Future sensing applications call for a thorough evaluation of network performance trade-offs so that desired guarantees can be provided for the realization of real-time wireless sensor networks (WSNs). Recent studies provide insight into the performance metrics in terms of first-order statistics, e.g., the expected delay. However, WSNs are characterized by the stochastic nature of the wireless channel and the queuing processes, which result in non-deterministic delay, throughput, and network lifetime. For the design of WSNs with predictable performance, probabilistic analysis of these performance metrics and their intrinsic trade-offs is essential. Moreover, providing stochastic guarantees is crucial since each deployment may result in a different realization. In this paper, the trade-offs between delay, throughput, and lifetime are quantified through a stochastic network design approach. To this end, two novel probabilistic network design measures, quantité and quantite interval, are defined to capture the dependability and predictability of the performance metrics, respectively. Extensive evaluations are conducted to explore the performance trade-offs in real-time WSNs.

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. Due to the non-deterministic impacts of the wireless channel and queuing state, probabilistic analysis of QoS is essential. For most WSNs applications, the end-to-end delay for packet delivery and the energy consumption are the most important QoS metrics. In this chapter, a comprehensive cross-layer probabilistic analysis framework is presented to investigate the probabilistic evaluation of QoS performance provided by WSNs. In particular, the QoS performance is evaluated in two levels. In the node level, using a Discrete-Time Markov queueing model, the distribution of single-hop delay and single-node energy consumption and lifetime are analyzed. In the network level, based on the node level analysis, the distributions of end-to-end delay, the network lifetime, and the event detection delay are then analyzed. Fluid models are utilized in the network level analysis. The framework also considers a realistic channel environments. Compared to the first-order QoS statistics, such as the mean and the variance, the distribution of QoS metrics reveals the relationship between the performance and reliability with QoS-based operations in WSNs. Using the framework, effective network development can be performed.

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. Traditional analysis work only focuses on the first-order statistics, such as the mean and the variance of the QoS performance. However, due to unique characteristics of WSNs, a cross-layer probabilistic analysis of QoS performance is essential. In this dissertation, a comprehensive cross-layer probabilistic analysis framework is developed to investigate the probabilistic evaluation and optimization of QoS performance provided by WSNs. In this framework, the distributions of QoS performance metrics are derived, which are natural tools to discover the probabilities to achieve given QoS requirements. Compared to first-order statistics, the distribution of these metrics reveals the relationship between the performance of QoS-based operations and the probability to achieve the performance. Using a Discrete-Time Markov queueing model in node-level analysis and fluid models in network-level analysis, the distributions of end-to-end delay, the network lifetime, and the event detection delay are then analyzed. Based on the evaluation of QoS metrics, a probabilistic optimization framework is developed to demonstrate the investigation of the optimal network and protocol parameters. Guidelines of designing networks and choosing optimal parameters for WSNs are provided using the optimization framework. Intensive testbed experiments and simulations are used to validate the accuracy of the proposed evaluation and optimization framework.

Emerging applications of wireless sensor networks (WSNs) require real-time event detection to be provided by the network. In a typical event monitoring WSN, multiple reports are generated by several nodes when a physical event occurs, and are then forwarded through multi-hop communication to a sink that detects the event. To improve the event detection reliability, usually timely delivery of a certain number of packets is required. Traditional timing analysis of WSNs are, however, either focused on individual packets or traffic flows from individual nodes. In this paper, a spatio-temporal fluid model is developed to capture the delay characteristics of event detection in large-scale WSNs. More specifically, the distribution of delay in event detection from multiple reports is modeled. Accordingly, metrics such as mean delay and soft delay bounds are analyzed for different network parameters. Motivated by the fact that queue build up in WSNs with low-rate traffic is negligible, a lower-complexity model is also developed. Testbed experiments and simulations are used to validate the accuracy of both approaches. The resulting framework can be utilized to analyze the effects of network and protocol parameters on event detection delay to realize real-time operation in WSNs. To the best of our knowledge, this is the first approach that provides a transient analysis of event detection delay when multiple reports via multi-hop communication are needed.

Limited energy resources in wireless sensor networks (WSNs) call for a comprehensive cross-layer analysis of energy consumption in a multi-hop network. In this paper, we provide a stochastic analysis of the energy consumption in a random network environment. Accordingly, a comprehensive cross-layer analysis framework, which employs a stochastic queueing model in realistic channel environments, is developed. This framework accurately predicts the distribution of energy consumption for nodes in WSNs during a given time period. We show that when the time duration is long, the energy consumption asymptotically approaches a Normal distribution. Using the distribution of energy consumption, the distribution of node lifetime is also investigated. With the help of this probabilistic model, a case study with an anycast protocol is conducted to show how the developed framework can analytically predict the distribution of energy consumption and lifetime. Comprehensive simulations and testbed experiments are provided to validate the developed model. The cross-layer framework is also used to identify relationships between the distribution of energy consumption and network parameters, such as network density, duty cycle, and traffic rate. To the best of our knowledge, this is the first work to investigate probabilistic distribution of energy consumption in WSNs.

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. However, designing real-time scheduling and communication solutions for these networks is challenging since the characteristics of QoS metrics in WSNs are not well known yet. Due to the nature of wireless connectivity, it is infeasible to satisfy worst-case QoS requirements in WSNs. Instead, probabilistic QoS guarantees should be provided, which requires the definition of probabilistic QoS metrics. To provide an analytical tool for the development of real-time solutions, in this paper, the distribution of end-to-end delay in multi-hop WSNs is investigated. Accordingly, a comprehensive and accurate cross-layer analysis framework, which employs a stochastic queueing model in realistic channel environments, is developed. This framework captures the heterogeneity in WSNs in terms of channel quality, transmit power, queue length, and communication protocols. A case study with the TinyOS CSMA/CA MAC protocol is conducted to show how the developed framework can analytically predict the distribution of end-to-end delay. Testbed experiments are provided to validate the developed model. The cross-layer framework can be used to identify the relationships between network parameters and the distribution of end-to-end delay and accordingly, to design real-time solutions for WSNs. Our ongoing work suggests that this framework can be easily extended to model additional QoS metrics such as energy consumption distribution. To the best of our knowledge, this is the first work to investigate probabilistic QoS guarantees in WSNs.

As a large-scale interconnected system of heterogeneous components integrating computation with physical processes, Cyber-Physical Systems (CPS) can greatly improve the efficiency of industrial process control systems. However, the inherent heterogeneity and the close integration of different components pose new challenges, which can only be solved by a new unifying network and control theory. This article investigates such challenges in industrial process control and proposes a CPS architecture for future research. Some open research issues are also suggested.

The localization performance of a wireless sensor network localization system is highly related to the rate at which it can transmit ranging signals and measurements. The underlying media access control (MAC) protocol and application level ranging and communication protocol of the cricket location-support system (CLS) is analyzed to identify bottlenecks. Unnecessary delays in the existing MAC layer, which is based on carrier sense multiple access with collision avoidance (CSMA/CA), are identified and removed. This greatly improves the rate at which radio frequency (RF) packets can be transmitted. To further improve localization performance, a time division multiple access (TDMA) protocol for an active architecture is proposed to replace the randomized schedule used by CLS to transmit ranging signals. Experiments show that the new protocols produce higher sampling rates than the original CLS communication protocols.

Current techniques for generating animated scenes involve either videos (whose resolution is limited) or a single image (which requires a significant amount of user interaction). In this paper, we describe a system that allows the user to quickly and easily produce a compelling-looking animation from a small collection of high resolution stills. Our system has two unique features. First, it applies an automatic partial temporal order recovery algorithm to the stills in order to approximate the original scene dynamics. The output sequence is subsequently extracted using a second-order Markov Chain model. Second, a region with large motion variation can be automatically decomposed into semiautonomous regions such that their temporal orderings are softly constrained. This is to ensure motion smoothness throughout the original region. The final animation is obtained by frame interpolation and feathering. Our system also provides a simple-to-use interface to help the user to fine-tune the motion of the animated scene. Using our system, an animated scene can be generated in minutes. We show results for a variety of scenes.

GUI(Graphic User Interface) based embedded applications are now widely in use.Common GUI products are usually large in size and complicated in detail.An embedded GUI system,which is more lightly weighted than most of current GUI products,was proposed in this paper.Due to adopting both object oriented modularized designing and a well-designed simulator,it is more convenient to develop this system.Its implementation in Video Conference Terminals was also introduced.In this implementation,this system took small memory size,and cost significantly short development time.It greatly reduced the cost of both product hardware and development labor.

ABU(亚广联)于2002年8月31日在日本东京举行了首届亚太地区大学生机器人大赛,CCTV中国大学生机器人大赛冠亚军得主中国科技大学机器人队代表中国参赛并荣获亚军.我们研制的由视觉系统控制的机器人还获得了最高技术奖,成为本次大赛的一个亮点.本文介绍了这个机器人的视觉控制系统.

介绍了舞蹈机器人步进电机驱动电路和程序设计.电路采用74373锁存,74LS244和ULN2003作电压和电流驱动,单片机AT89C52作工作脉冲序列信号发生器.程序设计基于中断服务和总线分时复用方式,实时更新各个电机的速度和方向.