Detecting daily activities is helpful for health care and clinical medicine. In this paper, we present ActDetector, a smartwatch based application which detects 8 common daily activities, including sitting, walking, running, going upstairs, going downstairs, eating, driving and sitting in a vehicle. By leveraging the built-in sensors on smartwatch, a multi-level classification system is proposed which considers both detection accuracy and energy efficiency. ActDetector is designed to work unobtrusively, no matter on which wrist the smartwatch is worn. We have implemented ActDetector on Sony Smartwatch 3 and evaluated its performance in real experiments involving 12 users. Experimental results show that ActDetector is energy efficient and can detect the daily activities with high accuracy.

Opportunistic mobile networks consisting of intermittently connected mobile devices have been exploited for various applications, such as computational offloading and mitigating cellular traffic load. In contrast to existing work, in this paper, we focus on cooperatively offloading data among mobile devices to maximally improve the probability of data delivery from a mobile device to intermittently connected infrastructure within a given time constraint, which is referred to as the cooperative offloading problem. Unfortunately, the estimation of data delivery probability over an opportunistic path is difficult and cooperative offloading is NP-hard. To this end, we first propose a probabilistic framework that provides the estimation of such probability. Based on the proposed probabilistic framework, we design a heuristic algorithm to solve cooperative offloading at a low computation cost. Due to the lack of global information, a distributed algorithm is further proposed. The performance of the proposed approaches is evaluated based on both synthetic networks and real traces. Experimental results show that the probabilistic framework can accurately estimate the data delivery probability, cooperative offloading greatly improves the delivery probability, the heuristic algorithm approximates the optimum, and the performance of both the heuristic algorithm and distributed algorithm outperforms other approaches.

Opportunistic mobile networks consisting of intermittently connected mobile devices have been exploited for various applications, such as computational offloading and mitigating cellular traffic load. Different from existing work, in this paper, we focus on cooperatively offloading data among mobile devices to maximally improve the probability of data delivery from a mobile device to an intermittently connected remote server or data center within a given time constraint, which is referred to as the cooperative offloading problem. Unfortunately, cooperative offloading is NP-hard. To this end, a heuristic algorithm is designed based on the proposed probabilistic framework, which provides the estimation of the probability of successful data delivery over the opportunistic path, considering both data size and contact duration. Due to the lack of global information, a distributed algorithm is further proposed. The performance of the proposed approaches is evaluated based on both synthetic networks and real traces, and simulation results show that cooperative offloading can significantly improve the data delivery probability and the performance of both heuristic algorithm and distributed algorithm outperforms other approaches.

This paper proposes SymDetector, a smartphone based application to unobtrusively detect the sound-related respiratory symptoms occurred in a user's daily life, including sneeze, cough, sniffle and throat clearing. SymDetector uses the built-in microphone on the smartphone to continuously monitor a user's acoustic data and uses multi-level processes to detect and classify the respiratory symptoms. Several practical issues are considered in developing SymDetector, such as users' privacy concerns about their acoustic data, resource constraints of the smartphone and different contexts of the smartphone. We have implemented SymDetector on Galaxy S3 and evaluated its performance in real experiments involving 16 users and 204 days. The experimental results show that SymDetector can detect these four types of respiratory symptoms with high accuracy under various conditions.

Vaccination is one of the most effective ways to protect people from being infected by infectious disease. However, it is often impractical to vaccinate all people in a community due to various resource constraints. Therefore, targeted vaccination, which vaccinates a small group of people, is an alternative approach to contain infectious disease spread. To achieve better performance in targeted vaccination, we collect student contact traces in a high school based on wireless sensors carried by students. With our wireless sensor system, we can record student contacts within the disease propagation distance, and then construct a disease propagation graph to model the infectious disease propagation. Based on this graph, we propose a metric called connectivity centrality to measure a node's importance during disease propagation and design centrality based algorithms for targeted vaccination. The proposed algorithms are evaluated and compared with other schemes based on our collected traces. Trace driven simulation results show that our algorithms can help to effectively contain infectious disease.

Community detection is an important issue due to its wide use in designing network protocols such as data forwarding in Delay Tolerant Networks (DTN) and worm containment in Online Social Networks (OSN). However, most of the existing community detection algorithms focus on binary networks. Since most networks are naturally weighted such as DTN or OSN, in this article, we address the problems of community detection in weighted networks, exploit community for data forwarding in DTN and worm containment in OSN, and demonstrate how community can facilitate these network designs. Specifically, we propose a novel community detection algorithm, and introduce two metrics: intra-centrality and inter-centrality, to characterize nodes in communities, based on which we propose an efficient data forwarding algorithm for DTN and a worm containment strategy for OSN. Extensive trace-driven simulation results show that the proposed community detection algorithm, the data forwarding algorithm, and the worm containment strategy significantly outperform existing works.

Mobile social networks have emerged as a new frontier in the mobile computing research society, and the commonly used social structure (i.e., community) has been exploited to facilitate the design of network protocols and applications, such as data forwarding and worm containment. However, community based approaches may not be accurate when applied for predicting node contacts and may separate two frequently contacted nodes into different communities. In this paper, to address these problems, we propose skeleton, a tree structure specially designed for organizing network nodes, as the underlying structure in mobile social networks. We address the challenges on how to uncover skeleton from network, how to adapt skeleton with dynamic network and how to leverage skeleton for network protocol designs. Skeleton is constructed based on best friendship and skeleton construction is simple and efficient (e.g., less computational complexity than community detection). Algorithms are also designed to adapt skeleton construction to dynamic network. Moreover, a data forwarding algorithm and a worm containment strategy are designed based on skeleton. Trace-driven simulation results show that the skeleton based data forwarding algorithm and worm containment strategy outperform existing schemes based on community.