Recently, various technologies for utilizing unmanned aerial vehicles have been studied. Drones are a kind of unmanned aerial vehicle. Drone-based mobile surveillance systems can be applied for various purposes such as object recognition or object tracking. In this paper, we propose a mobility-aware dynamic computation offloading scheme, which can be used for tracking and recognizing a moving object on the drone. The purpose of the proposed scheme is to reduce the time required for recognizing and tracking a moving target object. Reducing recognition and tracking time is a very important issue because it is a very time critical job. Our dynamic computation offloading scheme considers both the dwell time of the moving target object and the network failure rate to estimate the response time accurately. Based on the simulation results, our dynamic computation offloading scheme can reduce the response time required for tracking the moving target object efficiently.

Mobile devices such as personal digital assistants (PDAs) and smartphones are widely used not only in our everyday lives but also in various industrial fields. Most of these mobile devices have multiple wireless network interfaces, such as Bluetooth, 3G, and Wi-Fi. A considerable amount of energy is consumed to transfer the data through wireless communication. Moreover, most of these mobile devices operate on limited battery power. In industrial environments, changes in the communication environment are severe due to significant noise sources and due to distortion in the transceiver circuitry of strong motors, static frequency changers, electrical discharge devices, and other devices. It is necessary to select the network interface efficiently in order to extend the lifetimes of mobile devices and their applications. Therefore, in this paper, we propose an energy-efficient adaptive wireless network interface-selection scheme (AWNIS). Our scheme is proposed based on the mathematical modeling of energy consumption and data transfer delay patterns. Our scheme selects the best wireless network interface in terms of energy consumption by considering the link quality and adapting a dynamic network interface-selection interval according to the network environment. The simulation results show that proposed scheme effectively improves the energy efficiency while guaranteeing a certain level of data transfer delay.

A heterogeneous computing environment has been widely used with UAVs, edge servers, and cloud servers operating in tandem. Various applications can be allocated and linked to the computing nodes that constitute this heterogeneous computing environment. Efficiently offloading and allocating computational tasks is essential, especially in these heterogeneous computing environments with differentials in processing power, network bandwidth, and latency. In particular, UAVs, such as drones, operate using minimal battery power. Therefore, energy consumption must be considered when offloading and allocating computational tasks. This study proposed an energy consumption fairness-aware computational offloading scheme based on a genetic algorithm (GA). The proposed method minimized the differences in energy consumption by allocating and offloading tasks evenly among drones. Based on performance evaluations, our scheme improved the efficiency of energy consumption fairness, as compared to previous approaches, such as Liu et al.’s scheme. We showed that energy consumption fairness was improved by up to 120%.

In large-scale Grid computing environments, providing fault-tolerance is required for both scientific computation and file-sharing to increase their reliability. In previous works, several mechanisms were proposed for the Grids or distributed computing systems. However, some of them used only space redundancy (hardware replication), and others used only time redundancy (checkpointing and rollback). For this reason, the existing mechanisms are inefficient in terms of their resource utilization on the Grids. The main goal of ART is reducing the number of replications by using checkpointing and rollback scheme for each replication. In ART, the minimum number of replications is adaptively selected based on analysis of probability of successful execution within the given deadline and reliability requirement of each task. Our simulation results show that ART can significantly reduce the number of replications and improve scalability compared with existing mechanisms.

Remote sensing using drones has the advantage of being able to quickly monitor large areas such as rivers, oceans, mountains, and urban areas. In the case of applications dealing with large sensing data, it is not possible to send data from a drone to the server online, so it must be copied to the server offline after the end of the flight. However, online transmission is essential for applications that require real-time data analysis. The existing computation offloading scheme enables online transmission by processing large amounts of data in a drone and transferring it to the server, but without consideration for real-time constraints. We propose a novel computation offloading scheme which considers real-time constraints while minimizing the energy consumption of drones. Experimental results showed that the proposed scheme satisfied real-time constraints compared to the existing computation offloading scheme. Furthermore, the proposed technique showed that real-time constraints were satisfied even in situations where delays occurred on the server due to the processing of requests from multiple drones.

There are many applications related to interworking drones and ground wireless sensor networks to collect sensing data. A drone serves as a sink that collects all of the data on an entire wireless sensor network. Existing studies focus on reducing the energy consumption and flight distance of drones by using predetermined information such as the drone's trajectory, sensor locations, and the network topology, among other types. In this paper, we propose a rendezvous point estimation scheme that takes into account the drone speed and the mean of the data collection delay without any predetermined information. Simulation results show that our scheme is more reliable in terms of data collection.