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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11853565_29openalex-snapshot
A Wirelessly-Powered Platform for Sensing and Computation
inferredcites →https://doi.org/10.1007/978-3-642-12654-3_21openalex-snapshot
Supporting Energy-Efficient Uploading Strategies for Continuous Sensing Applications on Mobile Phones
inferredcites →https://doi.org/10.1007/978-3-642-12654-3_9openalex-snapshot
Recruitment Framework for Participatory Sensing Data Collections
inferredcites →https://doi.org/10.1007/978-3-642-21726-5_9openalex-snapshot
Identifying Important Places in People’s Lives from Cellular Network Data
inferredcites →https://doi.org/10.1007/978-3-662-02377-8openalex-snapshot
Handbook of Stochastic Methods
inferredcites →https://doi.org/10.1007/978-3-662-05389-8openalex-snapshot
Handbook of Stochastic Methods
inferredcites →https://doi.org/10.1007/s10796-013-9476-zopenalex-snapshot
MPaaS: Mobility prediction as a service in telecom cloud
inferredcites →https://doi.org/10.1109/90.554719openalex-snapshot
Reducing location update cost in a PCS network
inferredcites →https://doi.org/10.1109/ccnc.2010.5421769openalex-snapshot
Parallel Connections and their Effect on the Battery Consumption of a Mobile Phone
inferredcites →https://doi.org/10.1109/infcom.2012.6195568openalex-snapshot
Energy-efficient collaborative sensing with mobile phones
inferredcites →https://doi.org/10.1109/inss.2012.6240525openalex-snapshot
Compressed sensing method for human activity sensing using mobile phone accelerometers
inferredcites →https://doi.org/10.1109/iswc.2012.25openalex-snapshot
Energy-Efficient Activity Recognition Using Prediction
inferredcites →https://doi.org/10.1109/mass.2010.5663996openalex-snapshot
StreamShaper: Coordination algorithms for participatory mobile urban sensing
inferredcites →https://doi.org/10.1109/mcom.2011.6069707openalex-snapshot
Mobile crowdsensing: current state and future challenges
inferredcites →https://doi.org/10.1109/mdm.2012.58openalex-snapshot
Using On-the-Move Mining for Mobile Crowdsensing
inferredcites →https://doi.org/10.1109/mobhoc.2007.4428679openalex-snapshot
Opportunistic Mobile Sensor Data Collection with SCAR
inferredcites →https://doi.org/10.1109/percom.2011.5767577openalex-snapshot
Participatory sensing algorithms for mobile object discovery in urban areas
inferredcites →https://doi.org/10.1109/percom.2013.6526718openalex-snapshot
METIS: Exploring mobile phone sensing offloading for efficiently supporting social sensing applications
inferredcites →https://doi.org/10.1109/percom.2013.6526723openalex-snapshot
Probabilistic registration for large-scale mobile participatory sensing
inferredcites →https://doi.org/10.1109/percom.2013.6526731openalex-snapshot
DrOPS: Model-driven optimization for Public Sensing systems
inferredcites →https://doi.org/10.1109/percomw.2013.6529564openalex-snapshot
Connectivity and energy usage in low-power wireless: An experimental study
inferredcites →https://doi.org/10.1109/percomw.2014.6815273openalex-snapshot
From participatory sensing to Mobile Crowd Sensing
inferredcites →https://doi.org/10.1109/sahcn.2011.5984884openalex-snapshot
Distance and time based node selection for probabilistic coverage in People-Centric Sensing
inferredcites →https://doi.org/10.1145/1374618.1374670openalex-snapshot
Fast and quality-guaranteed data streaming in resource-constrained sensor networks
inferredcites →https://doi.org/10.1145/1555816.1555835openalex-snapshot
A framework of energy efficient mobile sensing for automatic user state recognition
inferredcites →https://doi.org/10.1145/1644893.1644927openalex-snapshot
Energy consumption in mobile phones
inferredcites →https://doi.org/10.1145/1978622.1978628openalex-snapshot
Towards ubiquitous computing with call prediction
inferredcites →https://doi.org/10.1145/1999995.2000025openalex-snapshot
Energy-efficient trajectory tracking for mobile devices
inferredcites →https://doi.org/10.1145/2019583.2019588openalex-snapshot
Behavior-based adaptive call predictor
inferredcites →https://doi.org/10.1145/2030112.2030160openalex-snapshot
Enabling large-scale human activity inference on smartphones using community similarity networks (csn)
inferredcites →https://doi.org/10.1145/2030112.2030163openalex-snapshot
Accurate and privacy preserving cough sensing using a low-cost microphone
inferredcites →https://doi.org/10.1145/2070942.2070949openalex-snapshot
Balancing energy, latency and accuracy for mobile sensor data classification
inferredcites →https://doi.org/10.1145/2370216.2370218openalex-snapshot
Improving energy efficiency of personal sensing applications with heterogeneous multi-processors
inferredcites →https://doi.org/10.1145/2370216.2370233openalex-snapshot
An ultra-low-power human body motion sensor using static electric field sensing
inferredcites →https://doi.org/10.1145/2370216.2370241openalex-snapshot
An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data
inferredcites →https://doi.org/10.1145/2370216.2370288openalex-snapshot
Automatically characterizing places with opportunistic crowdsensing using smartphones
inferredcites →https://doi.org/10.1145/2389148.2389158openalex-snapshot
Can crowdsensing beat dynamic cell-ID?
inferredcites →https://doi.org/10.1145/2426656.2426660openalex-snapshot
Broadcast-free collection protocol
inferredcites →https://doi.org/10.1145/2444776.2444789openalex-snapshot
Lowering the barriers to large-scale mobile crowdsensing
inferredcites →https://doi.org/10.1198/016214506000001455openalex-snapshot
Bayesian Forecasting of an Inhomogeneous Poisson Process With Applications to Call Center Data
inferredcites →https://doi.org/10.2307/1913472openalex-snapshot
Pseudo Maximum Likelihood Methods: Applications to Poisson Models
inferredcites →https://doi.org/10.48550/arxiv.1210.0137openalex-snapshot
Data for Development: the D4D Challenge on Mobile Phone Data
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-39446-1_3openalex-snapshot
Monitoring People’s Mobility in the Cities: A Review of Advanced Technologies
inferred← citeshttps://doi.org/10.1007/978-3-319-98161-1_6openalex-snapshot
Opportunities and Risks of Delegating Sensing Tasks to the Crowd
inferred← citeshttps://doi.org/10.1016/j.comcom.2020.04.006openalex-snapshot
Optimization strategies for the selection of mobile edges in hybrid crowdsensing architectures
inferred← citeshttps://doi.org/10.1016/j.comnet.2015.12.021openalex-snapshot
ICP: Instantaneous clustering protocol for wireless sensor networks
inferred← citeshttps://doi.org/10.1016/j.comnet.2018.06.013openalex-snapshot
Real-time and energy aware opportunistic mobile crowdsensing framework based on people’s connectivity habits
inferred← citeshttps://doi.org/10.1016/j.pmcj.2016.01.010openalex-snapshot
Power management techniques in smartphone-based mobility sensing systems: A survey
inferred← citeshttps://doi.org/10.1080/23307706.2022.2107096openalex-snapshot
Improving mobile mass monitoring in the IoT environment based on Fog computing using an improved forest optimization algorithm
inferred← citeshttps://doi.org/10.1109/access.2020.2999370openalex-snapshot
Maximizing Clearance Rate of Budget-Constrained Auctions in Participatory Mobile CrowdSensing
inferred← citeshttps://doi.org/10.1109/access.2020.3044495openalex-snapshot
Green CrowdSensing With Comprehensive Reputation Awareness and Predictive Device-Application Matching Using a New Real-Life Dataset
inferred← citeshttps://doi.org/10.1109/aiccsa.2016.7945725openalex-snapshot
Energy/coverage quality trade-off based tasks allocation for opportunistic real time mobile crowdsensing
inferred← citeshttps://doi.org/10.1109/comst.2019.2910855openalex-snapshot
Data-Oriented Mobile Crowdsensing: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1109/comst.2019.2914030openalex-snapshot
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
inferred← citeshttps://doi.org/10.1109/cscwd49262.2021.9437752openalex-snapshot
Recruiting MCS Workers Strategy with Non-Fixed Reward in Social Network
inferred← citeshttps://doi.org/10.1109/dyspan.2019.8935832openalex-snapshot
Crowdsourcing-based Spectrum Monitoring at A Large Geographical Scale
inferred← citeshttps://doi.org/10.1109/globecom42002.2020.9322541openalex-snapshot
Understanding Human Mobility for CrowdSensing Strategies with the ParticipAct Data Set
inferred← citeshttps://doi.org/10.1109/globecom42002.2020.9348085openalex-snapshot
Impact of Evolutionary Community Detection Algorithms for Edge Selection Strategies
inferred← citeshttps://doi.org/10.1109/glocom.2016.7842248openalex-snapshot
Secure and Deduplicated Spatial Crowdsourcing: A Fog-Based Approach
inferred← citeshttps://doi.org/10.1109/icccn.2018.8487375openalex-snapshot
Cumulative Participant Selection with Switch Costs in Large-Scale Mobile Crowd Sensing
inferred← citeshttps://doi.org/10.1109/infocom.2017.8057034openalex-snapshot
When data acquisition meets data analytics: A distributed active learning framework for optimal budgeted mobile crowdsensing
inferred← citeshttps://doi.org/10.1109/iscc47284.2019.8969704openalex-snapshot
Trustworthiness and Comfort-Aware Participant Recruitment for Mobile Crowd-Sensing in Smart Environments
inferred← citeshttps://doi.org/10.1109/ithings-greencom-cpscom-smartdata.2017.114openalex-snapshot
A Data Trustworthiness Enhanced Reputation Mechanism for Mobile Crowd Sensing
inferred← citeshttps://doi.org/10.1109/iwqos.2016.7590450openalex-snapshot
Enhancing participant selection through caching in mobile crowd sensing
inferred← citeshttps://doi.org/10.1109/jiot.2016.2608141openalex-snapshot
Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget
inferred← citeshttps://doi.org/10.1109/jiot.2017.2765699openalex-snapshot
A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing
inferred← citeshttps://doi.org/10.1109/jiot.2018.2801559openalex-snapshot
DTCS: An Integrated Strategy for Enhancing Data Trustworthiness in Mobile Crowdsourcing
inferred← citeshttps://doi.org/10.1109/jiot.2018.2815982openalex-snapshot
Task Allocation in Spatial Crowdsourcing: Current State and Future Directions
inferred← citeshttps://doi.org/10.1109/jiot.2018.2864341openalex-snapshot
Task Allocation in Mobile Crowd Sensing: State-of-the-Art and Future Opportunities
inferred← citeshttps://doi.org/10.1109/jiot.2018.2866973openalex-snapshot
Location-Dependent Task Allocation for Mobile Crowdsensing With Clustering Effect
inferred← citeshttps://doi.org/10.1109/jiot.2019.2940717openalex-snapshot
A Task Assignment Method for Sweep Coverage Optimization Based on Crowdsensing
inferred← citeshttps://doi.org/10.1109/jiot.2019.2957835openalex-snapshot
A Probabilistic Model for the Deployment of Human-Enabled Edge Computing in Massive Sensing Scenarios
inferred← citeshttps://doi.org/10.1109/jiot.2020.3011461openalex-snapshot
Empowering Self-Organized Feature Maps for AI-Enabled Modeling of Fake Task Submissions to Mobile Crowdsensing Platforms
inferred← citeshttps://doi.org/10.1109/jiot.2020.3019326openalex-snapshot
Secure and Efficient Probabilistic Skyline Computation for Worker Selection in MCS
inferred← citeshttps://doi.org/10.1109/jiot.2020.3022611openalex-snapshot
DeepSensing: A Novel Mobile Crowdsensing Framework With Double Deep Q-Network and Prioritized Experience Replay
inferred← citeshttps://doi.org/10.1109/jiot.2021.3094670openalex-snapshot
ChainSensing: A Novel Mobile Crowdsensing Framework With Blockchain
inferred← citeshttps://doi.org/10.1109/jiot.2022.3161046openalex-snapshot
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
inferred← citeshttps://doi.org/10.1109/jiot.2022.3161050openalex-snapshot
Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey
inferred← citeshttps://doi.org/10.1109/jiot.2022.3164672openalex-snapshot
BETA: From Behavior Sequentializing to Task Mapping in Mobile Crowdsensing
inferred← citeshttps://doi.org/10.1109/mass.2015.46openalex-snapshot
Dynamic Participant Recruitment of Mobile Crowd Sensing for Heterogeneous Sensing Tasks
inferred← citeshttps://doi.org/10.1109/mass.2018.00067openalex-snapshot
Multi-expertise Aware Participant Selection in Mobile Crowd Sensing via Online Learning
inferred← citeshttps://doi.org/10.1109/mcom.2014.6871668openalex-snapshot
4W1H in mobile crowd sensing
inferred← citeshttps://doi.org/10.1109/mcom.2017.1600658cmopenalex-snapshot
HySense: A Hybrid Mobile CrowdSensing Framework for Sensing Opportunities Compensation under Dynamic Coverage Constraint
inferred← citeshttps://doi.org/10.1109/mobilecloud.2017.14openalex-snapshot
Cowbird: A Flexible Cloud-Based Framework for Combining Smartphone Sensors and IoT
inferred← citeshttps://doi.org/10.1109/ojcs.2022.3177290openalex-snapshot
A Survey of Sparse Mobile Crowdsensing: Developments and Opportunities
inferred← citeshttps://doi.org/10.1109/pdgc.2018.8745988openalex-snapshot
Cost Effective Algorithms for Participant Selection Problem in Mobile Crowd Sensing Environment
inferred← citeshttps://doi.org/10.1109/percom.2017.7917851openalex-snapshot
Scalable privacy-preserving participant selection in mobile crowd sensing
inferred← citeshttps://doi.org/10.1109/tdsc.2018.2791432openalex-snapshot
Providing Task Allocation and Secure Deduplication for Mobile Crowdsensing via Fog Computing
inferred← citeshttps://doi.org/10.1109/tii.2022.3227618openalex-snapshot
Online Energy Balancing Strategy Based on Lyapunov Optimization in Mobile Crowdsensing
inferred← citeshttps://doi.org/10.1109/tmc.2014.2357791openalex-snapshot
EMC3: Energy-efficient data transfer in mobile crowdsensing under full coverage constraint
inferred← citeshttps://doi.org/10.1109/tmc.2018.2865355openalex-snapshot
Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing
inferred← citeshttps://doi.org/10.1109/tmc.2018.2869387openalex-snapshot
Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors
inferred← citeshttps://doi.org/10.1109/tmc.2018.2884945openalex-snapshot
Dynamic Participant Selection for Large-Scale Mobile Crowd Sensing
inferred← citeshttps://doi.org/10.1109/tmc.2019.2901463openalex-snapshot
A Signature Scheme with Unlinkable-yet-Accountable Pseudonymity for Privacy-Preserving Crowdsensing
inferred← citeshttps://doi.org/10.1109/tmc.2019.2955688openalex-snapshot
Expertise-Aware Truth Analysis and Task Allocation in Mobile Crowdsourcing
inferred← citeshttps://doi.org/10.1109/tmc.2020.2983688openalex-snapshot
Profit-Oriented Task Allocation for Mobile Crowdsensing With Worker Dynamics: Cooperative Offline Solution and Predictive Online Solution
inferred← citeshttps://doi.org/10.1109/tnse.2018.2791948openalex-snapshot
Scalable Privacy-Preserving Participant Selection for Mobile Crowdsensing Systems: Participant Grouping and Secure Group Bidding
inferred← citeshttps://doi.org/10.1109/tnse.2020.3020159openalex-snapshot
Privacy-Preserving Participant Grouping for Mobile Social Sensing Over Edge Clouds
inferred← citeshttps://doi.org/10.1109/tsc.2025.3583250openalex-snapshot
Multi-Agent Reinforcement Learning for Freshness-Aware Data Sensing Model in Vehicular Crowdsensing Systems
inferred← citeshttps://doi.org/10.1109/tsmc.2016.2523902openalex-snapshot
ecoSense: Minimize Participants’ Total 3G Data Cost in Mobile Crowdsensing Using Opportunistic Relays
inferred← citeshttps://doi.org/10.1109/tvt.2016.2614312openalex-snapshot
SpatialRecruiter: Maximizing Sensing Coverage in Selecting Workers for Spatial Crowdsourcing
inferred← citeshttps://doi.org/10.1109/wcnc45663.2020.9120489openalex-snapshot
Task Allocation for Mobile Crowdsensing with Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/wimob.2015.7348016openalex-snapshot
Leveraging CDR datasets for context-rich performance modeling of large-scale mobile pub/sub systems
inferred← citeshttps://doi.org/10.1145/2988336.2988353openalex-snapshot
Dos and Don'ts in Mobile Phone Sensing Middleware
inferred← citeshttps://doi.org/10.1145/3102301openalex-snapshot
Energy-Efficient Mobile Video Streaming
inferred← citeshttps://doi.org/10.1145/3326164openalex-snapshot
Efficient User Guidance for Validating Participatory Sensing Data
inferred← citeshttps://doi.org/10.1155/2022/2595143openalex-snapshot
A Task Assignment Method Based on User‐Union Clustering and Individual Preferences in Mobile Crowdsensing
inferred← citeshttps://doi.org/10.1155/2022/3693537openalex-snapshot
A Survey of Application and Key Techniques for Mobile Crowdsensing
inferred← citeshttps://doi.org/10.12720/jcm.14.8.745-757openalex-snapshot
IoT Technologies for Mobile Crowd Sensing in Smart Cities
inferred← citeshttps://doi.org/10.3390/s150818613openalex-snapshot
Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience
inferred← citeshttps://doi.org/10.3390/s150922060openalex-snapshot
Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks
inferred← citeshttps://doi.org/10.3390/s18051593openalex-snapshot
SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
inferred← citeshttps://doi.org/10.4018/978-1-5225-7501-6.ch077openalex-snapshot
Web Services Description and Discovery for Mobile Crowdsensing
inferred← citeshttps://doi.org/10.4018/ijismd.2016100103openalex-snapshot
Web Services Description and Discovery for Mobile Crowdsensing
inferred← citeshttps://doi.org/10.4018/ijitsa.326134openalex-snapshot
Social Welfare-Based Task Assignment in Mobile Crowdsensing
inferred← citeshttps://doi.org/10.70675/2731a1dfz9789z4e6bz9320zaae648fbbc94openalex-snapshot
Optimisation de la consommation d'énergie des systèmes mobiles par l'analyse des besoins de l'utilisateur
inferred← citeshttps://doi.org/10.70675/4f7c1881z1484z49eazb6f9zbac1b2963de0openalex-snapshot
Facilitating mobile crowdsensing from both organizers’ and participants’ perspectives
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