Mobile Phones as a Shared Sensing Platform Aman Kansal Researcher Networked Embedded Computing, MSR
Example: City Park User E-group Stitched view APPLICATION (Data centric coverage model) Upload Pictures, Video, Audio SenseWeb GROUP MEMBER PARK with people …and phones 1. Is the court wet? 2. What play structures are there? 3. Which bird sounds reported? SMS: Click picture of court. Group Points: 400
Mobile Phone Advantage • 2.14 billion phones and growing • Mobility • reach where static sensor cannot • increased spatial coverage • Phone exists for voice/data apps: • Piggybacking sensing is cost effective • Human assistance • Can sometimes help detect or aim at interesting phenomenon
Prototype Client on phone • Allows users to take pictures • Automatically uploads data to server • Location stamps using inbuilt/Bluetooth GPS SenseWeb • Server • Indexes images by location and time (SQL Server database) • Web service API for phones and apps. • Supports several sensor types • Example App: Portal • Displays sensor data by location and sensor type • Publicly accessible at http://atom.research.microsoft.com/sensormap Web service API’s allow building other apps.
Design Issues • Information value • Which data to collect and share: battery and bandwidth constraints • Coverage management • Which phone sensed where app needs coverage • Sensor tasking for application demands • Incentive mechanisms • Data verifiability, user privacy
Information Value of Images • Entropy of a single image: H(X) = -S(p.log(p)) [p: image histogram] • Value among multiple images • Consider common spatial coverage Buildings Data Size (MB) • H(X|Y) = -E[log2p(X|Y)] • H(X|Y1,…,Ym) = H(X|Z) (Z: common spatial coverage) Kitchen Value based selection Commonality: found using key feature based algorithm Relevance Value Cutoff (%) Details: ACM Sensys WSW 2006
Coverage Management • Which sensors does app access • Who sensed in required region during required time window? Mobile Sensor Swarm
Coverage Management • Which sensors does app access • Who sensed in required region during required time window? • Solution: location • Samples are geo-stamped • Apps do not track device • Trajectory • Connectivity • Sharing preferences • Device ID anonymized Application n Application 1 Data Centric Abstraction Mobile Sensor Swarm
Coverage Management • Several location technologies • GPS: does not work everywhere • Cell tower: coarse • Wifi: coarse • Human entered tags: approximate, high manual effort • Leverage camera data to enhance location • Refine location granularity • Room within building, aisle within store • Associate data when location not available • Verify location j Mij i • Algorithm • Images within vicinity organized as a graph • Edge weight by match • Relation R(i,j) by highest weight • Refined location zone: Transitive closure of R Details: ACM NOSSDAV 2007
Demand Based Sensor Tasking • Minimize sensing task overhead on phones • Sense to be most accurate on most used regions • Good model: determine where sensing needed • Learn most used: where apps need data • Task phones: battery, bandwidth, privacy, intrusion costs Phenomenon Demand Sensing cost Details: Andreas Krause, Intern project report
Demand Based Sensor Tasking • Set V of possible observations • For each subset A of V, define utility U(A) = Σi E[Di (Var(Si) – Var(Si | A)) ] Expectation over demand Di and observations A • Theorem: U(A) is submodular • Theorem [Nemhauser et al]: • For submodular U: • U(greedy solution) > • (1-1/e) U(optimal)
Conclusions • Mobile phones enable many sensing apps • Architecture to use a highly volatile swarm of mobile devices as a sensor network • Information value based data selection • Location based data centric abstraction • Coverage management and data addressing • Avoids burdening applications with managing device motion, connectivity, sharing • Efficient sensor tasking • Contact: firstname.lastname@example.org
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