No abstract

In recent years, Network Coding (NC) has been used to increase performance and efficiency in Wireless Sensor Networks (WSNs). In NC, Sensor Nodes (SNs) of network first store the received data as a packet, then process and combine them and eventually send them. Since the bandwidth of edges between SNs is limited, management and balancing bandwidth should be used for NS. In this paper, we present an optimization model for routing and balancing bandwidth consumption using NC and multicast flows in WSNs. This model minimizes the ratio of the total maximum bandwidth to the available bandwidth in network's edges and we use the dual method to solve this model. We also use the Karush-Kuhn-Tucker conditions (KKT) to calculate a lower bound and find the optimal solution and point in optimization model. For this purpose, we need to calculate the derivative of the Lagrangian function relative to its variables, in order to determine the condition as a multi-excited multi-equation device. But since the solution of equations KKT is centralized and for WSNs with a large number of SNs, it is very difficult and time consuming and almost impractical, we provide a distributed and repeatable algorithm for solving proposed model in which instead of deriving derivatives, combination Sub-gradient method and network flow separation method are used, thus allow each SN locally and based on the information of its neighboring nodes performs optimal routing and balances bandwidth consumption in the network. The effectiveness of the proposed optimization model and the proposed distributed algorithm with multiple runs of simulation in terms of the number of Source SNs (SSNs) and Lagrange coefficient and step size have been investigated. The results show that the proposed model and algorithm, due to informed routing and NC, can improve the parameters of the average required time to find the route optimal, the total amount of virtual flow in network's edges, the average latency end-to-end of the network, the consumed bandwidth, the average lifetime of the network and the consumed energy, or not very weak compared to other models. The proposed algorithm also has great scalability, because computations are done distributed and decentralized, and there is an insignificant dependence between the SNs.

This study uses the network coding (NC) and mobile sinks (MSs) for collecting coded data (CCD) of sensor nodes (SNs). MSs move on a steady, direct and predetermined path with constant velocity in wireless sensor networks. The authors present an optimisation model for CCD problem which is a generalisation of the previous works and an optimisation model based on the integrated linear programming model. Solving this problem in polynomial time is not possible. In this model for CCD, each SN and MS are assigned a time slice, and two conditions are presented for transmission range of SNs and at most number time slices for CCD. Then a centralised algorithm with polynomial time complexity is presented in proposed conditions for the problem solving. Finally, by simulating, different sets of SNs are deployed randomly with a fixed position, which was evaluated by influence of the number of SNs, travelled distance of MS at each time slice, the duration of time slice, velocity of MS on delay, the total amount of CCD and network efficiency. The simulation results represent scalability and performance superiority of the proposed algorithm compared to methods with no NC such as C-Schedule and GAP-based approximation algorithms.

Recent studies have shown that the use of mobile sinks and Network Coding (NC) and determining the Sink Optimal Route (SOR) in wireless sensor networks (WSNs) reduces the energy consumption. The purpose of this paper is to determine the multicast SOR to move mobile sinks at specific deadline using NC and modeling and problem formulating based on a Mixed Integer Linear Programming (MILP) in WSNs. In this paper, we first show that finding the SOR is NP-hard, and then for determining the SOR, several convex optimization models are presented using Support Vector Regression (SVR). Solving these models in a polynomial time is not possible due to various parameters and limited resources of WSNs. To solve this problem in polynomial time, a Tabu Search algorithm is proposed to reduce runtime and energy consumption. Simulation results show that optimization models and proposed Tabu Search algorithm significantly reduce energy consumption and required time for computing than non-NC methods.

For a particular Internet connection the number of outstanding packets is controlled by TCP's current window size. During congestion avoidance phase, the congestion window is increased by 1 packet roughly during every RTT. It means that in each second, the throughput of a typical node can be increased by 1/RTT pkts. Therefore, the sources with smaller RTT experience higher throughput than those with long RTT. Using Opnet Modeler simulator we show that how this RTT differences between competing traffic streams, can be the dominant factor in relative throughput. In order to decrease this discrimination, we introduce a parametric algorithm for congestion window (cwnd) and therefore for rate control. We show that using an appropriate formulation derived from overall optimization problem, the network implicit objective function provides a Lyapunov function for the dynamic system defined by the rate control algorithm. Then we show that how this mathematical model are being used to address fairness problem of the rate control algorithms in the Internet. Our simulation results show the success of the proposed algorithm.