The challenge of collecting large scale environmental data lies in reducing the cost of allocating data sensors while improving the data quality. Compared with traditional centralized data collecting methods, obtaining environmental data by distributed “crowds” can largely reduce the cost. However, designing such crowdsourcing systems requires an appropriate incentive mechanism to encourage providing accurate and rare environmental data. In this paper, we propose to utilize blockchain-based incentive mechanisms to address the problem. We design and evaluate a blockchain-based system for large scale environmental data acquisition, which consists of a sensor layer to collect distributed environmental data, a valuation layer to evaluate the quality of the data collected, a consensus layer to incentive and motivate high quality data collection, and a ledger layer to record the incentive transactions and the qualified environmental data. The incentive mechanism in the consensus layer is achieved with a Proof-of-Data-Value (PODV) protocol adapted from the Practical Byzantine Fault Tolerance (PBFT) [11] consensus algorithm. We carry out experiments to compare the PODV with contemporary blockchain census protocols including Proof-of-Work (POW) [16] and Proof-of-Stake (POS)[18]. The experimental results show that the proposed system outperforms in encouraging crowds to provide accurate and rare environmental data, and imply that the PODV are superior in terms of throughput and environmental friendliness.

Instead of relying on remote clouds, today's Augmented Reality (AR) applications usually send videos to nearby edge servers for analysis (such as objection detection) so as to optimize the user's quality of experience (QoE), which is often determined by not only detection latency but also detection accuracy, playback fluency, etc. Therefore, many studies have been conducted to help adaptively choose best video configuration, e.g., resolution and frame per second (fps), based on network bandwidth to further improve QoE. However, we notice that the video content itself has significant impacts on the configuration selection, e.g., the videos with high-speed objects must be encoded with a high fps to meet the user's fluency requirement. In this article, we aim to adaptively select configurations that match the time-varying network condition as well as the video content. We design Cuttlefish, a system that generates video configuration decisions using reinforcement learning (RL). Cuttlefish trains a neural network model that picks a configuration for the next encoding slot based on observations collected by AR devices. Cuttlefish does not rely on any pre-programmed models or specific assumptions on the environments. Instead, it learns to make configuration decisions solely through observations of the resulting performance of historical decisions. Cuttlefish automatically learns the adaptive configuration policy for diverse AR video streams and obtains a gratifying QoE. We compared Cuttlefish to several state-of-the-art bandwidth-based and velocity-based methods using trace-driven and real world experiments. The results show that Cuttlefish achieves a 18.4-25.8 percent higher QoE than the others.