Multi-Constrained Optimal Path Problem Research Articles

Energy-Efficient Algorithm for Reliable Routing of Wireless Sensor Networks

Quality of service (QoS) routing is one of the critical challenges in wireless sensor networks (WSNs), especially for surveillance systems. Multihop data transmission of WSNs, due to the high packet loss and energy-efficiency, requires reliable links for end-to-end data delivery. Current multipath routing works can provision QoS requirements like end-to-end reliability and delay, but suffer from a significant energy cost. To improve the efficiency of the network with multiconstraints QoS parameters, in this paper we model the problem as a multiconstrained optimal path problem and propose a distributed learning automaton (DLA) based algorithm to preserve it. The proposed approach leverages the advantage of DLA to find the smallest number of nodes to preserve the desired QoS requirements. It takes several QoS routing constraints like end-to-end reliability and delay into account in path selection. We simulate the proposed algorithm, and the obtained results verify the effectiveness of our solution. The results demonstrate that our algorithm has a better performance than current state-of-the-art competitive algorithms in terms of end-to-end delay and energy-efficiency.

An effective method for service components selection based on micro-canonical annealing considering dependability assurance

Distributed virtualization changes the pattern of building software systems. However, it brings some problems on dependability assurance owing to the complex social relationships and interactions between service components. The best way to solve the problems in a distributed virtualized environment is dependable service components selection. Dependable service components selection can be modeled as finding a dependable service path, which is a multiconstrained optimal path problem. In this paper, a service components selection method that searches for the dependable service path in a distributed virtualized environment is proposed from the perspective of dependability assurance. The concept of Quality of Dependability is introduced to describe and constrain software system dependability during dynamic composition. Then, we model the dependable service components selection as a multiconstrained optimal path problem, and apply the Adaptive Bonus-Penalty Microcanonical Annealing algorithm to find the optimal dependable service path. The experimental results show that the proposed algorithm has high search success rate and quick converges.

A survey on multi-constrained optimal path computation: Exact and approximate algorithms

The paper presents a survey on the techniques to solve the multi-constrained optimal path (MCOP) problem. Computing the MCOP is a task shared by many research areas, from transportation systems to telecommunication networks. In the latter, the MCOP is often related to the issue of Quality of Service (QoS) routing, which consists in finding a route between a couple of nodes that meets a series of QoS requirements such as bounded delay, packet loss, and other parameters. The MCOP problem has been faced by several authors and a plethora of solving methods is now available. In the present work, we draw the state of the art of exact and approximate MCOP computation algorithms, with particular attention to the networking area. We describe and analyse the most representative methods, and for each of them we derive the worst case computational complexity. In addition, we provide the reader with a uniform notation and with the detailed pseudo-code of various algorithms, so that the paper can indeed serve as a workable starting point for further studies on the MCOP problem.

Traffic driven multiple constraint-optimisation for QoS routing

The core of any QoS routing algorithm designed to solve the multi-constrained optimal path problem, is a length function that is used to find the optimal route across the network. User applications require diverse optimisation requirements but typical QoS-based algorithms have fixed length functions and do not offer a flexible optimisation framework. In this paper, we present a multiple-constraint-optimisation algorithm that adapts to user traffic optimisation needs without requiring a change to core logic or the length function. This routing paradigm searches for feasible paths satisfying multiple QoS requirements and implements a routing decision support system (RDSS) that separates the constraint path finding mechanism from the optimisation mechanism. Thereby the algorithm can optimise for any type or combination of metrics. Simulations compare the performance of the RDSS algorithm with other QoS algorithms and demonstrate the feasibility of the proposed approach in finding pareto optimal paths especially under strict constraints.