Asset management is important to all modern organizations. It becomes critical when assets are equipment on which lives depend. The development of communication and wireless technologies, such as the new Near Field Communication (NFC) has opened new doors for efficient asset management. Store managers and keepers can now use mobile lightweight devices for asset management. In this paper, we propose and implement the first NFC-based mobile asset management solution for medical equipment. Our solution provides a unique security feature that allows immediate detection of tampering with any equipment's description. Furthermore, we critically review existing works and compare them to our solution and show its efficiency.

The availability of low-cost virtual reality (VR) systems coupled with a growing population of researchers accustomed to newer interface styles makes this a ripe time to help domain science researchers cross the bridge to utilizing immersive interfaces. The logical next step is for scientists, engineers, doctors, etc. to incorporate immersive visualization into their exploration and analysis workflows. However, from past experience, we know having access to equipment is not sufficient. There are also several software hurdles to overcome. Obstacles must be lowered to provide scientists, engineers, and medical professionals low-risk means of exploring technologies beyond their desktops.

This paper investigates Internet service provider (ISP) incentives with a single-service class and with two-service classes in the Internet. We consider multiple competing ISPs who offer network access to a fixed user base, consisting of end-users who differ in their quality requirements and willingness to pay for the access. We model user-ISP interactions as a game in which each ISP makes capacity and pricing decisions to maximize its profits and the end-users only decide which service to buy (if any) and from which ISP. Our model provides pricing for networks with single- and two-service classes for any number of competing ISPs. Our results indicate that multiple service classes are socially desirable, but could be blocked due to the unfavorable distributional consequences that it inflicts on the existing Internet users. We propose a simple regulatory tool to alleviate the political economic constraints and thus make multiple service classes in the Internet feasible.

Femtocells or home base stations are a proposed solution to the problem of degraded indoor service from the macrocell base station in future 4G data networks. In this paper, we study user incentives for the adoption of femtocells and their resulting impact on network operator revenues. We model a monopolist network operator who offers the option of macrocell access or macro+femtocell access to a population of users who possess linear valuations for the data throughput. We compare the revenues from two possible spectrum schemes for femtocell deployment; the split spectrum scheme, where femtocells and macrocells operate on different frequencies and do not interfere, and, the common spectrum scheme, where they operate on the same frequencies (partially or fully) and interfere. Our results suggest that the optimal pricing scheme always charges a higher price for the femtocell service, i.e., the operator does not offer any subsidies for adoption. Yet, at the optimal prices, almost full adoption of femtocells is achieved even for many common spectrum schemes that degrade macrocell capacity. Femtocell deployments provide huge revenue gains when macrocell capacities are low. However, in this range, even common spectrum schemes that heavily degrade the macrocell capacity perform comparably to the split spectrum scheme. Some common spectrum schemes with moderate macrocell degradation yield revenues comparable or higher than the split spectrum scheme at all levels of macrocell congestion.

We adopt a machine learning approach towards the problem of identifying wireless systems present in a dynamic radio environment with heterogeneous usage. To classify the wireless systems, we utilize two features that typify spectrum use-center frequency and the frequency spread-and cluster the measurement data in this space. Since the systems are unknown prior to clustering, we use an unsupervised clustering method that uses the Chinese restaurant process implemented using Gibbs sampling. The system identification is divided into two parts: training and online classification. In the training phase, we assign wireless systems present in the surrounding to the clusters while the online classification uses this trained data to perform classification. By means of an extensive measurement campaign, we show that the proposed machine learning process achieves up to 90% correctness in classifying the wireless systems considered here.

In this paper, we devise a novel method for bottleneck analysis of UDP networks based on the concept of network utility maximization. To determine the losses on the links in a UDP network, we propose an optimization problem (geometric program) for which we find and prove conditions under which it accurately determines the true losses. We further extend this analysis to stochastic rates using stochastic optimization techniques and provide a new metric to flag bottleneck links. This method does not rely on time-consuming packet-level simulations, but is instead based on robust mathematical models. Alternatively, one could determine the losses by solving a fixed point problem and extend it to random rates using a Monte Carlo simulation. However, lack of knowledge of convergence makes it difficult to predict the end of such simulations. Our method is more advantageous as it involves solving an optimization problem, the solution to which can be numerically determined to the desired accuracy. Also, compared to a black and white approach between worst-case analysis and average-case analysis, our method offers network managers the flexibility of choosing the shades of gray in between.

We propose an economic model based on user utility to study the adoption of new network architectures such as IPv6. We use mathematical analysis and simulation studies to understand the role of various factors such as user and network benefits, switching costs, and the impact of converters on the adoption of new network architectures. In addition to corroborating various commonly held beliefs about new network architecture adoption, our analysis and simulation studies also reveal several surprising and non-intuitive results. For example, while in general, increasing the efficiency of converters hastens the adoption of new network architectures, there are cases in which more efficient converters hinder the adoption of such architectures. This and other results in the paper increase our understanding of new network architecture adoption and guide the design and implementation of mechanisms to hasten new network architecture adoption.