Peizhao Hu

An Autonomic Context Management System for Pervasive Computing

Context-aware applications adapt to changing computing environments or changing user circumstances/tasks. Context information that supports such adaptations is provided by the underlying infrastructure, which gathers, pre-processes and provisions context information from a variety of context information sources. Such an infrastructure is prone to failures and disconnections that negatively impact on the ability of context-aware applications to adapt (and therefore dramatically impact on their usability). This paper describes a model-based autonomic context management system (ACoMS) that can dynamically configure and reconfigure its context information gathering and pre-processing functionality in order to provide fault tolerant provisioning of context information. The approach uses standards based descriptions of context information sources to increase openness, interoperability and scalability of context-aware systems.

Sensor Standards: Overview and Experiences

Sensors are swiftly finding their way into real-world applications, from farming to factory floor monitoring. In parallel with this deployment, several standards are being developed to extend greater interoperability between sensor systems. In this paper, we explore a subset of the various sensor standards, and relate our experiences in attempting to integrate some of these standards for the purposes of developing an autonomic context manager.

Context-aware routing in wireless mesh networks

Wireless mesh networks promise to deliver resilient connectivity among network nodes, allowing data to be relayed seamlessly between mobile clients and infrastructure. Routing is the vital process in archiving self-configuration, self-healing and, to some degree, self-optimization. However, the heterogeneity of network nodes and highly dynamic network topologies create new challenges for developing efficient and adaptive routing solutions. The increasing amount and complexity of information that routing solutions have to consider, in order to cope with the changing network situation and/or user requirements, is a key challenge. We propose adopting a reconfigurable context management system to simplify the task of accessing a variety of information required by adaptive routing protocols and to hide the low-level complexities of information sources management. In addition, we show how our middleware supports faulttolerance of various information failures, freeing protocol developers to concentrate on improving the routing mechanism and the metric information model of routing.

Evaluations of MadWifi MAC layer rate control mechanisms

The 802.11 standards specify several transmission rates that can be used at the MAC layer protocol to adapt the transmission rate to channel conditions. Such dynamic adaptations can improve per-hop performance in Wireless Networks and therefore can have impact on the Quality of Service provided for communicating applications. In this paper we present a comprehensive evaluation of the performance of four rate control mechanisms used by the MadWifi driver in Linux: Onoe, AMRR, SampleRate and minstrel. The evaluation of these four rate control mechanisms was carried out in our platform for controllable and repeatable experiments.

Towards a standards-based autonomic context management system

Pervasive computing applications must be sufficiently autonomous to adapt their behaviour to changes in computing resources and user requirements. This capability is known as context-awareness. In some cases, context-aware applications must be implemented as autonomic systems which are capable of dynamically discovering and replacing context sources (sensors) at run-time. Unlike other types of application autonomy, this kind of dynamic reconfiguration has not been sufficiently investigated yet by the research community. However, application-level context models are becoming common, in order to ease programming of context-aware applications and support evolution by decoupling applications from context sources. We can leverage these context models to develop general (i.e., application-independent) solutions for dynamic, run-time discovery of context sources (i.e., context management). This paper presents a model and architecture for a reconfigurable context management system that supports interoperability by building on emerging standards for sensor description and classification.

Experimental evaluation of measurement-based SINR interference models

In 802.11-based wireless networks, the ability to accurately predict the impact of interference via the use of an interference model is essential to better and more efficient channel assignment algorithms and data routing protocols. Recently, there have been several works that proposed new interference models utilizing the well-known concept of signal-to-interference-plus-noise ratio (SINR). Using active measurements, these models construct a profile that maps either the measured received signal strength (RSS) or the computed SNR or SINR values at a receiver, to its packet delivery ratio (PDR) performance. The profile is then used by the models to predict the PDR performance in more complex scenarios involving multiple interferers. While comparison with other basic models (e.g. hop-based and distance-based) have been made in these works, there has as yet been no comprehensive comparison on the accuracy of these measurement-based SINR interference models. In this paper, we systematically evaluate the performance of three measurement-based SINR interference models in predicting the interference impact on the successful reception of packets. Our evaluations cover various interference scenarios with both 802.11 and non-802.11 interferers, in experiments carried out in both our conducted testbed and an over-the-air testbed. Our results show that an interference model that utilizes an SINR profile can accurately predict the PDR performance with a maximum root-mean-square error (RMSE) of 10.8% across all our evaluations. In contrast, interference models that rely on the SNR profile and the RSS profile perform poorly, with a maximum RMSE of 61.7% and 66.1% respectively.

Share your view: Wireless multi-hop video streaming using Android phones

With the rising penetration of smartphones in the consumer market, mobile multimedia content is becoming the dominant form of information that people produce and consume on a daily basis. In this paper we present a wireless multi-hop video streaming application for mobile phones with the Android operating system. This application allows to share live information captured by mobile phone sensors (e.g., camera, microphone) with persons that might be multiple wireless hops away. The video streaming is based on peer-to-peer communication between mobile phones, i.e. without the use of video processing servers or network infrastructure. We show the feasibility of such peer-to-peer video streaming application for Android phones in a variety of experiments that evaluate various video streaming scenarios, including various video codecs and various generations of Android phones.

Time-based and low-cost bandwidth estimation for IEEE 802.11 links

This paper presents a practical and low-cost approach to estimate the maximum achievable wireless link bandwidth based on the prevailing link conditions in an IEEE 802.11 network. This approach works by observing the number of bits successfully delivered over a link, divided by the corresponding channel occupancy time. The method is passive and does not introduce any extra overhead on the channel, and is based on the timing model of the IEEE 802.11 MAC layer. All required parameters are obtained locally at the sending node from the MAC layer. We evaluate the accuracy of the proposed method via a full implementation and experiments on our conducted testbed, which provides a more controlled environment and increased repeatability of experiments, compared to over-the-air wireless testbeds. The experiment results show that our method can accurately estimate the link bandwidth with both saturated and low traffic load across links with different transmission rates and link qualities. In addition, results also show that our method is able to track the link bandwidth as its link quality changes.

MeshVision: An Adaptive Wireless Mesh Network Video Surveillance System

The major surveillance camera manufacturers have begun incorporating wireless networking functionality into their products to enable wireless access. However, the video feeds from such cameras can only be accessed within the transmission range of the cameras. These cameras must be connected to backbone infrastructure in order to access them from more than one hop away. This network infrastructure is both time-consuming and expensive to install, making it impractical in many rapid deployment situations (for example to provide temporary surveillance at a crime scene). To overcome this problem, we propose the MeshVision system that incorporates wireless mesh network functionality directly into the cameras. Video streams can be pulled from any camera within a network of MeshVision cameras, irrespective of how many hops away that camera is. To manage the trade-off between video stream quality and the number of video streams that could be concurrently accessed over the network, MeshVision uses a Bandwidth Adaptation Mechanism. This mechanism monitors the wireless network looking for drops in link quality or signs of congestion and adjusts the quality of existing video streams in order to reduce that congestion. A significant benefit of the approach is that it is low cost, requiring only a software upgrade of the cameras.

Reconfigurable middleware for sensor based applications

The pervasive computing paradigm introduced context-aware applications that adapt themselves to their surrounding environment based on context information. Context information may be of different types including sensed context information gathered from a variety of sensors. In pervasive computing user intervention/interactions with their application should be minimised. On the other hand, pervasive computing environments are very dynamic environments -context information that supports adaptation decisions may not always be available due to user mobility and potential network and/or sensor failures. As context information supports autonomous adaptation of applications, the provision of context information itself has to be managed by an autonomic system able to both recognise and recover from context delivery failures. This paper presents the architecture of a context management system that is reconfigurable with regard to sensor and/or network failure and can support dynamic discovery and replacement of sensors. One of the components of this middleware architecture, the model and management of sensor descriptions, is described in more detail as it plays an important role in sensor discovery and replacement.