81 research outputs found
Service-oriented wireless sensor networks and an energy-aware mesh routing algorithm
Service-oriented wireless sensor networks (WSNs) are being paid more and more attention because service computing can hide complexity of WSNs and enables simple and transparent access to individual sensor nodes. Existing WSNs mainly use IEEE 802.15.4 as their communication specification, however, this protocol suite cannot support IP-based routing and service-oriented access because it only specifies a set of physical- and MAC-layer protocols. For inosculating WSNs with IP networks, IEEE proposed a 6LoWPAN (IPv6 over LoW Power wireless Area Networks) as the adaptation layer between IP and MAC layers. However, it is still a challenging task how to discover and manage sensor resources, guarantee the security of WSNs and route messages over resource-restricted sensor nodes. This paper is set to address such three key issues. Firstly, we propose a service-oriented WSN architectural model based on 6LoWPAN and design a lightweight service middleware SOWAM (service-oriented WSN architecture middleware), where each sensor node provides a collection of services and is managed by our SOWAM. Secondly, we develop a security mechanism for the authentication and secure connection among users and sensor nodes. Finally, we propose an energyaware mesh routing protocol (EAMR) for message transmission in a WSN with multiple mobile sinks, aiming at prolonging the lifetime of WSNs as long as possible. In our EAMR, sensor nodes with the residual energy lower than a threshold do not forward messages for other nodes until the threshold is leveled down. As a result, the energy consumption is evened over sensor nodes significantly. The experimental results demonstrate the feasibility of our service-oriented approach and lightweight middleware SOWAM, as well as the effectiveness of our routing algorithm EAMR.<br /
A Shadow-Like Task Migration Model Based on Context Semantics for Mobile and Pervasive Environments
Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi-modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users
An Adaptive Context-Aware Transaction Model for Mobile and Ubiquitous Computing
Transaction management for mobile and ubiquitous computing (MUC)aims at providing mobile users with reliable and transparent services anytime anywhere. Traditional mobile transaction models built on client-proxy-server architecture cannot make this vision a reality because (1) in these models, base stations (proxy) are the prerequisite for mobile hosts (client) to connect with databases (server), and 2)few models consider context-based transaction management. In this paper, we propose a new network architecture for MUC transactions, with the goal that people can get online network access and transaction even while moving around; and design a context-aware transaction model and a context-driven coordination algorithm adaptive to dynamically changing MUC transaction context. The simulation results have demonstrated that our model and algorithm can significantly improve the successful ratio of MUC transactions
Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis
Behavior prediction based on historical behavioral data have practical
real-world significance. It has been applied in recommendation, predicting
academic performance, etc. With the refinement of user data description, the
development of new functions, and the fusion of multiple data sources,
heterogeneous behavioral data which contain multiple types of behaviors become
more and more common. In this paper, we aim to incorporate heterogeneous user
behaviors and social influences for behavior predictions. To this end, this
paper proposes a variant of Long-Short Term Memory (LSTM) which can consider
context information while modeling a behavior sequence, a projection mechanism
which can model multi-faceted relationships among different types of behaviors,
and a multi-faceted attention mechanism which can dynamically find out
informative periods from different facets. Many kinds of behavioral data belong
to spatio-temporal data. An unsupervised way to construct a social behavior
graph based on spatio-temporal data and to model social influences is proposed.
Moreover, a residual learning-based decoder is designed to automatically
construct multiple high-order cross features based on social behavior
representation and other types of behavior representations. Qualitative and
quantitative experiments on real-world datasets have demonstrated the
effectiveness of this model
Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks
Prediction tasks about students have practical significance for both student
and college. Making multiple predictions about students is an important part of
a smart campus. For instance, predicting whether a student will fail to
graduate can alert the student affairs office to take predictive measures to
help the student improve his/her academic performance. With the development of
information technology in colleges, we can collect digital footprints which
encode heterogeneous behaviors continuously. In this paper, we focus on
modeling heterogeneous behaviors and making multiple predictions together,
since some prediction tasks are related and learning the model for a specific
task may have the data sparsity problem. To this end, we propose a variant of
LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the
student profile-aware representation from heterogeneous behavior sequences. The
proposed soft-attention mechanism can dynamically learn different importance
degrees of different days for every student. In this way, heterogeneous
behaviors can be well modeled. In order to model interactions among multiple
prediction tasks, we propose a co-attention mechanism based unit. With the help
of the stacked units, we can explicitly control the knowledge transfer among
multiple tasks. We design three motivating behavior prediction tasks based on a
real-world dataset collected from a college. Qualitative and quantitative
experiments on the three prediction tasks have demonstrated the effectiveness
of our model
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
Multi-behavioral sequential recommendation has recently attracted increasing
attention. However, existing methods suffer from two major limitations.
Firstly, user preferences and intents can be described in fine-grained detail
from multiple perspectives; yet, these methods fail to capture their
multi-aspect nature. Secondly, user behaviors may contain noises, and most
existing methods could not effectively deal with noises. In this paper, we
present an attentive recurrent model with multiple projections to capture
Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect
preferences from target behaviors, we propose a multi-aspect projection
mechanism for generating multiple preference representations from multiple
aspects. To extract multi-aspect intents from multi-typed behaviors, we propose
a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The
attention mechanism can filter out noises and generate multiple intent
representations from different aspects. To adaptively fuse user preferences and
intents, we propose a multi-aspect gated fusion mechanism. Extensive
experiments conducted on real-world datasets have demonstrated the
effectiveness of our model
Efficient spectral broadening and few-cycle pulse generation with multiple thin water films
High-energy, few-cycle laser pulses are essential for numerous applications
in the fields of ultrafast optics and strong-field physics, due to their
ultrafast temporal resolution and high peak intensity. In this work, different
from the traditional hollow-core fibers and multiple thin solid plates, we
represent the first demonstration of the octave-spanning supercontinuum
broadening by utilizing multiple ultrathin liquid films (MTLFs) as the
nonlinear media. The continuum covers a range from 380 to 1050 nm,
corresponding to a Fourier transform limit pulse width of 2.5 fs, when 35 fs
Ti:sapphire laser pulse is applied on the MTLFs. The output pulses are
compressed to 3.9 fs by employing chirped mirrors. Furthermore, a continuous
high-order harmonic spectrum up to the 33rd order is realized by subjecting the
compressed laser pulses to interact with Kr gas. The utilization of flowing
water films eliminates permanent optical damage and enables wider and stronger
spectrum broadening. Therefore, this MTLFs scheme provides new solutions for
the generation of highly efficient femtosecond supercontinuum and nonlinear
pulse compression, with potential applications in the fields of strong-field
physics and attosecond science.Comment: 9 pages, 5 figure
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