arXiv subjects

Athman Bouguettaya

Publications and source records attributed to Athman Bouguettaya.

At least 55 records · Page 3Linked to original sources

arXiv2022

Multi-Use Trust in Crowdsourced IoT Services

We introduce the concept of adaptive trust in crowdsourced IoT services. It is a customized fine-grained trust tailored for specific IoT consumers. Usage patterns of IoT consumers are exploited to provide an accurate trust value for service providers. A novel adaptive trust management framework is proposed to assess the dynamic trust of IoT services. The framework leverages a novel detection algorithm to obtain trust indicators that are likely to influence the trust level of a specific IoT service type. Detected trust indicators are then used to build service-to-indicator model to evaluate a service's trust at each indicator. Similarly, a usage-to-indicator model is built to obtain the importance of each trust indicator for a particular usage scenario. The per-indicator trust and the importance of each trust indicator are utilized to obtain an overall value of a given service for a specific consumer. We conduct a set of experiments on a real dataset to show the effectiveness of the proposed framework.

cs.CR↗

arXiv2022

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↗

arXiv2022

Wireless IoT Energy Sharing Platform

Wireless energy sharing is a novel convenient alternative to charge IoT devices. In this demo paper, we present a peer-to-peer wireless energy sharing platform. The platform enables users to exchange energy wirelessly with nearby IoT devices. The energy sharing platform allows IoT users to send and receive energy wirelessly. The platform consists of (i) a mobile application that monitors and synchronizes the energy transfer among two IoT devices and (ii) and a backend to register energy providers and consumers and store their energy transfer transactions. The eveloped framework allows the collection of a real wireless energy sharing dataset. A set of preliminary experiments has been conducted on the collected dataset to analyze and demonstrate the behavior of the current wireless energy sharing technology.

cs.DC↗

arXiv2022

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↗

arXiv2021

Composing Energy Services in a Crowdsourced IoT Environment

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users' energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

cs.DC↗

arXiv2021

Fairness-aware Crowdsourcing of IoT Energy Services

We propose a Novel Fairness-Aware framework for Crowdsourcing Energy Services (FACES) to efficiently provision crowdsourced IoT energy services. Typically, efficient resource provisioning might incur an unfair resource sharing for some requests. FACES, however, maximizes the utilization of the available energy services by maximizing fairness across all requests. We conduct a set of preliminary experiments to assess the effectiveness of the proposed framework against traditional fairness-aware resource allocation algorithms. Results demonstrate that the IoT energy utilization of FACES is better than FCFS and similar to Max-min fair scheduling. Experiments also show that better fairness is achieved among the provisioned requests using FACES compared toFCFS and Max-min fair scheduling.

cs.DC↗

arXiv2021

A Deep Reinforcement Learning Approach for Composing Moving IoT Services

We develop a novel framework for efficiently and effectively discovering crowdsourced services that move in close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT services considering quality parameters. Additionally, we develop a parallel flock-based service discovery algorithm as a ground-truth to measure the accuracy of the proposed approach. The experiments on two real-world datasets verify the effectiveness and efficiency of the deep reinforcement learning-based approach.

cs.LG↗

arXiv2021

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↗

arXiv2021

Top-k Dynamic Service Composition in Skyway Networks

We propose a novel top-k service composition framework for drone services under a dynamic environment. We develop a system model for formal modelling of drone services in a skyway network. The composition process is accomplished in two phases, i.e., computing top-k compositions and extending and ranking top-k compositions using probabilistic wait and recharge times under congestion conditions. We propose a top-k composition algorithm to compute the best service composition plan meeting user's requirements. A set of experiments with a real dataset is conducted to demonstrate the effectiveness of the proposed approach.

cs.MA↗

arXiv2021

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↗

arXiv2021

Occupancy Estimation from Thermal Images

We propose a non-intrusive, and privacy-preserving occupancy estimation system for smart environments. The proposed scheme uses thermal images to detect the number of people in a given area. The occupancy estimation model is designed using the concepts of intensity-based and motion-based human segmentation. The notion of difference catcher, connected component labeling, noise filter, and memory propagation are utilized to estimate the occupancy number. We use a real dataset to demonstrate the effectiveness of the proposed system.

cs.CV↗

arXiv2021

Dynamic Conflict Resolution of IoT Services in Smart Homes

We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. The proposed framework employs a preference extraction model based on a temporal proximity strategy. We design a preference aggregation model using a matrix factorization-based approach (i.e., singular value decomposition). The concepts of current resident item matrix and ideal resident item matrix are introduced as key criteria to cater to the conflict resolution framework. Finally, a set of experiments on real-world datasets are conducted to show the effectiveness of the proposed approach.

cs.CY↗

arXiv2021

IaaS Signature Change Detection with Performance Noise

We propose a novel framework to detect changes in the performance behavior of an IaaS service. The proposed framework leverages the concept of the IaaS signature to represent an IaaS service's long-term performance behavior. A new type of performance signature called categorical IaaS signature is introduced to represent the performance behavior more accurately. A novel performance noise model is proposed to accurately identify IaaS performance noise and accurate changes in the performance behavior of an IaaS service. A set of experiments based on real-world datasets is carried out to evaluate the effectiveness of the proposed framework.

cs.DC↗

arXiv2021

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↗

arXiv2021

Conflict Detection in IoT-based Smart Homes

We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home setting based on the context information. We propose a conflict taxonomy to capture different types of conflicts in a single resident smart home setting. A conflict detection algorithm is proposed to identify potential conflicts using the profiled knowledge graph. We conduct a set of experiments on real datasets and synthesized datasets to validate the effectiveness and efficiency of our proposed approach.

cs.AI↗

arXiv2021

Proactive Composition of Mobile IoT Energy Services

We propose a novel proactive composition framework of wireless energy services in a crowdsourced IoT environment. We define a new model for energy services and requests that includes providers' and consumers' mobility patterns and energy usage behavior. The proposed composition approach leverages the mobility and energy usage behavior to generate energy services and requests proactively. Preliminary experimental results demonstrate the effectiveness of generating proactive energy requests and composing proactive services.

cs.DC↗

arXiv2021

Adaptive Priority-based Conflict Resolution of IoT Services

We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. An adaptive priority model is developed considering the residents' contextual factors (e.g., age, illness, impairment). The proposed priority model is designed using the concept of the analytic hierarchy process. A set of experiments on real-world datasets are conducted to show the efficiency of the proposed approach.

cs.CY↗

arXiv2021

Robust Composition of Drone Delivery Services under Uncertainty

We propose a novel robust composition framework for drone delivery services considering changes in the wind patterns in urban areas. The proposed framework incorporates the dynamic arrival of drone services at the recharging stations. We propose a Probabilistic Forward Search (PFS) algorithm to select and compose the best drone delivery services under uncertainty. A set of experiments with a real drone dataset is conducted to illustrate the effectiveness and efficiency of the proposed approach.

cs.DC↗