Distance-Based Opportunistic Mobile Data Offloading

Lu, Xiaofeng; Lió, Píetro; Hui, Pan

doi:10.3390/s16060878

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…In terms of resource constraints, Elgendy et al [14] presented a multiuser resource allocation and an offloading algorithm with detailed processes to determine the optimal computation offloading decision. In terms of distance, Lu et al [15] proposed a distance‐based offloading node selection scheme, which determines the final offloading node by considering the distance between each node and the requesting node. In [16, 17], offloading nodes are selected based on community information.…”

Section: Related Workmentioning

confidence: 99%

Computation offloading time optimisation via Q‐learning in opportunistic edge computing

et al. 2020

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The emergence of computation offloading can meet the real‐time requirements of computing tasks with intensive computing demands. In this study, the authors use opportunistic communication to construct a network framework for opportunistic edge computing (OEC) to perform computation offloading. Specifically, OEC forms a computing resource pool near the edge servers in the edge layer by gathering idle computing resources. Firstly, the state of the system is defined by the attributes of the computing task, the execution location of the computing task and the location of the terminal device in OEC. Then the computation offloading time is calculated and learned by selecting different offloading nodes. Finally, an optimal offloading node selection strategy based on the Q‐learning algorithm is obtained. Extensive simulations show that the proposed strategy consumes the minimum computation offloading time compared with benchmark algorithms in aspects of the amount of uploaded data, the total number of CPU cycles of the task and the number of computing tasks.

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Section: Related Workmentioning

confidence: 99%

Computation offloading time optimisation via Q‐learning in opportunistic edge computing

et al. 2020

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“…In OppNets, at every contact opportunity, the sender tries to send its messages to some relay nodes having a good probability of meeting their destinations at some other places [3]. However, since the path from a source to a destination is intermittently connected, the conventional routing algorithms are not applicable [4].…”

Section: Introductionmentioning

confidence: 99%

FSF: Applying Machine Learning Techniques to Data Forwarding in Socially Selfish Opportunistic Networks

Souza

Mota

Soares

et al. 2019

Sensors

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Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our “FriendShip and Acquaintanceship Forwarding” (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness to consider those cases in which the relay node is socially selfish. To consider those cases in which the relay node does not accept receipt of the message because its device has resource constraints at that moment, FSF looks at the resource levels of the relay node. By using the ONE simulator to carry out trace-driven simulation experiments, we find that, when accounting for selfishness on routing decisions, our FSF algorithm outperforms previously proposed schemes, by increasing the delivery ratio up to 20%, with the additional advantage of introducing a lower number of forwarding events. We also find that the chosen buffer management algorithm can become a critical element to improve network performance in scenarios with selfish nodes.

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“…Opportunistic communications [ 1 , 2 , 3 , 4 , 5 ] are increasingly considered a promising Smart City enabling communication technology. They are applicable to situations when, for example, the cellular network infrastructure has become inefficient due to too many requests, or when no communication infrastructure is available at all [ 6 , 7 ] .…”

Section: Introductionmentioning

confidence: 99%

Friendly-Sharing: Improving the Performance of City Sensoring through Contact-Based Messaging Applications

Herrera-Tapia

Hernández-Orallo

Tomás

et al. 2016

Sensors

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Regular citizens equipped with smart devices are being increasingly used as “sensors” by Smart Cities applications. Using contacts among users, data in the form of messages is obtained and shared. Contact-based messaging applications are based on establishing a short-range communication directly between mobile devices, and on storing the messages in these devices for subsequent delivery to cloud-based services. An effective way to increase the number of messages that can be shared is to increase the contact duration. We thus introduce the Friendly-Sharing diffusion approach, where, during a contact, the users are aware of the time needed to interchange the messages stored in their buffers, and they can thus decide to wait more time in order to increase the message sharing probability. The performance of this approach is anyway closely related to the size of the buffer in the device. We therefore compare various policies either for the message selection at forwarding times and for message dropping when the buffer is full. We evaluate our proposal with a modified version of the Opportunistic Networking Environment (ONE) simulator and using real human mobility traces.

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Distance-Based Opportunistic Mobile Data Offloading

Cited by 6 publications

References 25 publications

Computation offloading time optimisation via Q‐learning in opportunistic edge computing

Computation offloading time optimisation via Q‐learning in opportunistic edge computing

FSF: Applying Machine Learning Techniques to Data Forwarding in Socially Selfish Opportunistic Networks

Friendly-Sharing: Improving the Performance of City Sensoring through Contact-Based Messaging Applications

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