WiFi-fingerprint localization is recognized as a promising indoor localization technique. However, it suffers from high implementation overhead such as heavy initial training and fingerprint map maintenance overtime. In this paper, we present the design, implementation, and evaluation of AP-Sequence. It is a fingerprint-based localization system that achieves extremely low overhead in fingerprint map construction and maintenance. AP-Sequence achieves this by treating a scan from any reference locations as an input to adjust a large portion of the fingerprint map. The power of AP-Sequence comes from dynamic region partitioning mechanism generating a fingerprint based on relative RSS values. AP-Sequence offers several advantages over existing methods with respect to robustness against environment noises, ability to handle dynamic power control, and mobile device heterogeneity. We have implemented AP-Sequence on an Android platform. Experiment results with over one month of evaluation demonstrate that our design achieves an average localization accuracy of 4-7.6 m over an extended time period with low-overhead in fingerprint map construction and maintenance.

Link correlation in wireless sensor networks has recently attracted a considerable amount of attention in the research community. Various pioneer works have empirically demonstrated the existence of link correlations and designed novel network protocols to exploit such link correlations. While all existing works focus on the correlated receptions at multiple receivers from a single sender, in this work we empirically demonstrate another type of link correlation, called sender-based link correlation. For sender-based link correlation, we observe wireless links from multiple senders to a single receiver are also correlated. Based on this observation, we design a two-tiered data forwarding scheme for improving the energy efficiency of unicast in the network. At the micro-level, individual nodes reduce their transmission energy consumption by temporarily switching to a new forwarder or suppressing the current transmission with the knowledge of link correlations. At the macro-level, we schedule the ordering of transmission times among neigh boring nodes so that the gains from all link correlation information in the network is maximized. Through trace-driven emulations and large scale simulations, we demonstrate that our design reduces data retransmissions by an average of 12% when compared with already highly energy efficient ETX-based protocols.

Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming increasingly popular for mobile applications. While GPS provides accurate outdoor locations, accurate indoor localization schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In order to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both social encounter and non-encounter events to cooperatively calibrate the estimation errors. We have fully implemented Social-Loc on the Android platform and demonstrated its performance on two underlying indoor localization systems: Dead-reckoning and WiFi fingerprint. Experiment results show that Social-Loc improves user's localization accuracy of WiFi fingerprint and dead-reckoning by at least 22% and 37%, respectively. Large-scale simulation results indicate Social-Loc is scalable, provides good accuracy for a long duration of time, and is robust against measurement errors.

A tree-based topology is often used to flood packets from the sink node in wireless sensor networks (WSNs). Therefore, flooding tree construction is an important and fundamental problem in WSNs, and has been extensively investigated in the literature. However, we note that the flooding tree construction problem in asynchronous low-duty-cycle WSNs has not been sufficiently investigated in existing work. In this work, we focus our investigation on minimum-delay and energy-efficient flooding tree construction considering the duty-cycle operation and unreliable wireless links. We formulate the problem as a undetermined-delay-constrained minimum spanning tree (UDC-MST) problem, where the delay constraint is known a posteriori. We design a distributed heuristic algorithm, named MDET, to solve the problem. Through extensive simulations, we demonstrate that MDET achieves a very good balance between flooding delay and energy efficiency.

An interactive and programmable musical yo-yo system is designed. The aim of it is to demonstrate the feasibility of converting any sensor-enabled objects into potential musical instruments. This involves three design phases. First, the physical yo-yo is designed to house Iris sensors. The software is developed to sense the movement of yo-yo and transmit its measurements to Max/MSP for corresponding music generation. Finally, aurally pleasing and real-time musical sounds are designed and generated in effect of yo-yo by the computer music composer.

In this paper, we propose Social-Loc, which uniquely utilizes social interactions in addition to common on-board sensors such as accelerometer and gyroscope on modern smartphones, to localize indoor mobile users. Specifically, Social-Loc takes the potential locations for individual users estimated by a novel particle filter tailored for indoor localization as input, and exploits both social encounter and non-encounter events to further improve the localization accuracy. We have implemented Social-Loc on the Android platform and extensively evaluated its performance. The simulation results demonstrate that Social-Loc improves the accuracy of the particle-filter-only scheme by as much as 560% on average and is able to achieve accuracy of few meters without any external ranging device or system training.