-RAN TS deals with the quality-of-service (QoS)-aware steering of the traffic by connectivity management (e.g., device-to-cell association, radio spectrum, and power allocation) for emerging heterogeneous networks (HetNets) in 5G-and-beyond systems. However, TS in HetNets is a complex problem in terms of efficiently assigning/utilizing the radio resources while satisfying the diverse QoS requirements of especially the cell-edge users due to their poor signal-to-interference-plus-noise ratio (SINR). In this respect, we propose an intelligent non-orthogonal multiple access (NOMA)-based RRM technique for a small cell base station (SBS) within a macro gNB. A Q-learning-assisted algorithm is designed to allocate the transmit power and frequency sub-bands at the O-RAN control layer such that interference from macro gNB to SBS devices is minimized while ensuring the QoS of the maximum number of devices. The numerical results show that the proposed method enhances the overall spectral efficiency of the NOMA-based TS use case without adding to the system's complexity or cost compared to traditional HetNet topologies such as co-channel deployments and dedicated channel deployments.
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| 3. |
- Akhtar, Muhammad Waseem, et al.
(author)
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Partial NOMA for Semi-Integrated Sensing and Communication
- 2023
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In: 2023 IEEE Globecom Workshops (GC Wkshps). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350370218 ; , s. 1129-1134
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Conference paper (peer-reviewed)abstract
- This paper proposes a novel partial non-orthogonal multiple access (P-NOMA)-based semi-integrated sensing and communication (ISaC) system design. As an example ISaC scenario, we consider a vehicle simultaneously receiving the communication signal from infrastructure-to-vehicle (I2V) and sensing signal from vehicle-to-vehicle (V2V). P-NOMA allows exploiting both the orthogonal multiple access (OMA) and NOMA schemes for interference reduction and spectral efficiency (SE) enhancement while providing the flexibility of controlling the overlap of the sensing and communication signals according to the channel conditions and priority of the sensing and communication tasks. In this respect, we derive the closed-form expressions for communication outage probability and sensing probability of detection in Nakagami-m fading by considering the interference from the composite sensing channel. Our extensive analysis allows capturing the performance trade-offs of the communication and the sensing tasks with respect to various system parameters such as overlapping partial NOMA parameter, target range, radar cross section (RCS), and parameter m of the Nakagami-m fading channel. Our results show that the proposed P-NOMA-based semi-ISaC system outperforms the benchmark OMA-and NOMA-based systems in terms of communication spectral efficiency and probability of detection for the sensing target.
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| 4. |
- Akhtar, M. W., et al.
(author)
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Q2A-NOMA : A Q-Learning-based QoS-Aware NOMA System Design for Diverse Data Rate Requirements
- 2022
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In: IEEE Transactions on Industrial Informatics. - 1551-3203 .- 1941-0050. ; 18:11, s. 7549-7559
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Journal article (peer-reviewed)abstract
- Wireless use cases in industrial internet-of-thing (IIoT) networks often require guaranteed data rates ranging from a few kilobits per second to a few gigabits per second. Supporting such a requirement in a single radio access technique is difficult, especially when bandwidth is limited. Although non-orthogonal multiple access (NOMA) can improve the system capacity by simultaneously serving multiple devices, its performance suffers from strong user interference. In this paper, we propose a Q-learning-based algorithm for handling many-to-many matching problems such as bandwidth partitioning, device assignment to sub-bands, interference-aware access mode selection (orthogonal multiple access (OMA), or NOMA), and power allocation to each device. The learning technique maximizes system throughput and spectral efficiency (SE) while maintaining quality-of-service (QoS) for a maximum number of devices. The simulation results show that the proposed technique can significantly increase overall system throughput and SE while meeting heterogeneous QoS criteria.
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| 5. |
- Alioua, A., et al.
(author)
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Incentive mechanism for competitive edge caching in 5G-enabled Internet of things
- 2022
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In: Computer Networks. - : Elsevier BV. - 1389-1286 .- 1872-7069. ; 213
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Journal article (peer-reviewed)abstract
- The fifth generation (5G) of cellular networks provides the enabling environment for the Internet of Things (IoT) applications. Hence, the vast proliferation of 5G-enabled IoT devices and services led to an overwhelming growth of data traffic that could saturate the core network's backhaul links. Nowadays, caching is an unavoidable technique to solve this issue, whereby popular contents are stored on edge nodes near to end-users. There exist several initiatives to motivate caching actors for improving the caching process, but not designed for the real-world competitive caching market. In this work, we propose an incentive caching strategy in a 5G-enabled IoT network by considering a completely competitive caching scenario with multiple 5G mobile network operators (MNOs) and multiple content providers (CPs). The MNOs manage a set of edge caches on their base stations and they are competing to fill these caching resources, while the CPs detain a set of popular contents and are in conflict to rent the MNOs’ caches. Each MNO aims to maximize its monetary profit and offload its backhaul links, as each CP aims to improve the quality of experience (QoE) of its end-users. Then, we formulate a multi-leader multi-follower Stackelberg game to model the interaction between MNOs and CPs and define the different players’ utilities. Subsequently, we propose an iterative algorithm based on the convex optimization method to investigate the Stackelberg equilibrium. Finally, the numerical results of the different experimentations demonstrate that our game-based incentive strategy can significantly alleviate the backhaul links while improving the user QoE.
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| 6. |
- Anjum, M., et al.
(author)
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A Multi-Level ML-Based Optimization Framework for IIoT Networks with Distributed IRS Assisted UAVs
- 2023
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In: 2023 IEEE Globecom Workshops (GC Wkshps). - : IEEE conference proceedings. - 9798350370218 ; , s. 1338-1343
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Conference paper (peer-reviewed)abstract
- The development of the fifth generation (5G) of cellular systems enables the realization of densely connected, seamlessly integrated, and heterogeneous device networks. While 5G systems were developed to support the Internet of Everything (IoE) paradigm of communication, their mass-scale implementations have excessive capital deployment costs and severely detrimental environmental impacts. Hence, these systems are not feasibly scalable for the envisioned real-time, high-rate, high-reliability, and low-latency requirements of connected consumer, commercial, industrial, healthcare, and environmental processes of the IoE network. The IoE vision is expected to support 30 billion devices by 2030, hence, green communication architectures are critical for the development of next-generation wireless systems. In this context, intelligent reflecting surfaces (IRS) have emerged as a promising disruptive technological advancement that can adjust wireless environments in an energy-efficient manner. This work utilizes and analyzes a multi-node distributed IRS-assisted system in variable channel conditions and resource availability. We then employ machine learning and optimization algorithms for efficient resource allocation and system design of a distributed IRS-enabled industrial Internet of Things (IoT) network. The results show that the proposed data-driven solution is a promising optimization architecture for high-rate, next-generation IoE applications.
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| 7. |
- Anjum, Mahnoor, et al.
(author)
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Analysis of RSSI Fingerprinting in LoRa Networks
- 2019
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In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). - : IEEE. - 9781538677476 ; , s. 1178-1183
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Conference paper (peer-reviewed)abstract
- Localization has gained great attention in recent years, where different technologies have been utilized to achieve high positioning accuracy. Fingerprinting is a common technique for indoor positioning using short-range radio frequency (RF) technologies such as Bluetooth Low Energy (BLE). In this paper, we investigate the suitability of LoRa (Long Range) technology to implement a positioning system using received signal strength indicator (RSSI) fingerprinting. We test in real line-of-sight (LOS) and non-LOS (NLOS) environments to determine appropriate LoRa packet specifications for an accurate RSSI-to-distance mapping function. To further improve the positioning accuracy, we consider the environmental context. Extensive experiments are conducted to examine the performance of LoRa at different spreading factors. We analyze the path loss exponent and the standard deviation of shadowing in each environment
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| 8. |
- Ansari, Rafay Iqbal, et al.
(author)
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Control-Data Separation Architecture for Dual-Band mmWave Networks : A New Dimension to Spectrum Management
- 2019
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In: IEEE Access. - 2169-3536. ; 7, s. 34925-34937
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Journal article (peer-reviewed)abstract
- The exponential growth in global mobile data traffic, especially with regards to the massive deployment of devices envisioned for the fifth generation (5G) mobile networks, has given impetus to exploring new spectrum opportunities to support the new traffic demands. The millimeter wave (mmWave) frequency band is considered as a potential candidate for alleviating the spectrum scarcity. Moreover, the concept of multi-tier networks has gained popularity, especially for dense network environments. In this article, we deviate from the conventional multi-tier networks and employ the concept of control-data separation architecture (CDSA), which comprises of a control base station (CBS) overlaying the data base station (DBS). We assume that the CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band mmWave capability, i.e., 26 GHz unlicensed band and 60 GHz licensed band. We formulate a multi-objective optimization (MOO) problem, which jointly optimizes conflicting objectives: the spectral efficiency (SE) and the energy efficiency (EE). The unique aspect of this work includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), dynamic sub-carrier allocation (DSA) and joint power and rate adaptation (JPRA) algorithms to show the performance gains achieved by the proposed algorithm.
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| 9. |
- Aslam, Muhammad Shehryar, et al.
(author)
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Exploring Multi-Hop LoRa for Green Smart Cities
- 2020
-
In: IEEE Network. - : IEEE Communications Society. - 0890-8044 .- 1558-156X. ; 34:2, s. 225-231
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Journal article (peer-reviewed)abstract
- With the growing popularity of Internet-of-Things (IoT)-based smart city applications, various long-range and low-power wireless connectivity solutions are under rigorous research. LoRa is one such solution that works in the sub-GHz unlicensed spectrum and promises to provide long-range communication with minimal energy consumption. However, the conventional LoRa networks are single-hop, with the end devices connected to a central gateway through a direct link, which may be subject to large path loss and hence render low connectivity and coverage. This article motivates the use of multi-hop LoRa topologies to enable energy-efficient connectivity in smart city applications. We present a case study that experimentally evaluates and compares single-hop and multi-hop LoRa topologies in terms of range extension and energy efficiency by evaluating packet reception ratio (PRR) for various source to destination distances, spreading factors (SFs), and transmission powers. The results highlight that a multi-hop LoRa network configuration can save significant energy and enhance coverage. For instance, it is shown that to achieve a 90% PRR, a two-hop network provides 50% energy savings as compared to a single-hop network while increasing 35% coverage at a particular SF. In the end, we discuss open challenges in multi-hop LoRa deployment and optimization.
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| 10. |
- Aydogan, Emre, et al.
(author)
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A Central Intrusion Detection System for RPL-Based Industrial Internet of Things
- 2019
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In: 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS). - : IEEE. - 9781728112688
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Conference paper (peer-reviewed)abstract
- Although Internet-of-Things (IoT) is revolutionizing the IT sector, it is not mature yet as several technologies are still being offered to be candidates for supporting the backbone of this system. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is one of those promising candidate technologies to be adopted by IoT and Industrial IoT (IIoT). Attacks against RPL have shown to be possible, as the attackers utilize the unauthorized parent selection system of the RLP protocol. In this work, we are proposing a methodology and architecture to detect intrusions against IIoT. Especially, we are targeting to detect attacks against RPL by using genetic programming. Our results indicate that the developed framework can successfully (with high accuracy, along with high true positive and low false positive rates) detect routing attacks in RPL-based Industrial IoT networks.
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