Resource Scopes: Toward Language Support for Compositional Determinism

Complex real-time embedded systems should be compositional and deterministic in the resource, time, and value domains. Determinism eases the engineering of correct systems and compositionality simplifies the assembly of complex systems out of smaller modules. This paper describes the PEACOD framework that is developed to support deterministic behavior for resource consumption, value passing, and timing. The paper introduces the notions of determinism in the context of the resource, value, and temporal domains, and present the resource-scope language construct that can be used to program such deterministic behaviors. Furthermore, the paper also provides semantics for the resource scope construct and uses these semantics to show that the program behavior is preserved under composition. The paper briefly describes the current implementation of PEACOD

Design Space Exploration of Object Caches with Cross-Profiling

To avoid data cache trashing between heapallocated data and other data areas, a distinct object cache has been proposed for embedded real-time Java processors. This object cache uses high associativity in order to statically track different object pointers for worst-case execution-time analysis. However, before implementing such an object cache, an empirical analysis of different organization forms is needed. We use a cross-profiling technique based on aspect-oriented programming in order to evaluate different object cache organizations with standard Java benchmarks. From the evaluation we conclude that field access exhibits some temporal locality, but almost no spatial locality. Therefore, filling long cache lines on a miss just introduces a high miss penalty without increasing the hit rate enough to make up for the increased miss penalty. For an object cache, it is more efficient to fill individual words within the cache line on a miss

Time-predictable Chip-Multiprocessor Design

Abstract—Real-time systems need time-predictable platforms to enable static worst-case execution time (WCET) analysis. Improving the processor performance with superscalar techniques makes static WCET analysis practically impossible. However, most real-time systems are multi-threaded applications and performance can be improved by using several processor cores on a single chip. In this paper we present a time-predictable chipmultiprocessor system that aims to improve system performance while still enabling WCET analysis. The proposed chip-multiprocessor (CMP) uses a shared memory with a time-division multiple access (TDMA) based memory access scheduling. The static TDMA schedule can be integrated into the WCET analysis. Experiments with a JOP based CMP showed that the memory access starts to dominate the execution time when using more than 4 processor cores. To provide a better scalability, more local memories have to be used. We add a processor local scratchpad memory and split data caches, which are still time-predictable, to the processor cores. I

Evaluating Latency in Multiprocessing Embedded Systems for the Smart Grid

Smart grid endpoints need to use two environments within a processing system (PS), one with a Linux-type operating system (OS) using the Arm Cortex-A53 cores for management tasks, and the other with a standalone execution or a real-time OS using the Arm Cortex-R5 cores. The Xen hypervisor and the OpenAMP framework allow this, but they may introduce a delay in the system, and some messages in the smart grid need a latency lower than 3 ms. In this paper, the Linux thread latencies are characterized by the Cyclictest tool. It is shown that when Xen hypervisor is used, this scenario is not suitable for the smart grid as it does not meet the 3 ms timing constraint. Then, standalone execution as the real-time part is evaluated, measuring the delay to handle an interrupt created in programmable logic (PL). The standalone application was run in A53 and R5 cores, with Xen hypervisor and OpenAMP framework. These scenarios all met the 3 ms constraint. The main contribution of the present work is the detailed characterization of each real-time execution, in order to facilitate selecting the most suitable one for each application.This work has been supported by the Ministerio de Economía y Competitividad of Spain within the project TEC2017-84011-R and FEDER funds as well as by the Department of Education of the Basque Government within the fund for research groups of the Basque university system IT978-16. It has also been supported by the Basque Government within the project HAZITEK ZE-2020/00022 as well as the Ministerio de Ciencia e Innovación of Spain through the Centro para el Desarrollo Tecnológico Industrial (CDTI) within the project IDI-20201264; in both cases, they have been financed through the Fondo Europeo de Desarrollo Regional 2014-2020 (FEDER funds). It has also been supported by the University of the Basque Country within the scholarship for training of research staff with code PIF20/135

Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography

Autonomous robots are complex systems that require the interaction between numerous heterogeneous components (software and hardware). Because of the increase in complexity of robotic applications and the diverse range of hardware, robotic middleware is designed to manage the complexity and heterogeneity of the hardware and applications, promote the integration of new technologies, simplify software design, hide the complexity of low-level communication and the sensor heterogeneity of the sensors, improve software quality, reuse robotic software infrastructure across multiple research efforts, and to reduce production costs. This paper presents a literature survey and attribute-based bibliography of the current state of the art in robotic middleware design. The main aim of the survey is to assist robotic middleware researchers in evaluating the strengths and weaknesses of current approaches and their appropriateness for their applications. Furthermore, we provide a comprehensive set of appropriate bibliographic references that are classified based on middleware attributes.http://dx.doi.org/10.1155/2012/95901

Adaptive service discovery on service-oriented and spontaneous sensor systems

Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief

Performance assessment of real-time data management on wireless sensor networks

Technological advances in recent years have allowed the maturity of Wireless Sensor Networks (WSNs), which aim at performing environmental monitoring and data collection. This sort of network is composed of hundreds, thousands or probably even millions of tiny smart computers known as wireless sensor nodes, which may be battery powered, equipped with sensors, a radio transceiver, a Central Processing Unit (CPU) and some memory. However due to the small size and the requirements of low-cost nodes, these sensor node resources such as processing power, storage and especially energy are very limited. Once the sensors perform their measurements from the environment, the problem of data storing and querying arises. In fact, the sensors have restricted storage capacity and the on-going interaction between sensors and environment results huge amounts of data. Techniques for data storage and query in WSN can be based on either external storage or local storage. The external storage, called warehousing approach, is a centralized system on which the data gathered by the sensors are periodically sent to a central database server where user queries are processed. The local storage, in the other hand called distributed approach, exploits the capabilities of sensors calculation and the sensors act as local databases. The data is stored in a central database server and in the devices themselves, enabling one to query both. The WSNs are used in a wide variety of applications, which may perform certain operations on collected sensor data. However, for certain applications, such as real-time applications, the sensor data must closely reflect the current state of the targeted environment. However, the environment changes constantly and the data is collected in discreet moments of time. As such, the collected data has a temporal validity, and as time advances, it becomes less accurate, until it does not reflect the state of the environment any longer. Thus, these applications must query and analyze the data in a bounded time in order to make decisions and to react efficiently, such as industrial automation, aviation, sensors network, and so on. In this context, the design of efficient real-time data management solutions is necessary to deal with both time constraints and energy consumption. This thesis studies the real-time data management techniques for WSNs. It particularly it focuses on the study of the challenges in handling real-time data storage and query for WSNs and on the efficient real-time data management solutions for WSNs. First, the main specifications of real-time data management are identified and the available real-time data management solutions for WSNs in the literature are presented. Secondly, in order to provide an energy-efficient real-time data management solution, the techniques used to manage data and queries in WSNs based on the distributed paradigm are deeply studied. In fact, many research works argue that the distributed approach is the most energy-efficient way of managing data and queries in WSNs, instead of performing the warehousing. In addition, this approach can provide quasi real-time query processing because the most current data will be retrieved from the network. Thirdly, based on these two studies and considering the complexity of developing, testing, and debugging this kind of complex system, a model for a simulation framework of the real-time databases management on WSN that uses a distributed approach and its implementation are proposed. This will help to explore various solutions of real-time database techniques on WSNs before deployment for economizing money and time. Moreover, one may improve the proposed model by adding the simulation of protocols or place part of this simulator on another available simulator. For validating the model, a case study considering real-time constraints as well as energy constraints is discussed. Fourth, a new architecture that combines statistical modeling techniques with the distributed approach and a query processing algorithm to optimize the real-time user query processing are proposed. This combination allows performing a query processing algorithm based on admission control that uses the error tolerance and the probabilistic confidence interval as admission parameters. The experiments based on real world data sets as well as synthetic data sets demonstrate that the proposed solution optimizes the real-time query processing to save more energy while meeting low latency.Fundação para a Ciência e Tecnologi