The dynamic network topology and mobile nature of nodes can cause challenges regarding connectivity and routing. Clustering in Mobile Ad-hoc Networks (MANETs) is one of the effective ways to organize a network according to the network topological changes. In this paper, we propose a self-organization based clustering scheme in MANET using zone based group mobility to improve scalability and stability of overall network. This proposed algorithm utilizes the bio-inspired behavioral study of birds flocking for formation and maintenance of clusters in MANETs. A dynamic mechanism for cluster size management is taken into account to reduce network congestion and improve the performance of the MANETs in group mobility. For proper use of resources and to reduce extra energy consumption, an algorithm is also proposed to handle the isolated nodes. Simulation result shows that proposed algorithm reduces the energy consumption and improves the network lifetime along with more robustness.

The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework for mobile traffic classification. Experimental results show that the proposed model based on machine learning technique is able to accurately model and classify the network traffic patterns. Furthermore, we demonstrate the application of such insights for resource optimisation.