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STEAM-Sim: filling the gap between time accuracy and scalability in simulation of wireless sensor networks
Abstract
After a decade of research in the field of wireless sensor networks the energy consumption remains the dominating constraint. Complex algorithms with non-negligible runtimes must be processed by resource-limited nodes, and therefore require in-depth knowledge of the temporal behavior of the software and hardware components. However, state-of-the-art simulators provide either accuracy or scalability and therefore somehow limit the development of such networks.
We present a novel and unique methodology for energy-aware, time-accurate, and scalable simulation of wireless sensor networks that considers software, hardware, and network components. Algorithms implemented in C are annotated with binary runtime information and are executed natively on the host cpu, i.e., the cpu where the simulation is run. Arbitrary hardware can be modeled at various levels of abstraction and is simulated together with the software. Important effects such as interrupt processing are simulated accurately.
As a proof of concept we implemented the proposed methodology and present STEAM-Sim, a novel simulation environment. We evaluated STEAM-Sim by means of a proprietary networking scenario typically used in industrial wireless sensor networks. Preliminary results regarding scalability and accuracy are presented.
... Further, PAWiS provides a clear separation of software and hardware components and enables the modeling of arbitrary hardware such as a radio transceiver, CPU, or a sensor interface. STEAM-Sim [2] is a recently published simulation environment which enables the simulation of the timing and functionality of real-life firmware, i.e., the software executed by a nodes CPU. The so called time annotation engine parses arbitrary firmware code written in the high-level language C and determines the execution time of code blocks. ...
... We compared the results regarding RSSI values and energy consumption between our "golden model" and the combined framework (MiXiM, PAWiS, and STEAM-Sim). The "golden model" is build upon STEAM-Sim which integrates PAWiS and was verified by means of measurements in [2]. To ensure comparable results we had to include the PAWiS free space propagation model as described in [4] in the combined framework. ...
After a decade of research in the field of wireless sensor networks (WSNs)
there are still open issues. WSNs impose several severe requirements regarding
energy consumption, processing capabilities, mobility, and robustness of
wireless transmissions. Simulation has shown to be the most cost-efficient
approach for evaluation of WSNs, thus a number of simulators are available.
Unfortunately, these simulation environments typically consider WSNs from a
special point of view. In this work we present the integration of three such
specialized frameworks, namely MiXiM, PAWiS, and STEAM-Sim. This integration
combines the strengths of the single frameworks such as realistic channel
models, mobility patterns, accurate energy models, and inclusion of real-life
application code. The result is a new simulation environment which enables a
more general consideration of WSNs. We implemented and verified our proposed
concept by means of static and mobile scenarios. As the presented results show,
the combined framework gives the same results regarding the functionality and
energy consumption as our "golden model". Therefore the system integration was
successful and the framework is ready to be used by the community.
In a wireless sensor network, the energy consumption of the nodes continues to impose a tight constraint. Researchers have therefore proposed several MAC protocols to decrease the energy consumption of the radio transceiver, which has increased the complexity of the firmware running on the nodes. In this paper, we show the consequences of ignoring runtimes of state-of-the-art MAC protocols, as proposed by some simulators. The quantitative discussion is based on a comparison between hardware measurements and simulations. We ported the Contiki operating system to STEAM-Sim, a recently developed simulator. With this setup it is possible to omit runtimes of firmware in a stepwise manner. We further used STEAM-Sim to accurately evaluate the X-MAC, Low Power Probing, and ContikiMAC radio duty-cycling protocols. The energy consumption profiles of hardware modules thus generated give interesting insights into the protocols. Scalability comparisons to the state-of-the-art simulator COOJA/MSPSim show that scalable and time-accurate simulation of WSNs in the nanosecond range is possible.
In this paper, we present a methodology to establish an accurate and power-aware simulation of wireless sensor networks. As the design of software applications running on resource-constrained sensor nodes mainly influences both timing and power consumption in the network, it is crucial to include these components in the simulation. Besides considering the software aspect in the network, it is also important to obtain a detailed and accurate power consumption profile of every hardware module present in the network. Our methodology extends the PAWiS framework, which builds upon the well known discrete event network simulator OMNeT++. The framework was extended to include natively executing real-life application code written in the C language. Using a time-annotation process brings the timing aspect of application code execution into the simulation, and therefore increases simulation accuracy. Moreover, the presented partitioning of the application code into software layers provides easy porting of the simulated code to real sensor nodes. This concept does not impose any restrictions with respect to the target platform used or the OS running on it. To demonstrate the functionality of this approach, the methodology was applied to a real-world networking test scenario, and the achieved simulation results were compared to real-world measurements. The performance of the simulation environment was evaluated and is presented.
Wireless sensor networks are composed of large numbers of tiny networked devices that communicate untethered. For large scale networks, it is important to be able to download code into the network dynamically. We present Contiki, a lightweight operating system with support for dynamic loading and replacement of individual programs and services. Contiki is built around an event-driven kernel but provides optional preemptive multithreading that can be applied to individual processes. We show that dynamic loading and unloading is feasible in a resource constrained environment, while keeping the base system lightweight and compact.
The PAWiS (power aware wireless sensors) simulation framework facilitates design and simulation of wireless sensor network models. Main focus is given to power efficiency and therefore on capturing inefficiencies in various aspect of the system. These aspects include all layers of the communication system, the targeted class of application itself, the power supply and energy management, the central processing unit (CPU) and the sensor-actuator interface. The proposed simulation framework is based on the OMNeT++ discrete event simulator and provides PAWiS specific features (e.g. a mechanism to handle RF channel transmissions) to simulate, analyze and optimize the aforementioned aspects.
Recently, the transaction-level modeling has been widely referred to in system-level design community. However, the transaction-level models (TLMs) are not well defined and the usage of TLMs in the existing design domains, namely modeling, validation, refinement, exploration, and synthesis, is not well coordinated. This paper introduces a TLM taxonomy and compares the benefits of TLMs' use.
Energy efficiency is one of the most important requirements for wireless sensor networks. Therefore, it is important to have accurate measurements of the energy consumption of the nodes in a network. We propose an measurement approach capable to handle this issue. In the development of wireless sensor network applications simulators are often used to verify the correctness of the application. Simulators as PowerTOSSIM are further capable to simulate the energy consumption. Doubtless, such energy simulator can be of great benefit, but their accuracy is an important issue. Previous measurements have shown that the results of PowerTOSSIM's energy simulation deviate up to 30% from our measurement results. Our previous measurement setup has been improved and a new prototype was build. Evaluation of our new prototype shows, that energy measurements of mica2 motes are possible with very good accuracy at low costs. The measurement setup can easily be adapted to other platforms. This paper shows an approach how to improve the accuracy of energy simulators for wireless sensor networks. We use our measurement setup to calibrate the energy model of PowerTOSSIM. Finally, our calibrated version of the energy model is evaluated and compared to the original version. Our results show that the accuracy of energy simulators can be greatly improved by our approach.
Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach
Software development for wireless sensor networks is a challenging and time consuming task. The resource limited hardware with limited I/O and debugging abilities combined with the often cumbersome hardware debugging tools makes low-level debugging on the target hardware difficult. We present MSPsim, an extensible sensor board platform and MSP430 instruction level simulator that simulates sensor boards with peripherals for the purpose of reducing development and debugging time. The use of a simulator also enables testing without access to the target hardware and makes more advanced debugging and instrumenting possible.
Power consumption is the most important metric in wireless sensor network research, but existing simulation tools for measuring
or estimating power consumption are either impractical or have unclear accuracy. We present COOJA/MSPSim, a practical simulation-based
tool for network-scale power estimation based on Contiki’s built-in power profiling mechanism, the COOJA sensor network simulator
and the MSPSim sensor node emulator. We compare experimental results measured on real sensor nodes with simulation results
for three different MAC protocols. The accuracy of our results indicates that COOJA/MSPSim enables accurate network-scale
simulation of the power consumption of sensor networks.
Software accounts for more than 80% of embedded system development efforts, so software performance estimation is a very important issue in system design. Recently, source level simulation (SLS) has become a state-of-the-art approach for software simulation in system level design. However, the simulation accuracy relies on the mapping between source code and binary code, which can be destroyed by compiler optimizations. This drawback strongly limits the usability of this technique in practical system design. We introduce an approach to overcome this limitation by converting source code to a low level representation, called intermediate source code (ISC). ISC has accounted for most compiler optimizations and has a structure close to binary code, so it allows for accurate back-annotation of timing information from the binary level. To show the benefits of our approach, we present a quantitative comparison of the related techniques with the proposed one, using a set of benchmarks.
This paper describes the C Intermediate Language: a highlevel representation along with a set of tools that permit easy analysis and source-to-source transformation of C programs.
Compared to C, CIL has fewer constructs. It breaks down certain complicated constructs of C into simpler ones, and thus it works at a lower level than abstract-syntax trees. But CIL is also more high-level than typical intermediate languages (e.g., three-address code) designed for compilation. As a result, what we have is a representation that makes it easy to analyze and manipulate C programs, and emit them in a form that resembles the original source. Moreover, it comes with a front-end that translates to CIL not only ANSI C programs but also those using Microsoft C or GNU C extensions.
We describe the structure of CIL with a focus on how it disambiguates those features of C that we found to be most confusing for program analysis and transformation. We also describe a whole-program merger based on structural type equality, allowing a complete project to be viewed as a single compilation unit. As a representative application of CIL, we show a transformation aimed at making code immune to stack-smashing attacks. We are currently using CIL as part of a system that analyzes and instruments C programs with run-time checks to ensure type safety. CIL has served us very well in this project, and we believe it can usefully be applied in other situations as well.
Developing sensor network applications demands a new set of tools to aid programmers. A number of simulation environments have been de- veloped that provide varying degrees of scalability, realism, and detail for understanding the behavior of sensor networks. To date, however, none of these tools have addressed one of the most important aspects of sensor application design: that of power consumption.While simple approximations of overall power usage can be derived from estimates of node duty cycle and communication rates, these techniques often fail to capture the detailed, low-level energy requirements of the CPU, radio, sensors, and other peripherals. In this paper, we present PowerTOSSIM, a scalable simulation en- vironment for wireless sensor networks that provides an accurate, per- node estimate of power consumption. PowerTOSSIM is an extension to TOSSIM, an event-driven simulation environment for TinyOS ap- plications. In PowerTOSSIM, TinyOS components corresponding to specific hardware peripherals (such as the radio, EEPROM, LEDs, and so forth) are instrumented to obtain a trace of each device's activ- ity during the simulation run. PowerTOSSIM employs a novel code- transformation technique to estimate the number of CPU cycles exe- cuted by each node, eliminating the need for expensive instruction-level simulation of sensor nodes. PowerTOSSIM includes a detailed model of hardware energy consumption based on the Mica2 sensor node plat- form. Through instrumentation of actual sensor nodes, we demonstrate that PowerTOSSIM provides accurate estimation of power consump- tion for a range of applications and scales to support very large simula- tions.
We present Castalia, a simulator for WSN that models many aspects of the WSN system and uses advanced models especially in terms of the channel and radio behaviour. We show the effects of these features in distributed algorithms that work fine with simpler simulators but fail under Castalia. The demo will present the differences, explain the failures and show how to redesign the algorithms to make them work under more realistic conditions.
Energy consumption is a crucial characteristic of sensor networks and their applications as sensor nodes are commonly battery-driven. Although recent research focuses strongly on energy-aware applications and operating systems, energy consumption is still a limiting factor Once sensor nodes are deployed, it is challenging and sometimes even impossible to change batteries. As a result, erroneous lifetime prediction causes high costs and may render a sensor network useless before its purpose is fulfilled. In this paper, we present AEON (Accurate Prediction of Power Consumption), a novel evaluation tool to quantitatively v predict energy consumption of sensor nodes and whole sensor networks. Our energy model, based on measurements of node current draw and the execution of real code, enables accurate prediction of the actual energy consumption of sensor nodes. Consequently, it prevents erroneous assumptions on node and network lifetime. Moreover, our detailed energy model allows to compare different low power and energy aware approaches in terms of energy efficiency. Thus, it enables a highly precise estimation of the overall lifetime of a sensor network.
CC2500 Low-Cost Low-Power 2.4 GHz RF Transceiver
- T Instruments
MSP430xG461x Mixed Signal Microcontroller slas508g edition
- T Instruments