CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations

Incentive mechanism design for mobile crowd sensing systems

The recent proliferation of increasingly capable and affordable mobile devices with a plethora of on-board and portable sensors that pervade every corner of the world has given rise to the fast development and wide deployment of mobile crowd sensing (MCS) systems. Nowadays, applications of MCS systems have covered almost every aspect of people's everyday living and working, such as ambient environment monitoring, healthcare, floor plan reconstruction, smart transportation, indoor localization, and many others. Despite their tremendous benefits, MCS systems pose great new research challenges, of which, this thesis targets one important facet, that is, to effectively incentivize (crowd) workers to achieve maximum participation in MCS systems. Participating in crowd sensing tasks is usually a costly procedure for individual workers. On one hand, it consumes workers' resources, such as computing power, battery, and so forth. On the other hand, a considerable portion of sensing tasks require the submission of workers' sensitive and private information, which causes privacy leakage for participants. Clearly, the power of crowd sensing could not be fully unleashed, unless workers are properly incentivized to participate via satisfactory rewards that effectively compensate their participation costs. Targeting the above challenge, in this thesis, I present a series of novel incentive mechanisms, which can be utilized to effectively incentivize worker participation in MCS systems. The proposed mechanisms not only incorporate workers' quality of information in order to selectively recruit relatively more reliable workers for sensing, but also preserve workers' privacy so as to prevent workers from being disincentivized by excessive privacy leakage. I demonstrate through rigorous theoretical analyses and extensive simulations that the proposed incentive mechanisms bear many desirable properties theoretically, and have great potential to be practically applied

Comparing Climate-Change Mitigating Potentials of Alternative Synthetic Liquid Fuel Technologies Using Biomass and Coal

Presenter: Robert H. Williams, Senior Research Scientist, Princeton University, Princeton, NJ. 19 pages (includes color illustrations). Contains references

Additive Manufacturing Of SiC-Sialon Refractory With Excellent Properties By Direct Ink Writing

Additive manufacturing of SiC-Sialon refractory with complex geometries was achieved using direct ink writing processes, followed by pressure less sintering under nitrogen. The effects of particle size of SiC powders, solid content of slurries and additives on the rheology, thixotropy and viscoelasticity of ceramic slurries were investigated. The optimal slurry with a high solid content was composed of 81 wt% SiC (3.5 µm+0.65 µm), Al2O3 and SiO2 powders, 0.2 wt% dispersant, and 2.8 wt% binder. Furthermore, the accuracy of the structure of specimens was improved via adjustment of the printing parameters, including nozzle size, extrusion pressure, and layer height. The density and flexural strength of the printed SiC-Sialon refractory sintered at 1600 °C were 2.43 g/cm3 and 85 MPa, respectively. In addition, the printed SiC-Sialon crucible demonstrated excellent corrosion resistance to iron slag. Compared to the printed crucible bottom, the crucible side wall was minimally affected by molten slag

Resilient Data Collection in Smart Grid

Sensors and measurement devices are widely deployed in Smart Grid (SG) to monitor the health of the system. However, these devices are subject to damage and attack so that they cannot deliver sensing data to the control center. In tree-based data collection schemes, a relay failure can further lead to unresponsiveness of all the devices in its sub-tree. In this paper, we study the resiliency issue in collecting data from SG measurement devices. We first design a protocol that guarantees successful data collection from all non-faulty devices in a backup-enabled tree structure. Then, we formulate the tree construction problem to optimize data collection time. Since the formulated problem is NP-hard, we propose a heuristic algorithm to solve it. We evaluate our algorithm using a real utility network topology. The experiment results show that our algorithm performs well in large scale networks.CREDCOpe

Secure Data Collection in Constrained Tree-Based Smart Grid Environments

To facilitate more efficient control, massive amounts of sensors or measurement devices will be deployed in the Smart Grid. Data collection then becomes non-trivial. In this paper, we study the scenario where a data collector is responsible for collecting data from multiple measurement devices, but only some of them can communicate with the data collector directly. Others have to rely on other devices to relay the data. We first develop a communication protocol so that the data reported by each device is protected again honest-but-curious data collector and devices. To reduce the time to collect data from all devices within a certain security level, we formulate our approach as an integer linear programming problem. As the problem is NP-hard, obtaining the optimal solution in a large network is not very feasible. We thus develop an approximation algorithm to solve the problem. We test the performance of our algorithm using real topologies. The results show that our algorithm successfully identifies good solutions within reasonable amount of time.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111643/1/Uludag_IEEE_SGC_14.pd

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape