Nodes in a mobile Ad Hoc network can change their location dynamically. The manner in which nodes can be moved can be represented by a mobility model. A mobility model defines the speed, pauses and direction of the nodes in a network. The way in which nodes move in an emergency scenario and rescue differs from a normal scenario, because the normal paths can be interrupted. AVM mobility models and UPM have been defined for this type of scenario. This paper presents a comparison of mobility models AVM, UPM, Waypoint, Brownian, Pursue on emergency and rescue scenario, with different routing protocols and different node density. The results show that Pursue, AVM and UPM models are more efficient than other models.

To take full advantage of the available network capacity, connections need to be able to use multiple paths to route their packets. Max-min fairness (MMF) can be effectively applied to single-path networks, but computing MMF rates in multipath networks requires solving a series of linear programming (LP) problems with high computational cost. Thus, a relaxation of MMF has been proposed, namely, upward max-min fairness (UMMF), which can be solved by simple combinatorial algorithms. Current proposals carry out incremental approximations emulating the waterfilling algorithm, which inherently establishes a dependency between the time required to achieve the optimal solution and the capacity of the links. Thus, the more capacity the network has, the less efficient the algorithms are. We defined the concept of the saturation level as the basis for the computation of fair shares. We developed the first centralized algorithm based on this concept, which we call c-SLEN. Unlike its predecessors, its convergence time does not depend on network capacity, and it does not incur link oversaturation. Based on c-SLEN, we derived d-SLEN, a distributed protocol that does not need to maintain per-subflow information in routers and guarantees constant processing time for control packets, making it a good candidate for practical use. Finally, through extensive simulations, we showed that d-SLEN is faster, lighter, and more accurate than its counterparts. Owing to its accuracy and convergence speed, it is able to maintain the size of link queues at minimal values at all times, thus proactively avoiding network congestion.

Propagation is an essential factor ensuring good coverage of wireless communications systems. Propagation models are used to predict losses in the path between transmitter and receiver nodes. They are usually defined for general conditions. Therefore, their results are not always adapted to the behavior of real signals in a specific environment. The main goal of this work is to propose a new model adjusting the loss coefficients based on empirical data, which can be applied in an indoor university campus environment. The Oneslope, Log-distance and ITU models are described to provide a mathematical base. An extensive measurement campaign is performed based on a strict methodology considering different cases in typical indoor scenarios. New loss parameter values are defined to adjust the mathematical model to the behavior of real signals in the campus environment. The experimental results show that the model proposed offers an attenuation average error of 2.5% with respect to the losses measured. In addition, comparison of the proposed model with existing solutions shows that it decreases the average error significantly for all scenarios under evaluation.