Wireless Sensor Network based Ubiquitous Multi-Context Modeling And Reasoning Maneesha V. Ramesh, Anjitha S, and Rekha P AMRITA Center for Wireless Networks and Applications Amrita Vishwa Vidyapeetham (AMRITA University) India
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[email protected] Abstract. Ubiquitous Computing with Context Awareness is emerging as a significant technology which is capable of supporting a wide variety of real world applications such as health care, environmental monitoring, security, etc. Most of the existing Context aware frameworks developed are singleapplication oriented. The key focus of our research work is to bring in multiple application support using single context aware framework. The proposed Ubiquitous Multi-Context Model (UMM) contains a new module “Context Categorizer” for spanning multiple real world applications. The designed model support non redundant information capturing and appropriate data sharing among multiple applications, by utilizing the potentials of wireless sensor networks. The implementation of the proposed model considers two relevant applications, health care and crowd behavior estimation, which are gaining attention nowadays. Keywords. Context Awareness, Crowd Behavior Estimation, Ubiquitous Computing, Health care, Wireless Sensor Networks.
1
Introduction
Design and deployment of context aware systems in open and dynamic environments is raising a new set of research challenges such as Sensor Data Acquisition, Context data fusion, Context Modeling & Reasoning, Service Discovery and Execution of services for the user. One of the crucial requirements of context-aware applications is real-time collection and aggregation of sensor data from sensors which are distributed in the environment. The key problem under consideration of our model is to develop a context aware architecture with key features such as reusability, non-redundancy and wide area coverage of context information. Context aware computing is the key enabling technology for pervasive computing. The major classification of context includes Computing context, user context and physical context. Computing context includes network connectivity, communication costs, communication bandwidth and nearby resources such as printers, displays and workstations. User context, specifies the user's profile, location, people nearby, even the current social situation and Physical context includes lighting, noise levels, traffic conditions and temperature.
The wireless sensor networks are capable of linking physical and digital world by capturing and revealing real-world phenomena and converting this into a form that can be processed, stored and acted upon. The integration into numerous machines, sensors are capable of providing societal benefits. The proposed Ubiquitous Multi-Context Model consist of three major phases, namely Context Acquisition phase, Context Modeling and Inference phase and Context based Action generation phase. The Open research challenges under consideration include the development of an integrated network with wireless sensor network and embedded smart phone sensor, determination of appropriate type, way and schedule of context acquisition and development of efficient algorithms for context modeling and reasoning. The paper is organized as follows: Section 2 discusses related works including the study of various context aware frameworks. Section 3 gives the proposed architecture of Ubiquitous Multi Context Model with detailed overview. Sections 4 provide the implementation details and experimental setup of the proposed model for crowd monitoring with Android application.
2
Related Works
The research on the importance of context-aware sensing to capture context information for providing effective service for people was discussed in [6]. It also provides a general overview of acquiring context information and various context aware applications. Table 1. Study of existing Context Aware Frameworks Context Aware Framework Specific Features Analyzed for the development of new multiple application supporting framework : UMM CoBrA[1] “Context Broker” component spans multiple application context aware agents by providing shared model of contexts. SOCAM [2] Distributed with centralized server and it performs context modeling based on ontologies Gaia project [3]
Distributed middleware infrastructure which is intended to coordinate the development and execution of mobile applications Cenceme[4] Sharing of Context information in web portal InContexto [5] Obtain Context from a Smartphone user Based on the analysis of various context aware frameworks, the observations that can be extended to our system under development for multiple application support include: the agent based architecture of CoBrA can be extended to support our pro-
posed idea of multiple application oriented architecture. The distributed middleware architecture of SOCAM and Gaia can be used to bring in the distributed architectural view for our proposed system.
3
Ubiquitous Multi-Context Model for Multiple Real World Applications
The development of standardized context framework to utilize context information for various real world applications is an active research area. The distribution of context information is a challenging task that involves various key modules of processing. The proposed system provides categorizing of single context for inference in multiple applications by the introduction of “Context Categorizer” module. “Context Categorizer” tags the aggregated context and share among multiple applications. The general context aware middleware architecture is shown in Figure 1. For example, for environmental monitoring, atmospheric temperature can be used in crop destruction study conducted by agriculture sectors as well as for health monitoring sectors studying the ”Sun burn” related health issues. For the implementation purpose, “Activity Recognition” is considered as the inferred context and this context can be used in Health-Care for Personal Health Analysis and it can also be used in “Crowd Monitoring” System. 3.1 Functional Phases The general functional phases in the Ubiquitous Multi-Context Model are Context Acquisition phase, Context Modeling and Inference Phase, and Context based Alert Generation Phase. The detailed architecture of the Ubiquitous Multi-Context Model is shown in Figure 2.
Fig. 1. General Diagram of Ubiquitous Multi-Context Model for Multiple Applications
Context Acquisition Module This module takes in context information from smart-phone sensors as well as from external wireless sensor networks. The information represented by context will not be perfect due to intermittent nature of wireless networks and mobility. In order to extract key information from raw context, Quality of Context (QoC) parameters are used. The major QoC parameters for information extraction are precision, freshness, period of time to which a single instance of context information is applicable and probability of correctness. Context Repository This module holds context information captured using the training mode. The repository keeps track of wide range of context information useful for multiple applications. Context Reasoning and Tagging This module supports Context Categorizer module by adding a tag to context information. The “tag” will be unique for each application say “health” for healthcare data.
Fig. 2. Detailed Architecture of Ubiquitous Multi-Context Model Context Categorizer This is the key module of the proposed system which provides the multiple application support. Based on the previous context information available in the Context Repository, this module performs categorizing of context data. Context categorizer forwards the information to corresponding application agents such as healthcare, security, environmental monitoring, etc based on the “tag” value assigned by Context Reasoning and Tagging module.
Multi-Application Agent Module This module consists of various application agents such as healthcare, security, environmental monitoring, etc. This module should contain application specific context processing algorithms. Based on the inference from the Context Aware Middleware, specific alert messages need to be displayed in the smart phone of the concerned individuals. If the risk factor is greater than a threshold value, then the propagation of alert message via Bluetooth technology is required. 3.2 System Algorithms Table 1. Mapping of aggregated context to context inference Aggregated Context (Caggregated)
Context Inference
Activity Recognition
Environmental Data Acquisition
Table 2. Context Reasoning and Tagging Context Inference
Activity Recognition
Possible application tags assigned by Context Categorizer based on threshold and Context Repository values a) b) c)
Environmental Data Acquisition
a) b) c)
“Health-care: Patient Monitoring” “Traffic: Road condition Monitoring” “Surveillance: Crowd Monitoring” “Agriculture: Prediction of Crop destruction” “Health-care: sun burn detection” “Surveillance: Crowd Monitoring”
Algorithm 1 Working of Context Categorizer Module 1: Begin 2: Input: Caggregated values and Context Inference data Output: Tagged Context data 3: while S in power-on state do 4: Analyze the Caggregated value and compare with application specific threshold values obtained by training. 5: Based on the analysis, tag each context inference data appropriately with specific applications. Some of the possible values for the tag are shown in Table 2.
6: send Tagged Context data to Multi-application Agent module. 7: end while 8: End
4
Ubiquitous Multi-Context Model (UMM) for crowd monitoring and health-care monitoring based on Activity Recognition
The proposed UMM supports the spanning of multiple applications using single context inferred from the multiple sensor readings. For the implementation and evaluation of the UMM, Activity Recognition is taken as the single context inference. This context inference can be used in two application areas, specifically for healthcare to monitor daily activities of an individual and for Crowd Abnormality Detection. The Activity Recognition context is inferred using the sensor values such as the acceleration values from tri-axial accelerometer of the Smartphones, location value from GPS and the time readings. The wireless sensor network based architecture for Health-care and Crowd Abnormality Detection is shown in Figure 3.
Fig. 3. Ubiquitous Multi-Context Model for Health-Care and Crowd Monitoring
5
Experimental Setup and Validation of Results
5.1
Training Phase for Activity Recognition.
Training phase was conducted to perform tri-axial accelerometer based activity recognition and thereby predicting the occurrence of stampede. The training was carried
out by a person of medium height and weight. The data collection was performed using HTC Google Nexus One by writing accelerometer values on to a file and stored in SD card for further analysis. Training was carried out for standing, walking, and slow-running conditions. The values were tabulated and analyzed with respect to each axis. The accelerometer in smart phones returns 3 current acceleration values in the units of m/s2 along the x, y, and z axes subtracted by gravity vector. X-axis (lateral): Sideways acceleration (left to right) Y-axis (longitudinal): Forward and backward acceleration Z-axis (vertical): Upward or downward acceleration
Fig. 4. User Interface for Activity Training and Accelerometer based Analysis for (a) Standing (b) Forward Walk (c) Slow-Run (d) Peak Shake
The feature extraction is performed on the raw values obtained from the smart phone‟s accelerometer along the three axes. For the experimentation, we have used a window size of 256 with 50% overlap. Each of the three axes is analyzed individually and the key features extracted are: MeanX, MeanY, MeanZ, MeanAcc, Standard deviation along X-axis (stdDevX), Standard deviation along Y-axis (stdDevY), Standard deviation along Z-axis (stdDevZ) and correlations. The study of crowd dynamics indicate that stampede in a crowd can be stimulated by the presence of “erratic shake” and “fall” experienced within the crowd. For the prediction of stampede, our system relies on detection of peak continuous shake or fall by „n‟ neighboring participant mobile phones. The threshold values, shakeThreshold and fallThreshold, were analyzed to estimate the peak shake and fall condition in a crowd. The user interface for activity training and accelerometer readings are shown in Figure 4.
6
Conclusion
The objective of our research work is to design and implement a Context aware framework with multiple application support. This research work has designed a Ubiquitous Multi-Context Model, by taking into account the capabilities of external wireless sensor networks and smart phone sensing. The research work performs the study of new framework in two major applications, health-care monitoring and Crowd monitoring. The implementation of the system is performed using Android Smart Phones by capturing light intensity, accelerometer readings, audio, images and weather condition of current location using Google Weather API and GPS facilities. The future work will focus on the development of improvised version of proposed framework by bringing in data mining and machine learning algorithms for “Context Categorizer” module. Acknowledgement. We would like to express our immense gratitude to our beloved Chancellor Mata Amritanandamayi Devi for providing a huge motivation and inspiration for doing this research work.
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