Detecting Communities in Sparse MANETs

Abstract

In sparse mobile ad hoc networks, placement of services and data is crucial to assure their availability to all nodes because sparse population of nodes can lead to (frequent) network partitions. If these dynamic networks display a fairly stable cluster structure, it is possible to utilize this structure to improve service and data availability. However, clustering in a dynamic network is a very challenging task due to the ever-changing topology and irregular density of such a network. In this paper, we investigate clustering of dynamic networks with the help of community detection mechanisms, using only topology information from the local routing table. The main aim of our approach is to reduce to "zero" the communication overhead needed for cluster management and to dynamically adapt to the size and layout of the network. We have performed extensive experiments to evaluate the consistency, quality, and stability of the clustering returned by our algorithms. The results show that our nonintrusive clustering indeed discovers temporary groups of nodes that form stable clusters in the network. Moreover, even though the local routing tables in general reflect slightly diverging topologies, our results still show only small differences between the communities detected at different nodes.Index Terms-Clustering, community detection algorithm, network topology, rescue operations, sparse mobile ad hoc networks (MANETs), wireless communications.

A survey of community detection methods in multilayer networks

“…To solve this problem, a layer aggregation approach (De Domenico et al 2014) is proposed to reduce data size or as a data filter to benefit network-analysis outcomes. Since Mucha et al (2010) introduced the multiplex modularity optimization method, numerous attempts were made in this field (Drugan et al 2011;Nguyen et al 2011;Li and Garcia-Luna-Aceves 2013), which opened up a upsurge in unveiling the communities in time-varying networks. Taylor et al (2017) proposed the random matrix theory and found layer aggregation to significantly influence detectability.…”

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

“…SSM is one of the most commonly used mobility models for simulating real-life mobile applications [212], [213]. This mobility model has been an attractive target for MANETs, and many scholars have employed it to conduct their experiments [214]- [216]. The Long Term Evolution (LTE) is one the use cases for this mobility model, which has gained lots of attention as it is being the defacto technology for 4G infrastructures [217]- [219].…”

CLAN: An Efficient Distributed Temporal Community Detection Protocol for MANETs

“…Drugan et. al [25] modify the modularity-based approach of Newman and Grivan [18], the random walk approach proposed by van Dongen [26] and the q-spin Potts model proposed by Reichard and Bornholdt [27], to accept as input the topology table maintained by the OLSR routing protocol [6] rather than a complete graph. The paper demonstrated that the OLSR topology table was sufficient to accurately detect the communities in the network without additional overhead, under the given network conditions.…”