This project aims at developing scientific and technological advances required for the deployment of real-time applications as part of the IoT paradigm. To provide support for real-time IoT traffic, we need to redesign various elements of today's Internet. This include how we reduce the impact of packet drops, how we route and schedule packets, and how we provide end-to-end support for such applications. The proposed research will focus on each of the above three issues related to IoT traffic control and communication, using real-time applications as the target domain.
Software Defined Networking (SDN) is an emerging paradigm in computer networking that allows a logically centralized software program to control the behavior of the entire network. The SDN controller is a critical piece in this structure, where it is considered the mastermind of SDN networks. Thus, its failure will cause the entire network to fail. In order to overcome the problem of the overloaded controller failure in SDN, this project aims at proposing a controller offloading solution based on a prediction module that anticipates the presence of harmful long-term load.
The expansion of wireless usage in different applications has resulted in a tremendous increase in energy consumption and this left a significant environmental effect. According to latest statistics reported, energy costs to operate wireless devices accounts for half of the operating expenses for any wireless deployment.
In this project, we aim at understanding and addressing buffering and packet scheduling requirements when data from multiple VLC links needs to be aggregated across an acoustic network. The lower capacity of acoustic link forms a network bottleneck, requiring us to research, explore, and devise mechanisms for buffer sizing to limit queuing delays. Further, packet scheduling techniques will also be explored to provide Quality of Service (QoS) guarantees to data packets.
In this project, we tackled the dense deployment and energy efficient operation of sensor systems in underwater and terrestrial environments. We proposed an optimal node placement strategy that builds an initial underwater wireless sensor network infrastructure for implementing further research. We formulated the problem as a nonlinear programming with objectives of minimizing the total transmission loss, minimizing the number of nodes, and maximizing the covered volume.