Lightweight Mobile Devices Research Articles

Bringing simulation to engineers in the field: a Web 2.0 approach

Field engineers working on water distribution systems have to implement day-to-day operational decisions. Since pipe networks are highly interconnected, the effects of such decisions are correlated with hydraulic and water quality conditions elsewhere in the network. This makes the provision of predictive decision support tools (DSTs) for field engineers critical to optimizing the engineering work on the network. We describe how we created DSTs to run on lightweight mobile devices by using the Web 2.0 technique known as Software as a Service. We designed our system following the architectural style of representational state transfer. The system not only displays static geographical information system data for pipe networks, but also dynamic information and prediction of network state, by invoking and displaying the results of simulations running on more powerful remote resources.

Statistical Inference-Based Cache Management for Mobile Learning

Supporting efficient data access in the mobile learning environment is becoming a hot research problem in recent years, and the problem becomes tougher when the clients are using light-weight mobile devices such as cell phones whose limited storage space prevents the clients from holding a large cache. A practical solution is to store the cache data at some proxies nearby, so that mobile devices can access the data from these proxies instead of data servers in order to reduce the latency time. However, when mobile devices move freely, the cache data may not enhance the overall performance because it may become too far away for the clients to access. In this article, we propose a statistical caching mechanism which makes use of prior knowledge (statistical data) to predict the pattern of user movement and then replicates/migrates the cache objects among different proxies. We propose a statistical inference based heuristic search algorithm to accommodate dynamic mobile data access in the mobile learning environment. Experimental studies show that, with an acceptable complexity, our algorithm can obtain good performance on caching mobile data.

Ubi-App: a ubiquitous application for universal access from handheld devices

Universal access from a handheld device (such as a PDA, cell phone) at any time or anywhere is now a reality. Ubicomp Assistant (UA) (Sharmin et al. in Proceedings of the 21st annual ACM symposium on applied computing (ACM SAC 2006), Dijon, France, pp 1013–1017, 2006) is an integral service of MARKS (Sharmin et al. in Proceedings of the third international conference on information technology: new generations (ITNG 2006), Las Vegas, Nevada, USA, pp 306–313, 2006). It is a middleware developed for handheld devices, and has been designed to accommodate different types of users (e.g., education, healthcare, marketing, or business). This customizable service employs the ubiquitous nature of current short range, low-power wireless connectivity and readily available, low-cost lightweight mobile devices. These devices can reach other neighbouring devices using a free short-range ad hoc network. To the best of the authors’ knowledge, the UA service is the only service designed for these devices. This paper presents the details of Ubi-App, a ubiquitous application for universal access from any handheld device, which uses UA as a service. The results of a usability test and performance evaluation of the prototype show that Ubi-App is useful, easy to use, easy to install, and does not degrade the performance of the device.

Efficient scalable encoding for distributed speech recognition

The problem of encoding speech features in the context of a distributed speech recognition system is addressed. Specifically, speech features are compressed using scalable encoding techniques to provide a multi-resolution bitstream. The use of this scalable encoding procedure is investigated in conjunction with a multi-pass distributed speech recognition (DSR) system. The multi-pass DSR system aims at progressive refinement in terms of recognition performance, (i.e., as additional bits are transmitted the recognition can be refined to improve the performance) and is shown to provide both bandwidth and complexity (latency) reductions. The proposed encoding schemes are well suited for implementation on light-weight mobile devices where varying ambient conditions and limited computational capabilities pose a severe constraint in achieving good recognition performance. The multi-pass DSR system is capable of adapting to varying network and system constraints by operating at an appropriate trade-off point between transmission rate, recognition performance and complexity to provide desired quality of service (QoS) to the user. The system was tested using two case studies. In the first, a distributed two-stage names recognition task, the scalable encoder operating at a bitrate of 4.6 kb/s achieved the same performance as that achieved using uncompressed features. In the second study, a two stage multi-pass continuous speech recognition task using HUB-4 data, the scalable encoder at a bitrate of 5.7 kb/s achieved the same performance as that achieved with uncompressed features. Reducing the bitrate to 4800 b/s resulted in a 1% relative increase in WER.