Emerging Internet of Things(IoT) applications are moving from silo and small scale sensor data sharing to composite and large scale ones. With the rapid growth of application scale, IoT applications is going to leverage cloud infrastructure for scalable solutions and real-time services. Thus large volumes of heterogeneous and high frequency sensor data are fed into IoT cloud services for real-time actionable insight, which raises great challenges of performance and adaptability on cloud solutions. In this paper, we propose a streaming based processing infrastructure for high throughput and low latency IoT real-time analytics services. A data adaptive mechanism is also introduced for heterogeneous data stream integration, interpreting and processing with application logics, as well as context stream. We implemented the proposed mechanisms with spark streaming, and deployed real time IoT analytics service in cloud. Experiment results show that the service has a good scalability and high throughput for IoT data analytics.

Push notification is an important approach to distribute interesting information to users timely. With the fast development of mobile devices and mobile applications, push notification is getting more and more popular. The convergence of mobile and IoT also bring new challenges on how the system can handle the mixed push channels designed for M2M communication and human interaction, and enable the effective interaction with both human and IoT devices involved. IoT devices may push notifications of sensor data in a high frequency. To enable push notification for both of mobile devices and IoT devices, the push notification system is required to achieve high throughput to handle the frequent notifications, and support content matching to filter out the undesired notifications. To enable effective push notification with both human and IoT devices involved, the system is required to understand the users' interests for notifications with the IoT devices providing the users' contexts. That is to say users need an intelligent push notification system. In this paper we propose a high performance context-aware push notification system for the converged mobile and IoT messaging. We designed high performance content matching engine as the core to enable efficient message dispatching for push notification according to highly personalized interest to ensure IoT messages to be involved in push notification. A user's interest of notifications is highly related with his context. Therefore, based on the content-matching engine, a framework for efficient context information fusion is built to support various types of context-aware push notification, towards intelligent push notification. Also we designed shared connection scheme to reduce the resource cost. Based on the content-match engine and context-aware features, the proposed push notification service can support both of group push notification and bi-directional push notification. Tests are conducted for performance evaluation.

Interactive screen sharing requires extremely low latency end-to-end transmission, which in turn requires highly efficient and low-complexity screen compression. In this paper, we present a block-based low-complexity screen compression scheme, in which multiple block modes are adopted to exploit the intra- and inter-frame redundancies. In particular, we classify the intra-coded blocks to pictorial blocks and textual blocks using a proposed fast block classification algorithm, which exploits the discriminative features between the pictorial and the textual blocks. Then, we design a low-complexity, yet efficient, algorithm to compress the textual blocks. We use base colors and escape colors to represent and quantize the textual pixels, which not only achieves high compression ratios but also preserves a high quality on textual pixels. The two-dimensionally predictive index coding and hierarchical pattern coding technologies are used to exploit local spatial correlations and global pattern correlation, respectively. To further utilize the correlation between the luminance and chrominance channels, we propose a joint-channel index coding method. We compare the coding efficiency and the computational complexity of the proposed scheme against the standard image coding schemes such as JPEG, JPEG2000, and PNG, the compound image compressor HJPC, and the popular video coding standard H.264. We also compare the visual quality of the proposed scheme against H.264 intra coding, JPEG2000, and HJPC. The evaluation results show that the proposed scheme achieves superior or comparable compression efficiency with much lower complexity than other schemes in most of the cases.

Screen is becoming a new dimension in cloud computing platforms, and low latency screen sharing in unreliable networks is becoming more and more important. Due to the different characteristics between the screen codecs and video codecs, current transmission technologies on low-latency video streaming cannot be directly applied to screen sharing. So in this paper we first theoretically analyze the difference in latency performance between ARQ and FEC for the UDP-based screen sharing. Then, considering the characteristics of the main-stream screen codecs, we propose an improved ARQ scheme to decrease the transmission latency. The experimental results show that the proposed system achieves better latency performance than the popular systems.

This paper presents a block-based low-complexity screen compression scheme. In this scheme, the input screen is split into non-overlapping blocks which are classified as pictorial blocks and textual blocks. We design a low-complexity yet efficient algorithm to compress the textual blocks. We use base colors plus escape pixels to represent and quantize the text pixels, and such quantization mechanism not only achieves high compression efficiency but also keeps low encoding/decoding complexity. We also propose the two-direction predictive index coding and hierarchical pattern coding technologies to utilize the local spatial correlation and the global pattern correlation for text pixels. In addition, to utilize the correlation between the luminance and chrominance channels, we propose a joint-channel index coding method to further improve the compression efficiency. The compression efficiency and complexity of the proposed method is evaluated against the popular image codecs JPEG/JPEG200 and PNG, a recent published screen compression scheme HJPC as well as the popular video codec H.264.

In this paper, we present a browser-friendly hybrid JPEG/PNG codec for compound images. First we employ a simple yet efficient block classification algorithm to identify the blocks to pictorial and textural ones. And then JPEG is used to encode the pictorial blocks and PNG is used for textual blocks. Our evaluation results show that our codec significantly outperforms JPEG and PNG in terms of rate-distortion performance, and also it outperforms JPEG, JPEG2000 and DjVu in terms of visual quality. Moreover, since JPEG and PNG are naturally supported by modern browsers, the coded images generated from our proposed coder can be natively supported by browsers and possible to be widely deployed in Web applications.

In this paper, we present a proxy-based mobile web browser with rich experiences. We use the server-side web parsing and rendering to leverage the browser computing logic. We use a composite screen format to represent the display of the web content, incorporating the web background screen and the dynamic web objects. And then we employ a slice-based screen encoding scheme to efficiently compress the web background screen. Besides the display screen of the web content, we also send the side information of the web objects to enable the designed object-level interaction mechanisms. The experimental results show that our browser can achieve the superior browsing speed, compared with the native browser and yield much better visual quality than the existing proxy-based browser