Simulators and Emulators for WSNs

Fahmy, Hossam Mahmoud Ahmad

doi:10.1007/978-981-10-0412-4_6

Cited by 11 publications

(3 citation statements)

References 62 publications

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“…Besides these type criteria, testbeds and hardware platforms are also considered in [31], in order to assessing different parameters required by WSN applications. In [32] [33], WSN simulators are described and classified according to their type: simulators (based on models) or emulators. The work presented in [34], describe thirty five simulators considering simulation models, emulation, and testbeds.…”

Section: Categories Of Scientific Articlesmentioning

confidence: 99%

Untitled

2019

IJWMN

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The large number of Wireless Sensor Networks (WSN) simulators available nowadays, differ in their design, goals, and characteristics. Users who have to decide which simulator is the most appropriate for their particular requirements, are today lost, faced with a panoply of disparate and diverse simulators. Hence, it is obvious the need for establishing guidelines that support users in the tasks of selecting a simulator to suit their preferences and needs. In previous works, we proposed a generic and novel approach to evaluate networks simulators, considering a methodological process and a set of qualitative and quantitative criteria. In particularly, for WSN simulators, the criteria include relevant aspects for this kind of networks, such as energy consumption modelling and scalability capacity. The aims of this work are: (i) describe deeply the criteria related to WSN aspects; (ii) extend and update the state of the art of WSN simulators elaborated in our previous works to identify the most used and cited in scientific articles; and (iii) demonstrate the suitability of our novel methodological approach by evaluating and comparing the three most cited simulators, specially in terms of energy modelling and scalability capacities. Results show that our proposed approach provides researchers with an evaluation tool that can be used to describe and compare WSN simulators in order to select the most appropriate one for a given scenario.

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Section: Categories Of Scientific Articlesmentioning

confidence: 99%

Untitled

2019

IJWMN

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“…Thus, TOSSIM, Cooja, and ns-3 are good choices to evaluate algorithms at design, development, and implementation stages. Sometimes, TOSSIM and Cooja are considered emulators as they execute the same code on real motes [53].…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

et al. 2018

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The Internet of Things (IoT) is the next big challenge for the research community where the IPv6 over low power wireless personal area network (6LoWPAN) protocol stack is a key part of the IoT. Recently, the IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for 6LoWPAN networks to alleviate the challenges of connecting low memory, limited processing capability, and constrained power supply sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and impacts on quality of service (QoS) aspects such as throughput, latency, energy consumption, reliability, and packet delivery. In this paper, we overview the protocol stack of 6LoWPAN networks and summarize a set of its protocols and standards. Also, we review and compare a number of popular congestion control mechanisms in wireless sensor networks (WSNs) and classify them into traffic control, resource control, and hybrid algorithms based on the congestion control strategy used. We present a comparative review of all existing congestion control approaches in 6LoWPAN networks. This paper highlights and discusses the differences between congestion control mechanisms for WSNs and 6LoWPAN networks as well as explaining the suitability and validity of WSN congestion control schemes for 6LoWPAN networks. Finally, this paper gives some potential directions for designing a novel congestion control protocol, which supports the IoT application requirements, in future work.

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“…However, it has low data rate and necessitates a strong microcontroller and its correspondent communication protocol. [39] is a general-purpose, cross-level, largescale and discrete-event simulator handling the communication, the mobility and the energy levels (battery, CPU, radio, protocol stack etc.). Each node entity is seen as an independent component.…”

Section: Energysimmentioning

confidence: 99%

Evaluation of simulator tools and power‐aware scheduling model for wireless sensor networks

Chéour

Jmal

Kanoun

et al. 2017

IET Computers & Digital Techniques

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The sharp increase of the wireless sensor networks (WSNs) performance has increased their power requirements. However, with a limited battery lifetime it is more and more difficult to deploy many more sensors with today's solutions. Therefore, the authors need to implement autonomous WSNs without any human intervention or external power supply. To this end, this study proposes an effective strategy to ensure an energy consumption gain that takes into account time constraints through a power-aware model based on the dynamic voltage and frequency scaling and the dynamic power management that are appropriate to the WSNs and on a global Earliest Deadline First scheduler. To select the most suitable simulator to integrate and simulate the developed models, >25 of the existing WSN simulators are outlined and evaluated. On the basis of this comparative study analysis, the authors chose the simulation tool for real-time multiprocessor scheduling (STORM) to validate their work for its multiple advantages. 2 Comparative study of WSNs simulators The simulation technologies have been widely used mainly, when the experimental approaches are difficult to achieve due to the complexity of the qualitative and quantitative changes applied

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Simulators and Emulators for WSNs

Cited by 11 publications

References 62 publications

Untitled

Untitled

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

Evaluation of simulator tools and power‐aware scheduling model for wireless sensor networks

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