BRuIT : Bandwidth Reservation under Interferences Influence

This paper deals with the bandwidth reservation problem in ad hoc networks and with the influence that interferences between signals have on this problem. We show that interferences could decrease the applications rates. This can be a real problem for applications that need guarantees. We propose a distributed protocol (called BRuIT) for bandwidth reservation in ad hoc networks that takes into account the existence of interferences from far transmissions. The protocol is analyzed through simulations carried out under NS: we evaluate the signaling overhead required for maintaining the knowledge of existing interferences ; we show that this knowledge reduces delays in case of congestion ; we measure the time for rebuilding broken routes ; and finally we show that this protocol maintains the rate of accepted applications.Cet article traite du problème de réservation de bande passante dans les réseaux ad-hoc et de l’influence des interférences hertziennes sur ce problème. Nous montrons que le phénomène d’interférences peut être à l’origine de pertes de bande passante qui peuvent être problématique pour les applications nécessitant des garanties. nous proposons un protocole distribué de réservation de bande passante pour réseaux ad-hoc appelé BRuIT. Ce protocole prend en compte l’existence d’interférences entre transmissions lointaines. Les performances de BRuIT sont analysées au moyen de simulations sous NS

Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks

In recent years there is a growing effort to provide learning algorithms for spectrum collaboration. In this paper we present a medium access control protocol which allows spectrum collaboration with minimal regret and high spectral efficiency in highly loaded networks. We present a fully-distributed algorithm for spectrum collaboration in congested ad-hoc networks. The algorithm jointly solves both the channel allocation and access scheduling problems. We prove that the algorithm has an optimal logarithmic regret. Based on the algorithm we provide a medium access control protocol which allows distributed implementation of the algorithm in ad-hoc networks. The protocol utilizes single-channel opportunistic carrier sensing to carry out a low-complexity distributed auction in time and frequency. We also discuss practical implementation issues such as bounded frame size and speed of convergence. Computer simulations comparing the algorithm to state-of-the-art distributed medium access control protocols show the significant advantage of the proposed scheme

A parallel interaction potential approach coupled with the immersed boundary method for fully resolved simulations of deformable interfaces and membranes

In this paper we show and discuss the use of a versatile interaction potential approach coupled with an immersed boundary method to simulate a variety of flows involving deformable bodies. In particular, we focus on two kinds of problems, namely (i) deformation of liquid-liquid interfaces and (ii) flow in the left ventricle of the heart with either a mechanical or a natural valve. Both examples have in common the two-way interaction of the flow with a deformable interface or a membrane. The interaction potential approach (de Tullio & Pascazio, Jou. Comp. Phys., 2016; Tanaka, Wada and Nakamura, Computational Biomechanics, 2016) with minor modifications can be used to capture the deformation dynamics in both classes of problems. We show that the approach can be used to replicate the deformation dynamics of liquid-liquid interfaces through the use of ad-hoc elastic constants. The results from our simulations agree very well with previous studies on the deformation of drops in standard flow configurations such as deforming drop in a shear flow or a cross flow. We show that the same potential approach can also be used to study the flow in the left ventricle of the heart. The flow imposed into the ventricle interacts dynamically with the mitral valve (mechanical or natural) and the ventricle which are simulated using the same model. Results from these simulations are compared with ad- hoc in-house experimental measurements. Finally, a parallelisation scheme is presented, as parallelisation is unavoidable when studying large scale problems involving several thousands of simultaneously deforming bodies on hundreds of distributed memory computing processors

A randomized primal distributed algorithm for partitioned and big-data non-convex optimization

In this paper we consider a distributed optimization scenario in which the aggregate objective function to minimize is partitioned, big-data and possibly non-convex. Specifically, we focus on a set-up in which the dimension of the decision variable depends on the network size as well as the number of local functions, but each local function handled by a node depends only on a (small) portion of the entire optimization variable. This problem set-up has been shown to appear in many interesting network application scenarios. As main paper contribution, we develop a simple, primal distributed algorithm to solve the optimization problem, based on a randomized descent approach, which works under asynchronous gossip communication. We prove that the proposed asynchronous algorithm is a proper, ad-hoc version of a coordinate descent method and thus converges to a stationary point. To show the effectiveness of the proposed algorithm, we also present numerical simulations on a non-convex quadratic program, which confirm the theoretical results