Minimum power multicasting with delay bound constraints in Ad Hoc wireless networks

In this paper, we design a new heuristic for an important extension of the minimum power multicasting problem in ad hoc wireless networks. Assuming that each transmission takes a fixed amount of time, we impose constraints on the number of hops allowed to reach the destination nodes in the multicasting application. This setting would be applicable in time critical or real time applications, and the relative importance of the nodes may be indicated by these delay bounds. We design a filtered beam search procedure for solving this problem. The performance of our algorithm is demonstrated on numerous test cases by benchmarking it against an optimal algorithm in small problem instances, and against a modified version of the well-known Broadcast Incremental Power (BIP) algorithm 20 for relatively large problems

On the Impact of Network Topology on Wireless Sensor Networks Performances - Illustration with Geographic Routing

Poster in the Tenth ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN 2013)Wireless Sensor Networks (WSN) are composed of constrained devices and deployed in unattended and hostile environments. Most papers presenting solutions for WSN evaluate their work over random topologies to highlight some of their "good" performances. They rarely study these behaviors over more than one topology. Yet, the topology used can greatly impact the routing performances. This is what we demonstrate in this paper. We present a study of the impact of network topology on algorithms performance in Wireless Sensor Networks and illustrate it with geographic routing. Geographic routing is a family of routing algorithms using nodes coordinates to route data packet from source to destination. We measure the impact of different network topologies from realistic ones to regular and unrealistic ones through extensive simulations. Studied algorithms are common geographic greedy algorithms with different heuristics from the literature. We show that different topologies can lead to a difference of up to 25% on delivery ratio and average route length and more than 100% on overall cost of transmissions

A Neural Separation Algorithm for the Rounded Capacity Inequalities

The cutting plane method is a key technique for successful branch-and-cut and branch-price-and-cut algorithms that find the exact optimal solutions for various vehicle routing problems (VRPs). Among various cuts, the rounded capacity inequalities (RCIs) are the most fundamental. To generate RCIs, we need to solve the separation problem, whose exact solution takes a long time to obtain; therefore, heuristic methods are widely used. We design a learning-based separation heuristic algorithm with graph coarsening that learns the solutions of the exact separation problem with a graph neural network (GNN), which is trained with small instances of 50 to 100 customers. We embed our separation algorithm within the cutting plane method to find a lower bound for the capacitated VRP (CVRP) with up to 1,000 customers. We compare the performance of our approach with CVRPSEP, a popular separation software package for various cuts used in solving VRPs. Our computational results show that our approach finds better lower bounds than CVRPSEP for large-scale problems with 400 or more customers, while CVRPSEP shows strong competency for problems with less than 400 customers

A note on the data-driven capacity of P2P networks

We consider two capacity problems in P2P networks. In the first one, the nodes have an infinite amount of data to send and the goal is to optimally allocate their uplink bandwidths such that the demands of every peer in terms of receiving data rate are met. We solve this problem through a mapping from a node-weighted graph featuring two labels per node to a max flow problem on an edge-weighted bipartite graph. In the second problem under consideration, the resource allocation is driven by the availability of the data resource that the peers are interested in sharing. That is a node cannot allocate its uplink resources unless it has data to transmit first. The problem of uplink bandwidth allocation is then equivalent to constructing a set of directed trees in the overlay such that the number of nodes receiving the data is maximized while the uplink capacities of the peers are not exceeded. We show that the problem is NP-complete, and provide a linear programming decomposition decoupling it into a master problem and multiple slave subproblems that can be resolved in polynomial time. We also design a heuristic algorithm in order to compute a suboptimal solution in a reasonable time. This algorithm requires only a local knowledge from nodes, so it should support distributed implementations. We analyze both problems through a series of simulation experiments featuring different network sizes and network densities. On large networks, we compare our heuristic and its variants with a genetic algorithm and show that our heuristic computes the better resource allocation. On smaller networks, we contrast these performances to that of the exact algorithm and show that resource allocation fulfilling a large part of the peer can be found, even for hard configuration where no resources are in excess.Comment: 10 pages, technical report assisting a submissio

A Lagrangian approach to Chance Constrained Routing with Local Broadcast

Mobile cellular networks play a pivotal role in emerging Internet of Things (IoT) applications, such as vehicular collision alerts, malfunctioning alerts in Industry-4.0 manufacturing plants, periodic distribution of coordination information for swarming robots or platooning vehicles, etc. All these applications are characterized by the need of routing messages within a given local area (geographic proximity) with constraints about both timeliness and reliability (i.e., probability of reception). This paper presents a Non-Convex Mixed-Integer Nonlinear Programming model for a routing problem with probabilistic constraints on a wireless network. We propose an exact approach consisting of a branch-and-bound framework based on a novel Lagrangian decomposition to derive lower bounds. Preliminary experimental results indicate that the proposed algorithm is competitive with state-of-the-art general-purpose solvers, and can provide better solutions than existing highly tailored ad-hoc heuristics to this problem

Infective flooding in low-duty-cycle networks, properties and bounds

Flooding information is an important function in many networking applications. In some networks, as wireless sensor networks or some ad-hoc networks it is so essential as to dominate the performance of the entire system. Exploiting some recent results based on the distributed computation of the eigenvector centrality of nodes in the network graph and classical dynamic diffusion models on graphs, this paper derives a novel theoretical framework for efficient resource allocation to flood information in mesh networks with low duty-cycling without the need to build a distribution tree or any other distribution overlay. Furthermore, the method requires only local computations based on each node neighborhood. The model provides lower and upper stochastic bounds on the flooding delay averages on all possible sources with high probability. We show that the lower bound is very close to the theoretical optimum. A simulation-based implementation allows the study of specific topologies and graph models as well as scheduling heuristics and packet losses. Simulation experiments show that simple protocols based on our resource allocation strategy can easily achieve results that are very close to the theoretical minimum obtained building optimized overlays on the network

Study of architecture and protocols for reliable multicasting in packet switching networks

Group multicast protocols have been challenged to provide scalable solutions that meet the following requirements: (i) reliable delivery from different sources to all destinations within a multicast group; (ii) congestion control among multiple asynchronous sources. Although it is mainly a transport layer task, reliable group multicasting depends on routing architectures as well. This dissertation covers issues of both network and transport layers. Two routing architectures, tree and ring, are surveyed with a comparative study of their routing costs and impact to upper layer performances. Correspondingly, two generic transport protocol models are established for performance study. The tree-based protocol is rate-based and uses negative acknowledgment mechanisms for reliability control, while the ring-based protocol uses window-based flow control and positive acknowledgment schemes. The major performance measures observed in the study are network cost, multicast delay, throughput and efficiency. The results suggest that the tree architecture costs less at network layer than the ring, and helps to minimize latency under light network load. Meanwhile, heavy load reliable group multicasting can benefit from ring architecture, which facilitates window-based flow and congestion control. Based on the comparative study, a new two-hierarchy hybrid architecture, Rings Interconnected with Tree Architecture (RITA), is presented. Here, a multicast group is partitioned into multiple clusters with the ring as the intra-cluster architecture, and the tree as backbone architecture that implements inter-cluster multicasting. To compromise between performance measures such as delay and through put, reliability and congestion controls are accomplished at the transport layer with a hybrid use of rate and window-based protocols, which are based on either negative or positive feedback mechanisms respectively. Performances are compared with simulations against tree- and ring-based approaches. Results are encouraging because RITA achieves similar throughput performance as the ring-based protocol, but with significantly lowered delay. Finally, the multicast tree packing problem is discussed. In a network accommodating multiple concurrent multicast sessions, routing for an individual session can be optimized to minimize the competition with other sessions, rather than to minimize cost or delay. Packing lower bound and a heuristic are investigated. Simulation show that congestion can be reduced effectively with limited cost increase of routings