In a densely populated city, effective land uses and redevelopment is critical to enable growth, development and renewal in all aspects of city planning. For instance, sufficient housing provision, adequate supporting infrastructure and accessible amenities are required to support population growth. Urban planners face the issue of where to locate facilities (e.g. clinics, elder/childcare centres and schools) such that they can best serve their intended target population. This presents the problem of arriving at a distribution of facilities that maximally optimizes coverage to target populations while minimizing the number of facilities to build. In this paper, we proposed a planning decision support platform for city planning with new flexible mixed integer programming models at the core and an interactive interface at the front-end that is integrated with a Geo-Information System. The system can generate scenarios, calculate travel time/distance based on road network, call and solve optimization models, and do scenario comparison and sensitivity analysis. Using existing and projected data for population growth and schools as an example, we examined the effectiveness and performance of our decision system and optimization approach for city planning. The results show that our optimization model and approach can solve real problems within a reasonable timeframe and can provide valuable insights for planners to inform land use planning decisions.

Internet of Things (IoT) is an emerging technological innovation and it could potentially changing the way people work, live and engage with devices. However, interaction with IoT devices through tangible user interface hasn't been getting much attention, particularly in the smart city domain. In this paper, we present a new urban scenario planning tool by introducing a new IoT based geospatial tangible user interface (GTUI). We discuss its advantages and disadvantages in the context of urban planning. We demonstrate three use cases where public and city planner can be benefited through the integration of IoT and tangible user interface.

The emerging vehicular communications will enable a variety of applications for safety, traffic efficiency, driver assistance and infotainment. Due to high vehicular speed, sporadic connection, limited communication range, and large volume of data that need to be transmitted, vehicular communications have the stringent requirement of real-time vehicular communications. This paper presents our tests with 802.11p device and presents its performance evaluation in terms of throughput, loss rate, communication range and resending rate. The experimental results show that the 802.11p device can achieve real-time vehicular communications and is suitable for the applications with medium throughput, and a few hundred meters of communication range.

We propose a novel demand-driven caching framework, called cache-on-demand (CoD). In CoD, intermediate/final answers of existing running queries are viewed as virtual caches that can be materialized if they are beneficial to incoming queries. Such an approach is essentially nonspeculative: the exact cost of investment and the return on investment are known, and the cache is certain to be reused! We address several issues for CoD to be realized. We also propose three optimizing strategies: Conform-CoD, Scramble-CoD, and Integrated-CoD. Conform-CoD and Scramble-CoD are based on a two-phase optimization framework, while Integrated-CoD operates in a single-phase framework. We conducted extensive performance study to evaluate the effectiveness of these algorithms. Our results show that all the CoD-based schemes can provide substantial performance improvement when compared with a predictive scheme and a no-caching scheme.

In a multi-user environment, multiple queries co-exist together in the system. Further, we observe that these queries usually share common sub-queries or reference common relations. By using cached results from earlier queries, better performance as well as faster evaluation of subsequent queries can be achieved. One promising concept that has been recently proposed is the cache-on-demand (CoD) schemes. CoD considers intermediate/final results of existing running queries as virtual caches, of which only the beneficial ones are materialized for incoming queries. In this paper, we propose a novel CoD-based strategy called Integrated-CoD. Unlike existing CoD-based schemes that operate in two phases, Integrated-CoD integrates the two phases into one. We evaluated the performance of Integrated-CoD against two known CoD-based schemes (Conform-CoD and Scramble-CoD). Our results show that Integrated-CoD offers superior performance at a marginal increase in optimization overhead.

Queries posed to a database usually access some common relations, or share some common sub-expressions. We examine the issue of caching using a novel framework, called cache-on-demand (CoD). CoD views intermediate/final answers of existing running queries as virtual caches that an incoming query can exploit. Those caches that are beneficial may then be materialized for the incoming query. Such an approach is essentially nonspeculative: the exact cost of investment and the return on investment are known, and the cache is certain to be reused. We address several issues for CoD to be realized. We also propose two optimizing strategies, Conform-CoD and Scramble-CoD, and evaluate their performance. Our results show that CoD-based schemes can provide substantial performance improvement.