Modeling obstacles in INET/Mobility framework: motivation, integration, and performance

Lichte, Hermann S.; Weide, Jannis

doi:10.4108/icst.simutools2009.5680

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…The mobile node (Figure 3 Additionally, the building structure with network infrastructure was modeled in OMNeT++ (with Inet/Mixim extensions). The implemented WSN model takes into account Wi-Fi radio propagation [34] and obstacles. The simulation parameters were described in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

Bernaś

Płaczek

2015

International Journal of Distributed Sensor Networks

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The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speedup of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in real world indoor environment by using both stationary and mobile sensor nodes.

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Section: Methodsmentioning

confidence: 99%

Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

Bernaś

Płaczek

2015

International Journal of Distributed Sensor Networks

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“…Radio transmission in real world is heavily affected by the signal shadowing effects in urban areas, due to buildings and other vehicles on the road which block radio propagation. In our simulation, we have used a simple obstacle shadowing model 24 . This is a simplified simulation scenario that benefits the eavesdropper as it assumes all the safety messages, within the communication range, are delivered to the eavesdropper without significant packet loss.…”

Section: Simulation Setupmentioning

confidence: 99%

“…In our simulation, we have used a simple obstacle shadowing model. 24 This is a simplified simulation scenario that benefits the eavesdropper as it assumes all the safety messages, within the communication range, are delivered to the eavesdropper without significant packet loss. In the following sections, we will discuss the specific mobility and adversary models used in our simulations and the different PCS that we have considered.…”

Section: Simulation Setupmentioning

confidence: 99%

Evaluating the effectiveness of pseudonym changing strategies for location privacy in vehicular ad‐hoc network

Saini

Saad

Jaekel

2019

Security and Privacy

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In a vehicular ad-hoc network, vehicles periodically broadcast safety messages to neighboring vehicles containing information about their identity, location, speed, and other relevant information. The time-critical nature of these messages means they are typically sent without encryption, which can compromise the privacy of the vehicle/driver. The use of pseudonyms, rather than the actual identity, has been suggested as a way to implement location privacy for drivers. However, such pseudonyms need to change frequently in order to prevent vehicle tracking. A pseudonym changing strategy (PCS) determines how frequently and under which conditions a vehicle should change its pseudonym. Many different PCS have been proposed in the literature over the last decade. However, there is a lack of a systematic analysis of the performance of these techniques, using a set of well-defined privacy metrics. In this article, we provide a thorough comparison of four main types of PCS, in terms of standard privacy metrics, and identify the advantages and limitations of each approach under different traffic conditions and attacker capabilities. The ultimate aim of this study is to help in the determination of the best PCS to use in different situations, to maximize the location privacy.

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“…is used which was first proposed in [32] and later improved in conjunction with the obstacle model in [33]. The values of the above mentioned propagation models are used to calculate the SINR on the receiver side:…”

Section: Propagationmentioning

confidence: 99%

Realistic and Efficient Radio Propagation Model for V2X Communications

2013

KSII TIIS

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Multiple wireless devices are being widely deployed in Intelligent Transportation System (ITS) services on the road to establish end-to-end connection between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. Vehicular ad hoc networks (VANETs) play an important role in supporting V2V and V2I communications (also called V2X communications) in a variety of urban environments with distinct topological characteristics. In fact, obstacles such as big buildings, moving vehicles, trees, advertisement boards, traffic lights, etc. may block the radio signals in V2X communications. Their impact has been neglected in VANET research. In this paper, we present a realistic and efficient radio propagation model to handle different sizes of static and moving obstacles for V2X communications. In the proposed model, buildings and large moving vehicles are modeled as static and moving obstacles, and taken into account their impact on the packet reception rate, Line-of-sight (LOS) obstruction, and received signal power. We use unsymmetrical city map which has many dead-end roads and open faces. Each dead-end road and open faces are joined to the nearest edge making a polygon to model realistic obstacles. The simulation results of proposed model demonstrates better performance compared to some existing models, that shows proposed model can reflect more realistic simulation environments.

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Modeling obstacles in INET/Mobility framework: motivation, integration, and performance

Cited by 6 publications

References 4 publications

Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

Evaluating the effectiveness of pseudonym changing strategies for location privacy in vehicular ad‐hoc network

Realistic and Efficient Radio Propagation Model for V2X Communications

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