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Balsam Alkouz

Publications and source records attributed to Balsam Alkouz.

At least 19 recordsLinked to original sources

Swarm Network-as-a-Service (SNaaS)

Emerging on-demand connectivity scenarios increasingly require networking solutions with stringent service-level guarantees. We propose Swarm Network-as-a-Service (SNaaS), a service-oriented framework that leverages fleets of drones to provide on-demand connectivity at scale. SNaaS explicitly models drone-to-device and drone-to-drone interactions as composable services, enabling consumers to request connectivity through Service-Level Agreements (SLAs). We formalize atomic and composite SNaaS services, present an SDN-inspired architecture that integrates the service-oriented triad of provider, consumer, and registry. We introduce a composition framework that orchestrates drones into end-to-end services. Within this framework, we define and analyze three composition strategies, i.e., direct, clustered, and parallel, and propose a queuing-theory-based heuristic for selecting the most suitable strategy under varying load conditions. A dedicated enforcement module continuously monitors queue stability and SLA latency, adaptively reconfiguring the swarm when violations occur. Experiments using real air-to-ground measurements show that the framework consistently outperforms fixed compositions, achieving lower latency, fewer SLA violations, and smoother adaptation as load and swarm size increase.

cs.NI

Modeling Inter-drone Interference as a Service in Skyway Networks

We present a novel investigation into the impact of inter-drone interference on delivery efficiencies within multi-drone skyway networks. We conduct controlled experiments to analyze the behavior of drones in an indoor testbed environment. Our study compares performance between solo flights and concurrent multi-drone operations along predefined routes. This analysis captures interference occurring during both flight and at charging stations, providing a comprehensive evaluation of its effects on overall network performance. We conduct a comprehensive series of experiments across diverse scenarios to systematically understand and model the dynamics of inter-drone interference. Key metrics, such as power consumption and delivery times, are considered. This generates a comprehensive dataset for in-depth analysis of interference at both the node and segment levels. These findings are then formalized into a predictive model. The results validate the effectiveness of the developed model, demonstrating its potential to accurately forecast inter-drone interferences.

cs.ET

Impact of Spatial Proximity on Drone Services

We demonstrate the peer-to-peer impact of drones flying in close proximity. Understanding these impacts is crucial for planning efficient drone delivery services. In this regard, we conducted a set of experiments using drones at varying positions in a 3D space under different wind conditions. We collected data on drone energy consumption traveling in a skyway segment. We developed a Graphical User Interface (GUI) that plots drone trajectories within a segment. The GUI facilitates analyzing the peer-to-peer influence of drones on their energy consumption. The analysis includes drones' positions, distance of separation, and wind impact.

cs.ET

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

Reactive Composition of UAV Delivery Services in Urban Environments

We propose a novel failure-aware reactive UAV delivery service composition framework. A skyway network infrastructure is presented for the effective provisioning of services in urban areas. We present a formal drone delivery service model and a system architecture for reactive drone delivery services. We develop radius-based, cell density-based, and two-phased algorithms to reduce the search space and perform reactive service compositions when a service failure occurs. We conduct a set of experiments with a real drone dataset to demonstrate the effectiveness of our proposed approach.

cs.RO

Immersive 3D Simulator for Drone-as-a-Service

We propose a 3D simulator tailored for the Drone-as-a-Service framework. The simulator enables employing dynamic algorithms for addressing realistic delivery scenarios. We present the simulator's architectural design and its use of an energy consumption model for drone deliveries. We introduce two primary operational modes within the simulator: the edit mode and the runtime mode. Beyond its simulation capabilities, our simulator serves as a valuable data collection resource, facilitating the creation of datasets through simulated scenarios. Our simulator empowers researchers by providing an intuitive platform to visualize and interact with delivery environments. Moreover, it enables rigorous algorithm testing in a safe simulation setting, thus obviating the need for real-world drone deployments. Demo: https://youtu.be/HOLfo1JiFJ0

cs.RO

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

Optimizing Drone Delivery in Smart Cities

We propose a novel context-aware drone delivery framework for optimizing package delivery through skyway networks in smart cities. We reformulate the problem of finding an optimal drone service delivery pathway as a more congruent and elegant drone delivery service composition problem. In this respect, we propose a novel line-of-sight heuristic-based context-aware composition algorithm that selects and composes near-optimal drone delivery services. We conducted an extensive experiment using a real dataset to show the robustness of our proposed approach.

cs.RO

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

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

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

Autonomous Delivery of Multiple Packages Using Single Drone in Urban Airspace

Current drone delivery solutions mainly focus on single package delivery using one drone. However, the recent developments in drone technology enable a drone to deliver multiple packages in a single trip. We use the nearest destination first strategy for the faster delivery of packages in a skyway network. This demonstration is a proof-of-concept prototype for the multi-package delivery in urban airspace following a skyway network. We deploy and test this multi-package drone delivery in an indoor testbed environment using a 3D model of Sydney CBD. Demo: https://youtu.be/YTwsIfUvWPc

cs.RO

Density-Based Pruning of Drone Swarm Services

We propose a novel framework for the recommendation of swarm-based drone delivery services based on the consumers preferences. We propose a density-based pruning approach that uses the concept of partnerships with charging station providers to reduce the search space of swarm-based drone service delivery providers. A weighted service composition algorithm is proposed that considers the providers capabilities and consumers' preferences in selecting the best next service. We propose a voting-based recommendation algorithm to select the best providers. We conduct a set of experiments to evaluate the efficiency of the framework in terms of consumer satisfaction, run-time, and search space reduction cost.

cs.RO

Constraint-based Formation of Drone Swarms

Drone swarms are required for the simultaneous delivery of multiple packages. We demonstrate a multi-stop drone swarm-based delivery in a smart city. We leverage formation flying to conserve energy and increase the flight range of a drone swarm. An adaptive formation is presented in which a swarm adjusts to extrinsic constraints and changes the formation pattern in-flight. We utilize the existing building rooftops in a city and build a line-of-sight skyway network to safely operate the swarms. We use a heuristic-based A* algorithm to route a drone swarm in a skyway network.

cs.RO

Service-Based Drone Delivery

Service delivery is set to experience a major paradigm shift with fast advances in drone technologies coupled with higher expectations from customers and increased competition. We propose a novel service-oriented approach to enable the ubiquitous delivery of packages in a drone-operated skyway network. We discuss the benefits, framework and architecture, contemporary approaches, open challenges and future visioned directions of service-based drone deliveries.

cs.RO

Constraint-Aware Trajectory for Drone Delivery Services

Drones are becoming a novel means for delivery services. We present a demonstration of drone delivery services in a skyway network that uses the service paradigm. A set of experiments is conducted using Crazyflie drones to collect the data on various positions of drones, wind speed, wind direction, and battery consumption. We run the experiments for a range of flight patterns including linear, rectangular, and triangular shapes.

cs.DC

A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

We propose a novel framework for the re-allocation of drone swarms for delivery services known as Swarm-based Drone-as-a-Service (SDaaS). The re-allocation framework ensures maximum profit to drone swarm providers while meeting the time requirement of service consumers. The constraints in the delivery environment (e.g., limited recharging pads) are taken into consideration. We utilize reinforcement learning (RL) to select the best allocation and scheduling of drone swarms given a set of requests from multiple consumers. We conduct a set of experiments to evaluate and compare the efficiency of the proposed approach considering the provider's profit and run-time efficiency.

cs.MA

Package Delivery Using Autonomous Drones in Skyways

Current drone delivery systems mostly focus on point-to-point package delivery. We present a multi-stop drone service system to deliver packages anywhere anytime within a specified geographic area. We define a skyway network which takes into account flying regulations, including restricted areas and no-fly zones. The skyway nodes typically represent building rooftops which may act as both recharging stations and delivery destinations. A heuristic-based A* algorithm is used to compute an optimal path from source to destination taking into account a number of constraints, including delivery time, availability of recharging stations, etc. We deploy our drone delivery system in an indoor testbed environment using a 3D model of Sydney CBD. We describe a graphical user interface to monitor the real-time package delivery in the skyway network.

cs.RO