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The performance of vehicular ad hoc networks (VANETs) is subject to frequent changes due to node mobility so that nonstationary/transient network behavior always exists. In addition, safety applications in VANETs are very sensitive to the real-time performance of delay and packet delivery ratio (PDR). Although there are extensive studies on the steady-state (SS) performance analysis of vehicular networks, little work exists on evaluating their time-varying behavior. In this paper, we develop a performance model to efficiently estimate the dynamic behavior of VANETs. In this paper, vehicle's transmission queue is modeled using fluid-flow (FF)-based differential equations, which are solved using numerical methods, whereas the network hearing topology is modeled by a time-varying adjacency matrix that can be determined from stochastic models, measurements, or discrete event simulations. Numerical results illustrate that our performance model is able to promptly respond to the ongoing dynamic conditions of VANETs and provide reasonably accurate performance results. [ABSTRACT FROM AUTHOR]

Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area. [ABSTRACT FROM AUTHOR]

In this paper, we develop a performance modeling technique for analyzing the time-varying network layer queuing behavior of multihop wireless networks with constant bit rate (CBR) traffic. Our approach is a hybrid of a time-varying adjacency matrix and a fluid flow queuing network model. The mobile network topology is modeled using a time-varying adjacency matrix, whereas node queues are modeled using fluid-flow-based differential equations that are solved using numerical methods. Numerical and simulation experiments show that this new approach can provide reasonably accurate results. Moreover, when compared with the computation time required in a standard discrete event simulator, the fluid-flow-based model is shown to be a more scalable tool. Finally, an illustrative example of our modeling technique application is given to show its capability of capturing the time-varying network performance as a function of traffic load, node mobility, and wireless link quality. [ABSTRACT FROM PUBLISHER]

This letter addresses the problem of wireless indoor positioning and proposes the concept of personal mobility map (PMM). As a graph describing an individual's typical mobility pattern over a physical or abstract space, PMM can be used as the building block for crowd-sourcing based simultaneous localization and mapping (SLAM). PMM can also enable new kinds of location-based services (LBSs). To construct a PMM, we conduct intra-sequence clustering on the sequences of Wi-Fi received signal strength (RSS) measurements to cluster the measurements into different location points (LPs), followed by personal inter-sequence LP assembling, which aligns these LPs with the concept of the Smith-Waterman algorithm originally used for protein sequencing. Experiments with Wi-Fi RSS measurements collected in the university campus prove that the PMM performs well in indoor mapping and path estimation. [ABSTRACT FROM PUBLISHER]

An introduction to the section of the journal in which the editor discusses various articles within the issue device-free wireless localization system; problem of network congestion and user mobility management in smart communications; and human motion.

In the field of tissue engineering, polymeric materials with high biocompatibility like polylactic acid and polyglycolic acid have been widely used for fabricating living constructs. For hypopharynx tissue engineering, skeletal muscle is one important functional part of the whole organ, which assembles the unidirectionally aligned myotubes. In this study, a polyurethane (PU) scaffold with microchannel patterns was used to provide aligning guidance for the seeded human myoblasts. Due to the low hydrophilicity of PU, the scaffold was grafted with silk fibroin (PU-SF) or gelatin (PU-Gel) to improve its cell adhesion properties. Scaffolds were observed to degrade slowly over time, and their mechanical properties and hydrophilicities were improved through the surface grafting. Also, the myoblasts seeded on PU-SF had the higher proliferative rate and better differentiation compared with those on the control or PU-Gel. Our results demonstrate that polyurethane scaffolds seeded with myoblasts hold promise to guide hypopharynx muscle regeneration. [ABSTRACT FROM AUTHOR]

Due to the wide deployment of wireless local area networks (WLAN), received signal strength (RSS)-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL) by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM). Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR) algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization.