Compressive Sensing. ⢠Scalability and Energy Efficiency ... Power of the IoT+CrowdSensing ... On phone, on broker (SenseDroid, SATWARE). ⢠Techniques ...
Essence: A Machine Learning Approach to Scalable and Energy-‐Efficient Sense-‐making for Internet-‐of-‐Things (IoT)
Santanu Sarma University of California Irvine September 2015
Overview • • • • • •
Sense-‐making in IoT Problem Descrip9on Compressive Sensing Scalability and Energy Efficiency A Middleware Perspec9ve Summary
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Internet-‐of-‐Things
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Internet-‐of-‐Things
[Vermesan2011]
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IoT Trends
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What is Sense-‐Making? • An informa9on-‐theore9c perspec9ve to mean the process of understanding the connec1ons (among people, places, environment, objects, and events) – in order to an9cipate their trajectories – develop meaningful insights – act effec9vely using empirical data /insights
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IoT+CrowdSensing
Pushing toward more interven9on
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Power of the IoT+CrowdSensing • Using mobile crowdsensing to – Leverage already deployed smartphones – Extend the ranges of exis9ng in-‐situ sensors – Send mobile users to specific loca9ons
• Crowdsensing broad use cases – Disaster and emergency response – Personal health monitoring and wellness – Smart spaces and their effec9ve u9liza9on IoT Symposium, Oct 2015
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Sensing -‐> Sense-‐making Severity Alert System
Personal Sensing to indicate Fall detec9ons, injury severity, alerts in old age people to provide scalable health care IoT Symposium, Oct 2015
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Sensing -‐> Sense-‐making Radia9on field near Fukushima
Hazardous gas in campus
Spa9al Field Sensing With Mobile Sensors
IoT Symposium, ct 2015 Latest Informa9on Crisis Map SOhowing
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Sensing -‐> Sense-‐making • Avoiding congested streets in a city • Finding the most popular booth in a fair • Searching for the ride with shortest lineup in an amusement park
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Sense-‐Making : Purpose & Goals u A Framework for Sense-‐making using mobile phone & infrastructure sensors to derive insights u Powerful addi9onal sensing abili9es and features for community of users by community of users u Understand user and group context efficiently u Develop a efficient mechanism (for energy , performance) IoT Symposium, Oct 2015
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The Problem – A cross layer, end to end issue § Several barriers and huge investment of 9me to build sense-‐making applica9ons § Lack of a framework hinders ease and speed the development of sense-‐making apps § Non-‐Scalable, Ad-‐hoc, non-‐standardized API § Unsupported network infrastructure, and configura9ons
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Middleware Plalorms and Techniques for Sense-‐making • On phone, on broker (SenseDroid, SATWARE) • Techniques implemented in middleware – Compressive and Collabora9ve Sensing – Virtual Sensing for Sense-‐making – Seman9cs Driven Sensing and Actua9on
• Combining In-‐situ Sensors with Mobile Crowdsensing
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Essence Hierarchical Architecture
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Essence Distributed Middleware APPS$2$ APPS$N$
APPS$1$ Cloud Mobile$Node$
S1$
Sn$
Sensing$&$ Sampling$
Manager$
Context$ Processing$ &$Fusion$$
Privacy$&$ se>ngs$
Communica.on$
Query$+$ Storage$
Query$ Query$&$$ Response$
Analysis$&$Processing$
AP
Communica.on$
Data$Collec.on&$ Comp.$Sampling$
Sn$
Sensing$&$ Sampling$
Manager$
Context$ Processing$ &$Fusion$
Privacy$&$ se>ngs$
Communica.on$
Query$+$ Storage$
Collabora.on$
Query$&$$ Response$
S1$
Response$
Broker$
Infrastructure$Sensing$$
…….$
Query$+$ Storage$
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Manager$
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S1$
S2$
…….$
Infrastructure$Sensors$
Sm$
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Sense-‐making Using Compressed Sensing • A random sampling technique that can represent Sparse signal with few random measurements • Represents a Sparse Signal with few salient coefficients in a transformed domain • Integrates sensing, compression, processing based on new uncertainty principles
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Compressive Sensing Comparison
[Baraniuk2008] IoT Symposium, Oct 2015
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Reconstruction##Error#(MSE)#
Collabora9ve Compressive Sensing
Legend
Sink Node(Broker)
Sampled Mobile Sensor
Mobile Node
No#of#Measurements##
Traded-‐off Number of Measurement Accuracy of Sensemaking Number of Measurement Energy Consumed in Sensing Accuracy of Sensemaking Scalability and Coverage IoT Symposium, Oct 2015
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Execu9on Time for Sense-‐Making
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Energy Consumed in Sense-‐Making
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Applica9on: Sparse Temperature based West Nile Virus (WNV) Predic9on
[Kaggle.com]
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Research Direc9ons • Energy Efficiency – Exploit collabora9ve & compressive sensing for energy efficiency
• Incen9ve Mechanisms – Device incep9ves for par9cipa9on and collabora9on
• Privacy Regula9on – Facilitate privacy preserving incen9ves
• Heterogeneity in Mobile Cloud – Use and exploit heterogeneity of sensors and devices IoT Symposium, Oct 2015
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Summary • Studies a new class of IoT based Sense-‐making using machine learning
– Geospa9al informa9on gathering – using combina9on of crowdsourcing and infrastructure sensing
• Proposes compressive sensing based Sense-‐making • Simula9on results are encouraging • Poten9al Extensions
– Implemen9ng a working prototype – Guide the workers to shoot photos using augmented reality – Quality assurance and cheat detec9on mechanisms
• Designed for collec9ng spa9al-‐temporal informa9on, but can be extended for event detec9on IoT Symposium, Oct 2015
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THANK YOU
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EXTRA SLIDES
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Sensors In Mobile Phones
• MEMS & sensors for cell phones, expanding from $ 3.5 bn in 2009 to $7.9 bn in 2015 [Yole Developpement] • Smartphone sensors to be $ 6 bn business by 2016 [Juniper Research] • 44 % of the mobile phones will be smartphones in 2015 • 7x increase in mobile health apps from 2010 to 2011 • moaon sensor in smartphones and tablets will expand to $ US 2.1 billion in IoT Symposium, Oct 2015 .Sarma billion in 2011 (IHS iSuppli) 2015 w ith a 25.3 % CAGR, up from © $S1.19
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Mobile Phone Trends • Mobile subscripaon 5.96 billion 2011 esamate • Smartphones (487.7 million) exceeding PCs (414.6 million) • More Mobile Internet Users Than Wireline Users in the U.S. by 2015 • Smartphone and bandwidth cost reduces • Smart devices contribute to more than 90% of mobile data traffic IoT Symposium, Oct 2015
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Mobile Sensors Trends
Source: EMS Market Tracker, April 2014. IoT Symposium, Oct 2015 IHS Consumer & Mobile © M S.Sarma
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Mobile Data Delivery Everywhere The exploding number of apps is driven by a huge upack in the number of smart devices
Smart devices contribute to more than 90% of mobile data traffic
~55%
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Cisco’s report 2014
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IoT for Emergency Use Cases • Earthquakes • Hurricanes • Tornadoes • Energy/utility outages • Fire hazards • Hazardous materials releases • Terrorism/
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IoT for Emergency Response
During Fire accidents can cause electric power failure. Mobile broadcast can be used to provide direcaons to the users about rescue operaaons. IoT Symposium, Oct 2015
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IoT for Emergency Response
Emergency situaaon Automaac Altering can be used to inform family, rescue teams, or nearby cars / passengers in case of accidents. IoT Symposium, Oct 2015
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