Body-worn inertial sensors has become a convenient tool for sensing and recognizing human activities. The work presented in this poster uses Nordic Semiconductor's Thingy:52 IoT Sensor kit as the data collection device. We built a body area network with four Thingy:52 devices that collect data of the wearer. In this work, we focused only on periodic and continuous activities and experimented with walking and running.

Location-based services have become an important market. At their core, location-based services generally rely on identifying the location of individuals or objects. In outdoor environments, determining the location is typically accomplished by means of GPS. In indoor environments, Bluetooth Low Energy (BLE) based localization has received a lot of attention. Most recently, an increasing number of mobile devices are capable of supporting ANT as an alternative low-power communication protocol. Unlike BLE, however, the suitability of ANT for localization has not been analyzed extensively. We report the initial results of an experimental study to compare ANT and BLE when used for localization. The results suggest that ANT-based localization can clearly outperform BLE, when using fingerprinting as the underlying localization principle.

In this work we explore the development of guidelines for creating "in-the-field" interfaces for enabling a single user to remotely control multiple robots. The problem of controlling a remote team of robots is complex, requiring a user to monitor and interpret robotic state and sensor information in real time, and to simultaneously communicate direction commands to the robots. The result is that a robot controller is often seated at a console; for many relevant applications such as search and rescue or firefighting this removes the user from the field of action, rendering them unable to directly participate in a task at hand.

Colocated groups using mobile devices do not share all of the benefits of face-to-face collaborators. Close interaction requires application support for awareness features, allowing participants to establish common ground. Following an overview of research on awareness and grounding, the results of an informal user study are presented, which demonstrate how current systems can deter users from engaging in close collaboration. Literature on awareness provides hope for improving this situation, but a naive transfer to mobile distributed user interfaces will not necessarily succeed. From prior art, a concise list of guidelines has been compiled to assist designers in providing awareness information to users of shared mobile workspaces. These guidelines can also serve as heuristics for the evaluation of future systems. An example is provided to demonstrate how these guidelines can be applied to the development of features for providing awareness of current location and browsing history to colocated users of mobile distributed user interfaces.

Data Grids provide services and infrastructure for distributed data-intensive applications accessing massive geographically distributed datasets. An important technique to speed access in Data Grids is replication, which provides nearby data access. Much of the work on the replica placement problem has focused on average system performance and ignored quality assurance issues. In a data grid environment, resource availability, network latency, and users' requests may change. Moreover, different sites may have different service quality requirements. In this paper, we introduce a new highly distributed and decentralized replica placement algorithm for hierarchical Data Grids that determines the positions of a minimum number of replicas expected to satisfy certain quality requirements. Our placement algorithm exploits the data access history for popular data files and computes replica locations by minimizing overall replication cost (read and update) while maximizing QoS satisfaction for a given traffic pattern. The problem is formulated using dynamic programming. We assess our algorithm using OptorSim. A comparison between our algorithm and its QoS-unconstrained counterpart shows that our algorithm can shorten job execution time greatly while consuming moderate bandwidth for data transfer.

Data grids support distributed data-intensive applications that need to access massive datasets stored around the world. Ensuring efficient access to such datasets is hindered by the high latencies of wide-area networks. To speed up access, files can be replicated so a user can access a nearby replica. Replication also provides improved availability, decreased bandwidth use, increased fault tolerance, and improved scalability. Since a grid environment is dynamic, resource availability, network latency, and user requests may change. To address these issues a dynamic replica placement strategy that adapts to changing behaviour is needed. In this paper, we introduce a highly distributed replica placement algorithm for hierarchical data grids. Our algorithm exploits data access histories to identify popular files and determines optimal replication locations to improve access performance by minimizing replication overhead (access and update) assuming a given traffic pattern. The problem is formulated using dynamic programming. We evaluate our algorithm using the OptorSim simulator and find that it offers shorter execution time and reduced bandwidth consumption compared to other dynamic replica placement methods.

Data grids provide geographically distributed storage for large-scale data-intensive applications. Ensuring efficient access to such large and widely distributed datasets is hindered by high latencies. To speed up data access, data grid systems replicate data in multiple locations so a user can access the data from a nearby site. In addition to reducing data access time, replication also aims to use network and storage resources efficiently. While replication is a well-known technique, the problem of replica placement has not been widely studied for data grid environments. To obtain the best possible gains from replication, strategic placement of the replicas is critical. In a grid environment resource availability, network latency, and userspsila requests can vary. To address these issues a placement strategy is needed that adapts to dynamic behavior. This paper proposes a new dynamic replica placement algorithm for hierarchical data grids based on file ldquopopularityrdquo. Our goal is to place replicas close to the clients to reduce access time while using the network and storage efficiently thereby effectively balancing storage cost and access latency. We evaluate our algorithm using OptorSim which shows that our approach outperforms other techniques in terms of access time and bandwidth used.

Heterogeneity of networks and end systems poses challenges for multicast based collaborative applications. In traditional multicasting, the sender transmits video at the same rate to all receivers independent of their network connection, end system equipment, and users' preferences. This wastes resources and may also result in some receivers having their quality expectations unsatisfied. This problem can be addressed, near the network edge, by applying dynamic, in-network transcoding of video streams. In this paper, we design, implement, and assess a network processor (NP) based video transcoding system using the Intel IXP1200. Experiments suggest that our system can adapt the video rate of MPEG-1 streams to a desired level on a per packet basis for moderate traffic levels.

Location determination is a fundamental component enabling context aware home applications. Ultrasound, floor sensors and computer vision, among others, have been proposed for location determination, each with its own benefits and limitations. In this paper we discuss the design, implementation and evaluation of a location determination system based on sensor networks. Our implementation includes three components: the location determination system which adapts and extends Motetrack for in-home use, a location storage system, and a middleware interface to allow home applications to access both current and historical location information. Our system uses Crossbow Mica2 and Mica2Dot sensors to provide 28th, 50th, 85th and 97th percentile location errors of under 1,1.5,2 and 3 meters, respectively. This will support most home services which typically require only "room level" accuracy. Using sensor nodes in our location determination scheme provides a low cost solution and eliminates dependence on the availability of existing, in-home equipment to perform the location determination computations

Advances in sensor nodes hardware, which comprises sensors, embedded processors, and communication components, have made the large-scale deployment of sensor networks a reality. Various sensor network applications ranging from monitoring to military require sensor nodes to collect data over a continuous time period. The placement, management, and processing of the sensor data necessitates an effective data storage, management and query processing policy. This poster attempts to identify the key query processing techniques in the sensor networks. Design goals and challenges of the query processing techniques are identified. The techniques are evaluated in terms of efficiency, scalability, applicability, and reliability. The evaluation of the techniques is guided by the distinctive query processing features supported by both types of sensor networks, conventional and wide area. Moreover, we argued for the integration of conventional and wide area sensor networks and addressed the integration issues and design goals. In particular, a query processing architecture is proposed to meet the emerging needs of the sensor networks. The architecture addressed the requirements for different layers of the integrated sensor network components such as base stations, sensor nodes, and the workstations in wide area. Additionally, the future research directions for the query processing are outlined

Dynamic task migration is an effective strategy to maximize the performance and resource utilization in metacomputing environments. Traditionally, however, a "task" means only the corresponding code of computation, i.e., the data related to this computation is usually neglected. As such, when one task is migrated from processor A to processor B, the data required by this task remains on processor A. Thus, the processor B has to perform remote communication when it executes this task, eliminating the advantage of task migration, or even further degrading the performance. Hence, the definition of the traditional "task" should be revisited. We define a task as follows: Task=Computation subtask+Data subtask. Computation subtask is the program code to be executed, while the data subtask is the operations to access the related data located in memory. In fact, as the speed gap between processors and memory becomes larger and larger, the importance of data subtask becomes more obvious than before. Therefore, we argue that both subtasks should be migrated to a new processor during task migration. Based on this observation, we propose a dynamic loop-level task migration scheme. This scheme is implemented within the context of the JIAJIA software DSM system (W. Ha et al., 1999). The evaluation results show that the task migration scheme improves the performance of our benchmark applications by 36% to 50% compared with static task allocation schemes. As a result, the new scheme performs an average of 30% better than other computation-only migration schemes.