POEM: Pricing Longer for Edge Computing in the Device Cloud

Multiple access mobile edge computing has been proposed as a promising technology to bring computation services close to end users, by making good use of edge cloud servers. In mobile device clouds (MDC), idle end devices may act as edge servers to offer computation services for busy end devices. Most existing auction based incentive mechanisms in MDC focus on only one round auction without considering the time correlation. Moreover, although existing single round auctions can also be used for multiple times, users should trade with higher bids to get more resources in the cascading rounds of auctions, then their budgets will run out too early to participate in the next auction, leading to auction failures and the whole benefit may suffer. In this paper, we formulate the computation offloading problem as a social welfare optimization problem with given budgets of mobile devices, and consider pricing longer of mobile devices. This problem is a multiple-choice multi-dimensional 0-1 knapsack problem, which is a NP-hard problem. We propose an auction framework named MAFL for long-term benefits that runs a single round resource auction in each round. Extensive simulation results show that the proposed auction mechanism outperforms the single round by about 55.6% on the revenue on average and MAFL outperforms existing double auction by about 68.6% in terms of the revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP

Doctor of Philosophy

dissertationWe are seeing an extensive proliferation of wireless devices including various types and forms of sensor nodes that are increasingly becoming ingrained in our daily lives. There has been a significant growth in wireless devices capabilities as well. This proliferation and rapid growth of wireless devices and their capabilities has led to the development of many distributed sensing and computing applications. In this dissertation, we propose and evaluate novel, efficient approaches for localization and computation offloading that harness distributed sensing and computing in wireless networks. In a significant part of this dissertation, we exploit distributed sensing to create efficient localization applications. First, using the sensing power of a set of Radio frequency (RF) sensors, we propose energy efficient approaches for target tracking application. Second, leveraging the sensing power of a distributed set of existing wireless devices, e.g., smartphones, internet-of-things devices, laptops, and modems, etc., we propose a novel approach to locate spectrum offenders. Third, we build efficient sampling approaches to select mobile sensing devices required for spectrum offenders localization. We also enhance our sampling approaches to take into account selfish behaviors of mobile devices. Finally, we investigate an attack on location privacy where the location of people moving inside a private area can be inferred using the radio characteristics of wireless links that are leaked by legitimate transmitters deployed inside the private area, and develop the first solution to mitigate this attack. While we focus on harnessing distributed sensing for localization in a big part of this dissertation, in the remaining part of this dissertation, we harness the computing power of nearby wireless devices for a computation offloading application. Specially, we propose a multidimensional auction for allocating the tasks of a job among nearby mobile devices based on their computational capabilities and also the cost of computation at these devices with the goal of reducing the overall job completion time and being beneficial to all the parties involved

Cooperative offloading based on online auction for mobile edge computing

In the field of edge computing, collaborative computing offloading, in which edge users offload tasks to adjacent mobile devices with rich resources in an opportunistic manner, provides a promising example to meet the requirements of low latency. However, most of the previous work has been based on the assumption that these mobile devices are willing to serve edge users, with no incentive strategy. In this paper, an online auction-based strategy is proposed, in which both users and mobile devices can interact dynamically with the system. The auction strategy proposed in this paper is based on an online approach to optimize the long-term utility of the system, such as start time, length and size, resource requirements, and evaluation valuation, without knowing the future. Experiments verify that the proposed online auction strategy achieves the expected attributes such as individual rationality, authenticity and computational ease of handling. In addition, the index of theoretical competitive ratio also indicates that the proposed online mechanism realizes near-offline optimal long-term utility performance

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models