In this paper, we present Orchestrator, an active resource orchestration framework for a PAN-scale sensor-rich mobile computing platform. Incorporating diverse sensing devices connected to a mobile phone, the platform will serve as a common base to accommodate personal context-aware applications. A major challenge for the platform is to simultaneously support concurrent applications requiring continuous and complex context processing, with highly scarce and dynamic resources. To address the challenge, we build Orchestrator, which actively coordinates applications' resource uses over the distributed mobile and sensor devices. As a key approach, it adopts an active resource use orchestration, which prepares multiple alternative plans for application requests and selectively applies them according to resource availability and demands at runtime. Through the selection, it resolves resource contention among applications and helps them efficiently share resources. With such system-level supports, applications become capable of providing long-running services under dynamic circumstances with scarce resources. Also, the platform can host a number of applications stably, exploiting its full resource capacity. We build an Orchestrator prototype on off-the-shelf mobile devices and sensor motes and show its effectiveness in terms of application supportability and resource use efficiency.

In this paper, we introduce Activity Travel Pattern (ATP) monitoring in a large-scale city environment. ATP represents where city residents and vehicles stay and how they travel around in a complex megacity. Monitoring ATP will incubate new types of value-added services such as predictive mobile advertisement, demand forecasting for urban stores, and adaptive transportation scheduling. To enable ATP monitoring, we develop ActraMon, a high-performanceATP monitoring framework. As a first step, ActraMon provides a simple but effective computational model of ATP and a declarative query language facilitating effective specification of various ATP monitoring queries. More important, ActraMon employs the shared staging architecture and highly efficient processing techniques, which address the scalability challenges caused by massive location updates, a number of ATP monitoring queries and processing complexity of ATP monitoring. Finally, we demonstrate the extensive performance study of ActraMon using realistic city-wide ATP workloads.

No abstract available.

In upcoming ubiquitous environment, many real-time sensing applications will emerge. These applications show different scale and characteristics on delivery demands. The applications commonly depend on real-time understanding on data from widely distributed data sources. Also, they have highly diverse and complex delivery demands in terms of data and delay, e.g., data value ranges of interest, spatial and temporal resolution and tolerable delay, etc. Due to the remarkable scale and complexity, however, existing data delivery schemes cannot support the applications effectively. We present a novel data delivery scheme to support real-time sensing applications. Our scheme provides efficient delivery paths over Internet-based delivery infrastructure, which is comprised of a collection of dispersed nodes forming an overlay network. It fully exploits the diversity of delivery demands on both data and delay requirements, thus achieving high level of service satisfaction and efficiency at the same time. Also, it distributes path management overhead to multiple nodes by adopting a hierarchical path management approach. We evaluate our scheme through a large-scale simulation. The results show that it achieves a high level of scalability and bandwidth efficiency.

In this paper, we present Orchestrator, an active resource orchestration framework for mobile context monitoring. Emerging pervasive environments will introduce a PAN-scale sensor-rich mobile platform consisting of a mobile device and many wearable and space-embedded sensors. In such environments, it is challenging to enable multiple context-aware applications requiring continuous context monitoring to simultaneously run and share highly scarce and dynamic resources. Orchestrator enables multiple applications to effectively share the resources while exploiting the full capacity of overall system resources and providing high-quality service to users. For effective orchestration, we propose an active resource use orchestration approach that actively finds appropriate resource uses for applications and flexibly utilizes them depending on dynamic system conditions. Orchestrator is built upon a prototype platform that consists of off-the-shelf mobile devices and sensor motes. We present the detailed design, implementation, and evaluation of Orchestrator. The evaluation results show that Orchestrator enables applications in a resource-efficient way.

We designed and implemented a pervasive game called Swan Boat that targets the bland and tedious nature of running on a treadmill, making it fun through social interaction and immersive game play. We developed Swan Boat on top of PSD, a platform for pervasive games, and using the Interactive Treadmill hardware. We conducted a user study to evaluate our game.

Large scale shared memory multiprocessors favor a directory-based cache coherence scheme for its scalability. The directory space needed to record the information for sharers has a complexity of /spl Theta/(N/sup 2/) when a full-mapped vector is used for an N-node system. Though this overhead can be reduced by limiting the directory size assuming that the sharing degree is small, it will experience significant inefficiency when a data is widely shared. In this paper, we propose a new directory scheme and a cache coherence scheme based on it for a mesh interconnection. Deterministic and wormhole routing enables a pointer to represent a set of nodes. Also a message traversing on the mesh performs a broadcast mission to a set of nodes without extra traffic, which can be utilized for the cache coherence problem. Only a slight change on a generic router is needed to implement our scheme. This scheme is also applicable to any k-ary n-cube networks including a mesh.

We propose a prefetching scheme for image processing which fully exploits regular memory references, and hides or reduces latencies from memory to cache in shared memory multiprocessors. Since the image processing applications usually require quite huge memory references, it is obvious that the memory latencies dominate the overall execution time. Though software-controlled prefetching can be also an alternative to alleviate such latency impact, it usually suffers from large overheads due to the prefetch instructions themselves which would offset the benefit obtained from prefetching. This paper proposes a new software-controlled prefetching scheme that prefetches multiple blocks using a prefetch instruction. It is designed in consideration of very regular memory references which is intrinsic to most image processing applications. Our scheme reduces almost all the memory stall time for loads and significantly the overall execution time. Simulation applications have shown a reduction in the execution time by 10-21% compared to the original methods without prefetching.