Architecture-Independent Programming for Wireless Sensor Networks / Wiley Series on Parallel and Distributed Computing (PDF)

Preface. Acknowledgments. 1. Introduction. 1.1 Sensor networks and traditional distributed systems. 1.2 Programming of distributed sensor networks. 1.2.1 Layers of programming abstraction. 1.2.1.1 Service-oriented specification. 1.2.1.2 Macroprogramming. 1.2.1.3 Node-centric programming. 1.2.2 Lessons from parallel and distributed computing. 1.3 Macroprogramming: What and Why? 1.4 Contributions and Outline. 2. The Abstract Task Graph. 2.1 Target applications and architectures. 2.2 Key Concepts. 2.2.1 Data Driven Computing. 2.2.1.1 Program flow mechanisms. 2.2.1.2 Why data driven? 2.2.2 Mixed Imperative-Declarative Specification. 2.3 Syntax. 2.3.1 The Structure of an ATaG Program. 2.3.2 More on Task Annotations. 2.3.3 Illustrative examples. 2.4 Semantics. 2.4.1 Terminology. 2.4.2 Firing rules. 2.4.3 Task graph execution. 2.4.4 get() and put(). 2.5 Programming idioms. 2.5.1 Object tracking. 2.5.2 Interaction within local neighborhoods. 2.5.3 In-network aggregation. 2.5.4 Hierarchical data fusion. 2.5.5 Event-triggered behavior instantiation. 2.6 Future work. 2.6.1 State-based dynamic behaviors. 2.6.2 Resource management in the runtime system. 2.6.3 Utility based negotiation for task scheduling and resource allocation. 2.6.4 Analyzing feasibility of compilation. 3. DART:The Data Driven ATaG Runtime. 3.1 Design objectives. 3.1.1 Support for ATaG semantics. 3.1.2 Platform independence. 3.1.3 Component-based design. 3.1.4 Ease of software synthesis. 3.2 Overview. 3.3 Components and functionalities. 3.3.1 Task, Data, and Channel Declarations. 3.3.2 UserTask. 3.3.2.1 Service. 3.3.2.2 Interactions. 3.3.2.3 Implementation. 3.3.3 DataPool. 3.3.3.1 Service. 3.3.3.2 Interactions. 3.3.3.3 Implementation. 3.3.4 AtagManager. 3.3.4.1 Service. 3.3.4.2 Interactions. 3.3.4.3 Implementation. 3.3.5 NetworkStack. 3.3.5.1 Service. 3.3.5.2 Interactions. 3.3.5.3 Implementation. 3.3.6 NetworkArchitecture. 3.3.6.1 Service. 3.3.6.2 Interactions. 3.3.6.3 Implementation. 3.3.7 Dispatcher. 3.3.7.1

Amol B. Bakshi, PhD, is a Research Assistant Professor in the
Ming Hsieh Department of Electrical Engineering at the University
of Southern California (USC), Los Angeles. He also manages the
Integrated Asset Management project at the USC-Chevron Center of
Excellence for Research and Academic Training on Interactive Smart
Oilfield Technologies. Dr. Bakshi's PhD research was on programming
models for networked sensor systems and led to the creation of the
ATaG programming model and software synthesis toolkit. His current
interests include semantic Web technologies for information
integration, smart oilfield technologies, model integrated
computing, and sensor networks.

Viktor K. Prasanna, PhD, is Charles Lee Powell Chair in
Engineering and Professor of Electrical Engineering and Professor
of Computer Science at the University of Southern California (USC),
Los Angeles. He is also an associate member of the Center for
Applied Mathematical Sciences (CAMS) at USC, and a member of the
USC-Chevron Center of Excellence for Research and Academic Training
on Interactive Smart Oilfield Technologies. He has served on the
editorial boards of the Journal of Parallel and Distributed
Computing, Proceedings of the IEEE, IEEE Transactions on VLSI
Systems, and IEEE Transactions on Parallel and Distributed Systems.
He was editor-in-chief of the IEEE Transactions on Computers and
was the founding chair of the IEEE Computer Society Technical
Committee on Parallel Processing. He is a Fellow of the IEEE and
the ACM.

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