data management in ambient assisted living platforms approaching iot: a case study

Transcription

1 data management in ambient assisted living platforms approaching iot: a case study Susanna Spinsante, Ennio Gambi, Laura Montanini, Laura Raffaeli December 6, 2015 Dipartimento di Ingegneria dell Informazione Universita Politecnica delle Marche Ancona, ITALY

2 table of contents 1. Introduction 2. SoA: Data Handling and Management in AAL 3. Data Management in AAL: a Case Study 4. Results and Discussion 5. Conclusion 2

3 introduction

4 data-related issues in aal platforms Heterogeneity (coarse classification): behavioral (or habits-related) [1, 2] physiological [3, 4] environmental (or ambient-related) [5, 6] healthcare [7, 8] Volume Velocity 4

5 aal, big data, and iot Variety, volume, and velocity: commonly mentioned features in the field of Big Data [9] Big Data commonly referred to in conjunction with Internet of Things (IoT) paradigm Remarkable affinity between AAL and IoT, but AAL exhibits specific requirements and constraints, about: data processing (local / remote) data delivery format (raw data / aggregated information) data sharing / access options 5

6 aims of the work To investigate the emerging approaches to data handling and management in IoT-oriented AAL platforms, by resorting to the state of the art literature To discuss the findings with respect to a practical AAL implementation, assumed as a case study To highlight the peculiarities expressed by AAL in data handling, thus contributing to the discussion around IoT reference models and architectures 6

7 soa: data handling and management in aal

8 data collection and communication Aims of IoT in AAL: to facilitate data collection (e.g. RFID, NFC), to make communication establishment easier and more effective [10] IoT frameworks for AAL shall support bidirectional flows of information (Closed Loop Principle) Communication protocols: trade-off among lifetime, security level, and pre-processing of sensors-generated data Data analysis capabilities needed to enable anomaly detection at the sensor level [11] Anytime + anywhere access to sensor data: constant network connectivity required Need to reduce the amount of data to be transmitted: event-based data transmission [12], processing tasks to aggregate data for a compact representation of relevant information [13], learning from user s decisions [14] 8

9 data processing Cloud-based big data management frameworks: data mining + machine learning algorithms: from sensors data to knowledge real-time processing of large data volumes: clustering, classification, feature selection [15] heterogeneous data collected by AAL platforms cannot be efficiently supported by flat and data-agnostic management frameworks, but demand for differentiated processing, to ensure service-specific requirements Local data processing: collected data analysis and validation [16] interoperability of AAL system with other platforms algorithms and protocols for data privacy, integrity, access control security mechanisms to protect locally stored data [17] 9

10 data management in aal: a case study

11 system architecture - i Several sub-systems / functional domains managed by a local server [18]: home automation sensors for behavior detection health monitoring human-system interfaces Heterogeneous devices (environmental sensors, home appliances, virtual sensors, smart objects) Heterogeneous transmission technologies and gateways: CAN-bus, ETH, WiFi, Bluetooth, NFC, SubGHz technologies Different interfaces tailored to users needs: Smart TV, touchscreen devices, NFC-enabled devices (tap-based interaction), depth sensors Local server: relational DB management, aggregation tasks (REST services) report tasks (JSON objects), connection to a remote cloud-based platform 11

12 system architecture - ii 12

13 living lab deployment 13

14 sensor-generated data and aggregated information - i The local server hosts a unified DB including: tables populated by each sensor / device aggregated tables generated by aggregation tasks Sensor-populated tables: power consumption table power metering sub-system sensors table environmental and AAL sub-systems user state table smart object (smart shoes) reminders table health operator / caregiver body sensor network concentrator (BSNC) table smart object (wearable device) Aggregated tables generated by REST services: report_consumption: hourly power consumption in a day report_activities: % amount of daily activity by monitored subject report_cooking: daily report on time spent cooking report_presence: daily report on time spent at home / out of home Aggregated tables transferred daily to the remote platform as JSON objects 14

15 sensor-generated data and aggregated information - ii Table 1: Records in the local DB tables hosting sensor-generated data power consumption user state Field Data Type Field Data Type id int(11) id int(11) id_meter int(5) shoe_type varchar(20) bnode varchar(20) state varchar(20) name varchar(50) date Date type varchar(50) time Time level int(2) timestamp bigint(20) date Date date Date time Time timestamp int(13) power int(11) reminders BSNC Field Data Type Field Data Type drug varchar(30) id int(11) quantity Text date / time Date / Time time Time family varchar(32) device varchar(32) code varchar(16) description varchar(64) date Date 15

16 sensor-generated data and aggregated information - iii Table 2: Records in the local DB tables hosting aggregated data report_consumption report_activity Field Data Type Field Data Type id int(11) id int(11) name Text name Text level int(1) state varchar(20) date Date date Date hourly_consumptions Text activities Text report_cooking report_presence Field Data Type Field Data Type id int(11) id int(11) name Text state varchar(20) date Date date Date events Text time Time timestamp int(13) 16

17 data management policies Bidirectional communications between local server and sub-systems within the AAL platform Data exchanges originated upon user s requests: Software applications running on physical devices / interfaces issue requests to the local server by HTTP protocol Local server replies in the form of JSON objects Data exchanges originated automatically (e.g. based on time triggers): Applications poll REST service on the local server REST service checks current time against pre-defined time triggers Match found: requested data transferred as JSON object to applications Unidirectional data flow from local server to remote cloud-based platform (JSON objects generated by REST services) 17

18 examples of local data exchanges 3 types of requests may be issued to the server: configure: get information on sub-system configuration state: get information on current state of specific sensors within sub-system command: issue commands to loads controlled by the sub-system (single or multiple loads) HTTP request format: address: port number]/* wildcard * may take different values: /domotic/configure/bnode, /domotic/state/meter/current, or /domotic/command?type=[...] Other parameters depend on specific command to issue 18

19 results and discussion

20 raw sensor data Experimental evaluation of data volumes locally generated by AAL platform and remotely transferred Figures referred to living-lab deployment Table 3: Daily and average amount of DB rows generated by AAL platform. Dates chosen randomly over four months. Sample # DB rows # DB rows Total daily Date (sensors data) (power meters data) # DB rows 18/02/ /03/ /03/ /04/ /04/ /04/ /05/ /05/ /05/ /06/ /06/ /06/ Average

21 aggregated data Each night server applications execute aggregation tasks Aggregated tables and so-called reports (JSON objects) are generated Reports are transferred to remote cloud-based platform: Activity: daily time % of sleeping, sitting, cooking, walking, stasis ( 1.3 KB) Presence: % of total daily time spent indoor / outdoor (< 1 KB) Energy: min, max, avg hourly power consumption for each device (72 values per meter) ( 42 KB) Graphical reports and statistics generated on remote cloud-based platform, accessible by web 21

22 remote platform view 22

23 discussion Different data management policies: Massive and unobtrusive data collection for behavior analysis, prediction, anomalies detection (e.g. worsening health conditions) Heterogeneous data fusion and synchronization issues Differentiated flows of operations: Almost static data (environmental sensors) local decisions (e.g. open / close windows, switch on / off lights...), daily basis processing, remote collection for long-term observation Time-sensitive data prompt local action (e.g. falls, alarming events) Interoperability issues: Heterogeneous nature of AAL data Required formatting operations performed before data transferring to common remote platform Ensure integrity and standard rules compliance of single sub-systems data (e.g. certified biomedical devices or telemedicine systems) 23

24 conclusion

25 conclusion AAL platforms have to cope with quite different data requirements and policies about transmission, storage capabilities, and data processing to create structured relations (long term behavioral analysis) Orchestration and data validation by a local server better than agnostic data management framework (vertical applications of IoT) Experimental case study presented Contribution to the definition of an AAL data management profile in IoT 25

26 Thanks for your attention! Questions? 26

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