The impact of physical conditions on network connectivity in wireless sensor network

In Wireless Sensor Networks, end-to-end routing paths need to be established when nodes want to communicate with the desired destination. For nodes assumed to be static, many routing protocols such as Directed Diffusion have been proposed to meet this requirement efficiently. The performance of such routing protocols is relative to the given network connectivity. This paper addresses mobile sensor nodes taking into account the diversity of scattered node density and investigates how physical conditions impact on network connectivity which in turn influences routing performance. Three analysis metrics: path availability, path duration, and interavailable path time are proposed to quantify the impact of different physical conditions on network connectivity. Simulation results show that the network connectivity varies significantly as a function of different physical conditions

On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study

Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces. The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos. The results consistently show a clear relation between mobility encounters and traffic across different buildings over multiple days, with encountered pairs showing higher traffic similarity than non-encountered pairs, and long encounters being associated with the highest similarity. We also investigate the feasibility of learning encounters through web traffic profiles, with implications for dissemination protocols, and contact tracing. This provides a compelling case to integrate both mobility and web traffic dimensions in future models, not only at an individual level, but also at pairwise and collective levels. We have released samples of code and data used in this study on GitHub, to support reproducibility and encourage further research (https://github.com/BabakAp/encounter-traffic).Comment: Technical report with details for conference paper at MSWiM 2018, v3 adds GitHub lin

Drop-Burst Length Evaluation of Urban VANETs

This is an Open Access Article. It is published by International Science and Engineering Society under the Creative Commons Attribution-ShareAlike 4.0 International Licence (CC BY-SA). Full details of this licence are available at: http://creativecommons.org/licenses/by-sa/4.0/Networks performance is traditionally evaluated using packet delivery ratio (PDR) and latency (delay).We propose an addition mechanism the drop-burst length (DBL). Many traffic classes display varying application-level performance according to the pattern of drops, even if the PDR is similar. In this paper we study a number of VANET scenarios and evaluate them with these three metrics. Vehicular Ad-hoc Networks (VANETs) are an emerging class of Mobile Ad-hoc Network (MANETs) where nodes include both moving vehicles and fixed infrastructure. VANETs aim to make transportation systems more intelligent by sharing information to improve safety and comfort. Efficient and adaptive routing protocols are essential for achieving reliable and scalable network performance. However, routing in VANETs is challenging due to the frequent, high-speed movement of vehicles, which results in frequent network topology changes. Our simulations are carried out using NS2 (for network traffic) and SUMO (for vehicular movement) simulators, with scenarios configured to reflect real-world conditions. The results show that OLSR is able to achieve a best DBL performance and demonstrates higher PDR performance comparing to AODV and GPSR under low network load. However, with GPSR, the network shows more stable PDR under medium and high network load. In term of delay OLSR is outperformed by GPSR

Drop-burst length evaluation of urban VANETs

Networks performance is traditionally evaluated using packet delivery ratio (PDR) and latency (delay).We propose an addition mechanism the drop-burst length (DBL). Many traffic classes display varying application-level performance according to the pattern of drops, even if the PDR is similar. In this paper we study a number of VANET scenarios and evaluate them with these three metrics. Vehicular Ad-hoc Networks (VANETs) are an emerging class of Mobile Ad-hoc Network (MANETs) where nodes include both moving vehicles and fixed infrastructure. VANETs aim to make transportation systems more intelligent by sharing information to improve safety and comfort. Efficient and adaptive routing protocols are essential for achieving reliable and scalable network performance. However, routing in VANETs is challenging due to the frequent, high-speed movement of vehicles, which results in frequent network topology changes. Our simulations are carried out using NS2 (for network traffic) and SUMO (for vehicular movement) simulators, with scenarios configured to reflect real-world conditions. The results show that OLSR is able to achieve a best DBL performance and demonstrates higher PDR performance comparing to AODV and GPSR under low network load. However, with GPSR, the network shows more stable PDR under medium and high network load. In term of delay OLSR is outperformed by GPSR

Performance Evaluation of MANET Based Routing Protocols for VANETs in Urban Scenarios

Abstract. Vehicular Ad hoc NETworks (VANETs) are self-organizing ad hoc networks that are specifically designed for communication among vehicles where vehicles are themselves the nodes. Although routing protocols have already been analyzed and compared in the past for Mobile Ad hoc Networks (MANETs), simulations and comparisons of routing protocols for VANETs have almost always been done considering random motions with non-urban specific parameters. This paper studies the performance of Ad hoc On-Demand Distance Vector (AODV) and Destination Sequenced Distance Vector (DSDV) which are popular routing protocols in MANETS for routing among vehicular nodes in VANETs. The effects of urban motions on the simulation parameters, their consequences on routing performance are compared between the two protocols in this study. The VANET simulations showed that on-demand based protocol AODV performs better than the table-driven based DSDV protocol for two performance metrics for vehicular nodes moving in urban scenarios

Impact of Correlated Mobility on Delay-Throughput Performance in Mobile Ad-Hoc Networks

Abstract—We extend the analysis of the scaling laws of wireless ad hoc networks to the case of correlated nodes movements, which are commonly found in real mobility processes. We consider a simple version of the Reference Point Group Mobility model, in which nodes belonging to the same group are constrained to lie in a disc area, whose center moves uniformly across the network according to the i.i.d. model. We assume fast mobility conditions, and take as primary goal the maximization of pernode throughput. We discover that correlated node movements have huge impact on asymptotic throughput and delay, and can sometimes lead to better performance than the one achievable under independent nodes movements. I. INTRODUCTION AND RELATED WORK In the last few years the store-carry-forward communication paradigm, which allows nodes to physically carry buffered dat

On the Validity of Geosocial Mobility Traces

Mobile networking researchers have long searched for largescale, fine-grained traces of human movement, which have remained elusive for both privacy and logistical reasons. Recently, researchers have begun to focus on geosocial mobility traces, e.g. Foursquare checkin traces, because of their availability and scale. But are we conceding correctness in our zeal for data? In this paper, we take initial steps towards quantifying the value of geosocial datasets using a large ground truth dataset gathered from a user study. By comparing GPS traces against Foursquare checkins, we find that a large portion of visited locations is missing from checkins, and most checkin events are either forged or superfluous events. We characterize extraneous checkins, describe possible techniques for their detection, and show that both extraneous and missing checkins introduce significant errors into applications driven by these traces

Discrete event simulation of wireless cellular networks

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