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Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks. [ABSTRACT FROM AUTHOR]

This work focuses on the incorporation of incentive stimulations into data dissemination in autonomous mobile social networks with selfish nodes. The key challenge of enabling incentives is to effectively track the value of a message under such a unique network setting with intermittent connectivity and multiple interest data types. We propose two data dissemination models: the data pulling model where mobile users pull data from data providers, and the data pushing model where data providers generate personalized data and push them to the intended users. For data pulling, we present effective mechanisms to estimate the expected credit reward of a message that helps intermediate nodes to evaluate the potential reward of it. Nodal message communication is formulated as a two-person cooperative game, whose solution is found by a heuristic approach which achieves Pareto optimality. Under the data pushing model, “virtual checks” are introduced to eliminate the needs of accurate knowledge about whom and how many credits data providers should pay. The check buying process is formulated as an online auction model to further accelerate the circulation of credits. Extensive simulations carried out based on real-world traces show the proposed schemes achieve better performance than fully cooperative scheme, but significantly reduce communication cost. [ABSTRACT FROM AUTHOR]

Delivery delay and communication costs are two conflicting design issues for mobile opportunistic networks with nonreplenishable energy resources. In this paper, we study the optimal data dissemination for resource constrained mobile opportunistic networks, i.e., the delay-constrained least-cost multicasting in mobile opportunistic networks. We formally formulate the problem and introduce a centralized heuristic algorithm which aims to discover a tree for multicasting, in order to meet the delay constraint and achieve low communication cost. While the above algorithm can be implemented by each individual node, it is intrinsically centralized (requiring global information) and, thus, impractical for real-world implementation. However, it offers useful insights for the development of a distributed scheme. The essence of the centralized approach is to first learn the probabilities to deliver the data along different paths to different nodes and then decide the optimal multicast tree by striking the balance between cost and delivery probability. In mobile opportunistic networks, even if the optimal routing tree can be computed by the centralized solution, it is the “best” only on a statistic basis for a large number of data packets. It is not necessarily the best solution for every individual transmission. Based on the above observation, we develop a distributed online algorithm using optimal stopping theory, in which in each meeting event, nodes make adaptive online decisions on whether this communication opportunity should be exploited to deliver data packets. We carry out simulations to evaluate the scalability of the proposed schemes. Furthermore, we prototype the proposed distributed online multicast algorithm using Nexus tablets and conduct an experiment that involves 37 volunteers and lasts for 21 days to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods. [ABSTRACT FROM AUTHOR]