With the advances in and convergence of Internet technologies, embedded computers, and wireless communication, computing devices have become part of our daily life. Hand-held devices and sensors with wireless connections create opportunities for many new nomadic applications. Service discovery is an essential component for cognitive science to discover existing network services just-in-time. Unlike many other approaches, we propose a service discovery model supporting nomadic users and services in public environments. Our model emphasizes secure and private service discovery in such environments. Location sensing is integrated for location dependent service discovery and is used to lessen service discovery network infrastructure requirements. We analyze the system performance and show our formal verification of the protocols. Our implementation shows that our model is feasible.

In this article, I will discuss three challenges in today’s data mining field. These challenges include: the transfer learning challenge, the social learning challenge and the mobile context mining challenge. I pick these three challenges because I think time is ripe for each of them to be addressed in a major way in the near future, given the current technological and societal readiness to tackle them. I also believe that each of the three challenges discussed in this article will help move the science and engineering of data mining forward, and have a great impact on society.

We introduce GreenOrbs, our recent effort in exploring the fundamental challenges and future direction of long-term large-scale applications of wireless sensor networks. An integrated framework with more than 1000 sensors has been implemented, including the indoor test bed, prototype systems, and forest deployment: comprehensively supporting research, development, and forestry applications.

This paper describes iSensNet, an infrastructure that we designed and implemented for various research and development projects in wireless sensor networks (WSNs). iSensNet is composed of a WSN platform and an event publish/subscribe middleware. On the WSN platform, users can test their protocols of any and all non-physical layers; the platform provides real time performance evaluation of the protocols. With the provided middleware, iSensNet is able to accept event subscriptions, to decompose an event to sub-events, to build up event detection trees composed of these sub-events, to collaborate in detecting the sub-events, and handle the event for various application programs. This paper focuses on the design of iSensNet, taking into consideration the severe resource constraints despite the high performance requirements of the applications.

A radio-frequency identification (RFID) system including hardware and software may be updated from time to time after first time deployment. To ensure the reliability of the system, extensive tests are required. However, enumerating all test cases is infeasible, especial- ly when the tests involve time-consuming hardware operations. To solve this problem, we propose a testing methodology for RFID systems which does not enumerate all test cases but rather those which are representative. A clustering method is adopted in selecting representative test cases. Although a small number of selected test cases are run, we can still obtain a relatively high bug detection rate compared with running the enumerated test cases. Our extensive experiments show the efficiency and effectiveness of our testing methodology.

With the concept of “Cognitive Sense of China” and “Smart Planet” proposed, wireless sensor networking is considered to be one of the most important technologies of the new century. In wireless sensor networks, how to extend battery lifetime is a core problem. In this paper, we address the problem of designing battery-friendly packet transmission policies for wireless sensor networks. Our objective is to maximize the lifetime of batteries for wireless sensor nodes subject to certain delay constraints. We present three packet transmission schemes and evaluate them with respect to battery performance. The first scheme, based on combining multiple packets, utilizes battery charge recovery effect, which allows some charge to be recovered during long idle periods. The second scheme, based on a modified version of lazy packet scheduling, draws smoother and lower current and is battery efficient. The final scheme, based on a combination of the two previous schemes has superior battery performance at the expense of larger average packet delay. All three schemes are simulated for a wireless network framework with internet traffic, and the results were validated.

With the advent of the Internet of Things (IoT) that offers capabilities to identify and connect worldwide physical objects into a unified system, the importance of modeling and processing IoT data has become significantly accentuated. IoT data is substantial in quantity, noisy, heterogeneous, inconsistent, and arrives at the system in a streaming fashion. Due to the unique characteristics of IoT data, the manipulation of IoT data for practical applications has encountered many fundamental challenging problems, such as data modeling and processing. This paper proposes the infrastructure for an IoT prototype system that aims to develop foundation models for IoT data. We illustrate major modules in the IoT prototype, as well as their functionalities, and provide our vision of the key techniques used for tacking the critical problems in each module.

This paper proposes a method to manage and utilize image and video data effectively in a smart city. Applying digital watermarking techniques, a framework for visual data security and management for smart cities is presented. In the framework, a reversible fragile or semi-fragile watermark embedded into the visual data is used to ensure trusted acquisition. Moreover, reversible metadata watermarks carrying information such as identification and other properties data is used to assist visual data management. A solution for tracing users on a large scale is presented using reversible watermarking.

This paper introduces the importance of 3D shape retrieval frameworks in 3D smart cities, and proposes a unified framework for 3D shape retrieval. The proposed method is based on the concept of visual representation, where each object is rendered with several depth images and binary images from distributed vertices in the regular polyhedron. For each image, several shape descriptors are utilized to extract features. Finally, different feature vectors are concatenated into a composite one. The experimental results show that the proposed method not only significantly improves the retrieval performance, but also achieves better retrieval effectiveness than other state-of-the-art algorithms, running princeton shape benchmark (PSB) and other standard evaluation measures.

We survey our recent work on interactive modeling, generation, and control of large-scale crowds and traffic for simulating digital cities. These include multi-agent navigation, simulating large crowds with emerging behaviors as well as interactive simulation of traffic on large road networks. We also highlight their performance on different scenarios.

High-efficiency transportation systems in urban environments are not only solutions for the growing public travel demands, but are also the premise for enlarging transportation capacity and narrowing the gap between urban and rural areas. Such transportation systems should have characteristics such as mobility, convenience and being accident-free. Ubiquitous-intelligent transportation systems (U-ITS) are next generation of intelligent transportation system (ITS). The key issue of U-ITS is providing better and more efficient services by providing vehicle to vehicle (V2V) or vehicle to infrastructure (V2I) interconnection. The emergence of cyber physical systems (CPS), which focus on information awareness technologies, provides technical assurance for the rapid development of U-ITS. This paper introduces the ongoing Beijing U-ITS project, which utilizes mobile sensors. Realization of universal interconnection between real-time information systems and large-scale detectors allows the system to maximize equipment efficiency and improve transportation efficiency through information services.