Realistic Simulation of Energy Consumption in Wireless Sensor Networks

Haas, Christian; Wilke, Joachim; Stöhr, Viktor

doi:10.1007/978-3-642-28169-3_6

Cited by 25 publications

(21 citation statements)

References 13 publications

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“…Running the benchmark at real time would imply waiting very long times for the measurements to be final. While testbeds provide more accurate performance measurements than simulator/emulators (such as, e.g., Avrora [38] or PowerTossim [33]), the latter provide reasonable approximations of performance in practice (e.g, as demonstrated by the comparisons of an improved version of Avrora with the SANDbed testbed [12]). By using emulation, we can carry out systematic experiments that cover a broader region of the WSN application design space in an efficient manner, whilst still obtaining sufficiently accurate performance data compared to the use of a testbed.…”

Section: Desiderata and Scopementioning

confidence: 99%

SensorBench

Galpin

Stokes

Valkanas

et al. 2014

Proceedings of the 26th International Conference on Scientific and Statistical Database Management

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Wireless sensor networks enable cost-effective data collection for tasks such as precision agriculture and environment monitoring. However, the resource-constrained nature of sensor nodes, which often have both limited computational capabilities and battery lifetimes, means that applications that use them must make judicious use of these resources. Research that seeks to support data intensive sensor applications has explored a range of approaches and developed many different techniques, including bespoke algorithms for specific analyses and generic sensor network query processors. However, all such proposals sit within a multi-dimensional design space, where it can be difficult to understand the implications of specific decisions and to identify optimal solutions. This paper presents a benchmark that seeks to support the systematic analysis and comparison of different techniques and platforms, enabling both development and user communities to make well informed choices. The contributions of the paper include: (i) the identification of key variables and performance metrics; (ii) the specification of experiments that explore how different types of task perform under different metrics for the controlled variables; and (iii) an application of the benchmark to investigate the behavior of several representative platforms and techniques.

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Section: Desiderata and Scopementioning

confidence: 99%

SensorBench

Galpin

Stokes

Valkanas

et al. 2014

Proceedings of the 26th International Conference on Scientific and Statistical Database Management

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“…During our evaluation, we compare the theoretical model from [6] with a more realistic evaluation approach using the emulation tool Avrora+ [18] and the real-world testbed SANDbed [5].…”

Section: Evaluation Tools and Modelsmentioning

confidence: 99%

“…To further improve the trustworthiness of these results, AVRORA has been extended by our research group. The energy usage data collected with the so called AVRORA+ has been compared to a real world testbed extensively in [4]. Still the best option regarding accuracy and trustworthiness of energyefficiency evaluations, however, is performing experiments and measurements on real sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the energy-efficiency of key exchange protocols in wirelesssensor networks

Haas

Wilke

2012

Proceedings of the 7th ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks

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The evaluation of the energy-efficiency of applications and protocols is one of the most important issues in Wireless Sensor Networks (WSN). However, this is a time consuming and challenging task. Therefore, a realistic energy-efficiency evaluation is often neglected or oversimplified by using simple theoretical models or unsuited simulation tools. In this work, we evaluate the energy-efficiency of two specific key exchange protocols, an Elliptic Curve Diffie-Hellman with authentication (ECDH-ECDSA) and a Kerberos based approach, while using different duty-cycling MAC layer protocols. In our evaluation, we compare the results from a theoretical model with simulation results using the AVRORA+ simulation tool and real-world measurements in our WSN testbed SANDbed. Using three MAC layer protocols, Tiny-OS built in Low-Power-Listening as well as S-MAC and standard 802.15.4, we show that there are several important cross-layer effects that should be considered when performing an energy-efficiency evaluation. Furthermore, we argue, that there are several evaluation metrics like the absolute energy consumption per key exchange or one key exchange per measured time interval.

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“…The energy consumption model of smart phones is based on the previous literature [54][55][56][57]. We assume that smart phones have three states: suspended state, idle states and high-power state.…”

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

“…Finally, the high-power case is the state when the portable device interacts with the cloud. The power utilisation of different states with regard to the elapsed time can be seen in Table 3, which is based on the average system power of specific smart phones for a selection of benchmarks in [55]. The energy utilization of different communication modes with respect to the amount of transferred data, as well as the associated transmission rate are shown in Table 4.…”

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

Emergency navigation without an infrastructure

Bi¹

2014

IEEE SENSORS 2014 Proceedings

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Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an ad hoc cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process.

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Realistic Simulation of Energy Consumption in Wireless Sensor Networks

Cited by 25 publications

References 13 publications

SensorBench

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Evaluating the energy-efficiency of key exchange protocols in wirelesssensor networks

Emergency navigation without an infrastructure

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