The explosive growth of mobile data traffic and the shortage of the available spectral resources have created new challenges for future cellular networks. In particular, resource management in heterogeneous network environment has become a critical issue. In this paper, we propose software-defined networking (SDN)-based resource management algorithms for future cellular network. Specifically, in this work, we have a threefold objective: i) alleviate spectrum shortage concerns by efficiently offloading traffic over the Wi-Fi network, ii) address network congestion by optimally balancing loads across multiple cells and iii) achieve the aforementioned objectives while taking network conditions and the end user quality-of-service (QoS) requirements into consideration. To this end, we present SDN-based partial data offloading and load balancing algorithms. The proposed algorithms exploit an SDN controller's global view of the network and take optimized resource allocation decisions. We analyze the performance of the proposed algorithms under realistic network model. Moreover, we also present an analytical framework to quantify the delay incurred due to the SDN-based data processing and forwarding. Our analysis and system-level simulations show that the proposed load balancing algorithm significantly improves the equilibrium extent and network stability as compared to the baseline algorithms. On the other hand, the proposed partial data offloading algorithm is shown to satisfy end user's quality-of-service while saving a significant amount of cellular resources.
Wireless sensor networks are an electro mechanical system which is used to transfer information to the base station from the beacon node. The objective of our project is to increase the lifetime of a beacon node, parallelly decreasing the energy using WSN by using appropriate scheduling mechanism. Each sensor performs different types of operation such as collecting the information from the sensors, processing it and communicating the source information when required, so energy consumption of these nodes is high. This can be reduced by using centralized approach and average consensus based Distributed algorithm. The lifetime of sensor is first increased by modifying M2CIC into multimodal set coverage problem and using this to produce NP-completeness. Another problem here is the network utility maximi-zation problem which consists of Mixed Integer programming. This is solved by splitting the multi period problem into a single period problem. It is future reduced to Pure Integer programming. This Pure Integer programming can be solved by a centralized way.
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