Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

[서평]「Component Software」 - Beyond Object-Oriented Programming -

생체의 모델링과 Simulation

The Internet of Things (IoT) envisages a future in which digital and physical things or objects (e.g., smartphones, TVs, cars) can be connected by means of suitable information and communication technologies, to enable a range of applications and services. The IoT’s characteristics, including an ultra-large-scale network of things, device and network level heterogeneity, and large numbers of events generated spontaneously by these things, will make development of the diverse applications and services a very challenging task. In general, middleware can ease a development process by integrating heterogeneous computing and communications devices, and supporting interoperability within the diverse applications and services. Recently, there have been a number of proposals for IoT middleware. These proposals mostly addressed wireless sensor networks (WSNs), a key component of IoT, but do not consider RF identification (RFID), machine-to-machine (M2M) communications, and supervisory control and data acquisition (SCADA), other three core elements in the IoT vision. In this paper, we outline a set of requirements for IoT middleware, and present a comprehensive review of the existing middleware solutions against those requirements. In addition, open research issues, challenges, and future research directions are highlighted.

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Middleware for Internet of Things: A Survey

State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing

Considerable research has been performed in applying run-time reconfigurable component models to the domain of wireless sensor networks The ability to dynamically deploy and reconfigure software components has clear advantages in sensor network deployments, which are typically large in scale and expected to operate for long periods in the face of node mobility, dynamic environmental conditions and changing application requirements To date, research on component and binding models for sensor networks has primarily focused on the development of specialized component models that are optimized for use in resource-constrained environments However, current approaches impose significant overhead upon developers and tend to use inflexible binding models based on remote procedure calls To address these concerns, we introduce a novel component and binding model for networked embedded systems (LooCI) LooCI components are designed to impose minimal additional overhead on developers Furthermore, LooCI components use a novel event-based binding model that allows developers to model rich component interactions, while providing support for easy interception, re-wiring and re-use A prototype implementation of our component and binding model has been realised for the SunSPOT platform Our preliminary evaluation shows that LooCI has an acceptable memory footprint and imposes minimal overhead on developers

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LooCI: a loosely-coupled component infrastructure for networked embedded systems

The domain of digital rights management (DRM) is currently lacking a generic architecture that supports interoperability and reuse of specific DRM technologies. This lack of architectural support is a serious drawback in light of the rapid evolution of a complex domain like DRM. It is highly unlikely that a single DRM technology or standard will be able to support the diversity of devices, users, platforms, and media, or the wide variety of system requirements concerning security, flexibility, and efficiency. This paper analyses state-of-the-art DRM technologies and extracts from them high level usage scenarios according to content consumers, producers, and publishers. In addition, the key services are identified both from a functional and security perspective. Identifying key DRM services and locating them in an overall structure brings us one step closer to a software architecture for DRM. Having available a software architecture should help the DRM community in reasoning about DRM systems, and in achieving reuse and interoperability of multiple domain-specific DRM technologies and standards.

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Towards a software architecture for DRM

When a self-adaptive system detects that its adaptation goals may be compromised, it needs to determine how to adapt to ensure its goals. To that end, the system can analyze the possible options for adaptation, i.e., the adaptation space, and pick the best option that achieves the goals. Such analysis can be resource and time consuming, in particular when rigorous analysis methods are applied. Hence, exhaustively analyzing all options may be infeasible for systems with large adaptation spaces. This problem is further complicated as the adaptation options typically include uncertainty parameters that can only be resolved at runtime. In this paper, we present a machine learning approach to tackle this problem. This approach enhances the traditional MAPE-K feedback loop with a learning module that selects subsets of adaptation options from a large adaptation space to support the analyzer with performing efficient analysis. We instantiate the approach for two concrete learning techniques, classification and regression, and evaluate the approaches for two instances of an Internet of Things application for smart environment monitoring with different sizes of adaptation spaces. The evaluation shows that both learning approaches reduce the adaptation space significantly without noticeable effect on realizing the adaptation goals.

Efficient analysis of large adaptation spaces in self-adaptive systems using machine learning

Flooding is a critical global problem, which is growing more severe due to the effects of climate change. This problem is particularly acute in the state of Sao Paulo, Brazil, where flooding during the rainy season incurs significant financial and human costs. Another critical problem associated with flooding is the high level of pollution present in urban rivers. Efforts to address these problems focus upon three key research areas: river monitoring, modelling of river conditions and incident response. This paper introduces a rich next-generation middleware platform designed to support wireless sensor network based environmental monitoring along with a supporting hardware platform. This system has been deployed and evaluated in a real-world river monitoring scenario in the city of Sao Carlos, Brazil.

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A middleware platform to support river monitoring using wireless sensor networks

Internet of Things (IoT) applications require diverse sensors and actuators. However, contemporary IoT devices provide limited support for the integration of third-party peripherals. To tackle this problem, we introduce μPnP: a hardware and software solution for plug-and-play integration of embedded peripherals with IoT devices. μPnP provides support for: driver development, automatic integration of third-party peripherals, discovery and remote access to peripheral services. This is achieved through a low-cost hardware identification approach, a lightweight driver language and a multicast network architecture. Evaluation shows that μPnP has a minimal memory footprint, reduces development effort and provides true plug-and-play integration at orders of magnitude less energy than USB.

Sam Michiels

Papers

LooCI: a loosely-coupled component infrastructure for networked embedded systems

Towards a software architecture for DRM

Efficient analysis of large adaptation spaces in self-adaptive systems using machine learning

A middleware platform to support river monitoring using wireless sensor networks

μPnP: plug and play peripherals for the internet of things