Location estimation methods using radio fingerprint have been studied extensively. The approach constructs a database that associates ambient radio signals with physical locations in training phase, and then estimates the location by finding the most similar signal pattern within the database. To achieve robust and accurate location estimation, the training phase should be conducted across the entire target space. In practice, however, a user may only access limited or authorized places in a building, that causes degradation in accuracy. In this paper, we present a smartphone-based autonomous indoor war-walking scheme, which automatically constructs the location fingerprint database, even covering unvisited locations. While a smartphone user explores the target area, the proposed system tracks the user's trajectory and simultaneously trains the location fingerprint database. Furthermore, our scheme interpolates radio signals in the database with an appropriate radio propagation model, and supplements fingerprints for unvisited places. As a result, although a user may sparsely explore the target site, the scheme returns the complete database. We implemented our solution and demonstrated the feasibility of the solution.

Finding a missing child is an important problem concerning not only parents but also our society. It is essential and natural to use serendipitous clues from neighbors for finding a missing child. In this paper, we explore a new architecture of crowd collaboration to expedite this mission-critical process and propose a crowd-sourced cooperative mobile application, CoSMiC. It helps parents find their missing child quickly on the spot before he or she completely disappears. A key idea lies in constructing the location history of a child via crowd participation, thereby leading parents to their child easily and quickly. We implement a prototype application and conduct extensive user studies to assess the design of the application and investigate its potential for practical use.

Smartphones enable the collection of mobility data using various sensors. The key challenge in the collection of continuous data is to overcome the limited battery capacity of the device. While extensive research has been conducted to solve energy issues in continuous mobility learning, we argue that previous works have not reached optimal performance. In this paper, we propose an energy-efficient mobility monitoring system, FreeTrack, to collect place-centric mobility data with minimum energy consumption in everyday life. We first analyzed the regularity of life patterns, cellular connection patterns, and battery charging behaviors of 94 smartphone users to examine important features related to human mobility. Based on our findings, we design an adaptive duty cycling scheme that uses zero-cost information (i.e., regular mobility, cell connection, and battery state) as low-level sensing to infer location change without the need to activate sensors. We model the location inference on the Hidden Markov Model and optimize the sensing schedule of individual smartphones for real-time operation. Our extensive experiment with 48 smartphone users shows that the proposed system achieves an energy saving of about 68% over previous works, yet still correctly traces 97% of mobility with 0.2±0.5 places misses in a day.

Monitoring a user's mobility during daily life is an essential requirement in providing advanced mobile services. While extensive attempts have been made to monitor user mobility, previous work has rarely addressed issues with predictions of temporal behavior in real deployment. In this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to provide contextual information about a user's mobility: time-resolved places and paths. Unlike previous approaches that focused on minimizing energy consumption for tracking raw coordinates, we propose efficient techniques to maximize the accuracy of monitoring meaningful places with a given energy constraint. SmartDC comprises unsupervised mobility learner, mobility predictor, and Markov decision process-based adaptive duty cycling. SmartDC estimates the regularity of individual mobility and predicts residence time at places to determine efficient sensing schedules. Our experiment results show that SmartDC consumes 81 percent less energy than the periodic sensing schemes, and 87 percent less energy than a scheme employing context-aware sensing, yet it still correctly monitors 90 percent of a user's location changes within a 160-second delay.

Modern mobile devices are equipped with multiple network interfaces, including 3G/LTE and WiFi. Bandwidth aggregation over LTE and WiFi links offers an attractive opportunity of supporting bandwidth-intensive services, such as high-quality video streaming, on mobile devices. However, achieving effective bandwidth aggregation in mobile environments raises several challenges related to deployment, link heterogeneity, network fluctuation, and energy consumption. We present GreenBag, an energy-efficient bandwidth aggregation middleware that supports real-time data-streaming services over asymmetric wireless links, requiring no modifications to the existing Internet infrastructure and servers. GreenBag employs several techniques, including medium load balancing, efficient segment management, and energy-aware mode control, to resolve such challenges. We implement a prototype of GreenBag on Android-based mobile devices which hosts, to the best knowledge of the authors, the first LTE-enabled bandwidth aggregation prototype for energy-efficient real-time video streaming. Our experiment results in both emulated and real-world environments show that GreenBag not only achieves good bandwidth aggregation to provide QoS in bandwidth-scarce environments but also efficiently saves energy on mobile devices. Moreover, energy-aware GreenBag can minimize video interruption while consuming 14-25% less energy than the non-energy-aware counterpart in real-world experiments.

A mobility model is an essential requirement in accurately predicting an individual's future location. While extensive studies have been conducted to predict human mobility, previous work used coarse-grained mobility data with limited ability to capture human movements at a fine-grained level. In this paper, we empirically analyze several mobility models for predicting temporal behavior of an individual user. Unlike previous approaches, which employed coarse-grained mobility data with partial temporal-coverage, we use fine-grained and continuous mobility data for the evaluation of mobility models.We explore the regularity and predictability of human mobility, and evaluate location-dependent and location-independent models with several feature-aided schemes. Our experimental results show that a location-dependent predictor is better than a location-independent predictor for predicting temporal behavior of individual user. The duration of stay at a location is strongly correlated to the arrival time at the current location and the return-tendency to the next location, rather than recent k location sequences.We also find that false-positive predictions can be reduced by adaptive use of mobility models.

The Wi-Fi fingerprinting (WF) technique normally suffers from the RSS (Received Signal Strength) variance problem caused by environmental changes that are inherent in both the training and localization phases. Several calibration algorithms have been proposed but they only focus on the hardware variance problem. Moreover, smartphones were not evaluated and these are now widely used in WF systems. In this paper, we analyze various aspect of the RSS variance problem when using smartphones for WF: device type, device placement, user direction, and environmental changes over time. To overcome the RSS variance problem, we also propose a smartphone-based, indoor pedestrian-tracking system. The scheme uses the location where the maximum RSS is observed, which is preserved even though RSS varies significantly. We experimentally validate that the proposed system is robust to the RSS variance problem.

Stateless address autoconfiguration protocols allow nodes to select addresses and validate the uniqueness of a selected address by duplicate address detection (DAD). The considerable cost of DAD results from the message complexity increase in multihop network topologies, such as wireless sensor networks. This paper proposes a lightweight, hybrid address autoconfiguration protocol, called Spectrum, that deploys IPv6-compatible addresses into 6LoWPAN networks in a distributed manner. Spectrum creates the virtual coordinate system on the network and deploys addresses based on the location of the nodes. The deployment policy based on the virtual locations of the nodes reduces the DAD cost in the initial configuration as well as the cost for additional configurations of newly arrived nodes. The authors implemented and tested the proposed scheme in a real environment. Simulations and experiments confirmed a reasonable message cost for both stateful and stateless autoconfigurations.

Indoor pedestrian tracking extends location-based services to indoor environments. Typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of accuracy and coverage, they require an indoor map, which is typically not available to the average user and involves significant training costs. A practical indoor pedestrian tracking approach should consider the indoor environment without a pretrained database or floor plan. In this paper, we present an indoor pedestrian tracking system, called SmartSLAM, which automatically constructs an indoor floor plan and radio fingerprint map for anonymous buildings using a smartphone. The scheme employs odometry tracing using inertial sensors, an observation model using Wi-Fi signals, and a Bayesian estimation for floor-plan construction. SmartSLAM is a true simultaneous localization and mapping implementation that does not necessitate additional devices, such as laser rangefinders or wheel encoders. We implemented the scheme on off-the-shelf smartphones and evaluated the performance in our university buildings. Despite inherent tracking errors from noisy sensors, SmartSLAM successfully constructed indoor floor plans.

With the widespread use of smartphones, the loss of a device is critical, both in disrupting daily communications, and in losing valuable property. When a mobile device is missing, localization techniques may assist in finding the device. Current techniques, however, hardly provide a complete solution because of inaccurate position estimation, especially in indoor environments. In this paper, we describe a software architecture called FindingMiMo, which tracks and locates a missing mobile device in indoor environments. The system consists of a missing mobile which logs diverse environmental features on a daily basis, and a chaser which traces the trail of the device using the observation log. During daily operation, the mobile device does not perform location estimation; it only observes the ambient features such as radio signals to minimize its operation cost. Instead, the chaser determines where the missing device measured the observations. This research implemented the scheme on Android-based smartphones. Real experiments with carefully designed, missing-and-tracking scenarios show that the participants successfully approached their lost phones within four meters distance, on average.

Monitoring a user's mobility during daily life is an essential requirement in providing advanced mobile services. While extensive attempts have been made to monitor user mobility, previous work has rarely addressed issues with battery lifetime in real deployment. In this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to provide contextual information about a user's mobility: time-resolved places and paths. Unlike previous approaches that focused on minimizing energy consumption for tracking raw coordinates, we propose efficient techniques to maximize the accuracy of monitoring meaningful places with a given energy constraint. SmartDC comprises unsupervised mobility learner, mobility predictor, and Markov decision process-based adaptive duty cycling. SmartDC estimates the regularity of individual mobility and predicts residence time at places to determine efficient sensing schedules. Our experiment results show that SmartDC consumes 81% less energy than the periodic sensing schemes, and 87% less energy than a scheme employing context-aware sensing, yet it still correctly monitors 80% of a user's location changes within a 160-second delay.

Estimating the geographical position of mobile device such as Smart phone in an indoor environment is not easy without the use of specific infrastructures. In this article, we introduce an indoor user tracking system. The system constructs a topological map with Wi-Fi signal calibrations, assigns semantically meaningful labels into the map, and estimates the semantic location of the user based on the current Wi-Fi observation. The system does not require a geometric map, or costly radio map building process. We implemented the system with the off-the-shelf Smart phone and experimentally validated the scheme.

Abstract Network reprogramming is a process used to update program codes of sensor nodes that are already deployed. To deal with potentially unstable link conditions of wireless sensor networks, the epidemic approach based on 3‐way advertise‐request‐data handshaking is preferred for network reprogramming. Existing epidemic protocols, however, require a long completion period and high traffic overhead in high‐density networks, mainly due to the hidden terminal problem. In this paper, we address this problem by dynamically adjusting the frequency of advertisement messages in terms of the density of sensor nodes, which is the number of sensor nodes in a certain area. We compare the performance of the proposed scheme, called DANP (Density‐Adaptive Network Reprogramming Protocol), with a well‐known epidemic protocol, Deluge. Simulations indicate that, in the grid topologies, DANP outperforms Deluge by about 30% in terms of the completion time and about 50% in terms of the traffic overhead. Significant performance gain is observed in random topologies as well. The performance of DANP is further confirmed via measurements in an experimental test bed. Copyright © 2009 John Wiley & Sons, Ltd.

As resource-constrained network technology develops, such as wireless sensor networks, connectivity to an IP-based network has become an important requirement. Assigning the global unique address to network nodes is a prerequisite to the connectivity to the IP-based networks. Since conventional address auto-configuration protocols require high network bandwidth and management cost, they are not suitable for wireless sensor networks. In this paper, we propose a lightweight address auto-configuration mechanism for resource-constrained networks. The proposed algorithm uses three coordinators that assign geometric information to the network to remove the assumption that each node has location information. Each node gathers the hop distance from the coordinators and generates a unique address based on the location information. The proposed algorithm is implemented with real hardware, and the performance is evaluated. The result shows that the mechanism efficiently assigns unique addresses to sensor nodes.

We show a mobile monitoring application, called Sensible Doctor, for sensor network diagnosis. The solution is applied to a restricted region of the network, which surrounds the mobile monitoring device. The network traffic is reduced to cover only the monitoring part of the network by adopting algorithms tailored to mobile monitoring. Sensible Doctor is implemented on the RETOS operating system and provides system information, system profiling, remote application installation and network topology monitoring.

As the use of wireless sensor networks (WSNs) becomes widespread, node density tends to increase. This poses a new challenge for medium access control (MAC) protocol design. Although traditional MAC protocols achieve low-power operation, they use only a single channel which limits their performance. Several multi-channel MAC protocols for WSNs have been recently proposed. One of the key observations is that these protocols are less energy efficient than single-channel MAC protocols under light traffic conditions. In this paper, we propose an energy efficient multichannel MAC protocol, Y-MAC, for WSNs. Our goal is to achieve both high performance and energy efficiency under diverse traffic conditions. In contrast to most of previous multi-channel MAC protocols for WSNs, we implemented Y-MAC on a real sensor node platform and conducted extensive experiments to evaluate its performance. Experimental results show that Y-MAC is energy efficient and maintains high performance under high-traffic conditions.

This paper presents the design principles, implementation, and evaluation of the RETOS operating system which is specifically developed for micro sensor nodes. RETOS has four distinct objectives, which are to provide (1) a multithreaded programming interface, (2) system resiliency, (3) kernel extensibility with dynamic reconfiguration, and (4) WSN-oriented network abstraction. RETOS is a multithreaded operating system, hence it provides the commonly used thread model of programming interface to developers. We have used various implementation techniques to optimize the performance and resource usage of multithreading. RETOS also provides software solutions to separate kernel from user applications, and supports their robust execution on MMU-less hardware. The RETOS kernel can be dynamically reconfigured, via loadable kernel framework, so a application-optimized and resource-efficient kernel is constructed. Finally, the networking architecture in RETOS is designed with a layering concept to provide WSN-specific network abstraction. RETOS currently supports Atmel ATmega128, TI MSP430, and Chipcon CC2430 family of microcontrollers. Several real-world WSN applications are developed for RETOS and the overall evaluation of the systems is described in the paper.

This demonstration shows the programming development suite of the RETOS operating system for sensor networks, which provides a robust and multithreaded programming interface to application programmers. We first demonstrate how to build the RETOS kernel on the TI MSP430, Atmel ATmega 128 and Chipcon CC2430 family of microcontrollers. The application or a kernel module is then compiled and disseminated, via wireless channel, to the target motes. The GUI-based RMon network management tool for RETOS is also demonstrated to monitor the networked sensors, and even to control the system's parameters or applications running on them via a remote shell. The system is demonstrated to run on a mixed set of MSP430, ATmega128 and CC2430-based motes. Overall, our demonstration will convince attendees of the programming convenience of developing sensor network applications using RETOS, which is, indeed, a mature and practical system that can be used to develop real-world applications.

Sensor nodes are typically battery powered and operate in unattended environments. Minimizing the energy consumption of sensor nodes is, therefore, important to prolong the network life time. Since the radio is a main energy consumer, most of the Medium Access Control (MAC) protocols for wireless sensor networks (WSNs) have focused on energy efficiency. With widespread use of sensor network applications, network bandwidth required by the applications tends to increase and even multiple applications may run in a sensor node. For these reasons, maximizing the bandwidth of WSNs has become a major consideration in MAC design.