Opportunistic computational offloading system for clusters of drones

Valentino, Rico; Jung, Woo‐Sik; Ko, Young-Bae

doi:10.23919/icact.2018.8323733

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…Combining Equations (8)- (10), the user's expected prospect theoretic utility by offloading b MEC n data to the UAV-mounted MEC server is defined as follows, jointly capturing the uncertainty of the UAV-mounted MEC server's computation resources, the pricing of the UAV-mounted MEC server, as well as the user's risk-aware characteristics in its data offloading decision:…”

Section: Users Prospect-theoretic Utility Function In Uav-assisted Mementioning

confidence: 99%

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Mitsis

Tsiropoulou

Papavassiliou

2020

Sensors

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Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits.

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Section: Users Prospect-theoretic Utility Function In Uav-assisted Mementioning

confidence: 99%

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Mitsis

Tsiropoulou

Papavassiliou

2020

Sensors

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“…In 2018, Valentino et al proposed an opportunistic and adaptive computational offloading scheme between UAV clusters [23]. A cluster head will broadcast a 'hello' message indicating their presence and available resources, then a local cluster sends an offloading request to the desired cluster head.…”

Section: ) Inter-uav Cooperationmentioning

confidence: 99%

Utility Based Scheduling for Multi-UAV Search Systems in Disaster-Hit Areas

et al. 2019

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Using micro or small unmanned aerial vehicles (UAVs) is a promising solution for search and rescue of missing persons who have disappeared during emergencies, such as natural disasters. In actual situations, the processing time of image data should be considered due to the wide variety of computing resources provided by UAVs. In addition, network connectivity and transmission speed could be unstable since communication infrastructure may have been damaged in disaster-hit areas. Thus, both the processing time of the acquired data and the data transfer time are critical in search and rescue missions. Unlike the solutions proposed in the past, we propose a scheduling method of multi-UAV search systems that takes into account both the processing time of image data and the data transfer time. We present a utility-based problem formulation that ensures continuously updating information while obtaining as many pieces of information as possible for a certain period. The simulation results indicate that the proposed scheduling method maximizes user utility and performs better than a conventional scheduling method in terms of user-centric evaluation metrics. INDEX TERMS Unmanned aerial vehicle, search and rescue, scheduling, edge computing.

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“…In that respect, the UAV-mounted MEC servers in combination with ground MEC servers collectively create a fog computing system [7], supporting end-users' applications' task offloading. Similarly, the use of clusters of UAV-mounted MEC servers is suggested [8], allowing the opportunistic task offloading to the neighboring UAV clusters with sufficient computing resources. In such a UAV-assisted network, computing intensive tasks are offloaded and executed in a nearby small-size edge data center, either directly connected with a wireless access point, or it is embedded on the UAV itself.…”

Section: Introductionmentioning

confidence: 99%

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Dechouniotis

Αθανασόπουλος

Leivadeas

et al. 2020

Sensors

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The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

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Opportunistic computational offloading system for clusters of drones

Cited by 10 publications

References 7 publications

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Utility Based Scheduling for Multi-UAV Search Systems in Disaster-Hit Areas

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

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