45 research outputs found
Energy-Efficient Task Mapping for Data-Driven Sensor Network Macroprogramming
International audienceData-driven macroprogramming of wireless sensor networks (WSNs) provides an easy to use high-level task graph representation to the application developer. However, determining an energy-efficient initial placement of these tasks onto the nodes of the target network poses a set of interesting problems. We present a framework to model this task-mapping problem arising in WSN macroprogramming. Our model can capture placement constraints in tasks, as well as multiple possible routes in the target network. Using our framework, we provide mathematical formulations for the task-mapping problem for two different metrics -- energy balance and total energy spent. For both metrics, we address scenarios where a) a single or b) multiple paths are possible between nodes. Due to the complex nature of the problems, these formulations are not linear. We provide linearization heuristics for the same, resulting in mixed-integer programming (MIP) formulations. We also provide efficient heuristics for the above. Our experiments show that our heuristics give the same results as the MIP for real-world sensor network macroprograms, and show a speedup of up to several orders of magnitude. We also provide worst-case performance bounds of the heuristics
Enabling High-Level Application Development for the Internet of Things
Application development in the Internet of Things (IoT) is challenging
because it involves dealing with a wide range of related issues such as lack of
separation of concerns, and lack of high-level of abstractions to address both
the large scale and heterogeneity. Moreover, stakeholders involved in the
application development have to address issues that can be attributed to
different life-cycles phases. when developing applications. First, the
application logic has to be analyzed and then separated into a set of
distributed tasks for an underlying network. Then, the tasks have to be
implemented for the specific hardware. Apart from handling these issues, they
have to deal with other aspects of life-cycle such as changes in application
requirements and deployed devices. Several approaches have been proposed in the
closely related fields of wireless sensor network, ubiquitous and pervasive
computing, and software engineering in general to address the above challenges.
However, existing approaches only cover limited subsets of the above mentioned
challenges when applied to the IoT. This paper proposes an integrated approach
for addressing the above mentioned challenges. The main contributions of this
paper are: (1) a development methodology that separates IoT application
development into different concerns and provides a conceptual framework to
develop an application, (2) a development framework that implements the
development methodology to support actions of stakeholders. The development
framework provides a set of modeling languages to specify each development
concern and abstracts the scale and heterogeneity related complexity. It
integrates code generation, task-mapping, and linking techniques to provide
automation. Code generation supports the application development phase by
producing a programming framework that allows stakeholders to focus on the
application logic, while our mapping and linking techniques together support
the deployment phase by producing device-specific code to result in a
distributed system collaboratively hosted by individual devices. Our evaluation
based on two realistic scenarios shows that the use of our approach improves
the productivity of stakeholders involved in the application development
A Compilation Framework for Macroprogramming Networked Sensors
Abstract. Macroprogramming—the technique of specifying the behavior of the system, as opposed to the constituent nodes—provides application developers with high level abstractions that alleviate the programming burden in develop- ing wireless sensor network (WSN) applications. However, as the semantic gap between macroprogramming abstractions and node-level code is considerably wider than in traditional programming, converting the high level specification to running code is a daunting process, and a major hurdle to the acceptance of macroprogramming.
In this paper, we propose a general compilation framework for a data-driven macroprogramming language that allows for plugging in different modules implementing various stages of compilation. We also demonstrate an actual instantiation of our framework by showing an end-to-end solution for compiling macro- programs. Our compiler provides the final code to be deployed on real nodes as well as an estimate of the costs the running system will incur, e.g., in terms of messages exchanged. We compared the auto-generated code against a hand- coded version for the same application behavior to verify the outcome of our compiler
Dioptase: a distributed data streaming middleware for the future web of things
International audienceThe Internet of Things (IoT) is a promising concept toward pervasive computing as it may radically change the way people interact with the physical world, by connecting sensors to the Internet and, at a higher level, to the Web, thereby enacting a Web of Things (WoT). One of the challenges raised by the WoT is the in-network continuous processing of data streams presented by Things, which must be investigated urgently because it affects the future data models of the IoT, and is critical regarding the scalability and the sustainability required by the IoT. This cross-cutting concern has been previously studied in the context of Wireless Sensor Networks (WSN) given the focus on the acquisition and in-network processing of sensed data. However, proposed solutions feature various proprietary and highly specialized technologies that are difficult to integrate and complex to use, which represents a hurdle to their wide deployment. At the other end of the spectrum, cloud-based solutions introduce a too high energy cost for the envisioned IoT scale, considering the energy cost of communication over computation. There is thus a need for a distributed middleware solution for data stream management that leverages existing WSN work, while integrating it with today's Web technologies in order to support the required flexibility and the interoperability of the IoT.Toward that goal, this paper introduces Dioptase, a lightweight Data Stream Management System for the WoT, which aims to integrate the Things and their streams into today's Web by presenting sensors and actuators as Web services. The middleware specifically provides a way to describe complex fully-distributed stream-based mashups and to deploy them dynamically, at any time, as task graphs, over available Things of the network, including resource-constrained ones