Event-based recursive distributed filtering over wireless sensor networks

In this technical note, the distributed filtering problem is investigated for a class of discrete time-varying systems with an event-based communication mechanism. Each intelligent sensor node transmits the data to its neighbors only when the local innovation violates a predetermined Send-on-Delta (SoD) data transmission condition. The aim of the proposed problem is to construct a distributed filter for each sensor node subject to sporadic communications over wireless networks. In terms of an event indicator variable, the triggering information is utilized so as to reduce the conservatism in the filter analysis. An upper bound for the filtering error covariance is obtained in form of Riccati-like difference equations by utilizing the inductive method. Subsequently, such an upper bound is minimized by appropriately designing the filter parameters iteratively, where a novel matrix simplification technique is developed to handle the challenges resulting from the sparseness of the sensor network topology and filter structure preserving issues. The effectiveness of the proposed strategy is illustrated by a numerical simulation.This work is supported by National Basic Research Program of China (973 Program) under Grant 2010CB731800, National Natural Science Foundation of China under Grants 61210012, 61290324, 61473163 and 61273156, and Jiangsu Provincial Key Laboratory of E-business at Nanjing University of Jiangsu and Economics of China under Grant JSEB201301

Quality of Service Behavioral Model from Event Trace Analysis

Proc. of the 7th international Command and Control Research and Technology Symposium (CCRTS 2002), Quebec City, Canada, pp. 1-16.The distributed command & control environment includes limited computer resources and numerous mission critical applications competing for these scarce resources. Additionally the stringent constraints and considerable complexity of distributed command & control systems can create a condition that places extreme demands upon the allocated resources and invites a potential for program errors. Consistent quality of service distribution can be a critical element in ensuring effective overall program completion while avoiding potential errors and process failures. The potential for errors and process failures can be understood and addressed by performing a practical analysis of the resource deployment procedures utilized within this environment. However, analyzing resource-based quality of service within a distributed command & control environment is a demanding endeavor. This difficult task can be simplified by directly examining specific quality of service actions that take place during program execution. Therefore, topragmatically isolate these actions and develop a practical quality of service behavioral model, the research discussed in this paper has implemented an event trace approach to examine the exact quality of service execution path during program operation

Exact and Approximate Probabilistic Symbolic Execution

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes

Paper Session II-B - Application of Information Technology to the National Launch System

The information needs of the National Launch System program had their beginnings with the Advanced Launch System (ALS). The Technical Reference Document for ALS called for a Unified Information System (UNIS) to provide, in a timely manner, all the information required to manage, design, manufacture, integrate, test, launch, operate, and support the ALS. UNIS 9 was to provide the link between distributed, heterogeneous workstations which were to make up both the ground and flight information systems. In addition, there was to be an Advanced Launch System Model (ALSYM), a set of computerized submodels, or tools, which would work together to simulate all aspects of the ALS. These conceptual requirements were transitioned to the NLS program, and UNIS and the system simulation exist today. The current version of the NLS UNIS links geographically dispersed users to databases, analysis tools, program management tools, and communications devices. UNIS development is continuing to provide the ultimate capabilities which were described in the ALS Technical Reference Document. The approach to that development, as well as the current and planned capabilities are described. The ALSYM requirement transitioned as a requirement for a largescale, end-to-end simulation of the Space Transportation Main Engine (STME) development program, named STESYM. The approach being used to satisfy that requirement incorporates object-oriented programming, discrete-event simulation, and knowledge-based techniques to produce a simulation that captures the technical characteristics of the hardware, the processing flows, and the scheduling requirements. The outputs of the simulation will include subsystem and system reliabilities, process infrastructure statistics, schedule performance statistics, and costs. Together, UNIS and STESYM will provide program managers, engineers, logisticians, and other program participants with communications connectivity and the information to support STME program analysis

The MONARC toolset for simulating large network-distributed processing systems

The next generation of High Energy Physics experiments have envisaged the use of network-distributed Petabyte-scale data handling and computing systems of unprecedented complexity. The general concept is that of a "Data Grid Hierarchy" in which the central facility at the European Laboratory for Particle Physics (CERN) in Geneva will interact and coherently manage tasks shared by and distributed amongst national "Tier1 (National) Regional Centres" situated in the US, Europe, and Asia. CERN and the Tier1 Centers will further communicate and task-share with the Tier2 Regional Centers, Tier3 centers serving individual universities or research groups, and thousands of "Tier4" desktops and small servers. The design and optimization of systems with this level of complexity requires a realistic description and modeling of the data access patterns, the data flow across the local and wide area networks, and the scheduling and workload presented by hundreds of jobs running concurrently on large scale distributed systems exchanging very large amounts of data. The simulation toolset developed within the "Models Of Networked Analysis at Regional Centers" - MONARC project provides a code and execution time-efficient design and optimisation framework for large scale distributed systems. A process-oriented approach for discrete event simulation has been adopted because it is well suited to describe various activities running concurrently, as well the stochastic arrival patterns typical of this class of simulations. Threaded objects or "Active Objects" provide a natural way to map the specific behaviour of distributed data processing (and the required flows of data across the networks) into the simulation program. This simulation program is based on Java2(™) technology because of the support for the necessary methods and techniques needed to develop an efficient and flexible distributed process oriented simulation. This includes a convenient set of interactive graphical presentation and analysis tools, which are essential for the development and effective use of the simulation system. The design elements, status and features of the MONARC simulation tool are presented. The program allows realistic modelling of complex data access patterns by multiple concurrent users in large scale computing systems in a wide range of possible architectures. Comparison between queuing theory and realistic client-server measurements is also presented

Event-based security control for discrete-time stochastic systems

This study is concerned with the event-based security control problem for a class of discrete-time stochastic systems with multiplicative noises subject to both randomly occurring denial-of-service (DoS) attacks and randomly occurring deception attacks. An event-triggered mechanism is adopted with hope to reduce the communication burden, where the measurement signal is transmitted only when a certain triggering condition is violated. A novel attack model is proposed to reflect the randomly occurring behaviours of the DoS attacks as well as the deception attacks within a unified framework via two sets of Bernoulli distributed white sequences with known conditional probabilities. A new concept of mean-square security domain is put forward to quantify the security degree. The authors aim to design an output feedback controller such that the closed-loop system achieves the desired security. By using the stochastic analysis techniques, some sufficient conditions are established to guarantee the desired security requirement and the control gain is obtained by solving some linear matrix inequalities with nonlinear constraints. A simulation example is utilised to illustrate the usefulness of the proposed controller design scheme.This work was supported in part by Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61573246 and 61374039, the Shanghai Rising-Star Programme of China under Grant 16QA1403000, the Program for Capability Construction of Shanghai Provincial Universities under Grant 15550502500 and the Alexander von Humboldt Foundation of Germany