Strategyproof auction mechanisms for network procurement

Deferred-acceptance auctions can be seen as heuristic algorithms to solve $${{\mathcal {N}}}{{\mathcal {P}}}$$ N P -hard allocation problems. Such auctions have been used in the context of the Incentive Auction by the US Federal Communications Commission in 2017, and they have remarkable incentive properties. Besides being strategyproof, they also prevent collusion among participants. Unfortunately, the worst-case approximation ratio of these algorithms is very low in general, but it was observed that they lead to near-optimal solutions in experiments on the specific allocation problem of the Incentive Auction. In this work, which is inspired by the telecommunications industry, we focus on a strategic version of the minimum Steiner tree problem, where the edges are owned by bidders with private costs. We design several deferred-acceptance auctions (DAAs) and compare their performance to the Vickrey–Clarke–Groves (VCG) mechanism as well as several other approximation mechanisms. We observe that, even for medium-sized inputs, the VCG mechanisms experiences impractical runtimes and that the DAAs match the approximation ratios of even the best strategy-proof mechanisms in the average case. We thus provide another example of an important practical mechanism design problem, where empirics suggest that carefully designed deferred-acceptance auctions with their superior incentive properties need not come at a cost in terms of allocative efficiency. Our experiments provide insights into the trade-off between solution quality and runtime and into the additional premium to be paid in DAAs to gain weak group-strategyproofness rather than just strategyproofness.

Multi Criterion Multicast Routing Algorithm Comparison for Large Networks

QoS enabled multicast routing is known to be of non-polynominal complexity, which leads to the necessity of using heuristic algorithms to find sub-optimal solutions to the problems of this class. The evaluation of such algorithms requires the use of the simulation techniques as the heuristics’ results are of stochastic nature. Because of the problem complexity the simulation times increase significantly in the function of the network size, therefore the results presented in the literature are often limited to only small models. In this article the results of the evaluation of different multicast QoS routing algorithms (further referred to as the Multi-Constrained Minimum Steiner Tree Problem - MCMST) have been presented for a wide range of network sizes reaching thousands of nodes.

Maintaining Connectivity of MANETs through Multiple Unmanned Aerial Vehicles

Recently, Unmanned Aerial Vehicles (UAVs) have emerged as relay platforms to maintain the connectivity of ground mobile ad hoc networks (MANETs). However, when deploying UAVs, existing methods have not consider one situation that there are already some UAVs deployed in the field. In this paper, we study a problem jointing the motion control of existing UAVs and the deployment of new UAVs so that the number of new deployed UAVs to maintain the connectivity of ground MANETs can be minimized. We firstly formulate the problem as a Minimum Steiner Tree problem with Existing Mobile Steiner points under Edge Length Bound constraints (MST-EMSELB) and prove the NP completeness of this problem. Then we propose three Existing UAVs Aware (EUA) approximate algorithms for the MST-EMSELB problem: Deploy-Before-Movement (DBM), Move-Before-Deployment (MBD), and Deploy-Across-Movement (DAM) algorithms. Both DBM and MBD algorithm decouple the joint problem and solve the deployment and movement problem one after another, while DAM algorithm optimizes the deployment and motion control problem crosswise and solves these two problems simultaneously. Simulation results demonstrate that all EUA algorithms have better performance than non-EUA algorithm. The DAM algorithm has better performance in all scenarios than MBD and DBM ones. Compared with DBM algorithm, the DAM algorithm can reduce at most 70% of the new UAVs number.

A New Hybrid GRASP with the Pilot Method Forthe Delay-Constrained Multicast Routing Problem

Multicast routing problem is a well know optimization problem for transmitting real-time multimedia applications in telecommunication networks. As the underpinning mathematical model, the constrained minimum Steiner tree problem in graphs is a well-known NP-complete problem. In this paper we investigate a new hybrid GRASP (Greedy Randomized Adaptive Search Procedure) approach where a pilot method is applied to further enhance the search for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. Experimental results demonstrate the efficiency of the hybrid GRASP algorithm and the contributions of the post-processing pilot method to better solutions in most cases. The proposed GRASP approach is a competitive approach in solving the DCLC multicast routing problem.