Modeling Analysis and Simulation of Wireless and Mobile Systems 

About: Modeling Analysis and Simulation of Wireless and Mobile Systems is an academic conference. The conference publishes majorly in the area(s): Wireless sensor network & Wireless network. Over the lifetime, 978 publications have been published by the conference receiving 22670 citations.

A group mobility model for ad hoc wireless networks

Abstract: In this paper, we present a survey of various mobility models in both cellular networks and multi-hop networks We show that group motion occurs frequently in ad hoc networks, and introduce a novel group mobility model Reference Point Group Mobility (RPGM) to represent the relationship among mobile hosts RPGM can be readily applied to many existing applications Moreover, by proper choice of parameters, RPGM can be used to model several mobility models which were previously proposed One of the main themes of this paper is to investigate the impact of the mobility model on the performance of a specific network protocol or application To this end, we have applied our RPGM model to two different network protocol scenarios, clustering and routing, and have evaluated network performance under different mobility patterns and for different protocol implementations As expected, the results indicate that different mobility patterns affect the various protocols in different ways In particular, the ranking of routing algorithms is influenced by the choice of mobility pattern

IEEE 802.11 rate adaptation: a practical approach

Abstract: Today, three different physical (PHY) layers for the IEEE 802.11 WLAN are available (802.11a/b/g); they all provide multi-rate capabilities. To achieve a high performance under varying conditions, these devices need to adapt their transmission rate dynamically. While this rate adaptation algorithm is a critical component of their performance, only very few algorithms such as Auto Rate Fallback (ARF) or Receiver Based Auto Rate (RBAR) have been published and the implementation challenges associated with these mechanisms have never been publicly discussed. In this paper, we first present the important characteristics of the 802.11 systems that must be taken into account when such algorithms are designed. Specifically, we emphasize the contrast between low latency and high latency systems, and we give examples of actual chipsets that fall in either of the different categories. We propose an Adaptive ARF (AARF) algorithm for low latency systems that improves upon ARF to provide both short-term and long-term adaptation. The new algorithm has very low complexity while obtaining a performance similar to RBAR, which requires incompatible changes to the 802.11 MAC and PHY protocol. Finally, we present a new rate adaptation algorithm designed for high latency systems that has been implemented and evaluated on an AR5212-based device. Experimentation results show a clear performance improvement over the algorithm previously implemented in the AR5212 driver we used.

Experimental evaluation of wireless simulation assumptions

Abstract: All analytical and simulation research on ad~hoc wireless networks must necessarily model radio propagation using simplifying assumptions. We provide a comprehensive review of six assumptions that are still part of many ad hoc network simulation studies, despite increasing awareness of the need to represent more realistic features, including hills, obstacles, link asymmetries, and unpredictable fading. We use an extensive set of measurements from a large outdoor routing experiment to demonstrate the weakness of these assumptions, and show how these assumptions cause simulation results to differ significantly from experimental results. We close with a series of recommendations for researchers, whether they develop protocols, analytic models, or simulators for ad~hoc wireless networks.

Do LoRa Low-Power Wide-Area Networks Scale?

Abstract: New Internet of Things (IoT) technologies such as Long Range (LoRa) are emerging which enable power efficient wireless communication over very long distances. Devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. Given the fact that a wide area is covered and that all devices communicate directly to a few sink nodes a large number of nodes have to share the communication medium. LoRa provides for this reason a range of communication options (centre frequency, spreading factor, bandwidth, coding rates) from which a transmitter can choose. Many combination settings are orthogonal and provide simultaneous collision free communications. Nevertheless, there is a limit regarding the number of transmitters a LoRa system can support. In this paper we investigate the capacity limits of LoRa networks. Using experiments we develop models describing LoRa communication behaviour. We use these models to parameterise a LoRa simulation to study scalability. Our experiments show that a typical smart city deployment can support 120 nodes per 3.8 ha, which is not sufficient for future IoT deployments. LoRa networks can scale quite well, however, if they use dynamic communication parameter selection and/or multiple sinks.

Smooth is better than sharp: a random mobility model for simulation of wireless networks

Abstract: This paper presents an enhanced random mobility model for simulation-based studies of wireless networks. Our approach makes the movement trace of individual mobile stations more realistic than common approaches for random movement.After giving a survey of mobility models found in the literature, we give a detailed mathematical formulation of our model and outline its advantages. The movement concept is based on random processes for speed and direction control in which the new values are correlated to previous ones. Upon a speed change event, a new target speed is chosen, and an acceleration is set to achieve this target speed. The principles for a direction change are similar. Moreover, we propose two extensions for modeling typical movement patterns of vehicles. Finally, we consider strategies for the nodes' border behavior (i.e., what happens when nodes move out of the simulation area) and point out a pitfall that occurs when using a bounded simulation area.