Channel and active component abstractions for WSN programming - a language model with operating system support

To support the programming of Wireless Sensor Networks, a number of unconventional programming models have evolved, in particular the event-based model. These models are non-intuitive to programmers due to the introduction of unnecessary, non-intrinsic complexity. Component-based languages like Insense can eliminate much of this unnecessary complexity via the use of active components and synchronous channels. However, simply layering an Insense implementation over an existing event-based system, like TinyOS, while proving efficacy, is insufficiently space and time efficient for production use. The design and implementation of a new language-specific OS, InceOS, enables both space and time efficient programming of sensor networks using component-based languages like Insense

Distributed computing in space-based wireless sensor networks

This thesis investigates the application of distributed computing in general and wireless sensor networks in particular to space applications. Particularly, the thesis addresses issues related to the design of "space-based wireless sensor networks" that consist of ultra-small satellite nodes flying together in close formations. The design space of space-based wireless sensor networks is explored. Consequently, a methodology for designing space-based wireless sensor networks is proposed that is based on a modular architecture. The hardware modules take the form of 3-D Multi-Chip Modules (MCM). The design of hardware modules is demonstrated by designing a representative on-board computer module. The onboard computer module contains an FPGA which includes a system-on-chip architecture that is based on soft components and provides a degree of flexibility at the later stages of the design of the mission.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network

AbstractThis paper presents computational and message complexity analysis for a multi-layer perceptron neural network, which is implemented in fully distributed and parallel form across a wireless sensor network. Wireless sensor networks offer a promising platform for parallel and distributed neurocomputing as well as potentially benefiting from artificial neural networks for enhancing their adaptation abilities and computational intelligence. Multilayer perceptron (MLP) neural networks are generic function approximators and classifiers with countless domain-specific applications as reported in the literature. Accordingly, embedding a multilayer perceptron neural network in a wireless sensor network in parallel and distributed mode offers synergy and is very promising. Accordingly, assessing the computational and communication complexity of such hybrid designs, namely an artificial neural network such as a multilayer perceptron network embedded within a wireless sensor network, of interest. This paper presents bounds and results of empirical study on the time, space and message complexity aspects of a wireless sensor network and multilayer perceptron neural network design

SystemC Model Generation for Realistic Simulation of Networked Embedded Systems

Verification and design-space exploration of today's embedded systems require the simulation of heterogeneous aspects of the system, i.e., software, hardware, communications. This work shows the use of SystemC to simulate a model-driven specification of the behavior of a networked embedded system together with a complete network scenario consisting of the radio channel, the IEEE 802.15.4 protocol for wireless personal area networks and concurrent traffic sharing the medium. The paper describes the main issues addressed to generate SystemC modules from Matlab/Stateflow descriptions and to integrate them in a complete network scenario. Simulation results on a healthcare wireless sensor network show the validity of the approach

Wireless sensor networks for active vibration control in automobile structures

International audienceWireless Sensor Network (WSN) are nowadays widely used in monitoring and tracking applications. This paper presents the feasibility of using Wireless Sensor Networks in active vibration control strategy. The active control method used is an active-structural acoustic control using piezoelectric sensors distributed on the car structure. This system aims at being merged in wireless sensor network whose head node collects data and process control law so as to command piezoelectric actuators wisely placed on the structure. We will study the feasibility of implementing WSN in active vibration control and introduce a complete design methodology to optimize hardware/software and control law synergy in mechatronic systems. A design space exploration will be conducted so as to identify the best Wireless Sensor Network platform and the resulting impact on control

Cross-layer design for wireless sensor relay networks

In recent years, the idea of wireless sensor networks has gathered a great deal of attention. A distributed wireless sensor network may have hundreds of small sensor nodes. Each individual sensor contains both processing and communication elements and is designed in some degree to monitor the environmental events specified by the end user of the network. Information about the environment is gathered by sensors and delivered to a remote collector. This research conducts an investigation with respect to the energy efficiency and the cross-layer design in wireless sensor networks. Motivated by the multipath utilization and transmit diversity capability of space-time block codes (STBC), a new energy efficient cooperative routing algorithm using the STBC is proposed. Furthermore, the steady state performance of the network is analyzed via a Markov chain model. The proposed approach in this dissertation can significantly reduce the energy consumption and improve the power efficiency. This work also studies the application of differential STBC for wireless multi-hop sensor networks over fading channels. Using differential STBC, multiple sensors are selected acting as parallel relay nodes to receive and relay collected data. The proposed technique offers low complexity, since it does not need to track or estimate the time-varying channel coefficients. Analysis and simulation results show that the new approach can improve the system performance. This dissertation models the cooperative relay method for sensor networks using a Markov chain and an M/G/1 queuing system. The analytical and simulation results indicate system improvements in terms of throughput and end-to-end delay. Moreover, the impact of network resource constraints on the performance of multi-hop sensor networks with cooperative relay is also investigated. The system performance under assumptions of infinite buffer or finite buffer sizes is studied, the go through delay and the packet drop probability are improved compared to traditional single relay method. Moreover, a packet collision model for crucial nodes in wireless sensor networks is introduced. Using such a model, a space and network diversity combining (SNDC) method is designed to separate the collision at the collector. The network performance in terms of throughput, delay, energy consumption and efficiency are analyzed and evaluated

A component-based model and language for wireless sensor network applications

Wireless sensor networks are often used by experts in many different fields to gather data pertinent to their work. Although their expertise may not include software engineering, these users are expected to produce low-level software for a concurrent, real-time and resource-constrained computing environment. In this paper, we introduce a component-based model for wireless sensor network applications and a language, Insense, for supporting the model. An application is modelled as a composition of interacting components and the application model is preserved in the Insense implementation where active components communicate via typed channels. The primary design criteria for Insense include: to abstract over low-level concerns for ease of programming; to permit worst-case space and time usage of programs to be determinable; to support the fractal composition of components whilst eliminating implicit dependencies between them; and, to facilitate the construction of low footprint programs suitable for resource-constrained devices. This paper presents an overview of the component model and Insense, and demonstrates how they meet the above criteria.Preprin