Crowdsensing the Speaker Count in the Wild: Implications and Applications

Abstract-The Mobile Crowd Sensing (MCS) paradigm enables large-scale sensing opportunities at lower deployment costs than dedicated infrastructures by utilizing the large number of today's mobile devices. In the context of MCS, end users with sensing and computing devices can share and extract information of common interest. In this article, we examine Crowd++, a MCS application, which accurately estimates the number of people talking in a certain place through unsupervised machine learning analysis on audio segments captured by mobile devices. Such a technique can find application in many domains, such as crowd estimation, social sensing, and personal well-being assessment. In this article, we demonstrate the utility of this technique in the context of conference room usage estimation, social diary, and social engagement in a power efficient manner followed by a discussion on privacy and possible optimizations to Crowd++ software

Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing

Smartphones are context-aware devices that provide a compelling platform for ubiquitous computing and assist users in accomplishing many of their routine tasks anytime and anywhere, such as sending and receiving emails. The nature of tasks conducted with these devices has evolved with the exponential increase in the sensing and computing capabilities of a smartphone. Due to the ease of use and convenience, many users tend to store their private data, such as personal identifiers and bank account details, on their smartphone. However, this sensitive data can be vulnerable if the device gets stolen or lost. A traditional approach for protecting this type of data on mobile devices is to authenticate users with mechanisms such as PINs, passwords, and fingerprint recognition. However, these techniques are vulnerable to user compliance and a plethora of attacks, such as smudge attacks. The work in this paper addresses these challenges by proposing a novel authentication framework, which is based on recognizing the behavioral traits of smartphone users using the embedded sensors of smartphone, such as Accelerometer, Gyroscope and Magnetometer. The proposed framework also provides a platform for carrying out multi-class smart user authentication, which provides different levels of access to a wide range of smartphone users. This work has been validated with a series of experiments, which demonstrate the effectiveness of the proposed framework

Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams

Ministry of Education, Singapore under its Academic Research Funding Tier

Halo: Managing Node Rendezvous in Opportunistic Sensor Networks

Abstract. One vision of an opportunistic sensor network (OSN) uses sensor access points (SAPs) to assign mobile sensors with sensing tasks submitted by applications that could be running anywhere. Tasked mobile sensors might upload sensed data back to these applications via subsequent encounters with this SAP tier. In a people-centric OSN, node mobility is uncontrolled and the architecture relies on opportunistic rendezvous between human-carried sensors and SAPs to provide tasking/uploading opportunities. However, in many reasonable scenarios application queries have a degree of time sensitivity such that the sensing target must be sampled and/or the resulting sensed data must be uploaded within a certain time window to be of greatest value. Halo efficiently, in terms of packet overhead and mobile sensor energy, provides improved delay performance in OSNs by: (i) managing tasking/uploading opportunity, and (ii) using mobility-informed scheduling at the SAP.