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SenSocial: A Middleware for Integrating Online Social Networks and Mobile Sensing Data Streams Abhinav Mehrotra, Veljko Pejovic, Mirco Musolesi School of Computer Science University of Birmingham

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Context-aware Systems Physical ContextMobile Sensors Social Context Emotional Context Online Social Networks 2

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Context-aware Systems Richer Contextual Information How to integrate? App 1 App 2 Integration Real Time Integration 3

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Sensor Data Notification Online Social Network Internet Server Notification Notice of friends arrival to Paris Sensor Data Social Links Application Scenarios 4

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Facebooks emotional contagion experiment, Kramer et al. (2014) EmotionSense, Rachuri et al. (2010) 5 How these can be improved ?

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SenSocial Richer Contextual Information 6 Online Social Network Data Mobile Sensor Data

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SenSocial Design 7

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Key Abstractions onDataSensed() Subscribe Context Data SenSocial Application subscribe() 8 Publish-Subscribe Interaction Paradigm Streams Stream Filters Stream Aggregators Multicast Multicast Streams

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Publish-Subscribe Interaction Paradigm Subscription Types - Topic-based (e.g., get users location). - Content-based (e.g., get users location when the user posts about football on his/her Facebook wall). onDataSensed() Subscribe subscribe() Context Data SenSocial Application 9

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Streams Types of Stream Continuous (e.g., get users location when the user is not moving). Social event-based (e.g., get users location when the user performs an action on Facebook). 10

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Stream Filters A condition comprises of a modality, a comparison operator, and a value. Example: get location only when a user is not moving. Condition c = new Condition(ModalityType.physical_activity, Operator.equals, ModalityValue.still); Refined Information Conditions Data Streams 11

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Stream Aggregator It wraps multiple streams received by the server into a single aggregated stream. Data from individual streams is multiplexed to the same join stream. It works similarly to a normal stream. 12

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Multicast Stream It creates duplicate streams on multiple clients. Clients are selected based on the OSN connectivity and geographic location of the users. OSN ConnectivityGeographic Location 13 Multicast

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Other Features Remote Stream Management Streams can be created, modified, or destroyed remotely on the mobile clients. Privacy Control By specifying the allowed data type (context modality) and the level of granularity (raw or classified). 14

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Evaluation 15 Energy Management

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OSN actions1234567 Charge consumed [AH]51.797.1142.5187.8233.2278.5324.3 Average battery consumption with varying number of OSN actions Note: the above OSN actions occurred within 20 minute time period and each triggered remote sampling of all five supported sensor modalities. CPU consumption with varying number streams Evaluation 16

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Prototype Applications Facebook Sensor MapConWeb 17

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Facebook Sensor Map 18

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Facebook Sensor Map: Code Snippet /*----------- Create streams----------------*/ SenSocialManager manager = SenSocialManager.getSenSocialManager(getApplicationContext()); String uid = manager.getUserId(); User user = manager.getUser(uid); Stream s1 = user.getDevice().getStream(SensorUtils.Sensor_Type_Accelerometer, "classified"); Stream s2 = user.getDevice().getStream(SensorUtils.Sensor_Type_Microphone, "classified"); Stream s3 = user.getDevice().getStream(SensorUtils.Sensor_Type_Location, "raw"); /*----------- Create list of filter condition(s) ----------------*/ ArrayList conditions = new ArrayList (); Condition c = new Condition(ModalityType.facebook_activity, Operator.equals, ModalityValue.active); conditions.add(c); /*----------- Add condition list to the filter -----------*/ Filter filter = new Filter(conditions); /*----------- Set filter to the streams -----------*/ s1 = s1.setFilter(filter); s2 = s2.setFilter(filter); s3 = s3.setFilter(filter); /*----------- Register streams -----------*/ manager.registerListener(this, stream1.getId()); manager.registerListener(this, stream2.getId()); manager.registerListener(this, stream3.getId()); /*----------- Start streams -----------*/ stream1.start(); stream2.start(); stream3.start(); 19

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Programming Effort Evaluation Application NameNumber of filesLines of code Facebook Sensor Map (M) (with SenSocial)8103 Facebook Sensor Map (S) (with SenSocial)2213 Facebook Sensor Map (M) (without SenSocial)682419 Facebook Sensor Map (S) (without SenSocial)421004 ConWeb (M) (with SenSocial)323 ConWeb (S) (with SenSocial)1107 ConWeb (M) (without SenSocial)612278 ConWeb (S) (without SenSocial)38945 Facebook Sensor Map : lines of code reduced by nine times (from 3423 to 316). ConWeb : lines of code reduced by twenty four times (from 3223 to 130). 20

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Summary We argue that the integration of OSNs and mobile sensor data streams can benefit in obtaining richer contextual information. We propose SenSocial to simplify the implementation of such ubiquitous computing applications. SenSocial is released as an open-source project. Available at: https://github.com/AbhinavMehrotra/SenSocial-Library 21

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http://www.cs.bham.ac.uk/~axm514/sensocial/ 22

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Thank You! Questions? Abhinav Mehrotra Email: a.mehrotra@cs.bham.ac.uk GitHub: https://github.com/AbhinavMehrotra/SenSocial-Library SenSocial: http://www.cs.bham.ac.uk/~axm514/sensocial/ 23