Abstract The commercial success of smartphones increases the feasibility of mobile ad hoc networking in daily life; we define such networks as spontaneous smartphone networks (SSNs). Efficient data delivery in SSNs is challenging because of the low node density, ambiguous contact opportunities, and short message lifetime. The existing schemes attempt to select optimal relays via various cumulative metrics (e.g., encounter history, social centrality, or contact distribution), whose effectiveness is ambiguous and suboptimal. In this paper, we introduce a Markov predictor‐based transient delivery scheme that quantifies the regularity of small time scale movement for forwarding decisions. Unlike previous works, we utilized fine‐grained mobility data to reduce errors of estimating contact opportunities and contact duration. On the basis of this forwarding strategy, we developed a multi‐copy routing scheme. The evaluation using real traces indicates that the proposed approach outperforms compared alternatives in terms of delivery rate and cost. Copyright © 2013 John Wiley & Sons, Ltd.

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.

With the growing number of smartphone users, peer-to-peer ad hoc content sharing is expected to occur more often. Thus, new content sharing mechanisms should be developed as traditional data delivery schemes are not efficient for content sharing due to the sporadic connectivity between smartphones. To accomplish data delivery in such challenging environments, researchers have proposed the use of store-carry-forward protocols, in which a node stores a message and carries it until a forwarding opportunity arises through an encounter with other nodes. Most previous works in this field have focused on the prediction of whether two nodes would encounter each other, without considering the place and time of the encounter. In this paper, we propose discover-predict-deliver as an efficient content sharing scheme for delay-tolerant smartphone networks. In our proposed scheme, contents are shared using the mobility information of individuals. Specifically, our approach employs a mobility learning algorithm to identify places indoors and outdoors. A hidden Markov model is used to predict an individual's future mobility information. Evaluation based on real traces indicates that with the proposed approach, 87 percent of contents can be correctly discovered and delivered within 2 hours when the content is available only in 30 percent of nodes in the network. We implement a sample application on commercial smartphones, and we validate its efficiency to analyze the practical feasibility of the content sharing application. Our system approximately results in a 2 percent CPU overhead and reduces the battery lifetime of a smartphone by 15 percent at most.

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.

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.

Currently available location technologies such as the global positioning system (GPS) or Wi-Fi fingerprinting are limited, respectively, to outdoor applications or require offline signal learning. In this paper, we present a smart phone-based autonomous construction and management of a personalized location provider in indoor and outdoor environments. Our system makes use of electronic compass and accelerometer, specifically for indoor user tracking. We mainly focus on providing point of interest (POI) locations with room-level accuracy in everyday life. We present a practical tracking model to handle noisy sensors and complicated human movements with unconstrained placement. We also employ a room-level fingerprint-based place-learning technique to generate logical location from the properties of pervasive Wi-Fi radio signals. The key concept is to track the physical location of a user by employing inertial sensors in the smartphone and to aggregate identical POIs by matching logical location. The proposed system does not require a priori signal training since each user incrementally constructs his/her own radio map into their daily lives. We implemented the system on Android phones and validated its practical usage in everyday life through real deployment. The extensive experimental results show that our system is indeed acceptable as a fundamental system for various mobile services on a smartphone.

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.

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.