Accepted Papers

    Kartikay Kaushik Department of Electronics Engineering,Indian Institute of Technology (Indian School of Mines), Dhanbad, India.

    The recent trends of increasing unpredictability of traffic demand and the proliferation of networked devices have led to a demand for a traffic engineering application which views network robustness as a crucial factor. Robustness refers to the resilience of infrastructure networks against random and targeted failures, caused by traffic shifts, natural disasters and Denial of Service (DoS) attacks. In this paper, the author developed an application to assign the links of the network a criticality value and finds the least critical path or in other terms, the most robust path. The dynamics of the network are translated to graph metrics and the application optimizes these metrics to determine the most robust path in the network. The developed application can be further extended by incorporating its output as a feedback mechanism for another application developed for automation of the network system for robustness. The application was written in Python 2 and implemented on Ubuntu 14.04.4.

    Hamed Rahimi1, Amin Pishevar2, Ali Akbar Safavi3,And Ali Akbar Safavi3Behnam Rahnama11 Department of Electrical and Computer Engineering, Shiraz University Shiraz, Iran 22 Department of Electrical and Computer Engineering, Shiraz University Shiraz, Iran 33 Department of Electrical and Computer Engineering, Shiraz University Shiraz, Iran 44 Department of Computer Engineering, Eastern Mediterranean University Gazimagusa, Cyprus

    this scenario presents the deployment of a group of wireless sensors and actuators networks (WSANs) in rural boundary areas using a drone to drop sensors and actuators at certain position as a mesh of a hexagonal form. Nodes are heterogeneous in hardware and functionality thus not all nodes are able to directly transfer data to the base station. Primitive ones are only capable of collecting local data. However, more sophisticated ones, are equipped with long distance radio telemetry and more computational power. Power optimization is one of the vital facts in designing WSANs. Total power consumption must be minimized as sensors are self-managed. It is not possible to collect sensors on time bases and recharge the batteries. Therefore, energy consumption optimization and harvesting green energy are other factors that are considered. In this regard, protocols are designed in a way to support such requirements. The preprocessed data are first collected and combined by the leaders at each hexagonal cell. Then, the information packets are sent to the head clusters. Consequently, head clusters reprocess the received information and depict a better global view of the zone, using variety of the received information. Finally, the processed information is sent to the nearest base station or a mobile drone.


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