effsense: Energy-Efficient and Cost-Effective Data Uploading in Mobile Crowdsensing

Transcription

1 effsense: Energy-Efficient and Cost-Effective Data Uploading in Mobile Crowdsensing Leye Wang, Daqing Zhang and Haoyi Xiong TELECOM SudParis, France PUCAA 2013

2 Outline Introduction Research Motivation User Needs and Objectives Some Observations Proposed Ideas Research Issues Main Contributions The Proposed effsense Framework Uploading Schemes in effsense System Evaluation Conclusions and Future Work 2

3 Research Motivation Mobile Crowdsensing can enable many applications, to encourage users to participate in Mobile Crowdsensing tasks, reducing cost incurred helps: Energy Consumption 3G Mobile Data Communication Cost 3 stages of a mobile crowdsensing task Task Assignment Sensing Data Uploading (our focus) 3

4 User Needs and Objectives User Types and Needs Data-plan users (DP) with unlimited data plan concerned about energy consumption Non-data-plan users (NDP) pay according to data size uploaded via 3G concerned about data cost Example: MIT Reality Mining ~30 users with data plan (data-plan users) ~70 users with no data plan (non-data-plan users) The data-plan/non-data-plan user ratio might change Objectives Data-plan users (DP) Reduce energy consumption Non-data-plan users (NDP) Reduce data cost 4

5 Some Observations Some Known techniques to Reduce Energy Consumption (for DP) Baseline: Initiate a 3G connection to upload data Uploading data via a Bluetooth gateway or WiFi consumes less energy Uploading data via 3G parallel with a phone call saves >75% energy J. Nurminen, Parallel connections and their effect on the battery consumption of a mobile phone, Some Known techniques to Eliminate Data Cost (for NDP) Uploading data via a Bluetooth gateway or WiFi Offloading data by Bluetooth to a DP user, who can later relay data to the server 5

6 Proposed Ideas for Energy/Data Cost Cut Leveraging Critical Events making a phone call encountering a Bluetooth gateway connecting to WiFi meeting another user Use Time to Trade-off Events - Delay-tolerant data uploading Traditionally, data is uploaded as soon as it is sensed. We allow delay between data sensing and uploading, so that critical events can happen and be leveraged to reduce energy and/or data cost. 6

7 A Running Example (How to Reduce Cost) Server S WiFi/LAN r i : sensing data of u i r i : sensing data of u i 3G (parallel with voice call) {r 2,r 1 } Bluetooth Gateway G 3 uploading paths: 1. u 3 G S 2. u 1 u 2 G S 3. u 2 u 1 S {r 1,r 2 } {r 3 } {r 1 } u 1 u 2 u 1 u 2 u 3 data-plan users {r 2 } non-data-plan users 7

8 Research Issues 1. Identify the critical events and predict the probability of future occurrence of these critical events. 2. Estimate data uploading energy consumption associated to each critical event. 3. Design a real-time algorithm distributed on each user s phone to decide whether to upload/offload data or keep data when a sequence of critical events occur. 8

9 Research Contributions Intend to achieve two objectives for two types of users for 1 st Time energy consumption (data-plan users) data cost (non-data-plan users) Propose a novel mechanism to address the problem delay-tolerant data uploading critical events Develop 4 different data uploading schemes Two for data-plan users Two for non-data-plan users Evaluate the proposed mechanism and schemes with two datasets MIT Reality Mining Nodobo 9

10 Outline Introduction Research Motivation User Needs and Objectives Some Observations Proposed Ideas Research Issues Main Contributions The Proposed effsense Framework Uploading Schemes in effsense System Evaluation Conclusions and Future Work 10

11 The Proposed effsense Framework Mobile Crowdsensing Framework Sensing Decision Making (offload or keep data) Data Uploading 11

12 The Proposed effsense Framework Key Concepts in effsense Framework Two types of crowdsensing users DP and NDP Critical Events Prediction for each user mobility prediction meet other user/ Bluetooth gateway/ WiFi AP call prediction Data Uploading Schemes Cold-Start works without user logs Prediction-Based - leverage user logs Force uploading at deadline At the end of the data uploading period (max delay), effsense checks data-plan users whether they have non-uploaded data: for those with data, effsense forces them to establish a 3G connection to upload data to the server. 12

13 Outline Introduction Research Motivation User Needs and Objectives Some Observations Proposed Ideas Research Issues Main Contributions The Proposed effsense Framework Uploading Schemes in effsense System Evaluation Conclusions and Future Work 13

14 Uploading Schemes in effsense When a critical event occurs, decide whether to offload/keep data For NDP SimpleGreedy (cold-start) AdvancedGreedy (prediction-based) For DP Greedy (cold-start) ExpectationBased (prediction-based) Assumption - Offload and Dismiss: Once a user offloads data to another user, won t be responsible for sending the data any more. Avoid redundant data offloading to reduce energy consumption 14

15 cold-start schemes for NDP/DP users SimpleGreedy ndp /Greedy dp Cold-start schemes make the decision directly according to the event type. without using user history logs NDP user: SimpleGreedy ndp DP user: Greedy dp upload data without incurring NDP users data cost Call Bluetooth Gateway upload data with less energy consumption than 3G Call Bluetooth Gateway Critical Event Connect WiFi Critical Event Connect WiFi meet DP user meet DP user meet NDP user meet NDP user 15

16 prediction-based scheme for NDP users AdvancedGreedy ndp Difference from SimpleGreedy ndp a NDP user might offload data to another NDP user encountered Call Bluetooth Gateway Critical Event Connect WiFi meet DP user the encountered user has higher probability to upload data without data cost meet NDP user Otherwise 16

17 prediction-based scheme for DP users ExpectationBased dp Calculate DP user s expected energy consumption under different conditions when encountering a critical event Call / Bluetooth Gateway / WiFi Upload data Keep Data Meet another DP user (suppose two users: u i and u j ) u i offloads data to u j u j offloads data to u i u i and u j both keep own data Decide according to the condition with smallest value Calculation is based on predict the future occurrence of critical events estimate the data transmission energy of critical events 17

18 Outline Introduction Research Motivation User Needs and Objectives Some Observations Proposed Ideas Research Issues Main Contributions The Proposed effsense Framework Uploading Schemes in effsense System Evaluation Conclusions and Future Work 18

19 Experiment Setup Two datasets MIT Reality Mining, Nodobo Max delay: 24 hours Start of the data uploading: 00:00:00 for each day Estimation of the energy consumption under critical events N. Balasubramanian, etc., Energy consumption in mobile phones: a measurement study and implications for network applications, 2009 Prediction Method count week-level frequency MIT #DP user #NDP user #BT gateway 2 0?WiFi traces no yes Nodobo e.g. 3 weeks out of 5-week logs, a user makes calls during Monday 8:00~12:00 predict that this user s call probability is 60% during next Monday 8:00~12:00 19

20 Questions NDP user (Data Cost) How many NDP users could successfully upload their sensed data to the server without data cost? DP user (Energy Consumption) How much energy do DP users save compared with traditional 3G data uploading? 20

21 NDP user (Data Cost) NDP users uploading data without data cost more than 45% (MIT) ; 54% (Nodobo) MIT (41 NDP users) Nodobo (16 NDP users) Working Days Holidays SimpleGreedy AdvancedGreedy All Days 18.8 (45%) 19.6 (48%) Working Days Holidays SimpleGreedy AdvancedGreedy All Days 8.64 (54%) 8.93 (56%) 21

22 DP user (Energy Consumption) Energy consumption of DP users 4 scheme pairs: 2 (NDP)*2 (DP) traditional: establish a new 3G connection to upload data Compared with traditional 3G Greedy dp reduces ~55% ExpectationBased dp reduces ~65%. 22

23 Conclusions and Future Work effsense is a novel data uploading framework for mobile crowdsensing reduce both energy consumption and data cost introduce delay-tolerant data uploading and critical events design cold-start/prediction-based uploading schemes for DP/NDP users evaluation more than 45% NDP users uploading data without data cost DP users reducing more than 55% energy compared with traditional 3G uploading Future Work Other critical events Precise energy consumption estimation mechanism Advanced mobility/call prediction method Incentives for DP users to relay data Real-life deployment 23

24