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.

Precise drone landing remains a persistent challenge due to the high level of accuracy needed. We propose a novel technique that collects actual drone landing data and employs machine learning algorithms to predict errors in autonomous landing. Our model considers variables like battery charge, flight path, altitude, and velocity for prediction. Various trends associated with the drone’s flight and landing are determined and visualised. We propose neural network models that use time series data from the drone’s flight before landing to predict its landing position. Our best model reduced landing error to 2.34 cm, a 7% improvement over the baseline. • A novel framework for predicting drone landing errors precisely. • A comprehensive dataset of real drone landings to ascertain influential factors impacting landing accuracy. • Application of advanced machine learning approaches to forecast the exact landing coordinates of drones.

We conduct a survey on drones used as a service, denoted as drone-as-a-service (DaaS). We develop a novel taxonomy based on DaaS functions, research tasks, and application domains. We provide a discussion on drones and their associated capabilities based on their type of use. We propose a three-layered DaaS system architecture that vertically integratescloudcomputing,drones, andservicesas a reference framework to compare existing drone service implementations. Additionally, we propose a representative uncertainty-aware DaaS model for delivery scenarios, illustrating how service definitions can incorporate both functional and nonfunctional attributes under dynamic environmental conditions. Finally, we identify and discuss future research directions and open problems related to the use of drones for service delivery.

Fog computing offers increased performance and efficiency for Industrial Internet of Things (IIoT) applications through distributed data processing in nearby proximity to sensors. Given resource constraints and their contentious use in IoT networks, current strategies strive to optimise which data processing tasks should be selected to run on fog devices. In this paper, we advance a more effective data processing architecture for optimisation purposes. Specifically, we consider the distinct functions of sensor data streaming, multi-stream data aggregation and event handling, required by IoT applications for identifying actionable events. We retrofit this event processing pipeline into a logical architecture, structured as a service function tree (SFT), comprising service function chains. We present a novel algorithm for mapping the SFT into a fog network topology in which nodes selected to process SFT functions (microservices) have the requisite resource capacity and network speed to meet their event processing deadlines. We used simulations to validate the algorithm’s effectiveness in finding a successful SFT mapping to a physical network. Overall, our approach overcomes the bottlenecks of single service placement strategies for fog computing through composite service placements of SFTs.

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.

We propose a novel service-oriented architecture for drone-based multi-package delivery. The proposed architecture provides a high-level design for deploying a skyway network in a city for the effective provisioning of drone-based service delivery. A graph-based heuristic is proposed to reduce the search space for optimal service selection in the skyway network. We then find an optimal solution using the selected drone services under a range of constraints. Experimental results demonstrate the efficiency and effectiveness of our proposed graph-based heuristic approach in terms of execution time and delivery time.

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.

We propose a novel game-theoretic approach for drone service composition considering recharging constraints. We design a non-cooperative game model for drone services. We propose a non-cooperative game algorithm for the selection and composition of optimal drone services. We conduct several experiments on a real drone dataset to demonstrate the efficiency of our proposed approach.

We propose a novel composition framework for drone-based package delivery services termed as Drone-as-a-Service (DaaS). The proposed framework includes a spatio-temporal service model and a quality model for DaaS. A drone service selection algorithm is designed using 3D Rtree. We develop a Dijkstra-based and a heuristic-based drone service composition approach to meet users' delivery requirements, i.e., expected delivery time and cost. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.

Information-centric networking (ICN) approaches have been considered as an alternative approach to TCP/IP. Contrary to the traditional IP, the ICN treats content as a first-class citizen of the entire network, where names are given through different naming schemes to contents and are used during the retrieval. Among ICN approaches, content centric networking (CCN) is one of the key protocols being explored for Internet of Things (IoT), names the contents using hierarchical naming. Moreover, CCN follows pull-based strategy and exhibits the communication loop problem because of its broadcasting mode. However, IoT requires both pull and push modes of communication with scalable and secured content names in terms of integrity. In this paper, we propose a hybrid naming scheme that names contents using hierarchical and flat components to support both push and pull communication and to provide both scalability and security, respectively. We consider an IoT-based smart campus scenario and introduce two transmission modes: 1) unicast mode and 2) broadcast mode to address loop problem associated with CCN. Simulation results demonstrate that proposed scheme significantly improves the rate of interest transmissions, number of covered hops, name aggregation, and reliability along with addressing the loop problem.