Elmurod Talipov

Mobility prediction-based smartphone energy optimization for everyday location monitoring

Monitoring a users 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 users 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 users location changes within a 160-second delay.

Evaluating mobility models for temporal prediction with high-granularity mobility data

A mobility model is an essential requirement in accurately predicting an individuals 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.

SmartDC: Mobility Prediction-Based Adaptive Duty Cycling for Everyday Location Monitoring

Monitoring a users 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 users 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 users location changes within a 160-second delay.

Autonomous Management of Everyday Places for a Personalized Location Provider

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.

Content Sharing over Smartphone-Based Delay-Tolerant Networks

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 individuals 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 lightweight stateful address autoconfiguration for 6LoWPAN

Sensor networks have become increasingly important in various areas, and most current applications require connectivity between sensor networks and the Internet. By being seamlessly integrated into IP network infrastructure, sensor network applications would benefit from standardized and established technology, as well as from the plethora of readily available applications. Preparing sensor networks for IP communication and integrating them into the IP network, however, present new challenges on the architecture and its functional blocks, e.g., the adaptation of the respective link technology for IP support, development of security mechanisms, and autoconfiguration to support ad hoc deployment. In this paper, we focus on the IPv6 address autoconfiguration issue and propose a proxy-based autoconfiguration protocol. The proposed protocol guarantees the assignment of a unique address to each node in the network. The protocol is simulated and implemented on off-the-shelf sensor network platforms. The experiment results show that our mechanism outperforms similar network address configuring mechanisms in terms of latency and overhead.

IPv6 lightweight stateless address autoconfiguration for 6LoWPAN using color coordinators

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 autoconfiguration protocols require high network bandwidth and management cost, they are not suitable for wireless sensor networks. In this paper, we propose a lightweight address autoconfiguration 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.

Data delivery scheme for intermittently connected mobile sensor networks

In mobile sensor networks, connectivity between nodes is intermittent due to blockage of radio and nodal mobility. Consequently, the efficient delivery of packets requires nodes communicate while connectivity is reliable and delay during other times. In such cases, the challenging issue is setting delay duration at each node. In this paper, we aim to provide a reliable data delivery scheme for mobile sensor networks with an enhanced delaying technique. In the proposed scheme, nodes estimate connectivity and expect inter-encounter time with sink nodes. Connectivity is estimated based on ratio of past and present connections. When the connectivity is unreliable, nodes delay the transmission for the remaining inter-encounter duration or per-hop lifetime. Since packets are forwarded if the connectivity reaches a reliable threshold before delay time expires, delivery latency is significantly reduced. Our scheme adopts receiver-based opportunistic forwarding to reduce delivery cost. To this end, we analyze the connectivity estimation and optimal delaying technique via theory and simulation. The proposed protocol deals well with network partitioning and indefinite link disconnection, which often arises in mobile sensor networks, and satisfies the requirement for delivery latency. Simulation results show that the proposed protocol outperforms other protocols in terms of packet delivery and per packet delivery cost.

Spectrum: Lightweight Hybrid Address Autoconfiguration Protocol Based on Virtual Coordinates for 6LoWPAN

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.

A context-rich and extensible framework for spontaneous smartphone networking

This paper presents the design principles, implementation, and evaluation of SPONET, a framework that has been specifically developed for spontaneous networking among smartphone users. SPONET has four distinct objectives, providing (1) a rich context for location-aware networking, (2) robust cognitive networking, (3) extensibility with various routing protocols, and (4) a convenient programming interface for delay-tolerant applications. The key technical challenges are, therefore, unsupervised place learning, network construction without user intervention, and a networking policy with low complexity. We have designed a place-learning algorithm using the properties of scanned Wi-Fi access points to identify meaningful places. SPONET provides dynamic neighbor discovery and data exchange mechanisms for autonomous networking. We have implemented SPONET on Android-based, off-the-shelf smartphones without any adaptation of their networking architecture. Experimental results show that the proposed system is indeed acceptable as a framework for various delay-tolerant applications in smartphones.