Zoltan Papp

An overview of non-centralized Kalman filters

The usage of wireless sensor networks (WSNs) for state-estimation has recently gained increasing attention due to its cost effectiveness and feasibility. One of the major challenges of state-estimation via WSNs is the distribution of the centralized state-estimator among the nodes in the network. Significant emphasis has been on developing non-centralized state-estimators considering communication, processing-demand and estimation-error. This survey paper presents different methodologies to obtain non-centralized state-estimators and focuses on the estimation algorithms and their implementation. The temperature distribution of a bar is used as a benchmark to assess the non-centralized state-estimators in terms of estimation-error and communication requirements.

Vehicle motion-state-estimation using distributed sensing

Knowing the position and speed of the vehicles on the road network in real-time is one of the major challenges that vehicle control and traffic management applications are facing. Wireless sensor networks received significant attention in the last decade and successful research put them in the forefront to answer this challenge. Wireless sensor networks are a new class of observation systems with a large amount of nodes communicating with each other via a wireless network channel. In this paper such a system is used to firstly detect a vehicle and secondly estimate its motion-state. The nodes are evenly distributed in a along the road surface and each node can detect when a vehicle passes. With the known position of the individual nodes, a motion-state of the vehicle is estimated. Three state-estimators are analyzed in this paper; a synchronous Kalman filter, an asynchronous Kalman filter and a mixture of Gaussians. After designing these state-estimators, simulations are performed to assess their performance.

Model-Based Architecture Optimization for Self-Adaptive Networked Signal Processing Systems

This short paper introduces a closed-loop design optimization method for self-organizing and self-optimizing networked systems with a focus on signal processing and control. The design process starts with creating graph-based model of the system using a dedicated modelling language. The design is exported and converted to executable code in order to obtain the properties of the runtime behaviour of the system using a simulation environment. The embedding optimization loop iteratively invokes the evaluation and searches for optimal architectures and parameterization in the user defined design space. A distinguishing feature of the tool is that it allows for runtime changes in the models, i.e. it is capable of evaluating runtime reconfigurable architectures. The design space is split into two disjunct sub-spaces: one of them defines the runtime reconfigurability (the self-capabilities), the other defines the region of design time optimization. The tool is demonstrated via a real-time monitoring application.

Model-based Design of Self-adapting Networked Signal Processing Systems

The paper describes a model based approach for architecture design of runtime reconfigurable, large-scale, networked signal processing applications. A graph based modeling formalism is introduced to describe all relevant aspects of the design (functional, concurrency, hardware, communication, energy, etc.). The formalism can be used to evaluate and suggest architectural patterns for signal processing type of applications. Moreover, an architecture design evaluation tool according to the modeling formalism is described, which can place the system design under various execution scenarios (e.g. node mobility, node failure, communication link deterioration, etc.) and determine emerging system properties (such as expected lifetime, availability of services, throughput, response times, etc.). Distinguishing feature of the tool is that it allows for modeling runtime reconfiguration solutions (e.g. distributed optimization, constraint solvers) and making changes in the underlying system architecture and thus evaluate the effects of the reconfiguration. The evaluation tool can be embedded into a design optimizer, which allows an optimal trade-off between design-time and runtime configuration to be determined. The effectiveness of the approach and the tool is demonstrated via two application cases (structural integrity monitoring, greenhouse temperature distribution estimation).

Model-Based Engineering of Runtime Reconfigurable Networked Embedded Systems

Today’s societal challenges, such as sustainable urban living and public safety and security require monitoring and control solutions for large-scale complex and dynamical systems. The distinguishing features of these systems are serious resource constraints, demanding non-functional requirements such as robustness, timeliness, lifetime and the capability of handling system evolution through runtime reconfiguration. In this chapter, a multi-aspect modeling language is introduced that allows system designers to model the architecture of large scale networked systems from different aspects. This modeling language introduces innovative concepts to model runtime reconfiguration at design-time. The proposed architecture for modeling runtime reconfiguration consists of primary tasks in one layer and secondary management tasks in another layer. Special reconfiguration primitives allow the description of four types of reconfiguration: re-parameterisation, re-instantiation, rewiring and relocation. The modeling language is accompanied by a modeling and design methodology (inspired by the MAPE-K technique [1]) and uses feedback loops in the system model to realize runtime reconfiguration. This chapter also proposes Key Performance Indicators (KPIs) that allow designers to quantify the “quality” of the system designs and pick the most promising one. Special attention is paid to the fact that the availability of a runtime reconfiguration (i.e. re-design capability) in a system requires KPIs to be derived and evaluated at runtime as a precondition for guiding the reconfiguration process.

An illustrative application example: cargo state monitoring

The previous chapters in this book provide the foundations and a brief description of the DEMANES tool chain. This chapter describes a real self-adaptive system developed using the DEMANES tool chain. This chapter focuses on the design and implementation stages of a real use case development. The use case under study is a subsystem, called Cargo Monitoring System (CMS) , that monitors the state of the container cargo and pushes all the data to a back office infrastructure for further processing. The containers can be on a truck, a train, or any other appropriate transportation means, or stacked in a container terminal or a cargo ship. A WSN will measure physical magnitudes (temperature, humidity and so on) inside a container and will forward data to others processing nodes in the CMS network. The CMS has to meet several self-adaptive requirements. For instance, the parameters of the CMS elements that monitor the container cargo state are reconfigured accordingly to adapt to internal or external changes (e.g. a low battery level or a container temperature out of the adequate bounds), and the CMS adapts the WSN nodes power transmission to save energy while providing an acceptable quality of service.

Designing Reconfigurable Systems: Methodology and Guidelines

One of the major challenges when designing software for complex systems relates to a lack of a specific and comprehensive set of rules and methodologies. Even more so, adaptation to field conditions is difficult to model and implement on systems composed of a larger number of devices/components, such as distributed systems or systems of systems. On state-of-the-art technology such as wireless sensor/actuator networks and cyber-physical systems, addressing the lack of a compressive set of rules for their design and realization offers considerable benefits. If successfully realized, it can accelerate and simplify their design and implementation. The main contribution of this chapter is a clear set of rules that are specific for the design of adaptive networked embedded systems. To be more specific, we discuss design-time vs. runtime trade-offs, introduce design patterns for reconfigurable real-time monitoring and control, propose techniques for runtime design space exploration (managing runtime reconfiguration) and a systems engineering process for runtime reconfigurable systems. We provide guidelines for all stages of the architectural process and help system and software designers in choosing wisely specific algorithms and techniques. In conclusion, this chapter introduces a set of rules (methodologies) that are specific for designing adaptive networked embedded systems.

Optimized power generation in offshore wind parks

Electricity generation on offshore wind parks has an increasing economic importance - the European Commission foresees that 12% of the wind energy will be produced on offshore installations by 2020, and this share is likely to increase further in the following years. However, the continuously varying offshore environment and the interaction among turbines at large park sites impose significant challenges to the efficient and economically feasible power production. In this work, we present a distributed control system that optimizes the operation of the wind park and adapts to different weather conditions and park configurations. The proposed optimization scheme uses a model for the interactions among the wind turbines in the wind park and the estimated energy production on turbine level to guide the optimization process. The solution can be extended to joint optimization of operation and maintenance on wind park level by incorporating health and wear models in the performance criteria and constraints. Extended simulations on large-scale wind parks show that the proposed scheme improves energy production in low-wind situation and is able to balance production to reduce wear.