arXiv subjects

Mohamed Abdallah

Publications and source records attributed to Mohamed Abdallah.

54 records · Page 3Linked to original sources

arXiv2020

Outage Analysis of Cognitive Electric Vehicular Networks over Mixed RF/VLC Channels

Modern transportation infrastructures are considered as one of the main sources of the greenhouse gases emitted into the atmosphere. This situation requires the decision-making players to enact the mass use of electric vehicles (EVs) which, in turn, highly demand novel secure communication technologies robust to various cyber-attacks. Therefore, in this paper, we propose a novel jamming-robust communication technique for different outdoor cognitive EV-enabled network cases over mixed radio-frequency (RF)/visible light communication (VLC) channels. One EV acts as a relaying node to allow an aggregator to reach the jammed EV and, at the same time, operates in both RF and VLC spectrum bands while satisfying interference constraints imposed by the primary network entities. We derive exact closed-form analytical expressions for the outage probability and also provide their asymptotic analysis while considering various channel state information quality scenarios. Moreover, we quantify the outage reduction achievable by deploying such mixed VLC/RF channels. Finally, analytical and simulation results validate the accuracy of our analysis.

cs.IT↗

arXiv2020

Exploiting Unlabeled Data in Smart Cities using Federated Learning

Privacy concerns are considered one of the main challenges in smart cities as sharing sensitive data brings threatening problems to 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 cities, hence there is a need to use semi-supervised learning. We propose a semi-supervised federated learning method called FedSem that exploits unlabeled data. The algorithm is divided into two phases where the first phase trains a global model based on the labeled data. In the second phase, we use semi-supervised learning based on the pseudo labeling technique to improve the model. We conducted several experiments using traffic signs dataset to show that FedSem can improve accuracy up to 8% by utilizing the unlabeled data in the learning process.

cs.LG↗

arXiv2020

Local Bitcoin Network Simulator for Performance Evaluation using Lightweight Virtualization

This paper presents a new blockchain network simulator that uses bitcoin's original reference implementation as its main application. The proposed simulator leverages the use of lightweight virtualization technology to build a fine tuned local testing network. To enable fast simulation of a large scale network without disabling mining service, the simulator can adjust the bitcoin mining difficulty level to below the default minimum value. In order to assess the performance of blockchain under different network conditions, the simulator allows to define different network topologies, and integrates Linux kernel traffic control (tc) tool to apply distinct delay or packet loss on the network nodes. Moreover, to validate the efficiency of our simulator we conduct a set of experiments and study the impact of the computation power and network delay on the network's consistency in terms of number of forks and mining revenues. The impact of applying different mining difficulty levels is also studied and the block time as well as fork occurrences are evaluated. Furthermore, a comprehensive survey and taxonomy of existing blockchain simulators are provided along with a discussion justifying the need of new simulator. As part of our contribution, we have made the simulator available on Github (https://github.com/noureddinel/core-bitcoin-net-simulator) for the community to use and improve it.

cs.CR↗

arXiv2020

Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method

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 for fingerprint-based localization. These solutions require frequent retraining of DL models with vast amounts of measurements. Although crowdsourcing is an excellent way to gather immense amounts of data, it jeopardizes the privacy of participants, as it requires to collect labeled data at a centralized server. Recently, federated learning has emerged as a practical concept in solving the privacy preservation issue of crowdsourcing participants by performing model training at the edge devices in a decentralized manner; the participants do not expose their data anymore to a centralized server. This paper presents a novel method utilizing federated learning to improve the accuracy of RSS fingerprint-based localization while preserving the privacy of the crowdsourcing participants. Employing federated learning allows ensuring \emph{preserving the privacy of user data} while enabling an adequate localization performance with experimental data captured in real-world settings. The proposed method improved localization accuracy by 1.8 meters when used as a booster for centralized learning and achieved satisfactory localization accuracy when used standalone.

cs.NI↗

arXiv2019

On Sharing Models Instead of Data using Mimic learning for Smart Health Applications

Electronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach. The idea is first to train a model on the original sensitive data, called the teacher model. Then, using this model, we can transfer its knowledge to another model, called the student model, without the need to learn the original data used in training the teacher model. The student model is then shared to the public and can be used to make accurate predictions. To assess the mimic learning approach, we have evaluated our scheme using different medical datasets. The results indicate that the student model mimics the teacher model performance in terms of prediction accuracy without the need to access to the patients' original data records.

cs.LG↗

arXiv2019

B-Ride: Ride Sharing with Privacy-preservation, Trust and Fair Payment atop Public Blockchain

Ride-sharing is a service that enables drivers to share their trips with other riders, contributing to appealing benefits of shared travel costs. However, the majority of existing platforms rely on a central third party, which make them subject to a single point of failure and privacy disclosure issues. Moreover, they are vulnerable to DDoS and Sybil attacks due to malicious users involvement. Besides, high fees should be paid to the service provider. In this paper, we propose a decentralized ride-sharing service based on public Blockchain, named B-Ride. Both riders and drivers can find rides match while preserving their trip data, including pick-up/drop-off location, and departure/arrival date. However, under the anonymity of the public blockchain, a malicious user may submit multiple ride requests or offers, while not committing to any of them, to discover better offer or to make the system unreliable. B-Ride solves this problem by introducing a time-locked deposit protocol for a ride-sharing by leveraging smart contract and zero-knowledge set membership proof. In a nutshell, both a driver and a rider have to show their commitment by sending a deposit to the blockchain. Later, a driver has to prove to the blockchain on the agreed departure time that he has arrived at the pick-up location. To preserve rider/driver location privacy by hiding the exact pick-up location, the proof is done using zero-knowledge set membership protocol. Moreover, to ensure a fair payment, a pay-as-you-drive methodology is introduced based on the elapsed distance of the driver and the rider. Also, we introduce a reputation-based trust model to rate drivers based on their past trips to allow riders to select them based on their history on the system. Finally, we implement B-Ride in a test net of Ethereum. The experiment results show the applicability of our protocol atop the existing real-world blockchain.

cs.CR↗

arXiv2019

Efficient and Privacy-Preserving Ride Sharing Organization for Transferable and Non-Transferable Services

Ride-sharing allows multiple persons to share their trips together in one vehicle instead of using multiple vehicles. This can reduce the number of vehicles in the street, which consequently can reduce air pollution, traffic congestion and transportation cost. However, a ride-sharing organization requires passengers to report sensitive location information about their trips to a trip organizing server (TOS) which creates a serious privacy issue. In addition, existing ride-sharing schemes are non-flexible, i.e., they require a driver and a rider to have exactly the same trip to share a ride. Moreover, they are non-scalable, i.e., inefficient if applied to large geographic areas. In this paper, we propose two efficient privacy-preserving ride-sharing organization schemes for Non-transferable Ride-sharing Services (NRS) and Transferable Ride-sharing Services (TRS). In the NRS scheme, a rider can share a ride from its source to destination with only one driver whereas, in TRS scheme, a rider can transfer between multiple drivers while en route until he reaches his destination. In both schemes, the ride-sharing area is divided into a number of small geographic areas, called cells, and each cell has a unique identifier. Each driver/rider should encrypt his trip's data and send an encrypted ride-sharing offer/request to the TOS. In NRS scheme, Bloom filters are used to compactly represent the trip information before encryption. Then, the TOS can measure the similarity between the encrypted trips data to organize shared rides without revealing either the users' identities or the location information. In TRS scheme, drivers report their encrypted routes, an then the TOS builds an encrypted directed graph that is passed to a modified version of Dijkstra's shortest path algorithm to search for an optimal path of rides that can achieve a set of preferences defined by the riders.

cs.CR↗

arXiv2019

Detecting Sybil Attacks using Proofs of Work and Location in VANETs

In this paper, we propose a Sybil attack detection scheme using proofs of work and location. The idea is that each road side unit (RSU) issues a signed time-stamped tag as a proof for the vehicle's anonymous location. Proofs sent from multiple consecutive RSUs is used to create vehicle trajectory which is used as vehicle anonymous identity. Also, one RSU is not able to issue trajectories for vehicles, rather the contributions of several RSUs are needed. By this way, attackers need to compromise an infeasible number of RSUs to create fake trajectories. Moreover, upon receiving the proof of location from an RSU, the vehicle should solve a computational puzzle by running proof of work (PoW) algorithm. So, it should provide a valid solution (proof of work) to the next RSU before it can obtain a proof of location. Using the PoW can prevent the vehicles from creating multiple trajectories in case of low-dense RSUs. Then, during any reported event, e.g., road congestion, the event manager uses a matching technique to identify the trajectories sent from Sybil vehicles. The scheme depends on the fact that the Sybil trajectories are bounded physically to one vehicle; therefore, their trajectories should overlap. Extensive experiments and simulations demonstrate that our scheme achieves high detection rate to Sybil attacks with low false negative and acceptable communication and computation overhead.

cs.CR↗

arXiv2018

Blockchain-based Firmware Update Scheme Tailored for Autonomous Vehicles

Recently, Autonomous Vehicles (AVs) have gained extensive attention from both academia and industry. AVs are a complex system composed of many subsystems, making them a typical target for attackers. Therefore, the firmware of the different subsystems needs to be updated to the latest version by the manufacturer to fix bugs and introduce new features, e.g., using security patches. In this paper, we propose a distributed firmware update scheme for the AVs' subsystems, leveraging blockchain and smart contract technology. A consortium blockchain made of different AVs manufacturers is used to ensure the authenticity and integrity of firmware updates. Instead of depending on centralized third parties to distribute the new updates, we enable AVs, namely distributors, to participate in the distribution process and we take advantage of their mobility to guarantee high availability and fast delivery of the updates. To incentivize AVs to distribute the updates, a reward system is established that maintains a credit reputation for each distributor account in the blockchain. A zero-knowledge proof protocol is used to exchange the update in return for a proof of distribution in a trust-less environment. Moreover, we use attribute-based encryption (ABE) scheme to ensure that only authorized AVs will be able to download and use a new update. Our analysis indicates that the additional cryptography primitives and exchanged transactions do not affect the operation of the AVs network. Also, our security analysis demonstrates that our scheme is efficient and secure against different attacks.

cs.CY↗

arXiv2018

Outage Probability of the EH-based Full-Duplex AF and DF Relaying Systems in α-μEnvironment

Wireless power transfer and energy harvesting have attracted a significant research attention in terms of their application in cooperative relaying systems. Most of existing works in this field focus on the half-duplex (HD) relaying mechanism over certain fading channels, however, in contrast, this paper considers a dual-hop full-duplex (FD) relaying system over a generalized independent but not identically distributed α-μfading channel, where the relay node is energy-constrained and entirely depends on the energy signal from the source node. Three special cases of the α-μmodel are investigated, namely, Rayleigh, Nakagami-m and Weibull fading. As the system performance, we investigate the outage probability (OP) for which we derive exact unified closed-form expressions. The provided Monte Carlo simulations validate the accuracy of our analysis. Moreover, the results obtained for the FD scenario are compared to the ones related to the HD. The results demonstrate that the decode-andforward relaying outperforms the amplify-and-forward relaying for both HD and FD scenarios. It is also shown that the FD scenario performs better than the HD relaying systems. Finally, we analyzed the impact of the fading parameters αand μon the achievable OP.

eess.SP↗

arXiv2017

Ergodic Capacity Analysis of Wireless Powered AF Relaying Systems over $α$-$μ$ Fading Channels

In this paper, we consider a two-hop amplify-and-forward (AF) relaying system, where the relay node is energy-constrained and harvests energy from the source node. In the literature, there are three main energy-harvesting (EH) protocols, namely, time-switching relaying (TSR), power-splitting (PS) relaying (PSR) and ideal relaying receiver (IRR). Unlike the existing studies, in this paper, we consider $α$-$μ$ fading channels. In this respect, we derive accurate unified analytical expressions for the ergodic capacity for the aforementioned protocols over independent but not identically distributed (i.n.i.d) $α$-$μ$ fading channels. Three special cases of the $α$-$μ$ model, namely, Rayleigh, Nakagami-mandWeibull fading channels were investigated. Our analysis is verified through numerical and simulation results. It is shown that finding the optimal value of the PS factor for the PSR protocol and the EH time fraction for the TSR protocol is a crucial step in achieving the best network performance.

cs.IT↗

arXiv2017

On the Capacity of Wireless Powered Cognitive Relay Network with Interference Alignment

In this paper, a two-hop decode-and-forward cognitive radio system with deployed interference alignment is considered. The relay node is energy-constrained and scavenges the energy from the interference signals. In the literature, there are two main energy harvesting protocols, namely, time-switching relaying and power-splitting relaying. We first demonstrate how to design the beamforming matrices for the considered primary and secondary networks. Then, the system capacity under perfect and imperfect channel state information scenarios, considering different portions of time-switching and power-splitting protocols, is estimated.

cs.IT↗

arXiv2017

Error Performance of Wireless Powered Cognitive Relay Networks with Interference Alignment

This paper studies a two-hop decode-and-forward underlay cognitive radio system with interference alignment technique. An energy-constrained relay node harvests the energy from the interference signals through a power-splitting (PS) relaying protocol. Firstly, the beamforming matrices design for the primary and secondary networks is demonstrated. Then, a bit error rate (BER) performance of the system under perfect and imperfect channel state information (CSI) scenarios for PS protocol is calculated. Finally, the impact of the CSI mismatch parameters on the BER performance is simulated.

cs.IT↗

arXiv2017

On the Feasibility of Interference Alignment in Compounded MIMO Broadcast Channels with Antenna Correlation and Mixed User Classes

This paper presents a generalized closed-form beamforming technique that can achieve the maximum degrees of freedom in compounded multiple-input multiple-output (MIMO) broadcast channels with mixed classes of multiple-antenna users. The contribution is firstly described within the context of a three-cell network and later extended to the general multi-cell scenario where we also show how to determine the conditions required to align the interference in a subspace that is orthogonal to the one reserved for the desired signals. This is then followed by an analysis of the impact of antenna correlation for different channel state information acquisition models. The proposed scheme is examined under both conventional and Large-scale MIMO systems. It will be shown that the proposed technique enables networks with any combination of user classes to achieve superior performance even under significant antenna correlation, particularly in the case of the Large-scale MIMO systems.

cs.IT↗

arXiv2015

A Stochastic Sizing Approach for Sharing-based Energy Storage Applications

In order to foster renewable energy integration, improve power quality and reliability, and reduce hydrocarbon emissions, there is a strong need to deploy energy storage systems (ESSs), which can provide a control medium for peak hour utility operations. ESSs are especially desired at the residential level, as this sector has the most untapped demand response potential. However, considering their high acquisition, operation, and maintenance costs, individual ESS deployment is not economically viable. Hence, in this paper, we propose a \emph{sharing-based} ESS architecture, in which the demand of each customer is modeled stochastically and the aggregate demand is accommodated by a combination of power drawn from the grid and the storage unit when the demand exceeds grid capacity. Stochastic framework for analyzing the optimal size of energy storage systems is provided. An analytical method is developed for a group customers with \emph{single} type of appliances. Then, this framework is extended to any network size with arbitrary number of customers and appliance types. The analytical method provides a tractable solution to the ESS sizing problem. Finally, a detailed cost-benefit analysis is provided, and the results indicate that sharing-based ESSs are practical and significant savings in terms of ESS size can be achieved.

math.OC↗

arXiv2015

Capacity Planning Frameworks for Electric Vehicle Charging Stations with Multi-Class Customers

In order to foster electric vehicle (EV) adoption, there is a strong need for designing and developing charging stations that can accommodate different customer classes, distinguished by their charging preferences, needs, and technologies. By growing such charging station networks, the power grid becomes more congested and, therefore, controlling of charging demands should be carefully aligned with the available resources. This paper focuses on an EV charging network equipped with different charging technologies and proposes two frameworks. In the first framework, appropriate for large networks, the EV population is expected to constitute a sizable portion of the light duty fleets. This which necessitates controlling the EV charging operations to prevent potential grid failures and distribute the resources efficiently. This framework leverages pricing dynamics in order to control the EV customer request rates and to provide a charging service with the best level of quality of service. The second framework, on the other hand, is more appropriate for smaller networks, in which the objective is to compute the minimum amount of resources required to provide certain levels of quality of service to each class. The results show that the proposed frameworks ensure grid reliability and lead to significant savings in capacity planning.

math.OC↗

arXiv2015

Energy Storage System Sizing for Peak Hour Utility Applications

In future smart grids, energy storage systems (ESSs) are expected to play a key role in reducing peak hour electricity generation cost and the associated level of carbon emissions. Considering their high acquisition, operation, and maintenance costs, ESSs are likely to serve a large number of users. Hence, optimal sizing of energy ESSs plays a critical role as over-provisioning ESS size leads to under-utilizing costly assets and under-provisioning it taxes operation lifetime. This paper proposes a stochastic framework for analyzing the optimal size of energy storage systems. In this framework the demand of each customer is modeled stochastically and the aggregate demand is accommodated by a combination of power drawn from the grid and the storage unit when the demand exceed grid capacity. In this framework an analytical method is developed, which provides tractable solution to the ESS sizing problem of interest. The results indicate that significant savings in terms of ESS size can be achieved.

math.OC↗

arXiv2014

A Survey on Energy Trading in Smart Grid

As the distributed energy generation and storage technologies are becoming economically viable, energy trading is gradually becoming a profit making option for end-users. This trend is further supported by the regulators and the policy makers as it aids the efficiency of power grid operations, reduces power generation cost and the Green House Gas (GHG) emissions. To that end, in this paper we provide an overview of distributed energy trading concepts in smart grid. First, we identify the motivation and the desired outcomes of energy trading framework. Then we present the enabling technologies that are required to generate, store, and communicate with the trading agencies. Finally, we survey on the existing literature and present an array of mathematical frameworks employed.

math.OC↗