The concept of vehicular ad hoc networks (VANETs) has emerged as an efficient way to improve the performance of transportation systems, enhance travel security and help address traffic control problems that modern society is facing today. Large amounts of VANET data are typically collected from highly heterogeneous sources paving the way for a new era of VANET-Big Data. This data must be processed rapidly, efficiently and cost-effectively by different users and systems to make timely decisions about traffic and transportation. VANET-Big Data is causing a shift from technology-driven to data-driven VANETs. The authors analyse and discuss Big Data solutions that can be leveraged to address some of the emerging challenges of VANETs.

Military environments require highly dynamic mobile ad hoc networks (MANETs) to meet operational mission requirements. Decision makers rely on the timely delivery of critical battlefield information to make informed determinations quickly and as accurately as possible. However, traditional MANET routing protocols do not provide quality of service (QoS). Furthermore, they do not implement active controls to minimise the impact of network congestion. This study proposes the use of the information embedded in an air tasking order (ATO) during the planning phase of military missions to optimise the network performance. The trajectories of relevant nodes (airborne platforms) participating in the MANET can be forecasted by parsing key information contained in the ATO. Using this idea it is possible to optimise network routes to minimise edge overutilisation and increase network throughput. In one simulated test case, there was a 25% improvement of network throughput, and 23% reduction on dropped packets. Using this technique, the authors can selectively preserve the QoS by establishing network controls that drop low-priority packets when necessary. The algorithm improves the overall MANET throughput while minimising the packets dropped due to network congestion.

Caching is a practical data access approach in mobile opportunistic networks, which enables mobile terminals to obtain data from adjacent nodes and to reduces accessing latency, whereby the updating files should be delivered to the caching nodes on time when the source is refreshed in order to maintain the freshness of the cached data. Due to the node mobility and short communication range, the contact duration in mobile opportunistic networks is short and this limits the amount of data transmitted in one contact. However, current cache refreshing schemes often ignore this critical issue. In this study, the authors study the contact duration aware cache refreshing in mobile opportunistic networks. Specifically, they establish a two-state Markov chain to describe the data delivered from one node to another. Based on the proposed model, they investigate a tree structure to refresh the cached data, where each node is responsible to refresh the data cached at its children and it may opportunistically rely on some relays outside the tree to improve the refreshing efficiency. Extensive trace-driven simulations validate the effectiveness of the authors’ scheme in terms of improving cache refreshing in challenging mobile environments.