Amol Bakshi

MILAN: A Model Based Integrated Simulation Framework for Design of Embedded Systems

We present MILAN, a model based extensible framework that facilitates rapid, multigranular performance evaluation of a large class of embedded systems, by seamlessly integrating different widely used simulators in to a unified environment. MILAN provides a formal paradigm for specification of structural and behavioral aspects of embedded systems, an integrated model-based approach, and a unified software environment for system design and simulation. This paper provides an overview of MILAN, discusses the Model Integrated Computing philosophy, and illustrates the high-level modeling concepts being developed in the MILAN project for embedded systems design and evaluation.

Algorithm design and synthesis for wireless sensor networks

Most of current research in wireless networked embedded sensing approaches the problem of application design as one of manually customizing network protocols. The design complexity and required expertise make this unsuitable for increasingly complex sensor network systems. We address this problem from a parallel and distributed systems perspective and propose a methodology that enables domain experts to design, analyze, and synthesize sensor network applications without requiring a knowledge of implementation details. At the core of our methodology is a virtual architecture for a class of sensor networks that hides enough system details to relieve programmers of the burden of managing low-level control and coordination, and provides algorithm designers with a clean topology and cost model. We illustrate this methodology using a real-world topographic querying application as a case study.

Expressing Sensor Network Interaction Patterns Using Data-Driven Macroprogramming

Wireless sensor networks (WSNs) are increasingly being employed as a key building block of pervasive computing infrastructures, owing to their ability to be embedded within the real world. So far, pervasive applications for WSNs have been developed in an ad-hoc manner using node-centric programming models, focusing on the behavior of single nodes. Instead, macro-programming models provide much higher levels of abstractions, allowing developers to reason on the sensor network as a whole. In this paper, we demonstrate how a wide range of interaction patterns commonly found in pervasive, embedded applications can be expressed using ATaG, a data-driven macro-programming language. To support this, we showcase real-world applications developed in ATaG, and consider both homogeneous, sense-only scenarios, and heterogeneous settings involving actuation on the environment under control

Enabling Scope-Based Interactions in Sensor Network Macroprogramming

Wireless sensor networks are increasingly employed to develop sophisticated applications where heterogeneous nodes are deployed, and multiple parallel activities must be performed. Therefore, application developers require the ability to partition the system based on the node characteristics, and specify complex interactions among different partitions. Existing programming abstractions for sensor networks tackled this problem by providing a notion of scoping. However, this rarely emerges as a first-class programming construct, hence limiting its applicability. To address this issue, in this paper we present a flexible notion of scoping in the context of a sensor network macroprogramming framework. Our approach enables the specification of complex interactions among system partitions, thus greatly simplifying the development process. Moreover, this is not detrimental to performance: our approach results reasonably close to an optimal solution computed with global system knowledge, while exhibiting a 70% gain w.r.t. baseline solutions.

A compilation framework for macroprogramming networked sensors

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 developing 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 macroprograms. 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 handcoded version for the same application behavior to verify the outcome of our compiler.

A Semantic Framework for Integrated Asset Management in Smart Oilfields

Integrated asset management (IAM) is the vision of IT- enabled transformation of oilfield operations where information integration from a variety of tools for reservoir modeling, simulation, and performance prediction will lead to rapid decision making for continuous production optimization. This paper describes the design of a model-based IAM system for production forecasting. Domain knowledge is captured through a formal modeling language that forms the basis for an intuitive user interface to the system. An IAM metacatalog captures domain knowledge as well as metadata about computational resources and data sets in a single ontological framework, thereby providing a unified mechanism for application, data, and workflow integration . The framework is designed to be portable across oilfield assets, to allow different classes of end users to interact with the integrated system, and to accomodate new domain knowledge, software applications, data sets, and workflows for IAM.

Structured Communication in Single Hop Sensor Networks

We propose a model for communication in single-hop wireless sensor networks and define and evaluate the performance of a robust, energy balanced protocol for a powerful and general routing primitive – (N,p,k 1,k 2) routing. This routing primitive represents the transfer of N packets among p nodes, where each node transmits at most k 1 packets and receives at most k 2 packets. Permutation routing is an instance of this primitive and has been recently studied by other researchers in the context of single-hop wireless radio networks. Our proposed protocol is the first to exploit the availability of a low-power control channel in addition to the “regular” data channel to achieve robustness in terms of handling node and link failures – both permanent and transient. We use a dynamic, distributed TDMA scheme for coordination over the control channel, which leads to collision-free transmissions over the data channel. The objective is to minimize overall latency, reduce per-node and overall energy consumption, and maintain energy balance. Our protocol is robust because the coordination is decentralized, there is no single point of failure, and one node failure affects only the packets destined for (and transmitted from) that particular node. Simulation results for different scenarios are presented.

Semantic web technologies for smart oil field applications

In model based oil field operations, engineers rely on simulations (and hence simulation models) to make important operational decisions on a daily basis. Three problems that are commonly encountered in such operations are: on-demand access to information, integrated view of information, and knowledge management. The first two problems of on-demand access and information integration arise because a number of different kinds of simulation models are created and used. Since these models are created by different processes and people, the same information could be represented differently across models. A unified view of the models and their simulations is desirable for decision making, and thus the necessity for information integration. Knowledge management refers to a systematic way to capture the rationale (knowledge) behind the various analyses performed by an engineer and decisions taken based on the analyses. It is critical to capture this knowledge for auditing, archiving, and training purposes. In this paper, we propose the application of semantic web technologies to address these problems. The key elements of the semantic web approach are the ontologies or the information schemas that model various elements from the domain, and a knowledge base (KB) which is a central repository of the instance information in the system. We present a modular approach for organizing the ontologies and outline the process that was followed to define the ontologies. We also describe the workflow that was used to populate the KB and briefly discuss some of our prototype applications that address the problems mentioned above. Based on our experience, semantic web technologies appear to be a highly promising approach to deal with these information management issues in the oilfield domain, although performance and tool support remain the key areas of concern at this stage.

Towards automatic synthesis of a class of application-specific sensor networks

Automatic synthesis of sensor network-based systems can be described as the process of translating a formal specification of application functionality into a particular task mapping, settings of available hardware knobs, and communication and coordination mechanisms among the sensor nodes, so as to meet the performance requirements and constraints. We propose a general methodology to tackle a specific class of this problem, based on analytical performance modeling, multigranularity system simulation, and automatic refinement of model parameters. To demonstrate the utility and feasibility of our proposed methodology, we define a system model for a class of sensor networks, and implement a software framework for its modeling and simulation. Our graphical design environment supports plug-and-play integration of different performance models, simulation and visualization suites, and even automatic design space exploration and optimization tools.

A Service Oriented Data Composition Architecture for Integrated Asset Management

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s).