International audience

In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy. Specifically, the load data can inadvertently reveal the daily routines of residential users, thereby posing a risk to their property security. While federated learning (FL) has been employed to safeguard user privacy by enabling model training without the exchange of raw data, these FL models have shown vulnerabilities to emerging attack techniques, such as Deep Leakage from Gradients and poisoning attacks. To counteract these, we initially employ a Secure-Aggregation (SecAgg) algorithm that leverages multiparty computation cryptographic techniques to mitigate the risk of gradient leakage. However, the introduction of SecAgg necessitates the deployment of additional sub-center servers for executing the multiparty computation protocol, thereby escalating computational complexity and reducing system robustness, especially in scenarios where one or more sub-centers are unavailable. To address these challenges, we introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. The Distributed Markovian Switching (DMS) topology shows strong robustness towards the poisoning attacks as well. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm. It not only significantly minimizes communication complexity but also maintains accuracy levels comparable to traditional FL methods, thereby enhancing the scalability of our load forecasting algorithm.

The stability of modern power grids relies heavily on effective frequency control, mainly achieved through automatic generation control (AGC) systems. However, AGC systems have become increasingly vulnerable to cyber-physical attacks due to their dependence on communication infrastructure and cyber-physical devices. In this article, we present a novel attack strategy that leverages the learning capabilities of a convolutional neural network and long short-term memory on encrypted data to predict the imminent operating state of a power system. By accurately forecasting vulnerable operational conditions through encrypted traffic analysis, attackers can orchestrate timely denial-of-service (DoS) attacks on AGC systems, significantly amplifying the physical impact of their cyber-attacks. Despite the implementation of network traffic protection measures through the utilization of the IPsec/ESP protocol, which offers encryption at the network layer to ensure the confidentiality of the original packet content and enhances the complexity of traffic analysis, our research demonstrates that attackers can still extract spatiotemporal features from high-entropy encrypted synchrophasor data packets. This finding underscores the inherent limitations of IPsec/ESP protocols in completely obstructing malicious analysis of network traffic and achieving absolute prevention of such nefarious activities. Consequently, the confidentiality of the power system's operational state remains compromised. To gauge the effectiveness of the proposed attack strategy, we conduct various test cases and simulations, revealing a significant increase in the success rate of DoS attacks. These findings underscore the urgency of implementing countermeasures to thwart attackers from exploiting traffic analysis techniques and emphasize the necessity for strengthened security measures in power grid infrastructure.

Power and energy systems are lifeline infrastructures to civilization. Their stable operation and security of supply are essential for the daily life of the people. Typically, they are characterized by a central generation infrastructure using large-scale power plants. The electricity is transported via long-distance transmission lines on high-voltage levels and distributed via distribution grids to customers on medium and low-voltage levels.

Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is assessed through numeric comparisons with an optimal and two heuristic charging strategies (i.e., myopic and greedy). The results demonstrate that the EBDC method can achieve a satisfactory performance in improving the scalability and the balance between the EV charging demand and wind power supply of buildings.Note to Practitioners—This paper is motivated by the challenging problem due to the substantial charging load of electric vehicles (EVs) on the power grid. Nowadays, modern technological advances and the rapid increase of high-rise buildings have promoted the development of building integrated/mounted wind turbines. As the EVs are usually parked in buildings for a large proportion of time every day, the issue to best utilize locally generated wind power of buildings to suffice EV travelling requirements shows vital significance in reducing their dependence on the power grid. However, there exist two main challenges including: 1) the multiple uncertainties regarding the uncertain wind power generation and the random driving behaviors of EVs and 2) the scalability of the solution method. To tackle the first challenge, the idea of model predictive control is introduced to make charging decisions at each stage based on a short-term prediction of the on-site wind power and the current collection of EVs parked there. To consider the scalability and overcome the lack of global charging information of all EVs in practical deployment, an iterative EV-based decentralized charging algorithm (EBDC) is derived, in which each EV can dynamically update its own charging decisions according to a dynamic charging “price” announced by the buildings. Alternatively, the buildings dynamically adjust the charging “price” to motivate the EVs to get charged during the time periods with sufficient wind power supply. Numeric results demonstrate that the EBDC method is scalable and performs well in improving the balance between the EV charging demand and the wind power supply of buildings.

Transmission system operators apply selective load-shedding plans to prevent system wide interruptions and black-outs due to generation and transmission adequacy issues. If the load-shedding plan is activated, the electricity supply is intentionally switched off in indicated areas for a fixed period of time. The burden of load-shedding plans thus falls on a subset of consumers, while the benefits accrue to all consumers. This results in public opposition as illustrated with the publication of the load-shedding plan in Belgium in the winter of 2014 - 2015. To improve the social acceptability of load-shedding plans, we analyzed the unfairness of load-shedding plans based on Gini-based inequality indices and studied a top-down socialized compensation scheme and bottom-up priority service contracts to indirectly reduce the unfairness. The analysis is executed for a simplified version of the Belgian load-shedding plan for the winter of 2014 - 2015.

Selection of the most appropriate communications technology for a smart grid (SG) application is far from trivial. We propose such a feasibility assessment starting from identification of key performance indicators (KPIs) required for peer-to-peer (P2P) energy trading and grid control operations from a communications perspective. A set of cross-disciplinary KPIs, both quantitative and qualitative, are considered from communications, power, business, actor involvement, financial, and demand side management categories. They serve as a general baseline for use cases, as there have been few previous works attempting to capture the essential features of P2P SG operations. The KPIs are briefly identified along with their relations to P2P energy trading and grid control. A straightforward comparison of the quantitative and qualitative KPIs' impact on technology selection is not feasible. This paper addresses the comparison with: 1) a prioritization of the KPIs using the analytic hierarchy process; 2) a comparison of technology solutions evaluated in our previous works against the KPIs' requirements; and 3) a total feasibility evaluation of the solutions against selected KPIs. The prioritization shows latency, reliability, security, scalability, robustness, costs of information and communication technologies (ICT) devices, and costs of ICT deployment are the most important KPIs in enabling P2P energy trading and grid control. Further, the technology feasibility assessment enables identification of the most suitable candidates for an SG application.

This paper describes a voltage stabilizing control mechanism using the available flexibility of smart devices within one household. The flexibility of all types of smart appliances is used, especially smart on/off devices. The main advantage of the developed control system is that it does not require a communication network between the different households, only locally available measurements, such as the household supply voltage, are taken into account. The control system will be rolled out in a real life pilot test. Simulation results point out that the amount of over and under voltage occurrences on average are lowered with 35%.

The novelty of this paper is that it incorporates real world situation like photovoltaic (PV) penetration variability in a test network in order to evaluate its impact on the reconfiguration of the network with an aim to reduce the losses. This paper shows that an incremental increase in PV penetration will result in either the same optimal configuration or in a configuration that is very similar in the sense that the new optimal configuration is obtained by simply transferring the terminal load between feeders of the previous optimal configuration. This result helps in avoiding the computational effort in finding the optimal configuration when an incremental change occurs in PV penetration. A simple parameter namely the minimum power loss reduction parameter helps in deciding the feeders involved in the load transfer.

This paper considers hourly reconfiguration of a low voltage distribution network with the objectives of minimizing power loss and voltage deviation. The uncertainty in photovoltaic (PV) generation which in turn will affect the optimum configuration is tackled with the help of scenario analysis. In the proposed strategy of the scenario analysis called the Internal method, final non-dominated solutions (configurations) will have a relative acceptable performance in all scenarios. The use of scenario analysis helps in reducing the number of switches that needs to be installed in order to cater for the uncertainty in PV generation. This is possible because the PV generation values for a given hour during different days are taken into account by means of defining various scenarios when finding out the optimum configuration. Non-dominated sorting genetic algorithm (NSGA-II) used in this paper generates non-dominated solutions, demonstrating their trade-offs. Finally, the best compromise solution can be selected depending on the decision maker's requirement.

Detailed large-scale simulations require a lot of data. Residential electrical load profiles are well protected by privacy laws. Representative residential electrical load generators get around the privacy problem and allow for Monte Carlo simulations. A top-down model of the residential electrical load, based on a dataset of over 1300 load profiles, is presented in this paper. The load profiles are clustered by a Mixed Model to group similar ones. Within the group, a behavior model is constructed with a Markov model. The states of the Markov models are based on the probability distribution of the electrical power. A second Markov model is created to randomize the behavior. A load profile is created by first performing a random-walking of the Markov models to get a sequence of states. The inverse of the probability distribution of the electrical power is used to translate the resulting states into electrical power.

An essential part of the bachelor program in Electrical Engineering at the Katholieke Universiteit Leuven since many years is a number of design projects that teach the basics of electronics design engineering to our students. The major project consists of an academic-year-long design task that is carried out by a group of about 20 students. These students are trained to operate as a multidisciplinary team based of sub- teams that handle the different design problems in a multidisciplinary way. Teaching assistants are added to the team to serve either as expert designer or as project leader of a team. In this way the students learn a lot about the circuits, systems and software they conceive but they also develop their teamwork, leadership and presentation skills. In this paper we will give an overview of the technical content of this project and describe the way how it is organised. Taking this project as an example, the rationale behind this kind of interdisciplinary design projects is depicted.

This paper proposes a cooperative mechanism, to tackle distributed denial of service (DDoS) attacks based on cooperation between the victim server and customer edge routers of the ISPs (internet service providers) that have traffic toward the victim server. The mechanism tackles the attack in three consecutive phases: first, before attack packets can converge to saturate the bandwidth, the victim server through edge routers of its ISP, regulates traffic rate at which traffic load falls below upper bound of its bandwidth (control phase); second, the victim server installs leaky- buckets at customer edge routers of all ISPs that have traffic toward it and then, through a feedback- control process adjusts the size of leaky buckets appropriately (stabilization phase); third, based on a finger- print test, the victim server requests those customer edge routers that purely carry good traffic to remove the leaky-bucket and then based on a reference profile fairly adjusts size of leaky-buckets for the remaining customer edge routers such that those routers that carry both good and attack traffic get bigger leaky-bucket sizes compared to those routers that totally carry attack traffic. Simulation results shows that our technique effectively, defenses a victim server against various DDoS attacks.

Overlay networks are used as proxies which mediate communication between an application and its users with-out revealing the application’s location (IP address). The capability that users can communicate with an application without knowing its location is called location-hiding. Al-though recent years have provided little literature about location-hiding such as Tor or anonymous web publishing, most Internet activities where anonymity is desired require only sender and relationship anonymity, thereby location-hiding needs more academic effort. This paper proposes a novel architecture to achieve location-hiding. We describe the design of a dynamic multilayer routing (DMR) where users can communicate withan application without knowing any information about its location (its IP address). The essential factors of DMR protocol are multi-layering, reconfiguration and host-diversity. The goal of DMR is to overcome or reduce several draw-backs of static structure based techniques. Through analytical analysis, this paper provides a de-tailed study of DMR architecture and shows that DMR is completely strong against penetration attacks. Our analysis shows that attackers have a negligible chance (e.g., 10−8)to penetrate the architecture and disclose the application’slocation.

Distributed intelligence and secure interconnected communication networks constitute recognized key factors for the economic operation of electricity infrastructures in competitive power markets. Hence, electric power utilities need to extend risk management frameworks with adequate tools for assessing consequences of ICT (Information and Communication Technologies) threats on their critical business. This requires realistic probability estimates to cyber threat occurrences and consequent failure modes. Due to data sensitivity and rapid discovery of new vulnerability exploits, historical data series of ICT failures affecting power control infrastructures are not sufficient for a timely risk treatment. Such lack of data can partially be overcome by setting up testbeds to run controlled experiments and collect otherwise unavailable data related to cyber misbehaviours in power system operation. Within the project CRUTIAL (CRitical UTility InfrastructurAL resilience) two testbed platforms have been set up for experimentally evaluating malicious threats on macro and micro grid control scenarios. Results from experimental campaigns are analyzed in the paper by means of an evaluation framework.

Filtering techniques are one of the main approaches to protect applications from denial of service attacks (DoS).However filtering techniques suffer from two main challenges: a) the accuracy detection of DoS traffic and b) processing time. Fosel (filtering with the help of an overlay security layer) has been proposed to protect application sites from denial-of-service attacks. The Fosel architecture addresses how an efficient and well-suited filter can be designed to improve the filtering challenges. This paper explores the effectiveness of the Fosel architecture by implementing an experimental testbed. Experimental study shows that by employing the Fosel architecture, DoS attacks have a negligible chance to saturate the target by malicious packets. These results confirm simulation study of Fosel and provide an empirical evidence that Fosel can be used to tolerate DoS attacks.

Today, overlay networks are used as a promising platform to deploy wide area of applications and services in the Internet. The application level state maintained by the overlay networks should have high degree of availability.This can be compromised when a significant percentage of overlay nodes fails simultaneously (massive failure). The first problem: when a significant fraction of nodes fails simultaneously the loss rate increases awfully. The second problem: End-to-end latency increases dramatically when massive failure occurs. The third problem: in some overlay networks, an adversary can discover connectivity information of routing path easily and consequently he can prevent communication by attacking only the specific nodes.Random path diversity (RPD) is a technique that we use to handle such problems. This paper shows that how sending the same packet through few overlay paths in a random manner improves robustness of overlay networks against massive failure and routing path discovery. The Chord network is a case study of this paper.Experimental results show RPD makes the Chord overlay network more robust against massive failure. Results show 4-RPD (i.e. simultaneously sending the same packet through four different paths) improves resilience of Chord against massive failure on average 40%. It also reduces end-to-end latency made by massive failure more than 30% (on average). By the way randomness gives the network anonymity that makes network more complicated for routing path discovery. Results show 4-RPD improves anonymity more than 55% in compare to no diversity.

Although the financial crisis shrinks oil price, human as an entire race still face the future of a hot, flat and crowded world [1]. Therefore, a critical transformation from our traditional energy consumption manner of fossil fuel combustion must be made. After years of endeavor in research and experiment, a number of Renewable Energy Sources (RES) has already become technically available as alternatives. A pivotal task still needed to be carried out is adapting the existing electricity infrastructure, which is still a very efficient energy delivery facility, to incorporate emerging RES openly and equally. For the purpose of wide adoption of RES, which results in the evolution to next generation infrastructure for electricity, impellers should contain economic and political measures, rather than only technology. In this paper, we introduced the architecture of future electricity infrastructure based on microgrids for high RES penetration. Moreover, we brief the mechanisms of a prevailing electricity market, for economical incentives of RES accommodation. Most important of all, the multi-agent model of the proposed system and its coordination mechanism within the market environment is elaborated.

In the last few years, P2P systems have rapidly evolved and emerged as a promising platform to deploy new applications and services in the internet. The main reasons for this is that P2P systems are self-organizing, have a decentralized nature, good scalability, efficient query search, and good resilience in the presence of node failures. However malicious faults (attacks) are serious threats in these systems.

Overlay networks enable applications to communicate with users without disclosing their IP addresses; hence overlay networks are used to protect applications against DoS attacks by hiding an applicationpsilas location. This paper analyzes three popular overlay networks (Chord, CAN, and Pastry) by simulation to answer to this question: which topology is more favorable for location-hiding and resisting against DoS attacks? Simulation results show that a CAN overlay network has a better topology structure for location-hiding and resistance against DoS attacks than Chord and Pastry. Simulation results also show the topologies with low average vertex degree are favorable for location-hiding techniques.

Today Internet is becoming an emerging technology for remote control of industrial applications, where one site needs to control another site remotely (e.g. power plants controllers). Denial-of-Service (DoS) attacks may cause significant disruptions to the Internet which will threaten the operation of such network based control systems. Overlay networks have been proposed to protect Internet application sites by location-hiding technique. This paper analyzes a large domain of previous approaches against this problem. This paper addresses how an interface to an overlay network can be designed such that communication services among geographically distributed application sites are secured against DoS attacks. This paper presents a novel architecture called overlay protection layer (OPL) that proactively protect application sites from DoS attacks. Through simulation this paper shows DoS attacks have a negligible chance to disrupt communications services via the OPL architecture. Even if attackers attack 50% of overlay nodes via a Distributed DoS attack still 75% of communication channels are available.

Denial of service (DoS) attacks are major threat againstavailability in the Internet. A large number of countermea-sure techniques try to detect attack and then filter out DoSattack packets. Unfortunately these techniques that filterDoS traffic by looking at known attack patterns or statisticalanomalies in the traffic patterns can be defeated by chang-ing the attack patterns and masking the anomalies that aresought by the filter. Hence, detecting DoS traffic is one ofthe main challenges for filtering techniques. Furthermoretechniques that drop any malicious packet need to processthe packet and processing is time-consuming.This paper addresses how an efficient and good filter canbe designed by helping an overlay network layer to mitigateDoS attacks. Fosel (Filtering by helping an Overlay Secu-rity Layer) filter is independent from DoS attack types, sowe do not worry about the changing attack patterns. Fur-thermore it reduces processing time noticeably.Through simulation this paper shows by employing Foselfilter, DoS attacks have a negligible chance to saturate thetarget by malicious packets. Our simulation demonstratesthat Fosel architecture reduces the probability of successfulattack to minuscule levels. Furthermore Fosel is between10% and 50% faster than SOS (Secure Overlay Services) architecture to drop malicious packets based on attackrate.

Today, peer-to-peer (P2P) systems are used as a promising platform to deploy new applications and services in the internet. There are much interest in emerging P2P overlay network because they provide a good substrate for creating large scale data sharing, content distribution and application-level multicast applications. Overlay networks also have following advantages: redundant storage, efficient query search, anonymity, fault-tolerance, trust and authentication.This paper compares and analyzes various types of P2P overlay networks from dependability view. Main topics of comparison and analysis are scalability, reliability, security, availability, integrity and anonymity. We verify dependability analysis of P2P overlay networks by simulation. Both analysis and simulation results show that the purely structured decentralized type (e.g.Chord) is the most dependable P2P overlay network.

The general approach to fault tolerance in uniprocessor systems is to maintain enough time redundancy in the schedule so that any task instance can be re-executed in presence of faults during the execution. In this paper a scheme is presented to add enough and efficient time redundancy to the earliest-deadline-first (EDF) scheduling policy for periodic real-time tasks. This scheme can be used to tolerate transient faults during the execution of tasks. We describe a recovery scheme which can be used to re-execute tasks in the event of transient faults and discuss conditions that must be met by any such recovery scheme. For performance evaluation of this idea a tool is developed.

The domestic consumer has to take advantage of the opportunities from the liberalized energy markets and this calls for technological support, without which the changes threaten to remain rather theoretical. In the following we describe a proposed ICT solution for the communication in the new intelligent electrical residential installations.

This paper presents the performance analysis of several well-known partitioning scheduling algorithms in real-time and fault-tolerant multiprocessor systems. Both static and dynamic scheduling algorithms are analyzed. Partitioning scheduling algorithms, which are studied, are heuristic algorithms that are formed by combining any of the bin-packing algorithms with any of the schedulability conditions for the rate-monotonic (RM) and earliest-deadline-first (EDF) policies. A tool is developed which enables to experimentally evaluate the performance of the algorithms from the graph of tasks. The results show that among several partitioning algorithms evaluated, the RM-small-task (RMST) algorithm is the best static algorithm and the EDF-best-fit (EDF-BF) is the best dynamic algorithm, for non fault-tolerant systems. For fault-tolerant systems which require about 49% more processors, the results show that the RM-first-fit decreasing utilization (RM-FFDU) is the best static algorithm and the EDF-BF is the best dynamic algorithm. To decrease the number of processors in fault-tolerant systems, the RMST is modified. The results show that the modified RMST decreases the number of required processors between 7% and 78% in comparison with the original RMST, the RM-FFDU and other well-known static partitioning scheduling algorithms

The general approach to fault tolerance in uniprocessor systems is to use time redundancy in the schedule so that any task instance can be re-executed in presence of faults during the execution. In this paper a scheme is presented to add enough and efficient time redundancy to the rate-monotonic (RM) scheduling policy for periodic real-time tasks. This scheme can be used to tolerate transient faults during the execution of tasks. For performance evaluation of this idea a tool is developed

Electrical energy applications rely ever more on an information infrastructure (info'structure), consisting of heterogeneous off-the-shelf information and communication technology for hardware, software and networking. This provides flexibility for the application, but also implies vulnerabilities as the electrical energy infrastructure depends on the correct functioning of the info'structure, in spite of random (physical) and malicious faults. In order to provide a robust application behaviour for the energy application, the info'structure needs to be fault-tolerant and able to deal with a dynamic environment; middleware can provide the required graceful degradation in case of unrecovered failures, rather than resulting in a complete breakdown.

Users expect future handheld devices to provide extended multimedia functionality and have long battery life. This type of application imposes heavy constraints on performance and power consumption and forces designers to optimize all parts of their platform. Evaluating the overall platform power breakdown is therefore critical to determine where to spend the efforts on power optimization. Surprisingly, few studies exist on that topic and decisions generally rely on common belief. We have realized a complete power breakdown for a realistic platform to identify the major power bottlenecks. This paper presents this power assessment of a realistic heterogeneous network on chip platform including processors, network and data/instruction memory hierarchy, running a video processing chain from camera to display. Our power breakdown identifies the main bottlenecks in the memory hierarchy and the foreground memory, and shows that global interconnect is not that critical for a well-optimized application mapping.

Current loop buffer organizations for very large instruction word processors are essentially centralized. As a consequence, they are energy inefficient and their scalability is limited. To alleviate this problem, we propose a clustered loop buffer organization, where the loop buffers are partitioned and functional units are logically grouped to form clusters, along with two schemes for buffer control, which regulate the activity in each cluster. Furthermore, we propose a design-time scheme to generate clusters by analyzing an application profile and grouping closely related functional units. The simulation results indicate that the energy consumed in the clustered loop buffers is, on average, 63 percent lower than the energy consumed in an uncompressed centralized loop buffer scheme, 35 percent lower than a centralized compressed loop buffer scheme, and 22 percent lower than a randomly clustered loop buffer scheme.

Portable consumer devices are increasing their capabilities more and more and can now implement new multimedia algorithms that were reserved only for powerful workstations a few years ago. Unfortunately, the original design characteristics of such algorithms do not often allow them to be ported directly to current embedded devices. These algorithms share complex and intensive dynamic memory use and actual embedded systems cannot provide efficient general-purpose memory management as it is needed. As a result, dynamic memory optimizations are a requirement when porting these applications. Within these optimizations, the refinement of the dynamically (de)allocated abstract data type implementations in the complex multimedia applications involved is one of the most important and difficult parts for an efficient mapping of the algorithms on low-power and high-speed embedded consumer devices. We describe a high-level approach for modeling and refining complex data types using abstract derived classes in C++. This approach enables the multimedia developer to compose, evaluate and refine complex data types in a conceptually straightforward way, without a time-consuming programming effort

Users expect future handheld devices to provide extended multimedia functionality and have long battery life. This type of application imposes heavy constraints on both (realtime) performance and energy consumption and forces designers to optimise all parts of their platform. In this experiment we focus on the different processor core design options for embedded platforms, including the effect of instruction memory hierarchy on the energy consumption. The results show that significant improvements for energy efficiency and/or performance over currently used RISC or VLIW processors can be achieved. We conclude, based on concrete data for a realistic application, that different styles, including both configurable hardware and instruction set processors, find their way into heterogeneous platforms and designers need to be aware of the trade-offs. Secondly, we show for the same application task that a heavily optimised instruction/configuration memory hierarchy can significantly reduce the energy consumption of this part, so it forms a crucial part of every energy aware design.

The DepAuDE architecture provides middleware to integrate fault tolerance support into distributed embedded automation applications. It allows error recovery to be expressed in terms of recovery strategies, i.e., lightweight code fragments separated from the application code. At run time, the middleware orchestrates their execution. This paper reports on the integration of different recovery scripts into a distributed run-time environment applied to the embedded automation system of a primary substation. An instrumented automata-based design environment allows the application to be deployed on a heterogeneous platform with several real-time operating systems. While the middleware detects the errors and selects the correct recovery scripts to be executed, the application functionality is maintained through system reconfiguration or graceful degradation. The added value comes from the flexibility to modify recovery strategies without requiring major modifications to the application, while tolerating the same physical faults as in the dedicated hardware solutions.

Due to the heterogeneous nature of networks and end-systems in distributed multimedia systems, multimedia applications should ideally be designed to counteract fluctuations in network bandwidth and end-system processing capacities for providing end users a certain degree of quality of service (QoS). This requirement can be satisfied with scalable applications. In addition, with the current evolution in run-time reconfigurable computing, run-time reconfigurable multimedia platforms are becoming increasingly viable. In this paper, an end-to-end delivery chain framework for mapping scalable networked multimedia applications on reconfigurable platforms is presented. The framework is demonstrated by a case study of a 3D game running on a prototype run-time reconfigurable platform.

Efficient exploitation of temporal locality in the memory accesses on array signals can have a very large impact on the power consumption in embedded data dominated applications. The effective use of an optimized custom memory hierarchy or a customized software controlled mapping on a predefined hierarchy is crucial for this. Only recently have effective systematic techniques to deal with this specific design step begun to appear. They are still limited in their exploration scope. In this paper we construct the design space by introducing three parameters which determine how and when copies are made between different levels in a hierarchy, and determine their impact on the total memory size, storage-related power consumption, and code complexity. Strategies are then established for an efficient exploration, such that cost-effective solutions for the memory size/power trade-off can be achieved. The effectiveness of the techniques is demonstrated for several real-life image processing algorithms.

Efficient exploitation of temporal locality in the memory accesses on array signals can have a very large impact art the power consumption in embedded data dominated applications. The effective use of an optimized custom memory hierarchy or a customized software controlled mapping on a predefined hierarchy, is crucial for this. Only recently have effective systematic techniques to deal with this specific design step begun to appear They were still limited in their exploration scope. In this paper we introduce an extended formalized methodology based on an analytical model of the data reuse of a signal. The cost parameters derived from this model define the search space to explore and allow us to exploit the maximum data reuse possible. The result is an automated design technique to find power efficient memory hierarchies and generate the corresponding optimized code.

Depending on varying prices of electricity, an optimal power-dispatching system (OPDS) is developed to minimize the cost of power consumption in the electrochemical process of zinc (EPZ). Due to the complexity of the EPZ, the main factors influencing the power consumption are determined by qualitative analysis, and a series of conditional experiments is conducted to acquire sufficient data, then two backpropagation neural networks are used to describe these relationships quantitatively. An equivalent Hopfield neural network is constructed to solve the optimization problem where a penalty function is introduced into the network energy function so as to meet the equality constraints, and inequality constraints are removed by alteration of the Sigmoid function. This OPDS was put into service in a smeltery in 1998. The cost of power consumption has decreased significantly, the total electrical energy consumption is reduced, and it is also beneficial to balancing the load of the power grid. The actual results show the effectiveness of the OPDS. This paper introduces a successful industrial application and mainly presents how to utilize neural networks to solve particular problems for the real world.

This paper describes a novel approach to software-implemented fault tolerance for distributed applications. This new approach can be used to enhance the flexibility and maintainability of the target applications in a cost-effective way. This is reached through a framework-approach including: (1) a library of fault tolerance functions; (2) a middleware application coordinating these functions; and (3) a language for the expression of nonfunctional services, including configuration, error recovery and fault injection. This framework-approach increases the availability and reliability of the application at a justifiable cost, also thanks to the re-usability of the components in different target systems. This framework-approach further increases the maintainability due to the separation of the functional behavior from the recovery strategies that are executed when an error is detected, because the modifications to functional and nonfunctional behavior are, to some extent, independent, and hence less complex to deal with. The resulting tool matches well, e.g., with current industrial requirements for embedded distributed systems, calling for adaptable and reusable software components. The "integration of this approach in an automation system of a substation for electricity distribution" reports this experience. This case study shows in particular the ability of the configuration-and-recovery language ARIEL to allow adaptability to changes in the environment. This framework-approach is also useful in the context of distributed automation systems that are interconnected via a nondedicated network.

Industrial embedded supercomputing applications benefit from a systematic approach to fault tolerance. The EFTOS (embedded fault-tolerant supercomputing) framework provides a flexible and adaptable set of fault-tolerance tools from which the application developer can choose to make an embedded application on a parallel or distributed system more dependable. A high-level description (recovery language) helps the developer specify the fault-tolerance strategies of the application as a second application layer; this separates functional from fault-tolerance aspects of an application, thus shortening the development cycle and improving maintainability. The framework incorporates a backbone (to hook a set of fault-tolerance tools onto, and to coordinate the fault-tolerance actions) and a presentation layer (to monitor and test the fault tolerance behavior). A practical implementation is described with its performance evaluation, using an industrial case study from the energy-transport area, as well as an analytic deduction of the appropriateness of fault-tolerance techniques for various application profiles.

This paper presents the design alternatives for reconfigurable instruction set processors (RISP) from a hardware/software point of view. Reconfigurable instruction set processors are programmable processors that contain reconfigurable logic in one or more of its functional units. Hardware design of such a type of processors can be split in two main tasks: the design of the reconfigurable logic and the design of the interfacing mechanisms of this logic to the rest of the processor. Among the most important design parameters are: the granularity of the reconfigurable logic, the structure of the configuration memory, the instruction encoding format, and the type of instructions supported. On the software side, code generation tools require new techniques to cope with the reconfigurability of the processor. Aside from traditional techniques, code generation requires the creation and evaluation of new reconfigurable instructions and the selection of instructions to minimize reconfiguration time. The most important design alternative on the software side is the degree of automatization present in the code generation tools.

User-triggered checkpointing and rollback is proposed as a system-independent and flexible way to integrate backward error recovery in long-running, computation-intensive message-passing applications on large parallel multicomputers. It employs library calls to coordinate the checkpointing, allowing a non-blocking and scalable approach that requires no protocol to save a consistent state because the coordination among the processes is implicit. The explicit indication of the checkpoint contents (i.e. the items of which the state must be saved) allows one to significantly reduce the amount of checkpoint data and the overhead. In contrast to other checkpointing approaches, the implementation does not rely on system-dependent features (like saving register-values or communication status) to save the state. Instead, re-executing the first part of the application brings the system-specific items into a consistent state with the rest of the checkpoint contents that is restored from the saved checkpoint data.