Federated Sensor Network architectural design for the Internet of Things (IoT)

An information technology that can combine the physical world and virtual world is desired. The Internet of Things (IoT) is a concept system that uses Radio Frequency Identification (RFID), WSN and barcode scanners to sense and to detect physical objects and events. This information is shared with people on the Internet. With the announcement of the Smarter Planet concept by IBM, the problem of how to share this data was raised. However, the original design of WSN aims to provide environment monitoring and control within a small scale local network. It cannot meet the demands of the IoT because there is a lack of multi-connection functionality with other WSNs and upper level applications. As various standards of WSNs provide information for different purposes, a hybrid system that gives a complete answer by combining all of them could be promising for future IoT applications. This thesis is on the subject of `Federated Sensor Network' design and architectural development for the Internet of Things. A Federated Sensor Network (FSN) is a system that integrates WSNs and the Internet. Currently, methods of integrating WSNs and the Internet can follow one of three main directions: a Front-End Proxy solution, a Gateway solution or a TCP/IP Overlay solution. Architectures based on the ideas from all three directions are presented in this thesis; this forms a comprehensive body of research on possible Federated Sensor Network architecture designs. In addition, a fully compatible technology for the sensor network application, namely the Sensor Model Language (SensorML), has been reviewed and embedded into our FSN systems. The IoT as a new concept is also comprehensively described and the major technical issues discussed. Finally, a case study of the IoT in logistic management for emergency response is given. Proposed FSN architectures based on the Gateway solution are demonstrated through hardware implementation and lab tests. A demonstration of the 6LoWPAN enabled federated sensor network based on the TCP/IP Overlay solution presents a good result for the iNET localization and tracking project. All the tests of the designs have verified feasibility and achieve the target of the IoT concept

Regression results for the impact of elimination of the mandatory retirement policy on the number of new hires.

Regression results for the impact of elimination of the mandatory retirement policy on the number of new hires.</p

The age distribution of faculty members in 1995 and 2010 and the model’s prediction for 2010 (the correlation between predicted data and observed data in 2010 is 0.95).

The age distribution of faculty members in 1995 and 2010 and the model’s prediction for 2010 (the correlation between predicted data and observed data in 2010 is 0.95).</p

Regression results for the impact of elimination of the mandatory retirement policy on the age of tenure-track faculty.

Regression results for the impact of elimination of the mandatory retirement policy on the age of tenure-track faculty.</p

Late retirement, early careers, and the aging of U.S. science and engineering professors - Fig 4

(a) The trend of average age of tenure-track faculty from 1983 to 2010 by type of institution (whether they eliminated mandatory retirement early or late); (b) the trend of the average number of new hires for each institution from 1993 to 2001 by type of institution. Note: Black solid line (universal implementation) indicates the time of elimination of mandatory retirement on the federal level for all schools, black dashed line (early implementation) indicates the elimination of mandatory retirement on the state level for early uncapped schools.</p

Age distribution pattern in US academia.

(a) Age distribution of faculty in 1995 and 2010. (b) Annual hazard rates of attrition based on 1995–1999 surveys and 2008–2010 surveys. (c) Age distribution of new tenure-track hires in 1995 and 2010. Note: we define a new faculty member as a tenure-track faculty member hired within two years when observed.</p

Changes in age measures of faculty members between 1995 and 2010.

Changes in age measures of faculty members between 1995 and 2010.</p

A simple representation of a stock-flow model of faculty members.

Note: the value for faculty positions is not necessarily constant; numbers depict age; N: population; i: hiring inflow; e: exit rate; o: aging; h: hazard rate of attrition.</p

Simulation results.

Simulation results.</p

Model Documentation.

The details of simulation model formulation, calibration and improvement opportunities are explained. Table A. List of parameters estimated in model calibration and their search (feasible) ranges. (DOCX)</p