ADMP: an adaptive multicast routing protocol for mobile ad hoc networks

We present ADMP, the adaptive mesh-based multicast routing protocol, in which nodes are able to independently tune the amount of redundancy used to transmit data packets with the goal of improving the overall packet delivery ratio while keeping the retransmission overhead as low as possible. ADMP is based on a novel distributed algorithm for computing connected dominating sets. ADMP uses a single type of control packet, called multicast announcement, which is used to build the meshes of multicast groups, elect the core of each mesh and obtain two-hop neighborhood information. Using detailed simulations for different scenarios, we show that ADMP achieves similar or better reliability than two mesh-based multicast protocols that are very resilient (ODMRP and PUMA) while inducing low packet retransmission overhead.1st IFIP International Conference on Ad-Hoc NetWorkingRed de Universidades con Carreras en Informática (RedUNCI

ADMP: an adaptive multicast routing protocol for mobile ad hoc networks

We present ADMP, the adaptive mesh-based multicast routing protocol, in which nodes are able to independently tune the amount of redundancy used to transmit data packets with the goal of improving the overall packet delivery ratio while keeping the retransmission overhead as low as possible. ADMP is based on a novel distributed algorithm for computing connected dominating sets. ADMP uses a single type of control packet, called multicast announcement, which is used to build the meshes of multicast groups, elect the core of each mesh and obtain two-hop neighborhood information. Using detailed simulations for different scenarios, we show that ADMP achieves similar or better reliability than two mesh-based multicast protocols that are very resilient (ODMRP and PUMA) while inducing low packet retransmission overhead.1st IFIP International Conference on Ad-Hoc NetWorkingRed de Universidades con Carreras en Informática (RedUNCI

Clustering Improves the Goemans–Williamson Approximation for the Max-Cut Problem

MAX−CUT is one of the well-studied NP-hard combinatorial optimization problems. It can be formulated as an Integer Quadratic Programming problem and admits a simple relaxation obtained by replacing the integer “spin” variables xi by unitary vectors v⃗ i. The Goemans–Williamson rounding algorithm assigns the solution vectors of the relaxed quadratic program to a corresponding integer spin depending on the sign of the scalar product v⃗ i⋅r⃗ with a random vector r⃗ . Here, we investigate whether better graph cuts can be obtained by instead using a more sophisticated clustering algorithm. We answer this question affirmatively. Different initializations of k-means and k-medoids clustering produce better cuts for the graph instances of the most well known benchmark for MAX−CUT. In particular, we found a strong correlation of cluster quality and cut weights during the evolution of the clustering algorithms. Finally, since in general the maximal cut weight of a graph is not known beforehand, we derived instance-specific lower bounds for the approximation ratio, which give information of how close a solution is to the global optima for a particular instance. For the graphs in our benchmark, the instance specific lower bounds significantly exceed the Goemans–Williamson guarantee

Unsatisfiability Bounds for Random Constraint Satisfaction Problems from an Energetic Interpolation Method

The interpolation method, originally developed in statistical physics, transforms distributions of random Constraint Satisfaction Problems (CSPs) to distributions of much simpler problems while bounding the change in a number of associated statistical quantities along the transformation path. By now, it is known that, in principle, the method can yield rigorous unsatisfiability results if one ``plugs in an appropriate functional distribution'' to the derived expressions. A drawback of the method is that identifying the appropriate distribution leads to major analytical challenges as the relevant distributions are, in fact, infinite dimensional objects. We develop a variant of the interpolation method for random CSPs on arbitrary sparse degree distributions which trades accuracy for tractability. In particular, our bounds only require the solution of a 1-dimensional optimization problem (which typically turns out to be very easy) and as such can be used to compute explicit rigorous unsatisfiability bounds. We use this new method to analyze the performance of a number of algorithms on random 3-CNF formulas with n variables and m=rn clauses. A long series of papers analyzing so-called ``myopic'' algorithms has provided a sequence of lower bounds for the satisfiability threshold, which is widely believed to be r~4.26. Indeed, for each myopic algorithm A it is known that there exists an algorithm-specific clause-density, r_A, such that if r2.78 and the same is true for generalized unit clause for all r>3.1. Our results imply exponential lower bounds for many other myopic algorithms for densities similarly close to the corresponding r_A

Unsatisfiability Bounds for Random Constraint Satisfaction Problems from an Energetic Interpolation Method

The interpolation method, originally developed in statistical physics, transforms distributions of random Constraint Satisfaction Problems (CSPs) to distributions of much simpler problems while bounding the change in a number of associated statistical quantities along the transformation path. By now, it is known that, in principle, the method can yield rigorous unsatisfiability results if one ``plugs in an appropriate functional distribution'' to the derived expressions. A drawback of the method is that identifying the appropriate distribution leads to major analytical challenges as the relevant distributions are, in fact, infinite dimensional objects. We develop a variant of the interpolation method for random CSPs on arbitrary sparse degree distributions which trades accuracy for tractability. In particular, our bounds only require the solution of a 1-dimensional optimization problem (which typically turns out to be very easy) and as such can be used to compute explicit rigorous unsatisfiability bounds. We use this new method to analyze the performance of a number of algorithms on random 3-CNF formulas with n variables and m=rn clauses. A long series of papers analyzing so-called ``myopic'' algorithms has provided a sequence of lower bounds for the satisfiability threshold, which is widely believed to be r~4.26. Indeed, for each myopic algorithm A it is known that there exists an algorithm-specific clause-density, r_A, such that if r2.78 and the same is true for generalized unit clause for all r>3.1. Our results imply exponential lower bounds for many other myopic algorithms for densities similarly close to the corresponding r_A

A Window-Based, Server-Assisted P2P Network for VoD Services with QoE Guarantees

We describe a Peer-to-Peer (P2P) network that is designed to support Video on Demand (VoD) services. This network is based on a video-file sharing mechanism that classifies peers according to the window (segment of the file) that they are downloading. This classification easily allows identifying peers that are able to share windows among them, so one of our major contributions is the definition of a mechanism that could be implemented to efficiently distribute video content in future 5G networks. Considering that cooperation among peers can be insufficient to guarantee an appropriate system performance, we also propose that this network must be assisted by upload bandwidth from servers; since these resources represent an extra cost to the service provider, especially in mobile networks, we complement our work by defining a scheme that efficiently allocates them only to those peers that are in windows with resources scarcity (we called it prioritized windows distribution scheme). On the basis of a fluid model and a Markov chain, we also developed a methodology that allows us to select the system parameters values (e.g., windows sizes or minimum servers upload bandwidth) that satisfy a set of Quality of Experience (QoE) parameters

Opportunistic Mobile Sensing in the Fog

The increasing adoption of mobile personal devices and Internet of Things devices is leveraging the emergence of a wide variety of opportunistic sensing applications. However, the designers of this type of applications face a set of technical challenges related to the limitations and heterogeneity of the hardware and software platforms and to the dynamics of the scenarios where they are deployed. In this paper, we introduce a Semantic-Centric Fog-based framework aimed at effectively and efficiently supporting this type of applications. The proposed framework is composed of local and distributed algorithms that support the establishment and coordination of sensing tasks in the Fog. First, it performs ontology-driven in-network processing to locate the most adequate devices to carry out a given sensing task and then, it establishes efficient multihop routes that are used to coordinate relevant devices and to transport the collected sensory data to Fog sinks. We present a set of theorems that prove that the proposed algorithms are correct and the results of a series of detailed simulation-based experiments in NS3 that characterize the performance of the proposed platform. The results show that the proposed framework outperforms traditional sensing platforms that are based on centralized services

A comprehensive analytical framework for VoD services in hybrid CDN-P2P systems

International audienceVideo on Demand services generate the largest amount of traffic in networks nowadays. Previous works have proposed integrating Content Delivery Networks (CDN) and Peer-to-Peer (P2P) networks to satisfy this demand. However, their analytical methods do not consider all the factors that affect the performance of these systems. Hence, we present a novel comprehensive framework, based on a fluid model, to evaluate VoD services over hybrid CDN-P2P systems. The proposed framework considers features of the service (e.g., size, coding-rate and popularity of the video), network attributes (e.g., upload data-rate capacity of servers and peers) and characteristics of the behavior of the users (e.g., sojourn time, cooperativeness and frequency of random-seeks). Our framework allows a system to be evaluated under a wide variety of scenarios in terms of network-cost and Quality-of-Service (QoS) parameters and is flexible enough to model different resource allocation schemes, including a novel scheme to distribute the upload network capacity. Despite the wide variety of considered factors, our framework is tractable and as accurate as discrete-models based on Markov chains