Collaborative opportunistic sensing with mobile phones

Castro, Luís A.; Beltrán, Jessica; Pérez, Moisés; Quintana, Eduardo Martín; Favela, Jesús; Chávez, Edgar; Rodríguez, Marcela D.; Navarro, René F.

doi:10.1145/2638728.2638814

Cited by 11 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these studies do not focus on underserved regions. While other mobile sensing platforms (like FunToolkit [20], InCense [21]) are successful in collecting data, they are inadequate for our context where individuals do not have the resources to pay for a monthly data usage plan, and the neighborhood has limited Wi-Fi access and poses several security challenges.…”

Section: Related Workmentioning

confidence: 99%

Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region

2020

View full text Add to dashboard Cite

Living in an underdeveloped region implies a higher cost of living: access to services, such as school, work, medical care, and groceries, becomes more costly than those who live in regions with better infrastructure. We are interested in studying how mobility affects the cost of living and the subjective wellbeing of residents in underdeveloped regions. We conducted a four-weeks sensing campaign with 14 users in Camino Verde (an underserved region in Tijuana, Mexico). All of the participants used a mobile system that we developed to track their daily mobility. The participants were indicated not to change their daily routine for the study as they carried the tracking device. We analyzed 537 individual routes from different city points and calculated their mobility divergences, while comparing the actual route chosen against the route that was suggested by Google Maps and using this not as the optimal route, but as the baseline. Our results allowed for us to quantify and observe how Camino Verde residents are affected in their mobility in four crucial aspects: geography, time, economy, and safety. A posteriori qualitative analysis, using semi-structured interviews, complemented the quantitative observations and provided insights into the mobility decisions that those people living in underserved regions have to take.

show abstract

Section: Related Workmentioning

confidence: 99%

Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region

2020

View full text Add to dashboard Cite

show abstract

“…Local Collaborative Sensing: There are also works on local collaborative sensing; however, these works do not consider the sequential nature of past information into the collaboration process [12]. For example, Mantyjarvi et al [13] present a collaborative sensing approach where a device, upon noticing a change in its local context beyond a threshold value, requests contexts from its surrounding devices so as to increase the accuracy of its context vector.…”

Section: Related Workmentioning

confidence: 99%

HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering

Sadhu

Zonouz

Sritapan

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications (PerCom

View full text Add to dashboard Cite

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in "Location X") are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. To this end, two stochastic models based on the theory of Hidden Markov Models (HMMs) to obtain mobile context are proposed-personalized model (HPContext) and collaborative filtering model (HCFContext). The former predicts the current context using sequential history of the user's past context observations; the latter enhances HPContext with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in company, gym friends, family members, etc. Each of the proposed models can also be used to enhance/complement the context obtained from sensors. Furthermore, since privacy is a concern in collaborative filtering, a privacy-preserving method is proposed to derive HCFContext model parameters based on the concepts of homomorphic encryption. Finally, these models are thoroughly validated on a real-life dataset.

show abstract

“…Finally, the "optimum node" module elects the clique leader (the most informative smartphone) to record the audio data and notifies the condition of deactivation to the other smartphones from capturing the duplicate audio signal. It also helps in sorting the smartphone list based on their audio signal strength which is Mobile Information Systems eventually utilized by the locomotive "signature collection" module to opportunistically check on and trigger the accelerometer sensor on the smartphones [41]. e server-side architecture consists of two main logical subcomponents: (i) occupancy context model and (ii) location context model.…”

Section: Overall System Architecturementioning

confidence: 99%

Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments

Khan

Roy

Hossain

2018

Mobile Information Systems

View full text Add to dashboard Cite

Occupancy detection helps enable various emerging smart environment applications ranging from opportunistic HVAC (heating, ventilation, and air-conditioning) control, effective meeting management, healthy social gathering, and public event planning and organization. Ubiquitous availability of smartphones and wearable sensors with the users for almost 24 hours helps revitalize a multitude of novel applications. The inbuilt microphone sensor in smartphones plays as an inevitable enabler to help detect the number of people conversing with each other in an event or gathering. A large number of other sensors such as accelerometer and gyroscope help count the number of people based on other signals such as locomotive motion. In this work, we propose multimodal data fusion and deep learning approach relying on the smartphone’s microphone and accelerometer sensors to estimate occupancy. We first demonstrate a novel speaker estimation algorithm for people counting and extend the proposed model using deep nets for handling large-scale fluid scenarios with unlabeled acoustic signals. We augment our occupancy detection model with a magnetometer-dependent fingerprinting-based localization scheme to assimilate the volume of location-specific gathering. We also propose crowdsourcing techniques to annotate the semantic location of the occupant. We evaluate our approach in different contexts: conversational, silence, and mixed scenarios in the presence of 10 people. Our experimental results on real-life data traces in natural settings show that our cross-modal approach can achieve approximately 0.53 error count distance for occupancy detection accuracy on average.

show abstract

Collaborative opportunistic sensing with mobile phones

Cited by 11 publications

References 6 publications

Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region

Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region

HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering

Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments

Contact Info

Product

Resources

About