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Abdallah Lakhdari

Publications and source records attributed to Abdallah Lakhdari.

At least 19 recordsLinked to original sources
arXiv2025

Energy-Predictive Planning for Optimizing Drone Service Delivery

We propose a novel Energy-Predictive Drone Service (EPDS) framework for efficient package delivery within a skyway network. The EPDS framework incorporates a formal modeling of an EPDS and an adaptive bidirectional Long Short-Term Memory (Bi-LSTM) machine learning model. This model predicts the energy status and stochastic arrival times of other drones operating in the same skyway network. Leveraging these predictions, we develop a heuristic optimization approach for composite drone services. This approach identifies the most time-efficient and energy-efficient skyway path and recharging schedule for each drone in the network. We conduct extensive experiments using a real-world drone flight dataset to evaluate the performance of the proposed framework.

cs.RO↗
arXiv2025

Privacy-aware IoT Fall Detection Services For Aging in Place

Fall detection is critical to support the growing elderly population, projected to reach 2.1 billion by 2050. However, existing methods often face data scarcity challenges or compromise privacy. We propose a novel IoT-based Fall Detection as a Service (FDaaS) framework to assist the elderly in living independently and safely by accurately detecting falls. We design a service-oriented architecture that leverages Ultra-wideband (UWB) radar sensors as an IoT health-sensing service, ensuring privacy and minimal intrusion. We address the challenges of data scarcity by utilizing a Fall Detection Generative Pre-trained Transformer (FD-GPT) that uses augmentation techniques. We developed a protocol to collect a comprehensive dataset of the elderly daily activities and fall events. This resulted in a real dataset that carefully mimics the elderly's routine. We rigorously evaluate and compare various models using this dataset. Experimental results show our approach achieves 90.72% accuracy and 89.33% precision in distinguishing between fall events and regular activities of daily living.

eess.SP↗
arXiv2024

Impact Conflict Detection of IoT Services in Multi-resident Smart Homes

We propose a novel impact conflict detection framework for IoT services in multi-resident smart homes. The proposed impact assessment model is developed based on the integral of a signal deviation strategy. We mine the residents' previous service usage records to design a robust preference estimation model. We design an impact conflict detection approach using temporal proximity and preferential proximity techniques. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach.

cs.DC↗
arXiv2023

Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services

We propose a novel framework for crowdsourced images to determine the likelihood of an image being fake. We use a service-oriented approach to model and represent crowdsourced images uploaded on social media, as image services. Trust may, in some circumstances, be determined by using only the non-functional attributes of an image service, i.e., image metadata. We define intention of changes as a key parameter to ascertain fake image services. A novel framework is proposed to estimate the intention of underlying changes considering change in semantics of an image. Our experiments show high accuracy using a large real dataset.

cs.CV↗
arXiv2023

Positional Encoding-based Resident Identification in Multi-resident Smart Homes

We propose a novel resident identification framework to identify residents in a multi-occupant smart environment. The proposed framework employs a feature extraction model based on the concepts of positional encoding. The feature extraction model considers the locations of homes as a graph. We design a novel algorithm to build such graphs from layout maps of smart environments. The Node2Vec algorithm is used to transform the graph into high-dimensional node embeddings. A Long Short-Term Memory (LSTM) model is introduced to predict the identities of residents using temporal sequences of sensor events with the node embeddings. Extensive experiments show that our proposed scheme effectively identifies residents in a multi-occupant environment. Evaluation results on two real-world datasets demonstrate that our proposed approach achieves 94.5% and 87.9% accuracy, respectively.

cs.LG↗
arXiv2023

Detecting Changes in Crowdsourced Social Media Images

We propose a novel service framework to detect changes in crowdsourced images. We use a service-oriented approach to model and represent crowdsourced images as image services. Non-functional attributes of an image service are leveraged to detect changes in an image. The changes are reported in form of a version tree. The version tree is constructed in a way that it reflects the extent of changes introduced in different versions. Afterwards, we find semantic differences in between different versions to determine the extent of changes introduced in a specific version. Preliminary experimental results demonstrate the effectiveness of the proposed approach.

cs.SI↗
arXiv2023

Context-Aware Trustworthy IoT Energy Services Provisioning

We propose an IoT energy service provisioning framework to ensure consumers' Quality of Experience (QoE). A novel context-aware trust assessment model is proposed to evaluate the trustworthiness of providers. Our model adapts to the dynamic nature of energy service providers to maintain QoE by selecting trustworthy providers. The proposed model evaluates providers' trustworthiness in various contexts, considering their behavior and energy provisioning history. Additionally, a trust-adaptive composition technique is presented for optimal energy allocation. Experimental results demonstrate the effectiveness and efficiency of the proposed approaches.

eess.SY↗
arXiv2023

Flow-Based Energy Services Composition

We propose a novel spatio-temporal service composition framework for crowdsourcing multiple IoT energy services to cater to multiple energy requests. We define a new energy service model to leverage the wearable-based energy and wireless power transfer technologies. We reformulate the problem of spatio-temporal service composition to provision multiple energy requests as a matching problem. We leverage the fragmented nature of energy to offer partial services to maximize the utilization of energy services. We propose EnergyFlowComp, a modified Maximum Flow matching algorithm that efficiently provisions IoT energy services to accommodate multiple energy requests. Moreover, we propose PartialFlowComp, an extension of the EnergyFlowComp approach that considers the partial-temporal overlap between services and requests in provisioning. We conduct an extensive set of experiments to assess the effectiveness and efficiency of the proposed framework.

cs.DC↗
arXiv2023

Failure-Sentient Composition For Swarm-Based Drone Services

We propose a novel failure-sentient framework for swarm-based drone delivery services. The framework ensures that those drones that experience a noticeable degradation in their performance (called soft failure) and which are part of a swarm, do not disrupt the successful delivery of packages to a consumer. The framework composes a weighted continual federated learning prediction module to accurately predict the time of failures of individual drones and uptime after failures. These predictions are used to determine the severity of failures at both the drone and swarm levels. We propose a speed-based heuristic algorithm with lookahead optimization to generate an optimal set of services considering failures. Experimental results on real datasets prove the efficiency of our proposed approach in terms of prediction accuracy, delivery times, and execution times.

cs.RO↗
arXiv2023

CrowdWeb: A Visualization Tool for Mobility Patterns in Smart Cities

Human mobility patterns refer to the regularities and trends in the way people move, travel, or navigate through different geographical locations over time. Detecting human mobility patterns is essential for a variety of applications, including smart cities, transportation management, and disaster response. The accuracy of current mobility prediction models is less than 25%. The low accuracy is mainly due to the fluid nature of human movement. Typically, humans do not adhere to rigid patterns in their daily activities, making it difficult to identify hidden regularities in their data. To address this issue, we proposed a web platform to visualize human mobility patterns by abstracting the locations into a set of places to detect more realistic patterns. However, the platform was initially designed to detect individual mobility patterns, making it unsuitable for representing the crowd in a smart city scale. Therefore, we extend the platform to visualize the mobility of multiple users from a city-scale perspective. Our platform allows users to visualize a graph of visited places based on their historical records using a modified PrefixSpan approach. Additionally, the platform synchronizes, aggregates, and displays crowd mobility patterns across various time intervals within a smart city. We showcase our platform using a real dataset.

cs.SI↗
arXiv2023

Energy Loss Prediction in IoT Energy Services

We propose a novel Energy Loss Prediction(ELP) framework that estimates the energy loss in sharing crowdsourced energy services. Crowdsourcing wireless energy services is a novel and convenient solution to enable the ubiquitous charging of nearby IoT devices. Therefore, capturing the wireless energy sharing loss is essential for the successful deployment of efficient energy service composition techniques. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices in a crowdsourced energy sharing environment. The predicted battery levels are used to estimate the energy loss. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework. We conducted extensive experiments on real wireless energy datasets to demonstrate that our framework significantly outperforms existing methods.

cs.DC↗
arXiv2023

Activity-based Profiling for Energy Harvesting Estimation

We propose a novel activity-based profiling framework to estimate IoT users' harvested energy based on their daily activities. Energy is harvested from natural sources such as the kinetic movement of IoT users. The profiling framework captures the users' physical activity data to define activity-based profiles. These profiles are utilized to estimate the harvested energy by IoT users. We train and evaluate our framework based on a real Fitbit dataset.

cs.DC↗
arXiv2023

Service-based Trajectory Planning in Multi-Drone Skyway Networks

We present a demonstration of service-based trajectory planning for a drone delivery system in a multi-drone skyway network. We conduct several experiments using Crazyflie drones to collect the drone's position data, wind speed and direction, and wind effects on voltage consumption rates. The experiments are run for a varying number of recharging stations, wind speed, and wind direction in a multi-drone skyway network. Demo: https://youtu.be/zEwqdtEmmiw

cs.RO↗
arXiv2023

Drone Formation for Efficient Swarm Energy Consumption

We demonstrate formation flying for drone swarm services. A set of drones fly in four different swarm formations. A dataset is collected to study the effect of formation flying on energy consumption. We conduct a set of experiments to study the effect of wind on formation flying. We examine the forces the drones exert on each other when flying in a formation. We finally identify and classify the formations that conserve most energy under varying wind conditions. The collected dataset aims at providing researchers data to conduct further research in swarm-based drone service delivery. Demo: https://youtu.be/NnucUWhUwLs

cs.RO↗
arXiv2023

Monitoring Efficiency of IoT Wireless Charging

Crowdsourcing wireless energy is a novel and convenient solution to charge nearby IoT devices. Several applications have been proposed to enable peer-to-peer wireless energy charging. However, none of them considered the energy efficiency of the wireless transfer of energy. In this paper, we propose an energy estimation framework that predicts the actual received energy. Our framework uses two machine learning algorithms, namely XGBoost and Neural Network, to estimate the received energy. The result shows that the Neural Network model is better than XGBoost at predicting the received energy. We train and evaluate our models by collecting a real wireless energy dataset.

cs.NI↗
arXiv2022

Towards peer-to-peer sharing of wireless energy services

Crowdsourcing wireless energy services is a novel convenient alternative to charge IoT devices. We demonstrate peer-to-peer wireless energy services sharing between smartphones over a distance. Our demo leverages (1) a service-based technique to share energy services, (2) state-of-the-art power transfer technology over a distance, and (3) a mobile application to enable communication between energy providers and consumers. In addition, our application monitors the charging process between IoT devices to collect a dataset for further analysis. Moreover, in this demo, we compare the peer-to-peer energy transfer between two smartphones using different charging technologies, i.e., cable charging, reveres charging, and wireless charging over a distance. A set of preliminary experiments has been conducted on a real collected dataset to analyze and demonstrate the behavior of the current wireless and traditional charging technologies.

cs.NI↗
arXiv2022

In-Flight Energy-Driven Composition of Drone Swarm Services

We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations. The proposed framework considers various intrinsic and extrinsic delivery constraints. We propose to use support drones whose sole purpose is to recharge other drones in the swarm during their flight. In this respect, we compute the optimal set of optimal support drones to minimize the probability of delivery services and recharging time at the next stations. We also use two settings to position the support drones in a flight formation for comparative purposes. Two novel energy sharing methods are proposed, namely, Priority-based and Fairness-based methods. A re-ordering method of the delivery drones is presented to facilitate the in-flight energy composition process. An enhanced A* algorithm is implemented to compose the optimal services in terms of delivery time. Experimental results prove the efficiency of our proposed approach.

cs.RO↗
arXiv2022

Service-Based Wireless Energy Crowdsourcing

We propose a novel service-based ecosystem to crowdsource wireless energy to charge IoT devices. We leverage the service paradigm to abstract wireless energy crowdsourcing from nearby IoT devices as energy services. The proposed energy services ecosystem offers convenient, ubiquitous, and cost-effective power access to charge IoT devices. We discuss the impact of a crowdsourced wireless energy services ecosystem, the building components of the ecosystem, the energy services composition framework, the challenges, and proposed solutions.

cs.NI↗