arXiv subjects

Yehia Massoud

Publications and source records attributed to Yehia Massoud.

27 records · Page 2Linked to original sources

arXiv2020

Scalable and Secure Architecture for Distributed IoT Systems

Internet-of-things (IoT) is perpetually revolutionizing our daily life and rapidly transforming physical objects into an ubiquitous connected ecosystem. Due to their massive deployment and moderate security levels, those devices face a lot of security, management, and control challenges. Their classical centralized architecture is still cloaking vulnerabilities and anomalies that can be exploited by hackers for spying, eavesdropping, and taking control of the network. In this paper, we propose to improve the IoT architecture with additional security features using Artificial Intelligence (AI) and blockchain technology. We propose a novel architecture based on permissioned blockchain technology in order to build a scalable and decentralized end-to-end secure IoT system. Furthermore, we enhance the IoT system security with an AI-component at the gateway level to detect and classify suspected activities, malware, and cyber-attacks using machine learning techniques. Simulations and practical implementation show that the proposed architecture delivers high performance against cyber-attacks.

cs.CR↗

arXiv2020

A Trustworthy Recruitment Process for Spatial Mobile Crowdsourcing in Large-scale Social IoT

Spatial Mobile Crowdsourcing (SMCS) can be leveraged by exploiting the capabilities of the Social Internet-of-Things (SIoT) to execute spatial tasks. Typically, in SMCS, a task requester aims to recruit a subset of IoT devices and commission them to travel to the task location. However, because of the exponential increase of IoT networks and their diversified devices (e.g., multiple brands, different communication channels, etc.), recruiting the appropriate devices/workers is becoming a challenging task. To this end, in this paper, we develop a recruitment process for SMCS platforms using automated SIoT service discovery to select trustworthy workers satisfying the requester requirements. The method we purpose includes mainly two stages: 1) a worker filtering stage, aiming at reducing the workers' search space to a subset of potential trustworthy candidates using the Louvain community detection algorithm (CD) applied to SIoT relation graphs. Next, 2) a selection process stage that uses an Integer Linear Program (ILP) to determine the final set of selected devices/workers. The ILP maximizes a worker efficiency metric incorporating the skills/specs level, recruitment cost, and trustworthiness level of the recruited IoT devices. Selected experiments analyze the performance of the proposed CD-ILP algorithm using a real-world dataset and show its superiority in providing an effective recruitment strategy compared to an existing stochastic algorithm.

cs.SI↗

arXiv2020

A Latency-Aware Task Offloading in Mobile Edge Computing Network for Distributed Elevated LiDAR

Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since ELiDs have been removed from the vehicle, their data must be processed externally in the cloud or on the edge, necessitating an optimized backhaul system that allocates data efficiently to compute servers. In this paper, we address this need for an optimized backhaul system by formulating a mixed-integer programming problem that minimizes the average latency of the uplink and downlink hop-by-hop transmission plus computation time for each ELiD while considering different bandwidth allocation schemes. We show that our model is capable of allocating resources for differing topologies, and we perform a sensitivity analysis that demonstrates the robustness of our problem formulation under different circumstances.

eess.SP↗

arXiv2020

Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach

In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.

cs.RO↗

arXiv2020

Automated Service Discovery for Social Internet-of-Things Systems

In this paper, we propose to design an automated service discovery process to allow mobile crowdsourcing task requesters select a small set of devices out of a large-scale Internet-of-things (IoT) network to execute their tasks. To this end, we proceed by dividing the large-scale IoT network into several virtual communities whose members share strong social IoT relations. Two community detection algorithms, namely Louvain and order statistics local method (OSLOM) algorithms, are investigated and applied to a real-world IoT dataset to form non-overlapping and overlapping IoT devices groups. Afterwards, a natural language process (NLP)-based approach is executed to handle crowdsourcing textual requests and accordingly find the list of IoT devices capable of effectively accomplishing the tasks. This is performed by matching the NLP outputs, e.g., type of application, location, required trustworthiness level, with the different detected communities. The proposed approach effectively helps in automating and reducing the service discovery procedure and recruitment process for mobile crowdsourcing applications.

cs.SI↗

arXiv2020

Design of a Scalable DNA Shearing System Using Phased-Array Fresnel Lens Transducers

In this paper, we present the design of a Deoxyribonucleic Acid (DNA) shearing system based on unique acoustic waves generated using a phased-array Fresnel Lens transducer. Four 90-degree sector-transducers are used to build a circular array transducer. Acoustic simulation results for particle displacement are provided for cases when one, two, three, and all four transducers in the array are excited with RF signals. Each 90degree-transducer is excited with separate RF signal of same or different phase. The proposed transducer structure generates bulk lateral ultrasonic waves in the DNA sample; the lateral waves produce both convergence and vortexing effects in the sample. The converged lateral acoustic waves are required to break the DNA sample-meniscus inside the tube and the rotational component of acoustic field is used to recirculate the DNA sample to get homogeneous shearing with tight fragment distribution. Finally, we present the experimental results of DNA sheared to different mean fragment sizes using the proposed system and validate the shearing-capability of the proposed system.

physics.ins-det↗

arXiv2019

Analytical Modeling and Design of Fresnel Lens Transducers

In this paper, we present an analytical modeling technique for circularly symmetric piezoelectric transducers, also called as Fresnel Lens. We also present the design of a flat/piston transducer that can generate unique acoustic wave patterns, having both converging and vortexing effects. The converging effect is generated by designing the transducer electrodes in the shapes of circular rings using Fresnel formula and exciting it with an RF signal of resonant frequency. The vortexing effect is achieved by cutting the rings to different sector angles: 90, 120, 180 and 270 degrees. We use the analytical model to simulate the performance of these transducers.

physics.app-ph↗

arXiv2012

A Realizable Modified Tent Map for True Random Number Generation

Tent map is a discrete-time piecewise-affine I/O characteristic curve, which is used for chaos-based applications, such as true random number generation. However, tent map suffers from the inability to maintain the output state confined to the input range under noise and process variations. In this paper, we propose a modified tent map, which is interchangeable with the tent map for practical applications. In the proposed modified tent map, the confinement problem is solved while maintaining the functionality of the tent map. We also demonstrate simulation results for the circuit implementation of the presented modified tent map for true random number generation.

math.DS↗

arXiv2012

A Performance Metric for Discrete-Time Chaos-Based Truly Random Number Generators

In this paper, we develop an information entropy based metric that represents the statistical quality of the generated binary sequence in Truly Random Number Generators (TRNG). The metric can be used for the design and optimization of the TRNG circuits as well as the development of efficient post-processing units for recovering the degraded statistical characteristics of the signal due to process variations.

cs.IT↗