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Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.

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Future 5G mobile network architecture is expected to offer capacities to accommodate the inexorable rise in mobile data traffic and to meet further stringent latency and reliability requirements to support diverse high data rate applications and services. Mobile cloud computing (MCC) in 5G has emerged as a key paradigm, promising to augment the capability of mobile devices through provisioning of computational resources on demand, and enabling resource-constrained mobile devices to offload their processing and storage requirements to the cloud infrastructure. Follow-me cloud (FMC), in turn, has emerged as a concept that allows seamless migration of services according to the corresponding users mobility. Meanwhile, software-defined networking (SDN) is a new paradigm that permits the decoupling of the control and data planes of traditional networks and provides programmability and flexibility, allowing the network to dynamically adapt to change traffic patterns and user demands. While the SDN implementations are gaining momentum, the control plane is still suffering from scalability and performance concerns for a very large network. In this paper, we address these scalability and performance issues in the context of 5G mobile networks by introducing a novel SDN/OpenFlow-based architecture and control plane framework tailored for MCC-based systems and more specifically for FMC-based systems where mobile nodes and network services are subject to constraints of movements and migrations. Contrary to a centralized approach with a single SDN controller, our approach permits the distribution of the SDN/OpenFlow control plane on a two-level hierarchical architecture: a first level with a Global FMC Controller (G-FMCC), and a second level with several Local FMC Controllers (L-FMCCs). Thanks to our control plane framework and Network Function Virtualization (NFV) concept, the L-FMCCs are deployed on-demand, where and when needed, depending on the global system load. Results obtained via analysis show that our solution ensures more efficient management of control plane, performance maintaining, and network resources preservation.

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