Communication in Mobile Ad Hoc Network (MANET) is accomplished using routing protocols. These protocols provide an efficient and reliable path for data sharing. In static environment where the nodes are stationary these protocols performs exceptionally well but in an environment having mobile nodes the performance of these protocols degrade drastically. To investigate this factor various researchers developed mobility models using simulation tools such as QUALNET, NS-2 etc. These models represent the erratic movement of nodes and give us an idea regarding their location, velocity and acceleration change over time. This paper is an effort to study the effect of mobility models such as Random Way Point, File and Group Mobility Models on the performance of routing protocols using QUALNET simulator. The results show that the choice of mobility models affect the performance of routing protocol significantly.
In order to facilitate communication in Mobile Ad hoc Network (MANET), routing protocols are developed. The performance of these protocols depends upon various factors such as: transmission range, number of nodes deployed and mobility of the nodes. Another factor which affects the performance of MANET routing protocols is the environment in which ad hoc network is deployed. The MANET environment may contain obstacles such as mountains lakes, buildings and river. These obstacles restrict nodes movement but may or may not obstruct the effective transmission range of nodes deployed. This paper is an effort to evaluate the performance of MANET routing protocols in presence of obstacles by designing a simulator in MATLAB-10. To make the situation more realistic obstacle of different shapes, size, number and type were introduced in the simulation region. We found significant impact of the same on the performance of routing protocols.
We consider the decentralized convex optimization problem, where multiple agents must cooperatively minimize a cumulative objective function , with each local function expressible as an empirical average of data-dependent losses. State-of-the-art approaches for decentralized optimization rely on gradient tracking, where consensus is enforced via a doubly stochastic mixing matrix. Construction of such mixing matrices is not straightforward and requires coordination even prior to the start of the optimization algorithm. This paper puts forth a primal-dual framework for decentralized stochastic optimization that obviates the need for such doubly stochastic matrices. Instead, dual variables are maintained to track the disagreement between neighbors. The proposed framework is flexible and is used to develop decentralized variants of SAGA, L-SVRG, SVRG++, and SEGA algorithms. Using a unified proof, we establish that the oracle complexity of these decentralized variants is O(1/ ), matching the complexity bounds obtained for the centralized variants. Additionally, we also present a decentralized primal-dual accelerated SVRG algorithm achieving O(1/ √ ) oracle complexity, again matching the bound for the centralized accelerated SVRG. Numerical tests on the algorithms establish their superior performance as compared to the variance-reduced gradient tracking algorithms.
The mobility models are used to represent the unpredictable movement pattern of the nodes in Mobile Adhoc Network (MANET) and give us an idea regarding their location, velocity and acceleration change over time. These models are used for simulation purpose in standard software tools such as QualNet, ns-2 etc. This paper evaluates the performance of routing protocols for mobility models such as Random Way Point (RWP), Random Walk (RW) and Random Direction (RD) in presence of obstacles like mountain which restricts node movement as well obstruct transmission path between nodes based on a parameter termed as Probability of Reachability (POR). The POR is defined as the fraction of reachable routes to all possible routes between all pairs of sources and destinations. For this purpose a simulator is designed in MATLAB. We observe a marked difference in value of POR in presence of obstacles as well as variation in number of obstacles.
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