Applications and Routing Management of Wireless Sensor Networks

In this paper, we first describe some current and future applications of sensor networks. We present the conceptual framework of distributed routing strategies for wireless sensor networks. We show that under reasonable assumptions, this routing scheme guarantees ‘shortest path property’ which is quite desirable for sensor networks. We then discuss how this framework can be used to support distributed applications for sensor nodes acting as mobile devices. These schemes work well in low mobility conditions. We also discuss the performance of these heuristics

"Hierarchical routing in sensor networks using κ-dominating sets "

Michael Q. Rieck is an associate professor at Drake University in Des Moines, Iowa, USA. He holds a Ph. D. in mathematics from the University of South Florida. His primary research interests are in the areas of camera tracking and ad hoc wireless networks. He has also published results in the areas of triangle geometry, discrete mathematics, linear algebra, finite fields and association schemes.For a connected graph, representing a sensor network, distributed algorithms for the Set Covering Problem can be employed to construct reasonably small subsets of the nodes, called k-SPR sets. Such a set can serve as a virtual backbone to facilitate shortest path routing, as introduced in [4] and [14]. When employed in a hierarchical fashion, together with a hybrid (partly proactive, partly reactive) strategy, the κ-SPR set methods become highly scalable, resulting in guaranteed minimal path routing, with comparatively little overhead. © Springer-Verlag Berlin Heidelberg 2005

"Distributed routing schemes for ad hoc networks using d-SPR sets"

Michael Q. Rieck is an associate professor at Drake University in Des Moines, Iowa, USA. He holds a Ph. D. in mathematics from the University of South Florida. His primary research interests are in the areas of camera tracking and ad hoc wireless networks. He has also published results in the areas of triangle geometry, discrete mathematics, linear algebra, finite fields and association schemes.In this paper, we propose several new distributed algorithms for producing sets of nodes that can be used to form backbones of an ad hoc wireless network. Our focus is on producing small sets that are d-hop connected and d-dominating and have a desirable ‘d-shortest path property’ which we call d-SPR sets. These algorithms produce sets that are considerably smaller than those produced by an algorithm previously introduced by the authors. Our proposed algorithms, except the greedy ones, have constant time complexity in the restricted sense that the time required is unaffected by the size of the network, assuming however that the node degrees are bounded by a constant. The performance of the new algorithms are compared, and also compared with the authors' earlier algorithm, and with an adaptation of an algorithm of Wu and Li

CURRENT TRENDS IN GLOBAL IS OUTSOURCING

IT (Information Technology) outsourcing is one of the most prolific areas of research in the recent years. It is an act of delegating or transferring some or all of IT related decision making rights, business processes, internal activities, and services to external providers, who develop, manage, and administer these activities in accordance with agreed upon deliverables, performance standards and outputs, as set forth in the contractual agreement. Global offshore outsourcing, involves contracting with a low-cost offshore service provider that assumes responsibility for all or part of the information systems development lifecycle. In addition to lower cost, other benefits of offshore development and outsourcing include access to specialized technical skills and services, and the ability to respond to IT labor shortages according to variations in global supply and demand. Enterprises within and outside the IT industry have long used offshore development and outsourcing to reduce information systems development and maintenance costs and as a source of specialized, low-wage workers. In the last decade, there has been a spur of activities in offshore outsourcing which is driven by the e-Business revolution and a worldwide demand for IT skills. This contributed to the growth of IT related industries in countries such as Ireland and India. Meanwhile, vendors from the Philippines, Russia, Hungary, China, Taiwan, Mexico, and other countries entered the market, and in some cases, adapting business models established by Indian firms that have dominated the services sector in the past decade. The emergence of new offshore centers has been marked by new approaches and skill sets, adding to the services and value propositions that define the offshore sector today

Distributed Routing Schemes for Ad Hoc Networks Using d-SPR

In this paper, we propose several new distributed algorithms for producing sets of nodes that can be used to form backbones of an ad hoc wireless network. Our focus is on producing small sets that are d-hop connected and d-dominating and have a desirable “d-shortest path property” which we call d-SPR sets. These algorithms produce sets that are considerably smaller than those produced by an algorithm previously introduced by the authors. Our proposed algorithms, except the greedy ones, have constant time complexity in the restricted sense that the time required is unaffected by the size of the network, assuming however that the node degrees are bounded by a constant. The performance of the new algorithms are compared, and also compared with the authors ’ earlier algorithm, and with an adaptation of an algorithm of J. Wu and H. Li. Keywords dominating set, ad hoc wireless networks, routing algorithm, set covering problem, shortest path routing I

Challenges and business models for mobile location-based services and advertising

key insights professionals should be aware of technical-and business-related challenges as they develop solutions for location-based services. Location-based mobile advertising has potential to generate significant revenues leading to successful business models. awareness of multiple business models that can play key roles in mobile advertising-and how these models compare to one another-would be essential in the successful deployment of location-based services. in addtion to the current issues, professionals should also consider the imminent challenges as the develop and implement location-based services

Data Science for All

In this talk we will discuss the 3-year, NSF funded Data Science for All seminar series; including the motivation behind extra-curricular seminars, the targeted audience of students and faculty, our experiences developing and delivering the seminars, what’s worked (and what hasn’t), as well as the materials available for faculty at any institution to download and use. Two of the main goals of the seminar series are: (1) increase and diversify the number of undergraduate and community college students aware of data science, and (2) increase and diversify the population of undergraduate and community college students possessing data gathering, wrangling, and cleansing skills. There is currently a great need to grow our Nation’s data science capabilities. One way to meet this need is by creating data scientists through advanced degree programs – a costly and time-consuming approach. An alternative is to augment the data science workforce with graduates possessing basic data science skills. According to the National Academies of Science, Engineering, and Medicine (2017), many data science roles can be filled by undergraduate students, including data wrangling; the acquisition, profiling, and transforming of data prior to analysis that constitutes 80% of a data scientist’s work. Shifting data wrangling to lesser-trained employees allows data scientists to focus on complex tasks; extending existing data science resources and providing opportunities for a diverse population of undergraduate students. These seminars seek to provide this much-needed data wrangling workforce by introducing data science concepts to undergraduate students early in their academic careers through low-risk extracurricular seminars. Each seminar includes optional pre-seminar materials that provide a gentle introduction to the topic, a hands-on seminar without prerequisites, and an optional post-seminar assignment they can complete to earn a digital badge attesting to their new skills. Although some seminars (e.g., Python foundations) provide skills that are useful in other seminars, each seminar is designed as a stand-alone unit to minimize the commitment both by students attending and faculty wanting to implement it. To reach a diverse population and provide them skills in the discipline, the seminars focus on entry-level concepts in data science, using an experiential learning approach and current tools in the field. In 2019, the initial seminar series included foundational seminars in both statistical concepts and Python, data wrangling using Apache Spark and Jupyter notebooks, exploring graph databases, and an introduction to concepts in machine learning. The teaching materials available for download include: slides, Jupyter notebooks, exercises, teaching notes, datasets, Canvas course packages, test materials, and guidelines for setting up digital badges

A Building Permit System for Smart Cities: A Cloud-based Framework

In this paper we propose a novel, cloud-based framework to support citizens and city officials in the building permit process. The proposed framework is efficient, user-friendly, and transparent with a quick turn-around time for homeowners. Compared to existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of both the end user experience and the permitting and urban planning processes. This is enabled through a data mining-powered permit recommendation engine as well as a data analytics process that allow a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on their location, time, and type of request. The novelty of the proposed framework lies in the integration of a pre-permit processing front-end with permit processing and data analytics & mining modules, along with utilization of techniques for extracting knowledge from the data generated through the use of the system. The proposed framework is completely cloud-based, such that any city can deploy it with lower initial as well as maintenance costs. We also present a proof-of-concept use case, using real permit data from New York City