Investigating a physically-based signal power model for robust low power wireless link simulation

Rusak, Tal; Levis, Philip

doi:10.1145/1454503.1454514

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…This is consistent with previous findings reported [Cerpa et al 2003[Cerpa et al , 2005Rusak and Levis 2009;Srinivasan et al 2010]. In general, in simulation, it is easy to model good links, as they do not show significant variation with time [Metcalf 2007;Rusak and Levis 2008]. On the other hand, there is a significant difference between the models of intermediate links in simulation and the real world.…”

Section: Exploratory Data Analysissupporting

confidence: 90%

“…In contrast, earlier studies [Rusak and Levis 2008] have collected the same data at a lower frequency (4 Hz). To analyze the dependence of frequency of sending packets during the data collection phase on the quality of our model, we reduced the amount of data used for creating the model from the original 64 Hz down to 1 Hz.…”

Section: Sensitivity Analysis: Dependence On Ipi During Data Collectionmentioning

confidence: 98%

“…The CPM models helps capture the short-term link quality variation caused by factors, such as 802.11 traffic. In a recent paper [Rusak and Levis 2008], two approaches (Expected Value PMF and Average Signal Power Value) were proposed to estimate the signal power of missing packets in a packet reception trace, and using this data, the CPM algorithm models the variations in packet signal strength. These existing models require the modeling of two separate physical-layer measurements, namely, RSSI and noise/interference values to create a representative model of a real environment.…”

Section: Tossimmentioning

confidence: 99%

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Improving wireless link simulation using multilevel markov models

Kamthe

Carreira-Perpiñán

Cerpa

2013

ACM Trans. Sen. Netw.

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Modeling the behavior of 802.15.4 links is a nontrivial problem, because 802.15.4 links experience different level of dynamics at short and long time scales. This makes the design of a suitable model that combines the different dynamics at different time scales a nontrivial problem. We propose a novel multilevel approach, the M&M model, involving hidden Markov models (HMMs) and mixtures of multivariate Bernoullis (MMBs) for modeling the long and short time-scale behavior of wireless links from 802.15.4 test beds. We characterize the synthetic traces generated from our model of the wireless link in terms of the mean and variance of the packet reception rates from the data traces, comparison of distributions of run lengths, and conditional packet delivery functions of successive packet receptions (1's) and losses (0's). Our results show that when compared to the closest-fit pattern matching model in TOSSIM, the proposed modeling approach is able to mimic the behavior of the data traces quite closely, with differences in packet reception rates of the empirical and simulated traces of less than 1.9% on average and 6.6% in the worst case. Moreover, the simulated links from our proposed approach were able to account for long runs of 1's and 0's as observed in empirical data traces.

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Section: Exploratory Data Analysissupporting

confidence: 90%

Section: Sensitivity Analysis: Dependence On Ipi During Data Collectionmentioning

confidence: 98%

Section: Tossimmentioning

confidence: 99%

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Improving wireless link simulation using multilevel markov models

Kamthe

Carreira-Perpiñán

Cerpa

2013

ACM Trans. Sen. Netw.

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“…The static routing tables for the testbed are based on the same link data which were used to create the RealSim. To overcome the problem of transient link behavior (confer [14]) we used a lower transmission power for determining the routes than for the actual experiment. While this measure in many cases caused routes to be chosen too pessimistically and thereby artificially increased the diameter of the resulting routing topology, it turned out to be necessary to guarantee that the network was connected most of the time.…”

Section: Testbedmentioning

confidence: 99%

Route-Over Forwarding Techniques in a 6LoWPAN

Weigel¹,

Ringwelski²,

Turau³

et al. 2014

ICST Trans Mobile Comm Appl

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Abstract6LoWPAN plays a major role within the protocol stack for the future Internet of Things. Its fragmentation mechanism enables transport of IPv6 datagrams with the required minimum MTU of 1280 bytes over 802.15.4-based networks. With the goal of a fully standardized WSN protocol stack currently necessitating a route-over approach, i.e., routing at the IP-layer, there are two main choices for any 6LoWPAN implementation with regard to datagram fragmentation: Hop-by-hop assembly or a cross-layered direct mode, which forwards individual 6LoWPAN fragments before the whole datagram has arrived. In addition to these two straightforward approaches, we propose enhancements based on adaptive rate-restriction for the direct forwarding and a retry control for both modes to reduce the number of losses of larger datagrams. An evaluation of these modes in a simulation environment and a hardware testbed indicate that the proposed enhancements can considerably improve PRR and latency within 6LoWPAN networks.

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“…All scenarios are simulated in TOSSIM [8]. Although in TOSSIM, the signal power is a constant [9], a matrix of gain between nodes is developed and can be varied in each step of the simulation. With that improvement, any propagation model can be applied in the simulation.…”

Section: Simulationmentioning

confidence: 99%

A Model of Wireless Sensor Networks Using Opportunistic Routing in Logistic Harbor Scenarios

Son

Wenning

Timm‐Giel

et al. 2011

Dynamics in Logistics

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Abstract:Collection of information for management in logistics is of great importance today. Many applications in logistics are currently starting to use modern technologies such as RFID. However, these technologies have not provided enough information for management such as the real-time condition of goods or online item tracking during transport. In this paper, a model for Wireless Sensor Networks (WSNs) in logistics is proposed under the consideration of mobility and multiple WSN domains. With the use of WSNs, relevant information can be monitored down to the level of individual logistic items. For the communication reliability of sensor networks, a high packet reception ratio (PRR) is desired. To achieve this high PRR, many techniques are used in our model to conserve energy and to improve the reliability. The results show that a PRR of 90% can be reached in our simulation scenarios using the proposed model. Moreover, a logistic scenario in a harbor is examined as an example to investigate the capability to connect automatically in multiple sensor networks.

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Investigating a physically-based signal power model for robust low power wireless link simulation

Cited by 12 publications

References 11 publications

Improving wireless link simulation using multilevel markov models

Improving wireless link simulation using multilevel markov models

Route-Over Forwarding Techniques in a 6LoWPAN

A Model of Wireless Sensor Networks Using Opportunistic Routing in Logistic Harbor Scenarios

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