Takashi Miyaki

Energy-Efficient Activity Recognition Using Prediction

Energy storage is quickly becoming the limiting factor in mobile pervasive technology. For intelligent wearable applications to be practical, methods for low power activity recognition must be embedded in mobile devices. We present a novel method for activity recognition which leverages the predictability of human behavior to conserve energy. The novel algorithm accomplishes this by quantifying activity-sensor dependencies, and using prediction methods to identify likely future activities. Sensors are then identified which can be temporarily turned off at little or no recognition cost. The approach is implemented and simulated using an activity recognition data set, revealing that large savings in energy are possible at very low cost (e.g. 84% energy savings for a loss of 1.2 pp in recognition).

The TECO Envboard: A mobile sensor platform for accurate urban sensing — And more

Participatory Urban Sensing scenarios have increasingly been studied in the past years. At the same time, societys concern about the effects of pollutants on peoples personal health as well as on the environment grew. This, in conjunction with studies that helped to give a better understanding of those effects, lead to new and stricter regulations and standards set up by governments. Such standards define limits for concentrations which should or may not be exceeded. There are usually several of such maximum permissible values for different pollutants, and they may differ from country to country. As a result, we see the need for ways to take accurate, fine-grained and mobile measurements, e.g in order to identify hot spots or monitor people at risk. Standard fixed measuring methods are not suitable for such scenarios. This demo presents a generic platform for such measurements - the TECO Envboard.

Recognizing Group Activities Using Wearable Sensors

Pervasive computing envisions implicit interaction between people and their intelligent environments instead of between individuals and their devices, inevitably leading to groups of individuals interacting with the same intelligent environment. These environments must be aware of user contexts and activities, as well as the contexts and activities of groups of users. Here an application for in-network group activity recognition using only mobile devices and their sensors is presented. Different data abstraction levels for recognition were investigated in terms of recognition rates, power consumption and wireless communication volumes for the devices involved. The results indicate that using locally extracted features for global, multi-user activity recognition is advantageous (10% reduction in energy consumption, theoretically no loss in recognition rates). Using locally classified single-user activities incurred a 47% loss in recognition capabilities, making it unattractive. Local clustering of sensor data indicates potential for group activity recognition with room for improvement (40% reduction in energy consumed, though 20% loss of recognition abilities).

A distributed resource management architecture for interconnecting Web-of-Things using uBox

Although there are many smart devices and networked embedded object applications using World Wide Web technologies, it is still a big step to go towards a true Web of Things. It is e.g. difficult to build ubiquitous WoT applications that work in and accross multiple environments. Approaches which aggregate WoT ressources by centralizing all the resource information, have problems: total dependency on external infrasture, lack of private WoT management, inflexible communication patterns and limited dynamic ressource discovery and mapping. To solve these problems, we propose uBox, a local WoT platform which can be a stand-alone server to make your WoT environment, with interfaces to connect the other local WoT platforms. This way, which we call uBoXing, we can create World Wide WoT platform with a distributed architecture. This paper describes the concept of a distributed resource management architecture, and how we implement the concept into software. Also, we will discuss the platform with the example application in SmartTecO environment.

A Platform-as-a-Service for in-situ development of wireless sensor network applications

In this paper we present a Platform-as-a-Service (PaaS) approach for rapid development of wireless sensor network (WSN) applications based on the dinam-mite concept, i.e. an embedded web-based development environment and run-time platform for WSN systems integrated in a single information appliance. The PaaS is hosted by a cloud of dinam-mite nodes which facilitates the on-demand development, deployment and integration of WSN applications. We introduce the dinam Cloud architecture and focus, in this paper, on the PaaS layer established by the dinam-mite nodes. In addition to the description of this so-called dinam PaaS, a performance analysis of the dinam-mite node towards its applicability to forming a dinam PaaS layer is demonstrated. We then present the MASON mobile vehicular network as an example of such a WSN which delivers spatially and temporally fine-grained environmental measurements within the city of Beijing, and illustrate how to utilize the dinam PaaS for integrating the data from the MASON network into its back-end business system. Finally, we discuss the five essential properties of the Cloud Computing stack, according to the NIST definition, with respect to the dinam PaaS and illustrate the benefits of the dinam PaaS for system integration as well as WSN application development.

Self-optimized collaborative data communication in wireless sensor networks

Collaborative data communication is one of the efficient approaches in wireless sensor networks (WSN) in terms of life-time, reliability and quality of service (QoS) enhancement. In this paper, we propose a new self-optimized collaborative algorithm which minimizes the energy consumption by decreasing the number of collaborative nodes and at the same time guarantees the demanded quality. To do this, we focus on the fact that during the collaboration, a receiver node aggregates the signals of the collaborative nodes separately. The major task of this node is the time adjustment of the collaborative nodes to receive their signals synchroneously. The proposed algorithm performs an extra process to sort the aggregated signals based on their bit error rate (BER) as the quality and select the minimum number of the nodes with higher rank for collaboration. It is because the low quality signals have negative effect on the collaboration performance, as confirmed experimentally. The new algorithm gains higher level of energy storage balance without increasing of the inter-node communications or computational load by modification of the node selection metric. It also guarantees the demanded QoS through modification of the collaboration based on the signal quality at the destination which results in higher reliability. Based on the proposed algorithm, sensor nodes can gain the optimum efficiency during collaborative data communication without external management resources. The algorithm is applicable in various scenarios and network structures.

An experiment in hierarchical recognition of group activities using wearable sensors

Pervasive computing envisions implicit interaction between people and their intelligent environments instead of individual devices, inevitably leading to groups of individuals interacting with the same intelligent environment. These environments must therefore be aware not only of user contexts and activities, but the contexts and activities of groups of users as well. This poster will demonstrate an experiment conducted towards understanding hierarchical multi-user group activity recognition using wearable sensors. The experiment will explore different data abstraction levels in terms of recognition rates, power consumption and wireless communication volumes for the devices involved.

Social Geoscape: Visualizing an Image of the City for Mobile UI Using User Generated Geo-Tagged Objects

In this paper, we propose a people-powered location recognition application, Social Geoscape (SGS) that provides highly descriptive geographical information to mobile users. GPS provides the user with latitude and longitude values; however, these values are cumbersome for determining a precise location. A traditional reverse-geocoding database system (i.e., one that converts latitude and longitude values into street addresses) provides locative information based on administrative labeling, but people often do not recognize locations or their surrounding environs from street addresses alone. To address this problem with location recognition, we have created Social Geoscape, a reverse geocoding system that enhances locative data with user-generated information and provides assistance through a mobile interface.

SmartTecO: context-based ambient sensing and monitoring for optimizing energy consumption

This paper presents an energy-saving concept for home/office environments, which proposes to design a multi-layered architecture for an automatic monitoring and control. Based on wireless sensor networks and a context awareness system, the acquired data will be interpreted into different energy-related contextual information. A correlation module using Hidden Markov Models could then give the actuation module a certain context, which allows managing and saving the energy consumption of home/office appliances.

Collaborative Channel Equalization: Analysis and Performance Evaluation of Distributed Aggregation Algorithms in WSNs

In wireless sensor networks (WSN), collaboration is a way to improve the quality of data communication between sensor nodes with restricted resources in terms of memory, processing and energy storage. For receive collaboration, various array processing schemes such as receive beamforming and collaborative channel equalization (CCE) can be used for aggregating data received by each node in the network. The key challenge is the limitation on the number of nodes which can collaborate because of the increased computational load and memory demand when the multiple signals are aggregated. This problem arises when sensor nodes in a CDMA based WSN collaborate, although the low power property of CDMA technique makes it suitable for WSN applications. Here receive collaboration is investigated in CDMA networks using CCE as the collaboration algorithm. We present two novel distributed signal aggregation algorithms: partial and hierarchical aggregation, which distribute computational load and memory demands on collaborative nodes. The positive impacts of receive collaboration on the signal quality and reliability are confirmed experimentally in a WSN scenario using software radios. Then the requirements of collaborative reception using CCE combined with the novel aggregation methods in terms of computational and memory load, as well as energy consumption are evaluated. The results indicate that the distributed signal aggregation algorithms, especially hierarchical aggregation, have computational and memory requirements less than that of centralized CCE, providing greater flexibility and scalability which enables collaboration in WSNs on a larger scale than previously possible.