Mobile Crowd Sensing for Internet
of Things: A Credible Crowdsourcing
Model in Mobile-Sense Service
Speaker:陳凱鴻
This paper appears in:Multimedia Big Data (BigMM),
2015 IEEE International Conference on, Issue Date: 20-22
April 2015
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目錄
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簡介
介紹Mobile Crowd Sensing(MCS)相關技術
分析比較現有的CS server models
提出可信的IOT眾包服務模型
Key technologies of model
結論
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簡介
• 何謂Mobile Crowd Sensing?
現在手機等裝置越來越普遍，手機上都有不少感測器，當有很多
裝置與很多sensor一起聯合運作時就稱之。
• 為甚麼需要?
當我們想要實作大型環境應用，或增強環境應用效能時，我們就
可藉由這些裝置來達到很好的效果。
• 能帶來甚麼好處?
這跟以往大規模部屬感知器相比起來節省了大量人事物的消耗，
可塑性也較高。
• 人類在這之中扮演甚麼腳色?
從以往的使用者晉升為貢獻者或決策者
在這個網絡中，移動性隨機性和時間複雜度帶來的數據感測技術
挑戰，數據傳輸，如在以下幾個方面描述
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簡介
• 當有太多資訊要處理時，可能會有資訊負載過
大的問題
• 提供符合用戶喜好的服務，如群體行為模式，
網絡架構和服務屬性是關鍵。
• 資料的收集傳輸皆在裝置上進行。
• 此篇要討論的與以往不同，不假設皆在相互可
信任的狀態，因為現實中並沒有所謂預先存在
可信任的裝置
• 本文提出了基於群眾感知的可信賴眾包服務模
型，分析活動模式的關係和節點之間的網絡架
構
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簡介
• 未來討論方向為可靠的群眾資源、獎勵機
制、擴大感知深廣度
• 動態分配會是群眾感知的一個挑戰
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Mcs Technology
• 與傳統感測網路不同處為MCS以使用者為中
心。(人們在MCS扮演很重要的腳色)
• MCS模型分為參與式感知與機會感知
• 參與式感知:參與者決定where,when,how
• 機會感知:由應用程式主動收集資料
• 對兩種感知進行比較1.使用者負擔2.感知準
確性3.任務複雜度
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MCS Application
• MCS can be widely used in many fields such as transportation,
environmental monitoring, health care, mobile social
networks, etc.
• this paper divide them into three main areas: environment
sensing, infrastructure sensing and social sensing.
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Crowdsourcing service model
• In the loT crowdsourcing service based on MCS, the sensing
service in the target area is no longer achieved depending on
a single fixed node.
• While summarizing the characteristics of the mobile sensing
of loT, we need to consider the following aspects in modeling
process.
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Pervasive(無所不在,能帶來更便利舒適的生活)
Mobility(透過移動性可彌補傳統感知器的不足)
Heterogeneity(建立一個讓各種裝置都能溝通的平台)
Dynamic(透過智能化服務和調度機制的組合達到動態性)
Context-Awareness(情境感知,藉由學習與分析給使用者建議)
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Crowdsourcing service model
• This paper proposes a service-oriented architecture (SOA)
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Crowdsourcing service model
The model mainly includes the following parts.
• Mobile Service Interface
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service registration and context registration.
• Data Center Module
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As the storage center in our model.
Social attributes, activity patterns and community relationships of users are stored in the
data center.
• Mobile Crowd Sensing Module(core of model)
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Gathers real-time activity and interaction data (like GPS trail, external environment data)
from mobile devices that user carries.
By applying machine learning and data mining methods to the raw data gathered.
This information could be used to provide decision-making basis for the subsequent
process of crowdsourcing service assignment.
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Crowdsourcing service model
The model has the three main steps:
• Measurement and prediction of social relations of crowd sensing nodes(透
過取得活動模式,傳輸紀錄,來分析影響社會關係的關鍵因素)
• Discovery of service community in a directed weighted
network
• Discovery and selection of crowdsourcing service nodes
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Key technologies of model
A.
B.
C.
D.
E.
Multi-Source Perception Information Collection
Social Relationship Cognition
Credible Route for Crowdsourcing Service
Crowdsourcing Service Assignment Based on Ahp
Credible Crowdsourcing Services Platform
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Key technologies of model
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A.Multi-Source Perception Information
Collection
Information can be divided into equipment information and
mobile application information.
– The equipment records the node activities and interactive information.
(such as call recording, message logging, geographic information,
environmental information, etc.)
– The mobile applications mainly record information which is generated
in the context and on-line services.
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B. Social Relationship Cognition
• “how to deliver the requests to trusted SP” and “how to
deliver the data response to SR” become the primary
concerns of crowdsourcing service model.
• Defined different decision factors to quantize the social
relationship between users.
– location factor, interaction factor, service evaluation factor, and
feedback aggregation factor. (These factors try to reflect the complex,
dynamic, asymmetric and transitive features of social relationships)
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C. Credible Route for Crowdsourcing
Service
• Users within same community usually have similar social attributes
and service preferences, and they are not strangers to each other,
but with a certain trust relationship between themselves.
• In optimal path tree, similarity index and community dispersion
index are defined and calculated to achieve the community
detection, and an effective partitioning algorithm is proposed for
weighted and directed network.
• (During the process of establishing credible route, if SR and SP
belong to the same community, the trusted route can be
established by utilizing internal trust relationships between users in
same community.)自閱,報告刪除
• 插入圖表1
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D. Crowdsourcing Service Assignment
Based on AHP
The process of crowdsourcing service assignment can be
summarized as below.
– Detection of Crowdsourcing Service Candidates
– Crowdsourcing Decision-Making Factors
– Crowdsourcing Service Based on AHP
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Detection of Crowdsourcing Service
Candidates
• In order to realize crowdsourcing service in target area, the
set of users that can provide services need to be discovered.
• Trails of movement directly reflect users' activities. Collecting
and analyzing trail information should provide guideline for
the detection of crowdsourcing service candidate users.
• Through the observation of a user's everyday activity and the
analysis of its pattern, came to the conclusion that user's
movement is event-driven.
• For example…
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Crowdsourcing Decision-Making
Factors
• The mobility and sociality of mobile nodes will bring new
challenges to crowdsourcing service.
• Service Quality Factor (SQF), Link Reliability Factor (LRF) and
Region Heat Factor (RHF). These factors could be used for
describing the different attributes of user service preferences.
• In addition to provider's capacity, link reliability should be
taken into consideration when selecting crowdsourcing
service provide.
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Crowdsourcing Decision-Making
Factors
• Service Quality Factor (SQF) is the objective evaluation of a
user's historical quality of services.
• Link Reliability Factor (LRF) is determined by link stability,
attenuation factor and the number of forwarding users.rs.
• Region Heat Factor (RHF) represents how many users are
going on in specific region at a given time. RHF is decided by
the number of arrivals of users, and their duration time at the
specific region.
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Crowdsourcing Service Based on AHP
• Three decision-making factors in our paper are SQF, LRF and
RHF, and these factors describe user preferences from
different perspectives.
• The Analytic Hierarchy Process (AHP) is a structured technique
for organizing and analyzing complex decisions, which based
on the mathematics and psychology.
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E. Credible Crowdsourcing Services
Platform
• The system can be divided into different functional modules
according to the tasks they perform. each module realizes a
specific function and can be joined together to work as a
whole.
• The client-side can have the data such as mobile node
location, and sensing information, uploaded to the server.
• the server-side displays real-time location of each node in
tables, tree structures and topologies to present the node
social relations, community structure, credible links and other
information.
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E. Credible Crowdsourcing Services
Platform
1. When a node requests a crowdsourcing service, the client-side will
submit a service request (including the target area of perception,
crowdsourcing content, services preferences, etc.)
2. After receiving the perception service request from the client.
3. Server-side first analyzes the nodes that may appear in the target
area within the time threshold according to the mobile node
historical location, call and other information.
4. Measures and predicates the social relationship combining the
service interaction information.
5. Divides reasonable community structures.
6. Builds a credible request delivery path.
7. Selects and returns to the client the optimal paths based on user
preferences
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E. Credible Crowdsourcing Services
Platform
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Performance evaluation
1. When a node requests a crowdsourcing service, the client-side will
submit a service request (including the target area of perception,
crowdsourcing content, services preferences, etc.)
2. After receiving the perception service request from the client.
3. Server-side first analyzes the nodes that may appear in the target
area within the time threshold according to the mobile node
historical location, call and other information.
4. Measures and predicates the social relationship combining the
service interaction information.
5. Divides reasonable community structures.
6. Builds a credible request delivery path.
7. Selects and returns to the client the optimal paths based on user
preferences
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Conclusion
• This paper builds a credible crowdsourcing service model for
crowd sensing on the basis of the features of terminal
intelligence, sociality of service node and massive sensing
data under loT environment.
• A general and integrated service model and key techniques
are provided, which is useful for the analysis of the social
characteristics of mobile nodes, services preferences mining,
and service mode decision, etc.
• defined different decision factors to quantize the social
relationship between users
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• (1) 滿足應用程式的要求，最小化電量消耗
• (2)在有限的電量下，最大化情境感知的精準度。我們分別提出兩個最
佳化演算法和快速的演算法並用Java開發模擬器。從實驗結果表示，
快速的演算法可
以即時的解決問題、節省電量消耗和最佳化演算法平均只有∼ 3%的
差距。我們將演算實做到Android系統上並成功節省電量的消耗。在
論文的第二部分，我們推廣感測器排程的概念到多隻手機上並將其應
用到crowdsensing 系統中。我們設計一個crowdsensing系統，系統依照
手機使用者的位址和能力(ex: 剩餘電量)，找出最佳的工作分配方式
以降低碳排放的量。我們用數學式子表示問題並提出兩個最佳化/快
速的演算法。利用Java開發的模擬器所得到的結果表示，快速的演算
法可以減少364倍的碳排放量、加速工作完成(8倍)和最佳化演算法只
有∼ 2%的差距。
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•
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；Talasila 等人[4] 使用定位信息判断
用户提交的交通堵塞信息是否真实，
防止有人故意在出发前发布假的拥
堵信息用于清畅道路
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