Multi-Constrained Optimal Path 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.

New Path Centrality Based on Operator Calculus Approach for Wireless Sensor Network Deployment

One of the greatest challenges in computing and estimating the important node metrics of a structural graph is centrality. Since centrality is an essential concept in social network analysis (SNA), it is used to define the importance of a node in a network like a wireless sensor network (WSN). Route optimization is another important feature in a wireless sensor network. This paper proposes an alternative solution to route optimization problems by using the multi-constrained optimal path (MCOP) approach. A new metric called path operator calculus (POC) is proposed as an alternative way to determine nodes with high centrality in wireless sensor network deployment. The estimation of this new metric is based on network topology by using the operator calculus approach, which produces the feasible paths for each node to the sink node. In order to define the path centrality of the network, the proposed approach takes into account two constraints for each node: energy and bit error rate (BER). The experimental evaluation shows improved performance in terms of delivery ratio, latency, throughput, delay, energy remaining and routing overhead.

Operator calculus approach for route optimizing and enhancing wireless sensor network

Route optimization is one of important feature in wireless sensor networks in order to enhancing the life time of WSNs. Since Centrality is one of the greatest challenges in computing and estimating the important node metrics of a structural graph, it is necessary to calculate and determine the importance of a node in a network. This paper proposes an alternative way to optimizing the route problems which is based on multi-constrained optimal path (MCOP) and operator calculus approach. A novel routing protocol called Path Operator Calculus Centrality (POCC) is proposed as a new method to determine the main path which contains high centrality nodes in a wireless sensor network deployment. The estimation of centrality is using the operator calculus approach based on network topology which provides optimal paths for each source node to base station. Some constraints such as energy level and bit error rate (BER) of node are considered to define the path centrality in this approach. The simulation evaluation shows improved performance in terms of energy consumption and network lifetime.

CAT: Context-Aware Trust-Oriented Worker Selection in Social Crowd

Finding trustworthy worker is a longstanding issue in crowdsourcing systems. On traditional crowdsouring platforms, like Amazon Mechanical Turk1, the trustworthiness of a worker is usually based on the contextual information, like different types of tasks and different reward amounts of tasks. However, with the combination of OSNs and the crowdsourcing applications in social crowd, in addition to the above mentioned task based contexts, the social contexts like the social relationships and the social positions of participants can greatly assist requestors to select trustworthy workers. In order to select the trustworthy workers in social crowd, in this paper, we first present a contextual social network structure which contains complex social contexts. Then we propose a trust evaluation model taking both contexts information and the requirements of requestors into consideration, which leads to the trust worker selection in social crowd as a classical NP-Complete Multi-Constrained Optimal Path (MCOP) selection problem. For solving this challenging problem, we propose a new efficient and effective approximation Context-Aware Trust-Oriented Worker Selection algorithm CAT. The results of our experiments conducted on four real OSN datasets illustrate the superiority of our method in trustworthy worker selection.

Converged Network-Cloud Service Composition with End-to-End Performance Guarantee

The crucial role of networking in cloud computing calls for federated management of both computing and networking resources for end-to-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both cloud computing and networking enables a convergence of network and cloud service provisioning. One of the key challenges to high performance converged network-cloud service provisioning lies in composition of network and cloud services with end-to-end performance guarantee. In this paper, we propose a QoS-aware service composition approach to tackling this challenging issue. We first present a system model for network-cloud service composition and formulate the service composition problem as a variant of Multi-Constrained Optimal Path (MCOP) problem. We then propose an approximation algorithm to solve the problem and give theoretical analysis on properties of the algorithm to show its effectiveness and efficiency for QoS-aware network-cloud service composition. Performance of the proposed algorithm is evaluated through extensive experiments and the obtained results indicate that the proposed method achieves better performance in service composition than the best current MCOP approaches.

On modeling and optimization for composite network–Cloud service provisioning

Networking plays a crucial role in Cloud computing, which calls for a holistic vision of combined control and optimization for both networking and computing resources. Essentially the services received by Cloud users are composite services that consist of not only Cloud computing functions but also networking services. Therefore, composite network–Cloud service provisioning becomes one of the main networking challenges in Cloud computing. In this paper we tackle the problem of modeling and optimization for composite network–Cloud service provisioning. We first present a system model in which a Network-as-a-Service (NaaS) paradigm is given to facilitate convergence of network and Cloud services; then we formulate QoS-aware network–Cloud service selection as a variant of Multi-Constrained Optimal Path (MCOP) problem and propose an exact algorithm for solving this problem. Theoretical analysis shows that the proposed algorithm is light-weighted and cost-effective in selecting services for composite network–Cloud service provisioning. Experimental results reported in this paper also validate the efficiency of the proposed algorithm.

Finding the Optimal Social Trust Path for the Selection of Trustworthy Service Providers in Complex Social Networks

Online Social networks have provided the infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers or the recommendation of files as services. In these applications, trust is one of the most important factors in decision making by a service consumer, requiring the evaluation of the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider. However, there are usually many social trust paths between two participants who are unknown to one another. In addition, some social information, such as social relationships between participants and the recommendation roles of participants, has significant influence on trust evaluation but has been neglected in existing studies of online social networks. Furthermore, it is a challenging problem to search the optimal social trust path that can yield the most trustworthy evaluation result and satisfy a service consumer's trust evaluation criteria based on social information. In this paper, we first present a novel complex social network structure incorporating trust, social relationships and recommendation roles, and introduce a new concept, Quality of Trust (QoT), containing the above social information as attributes. We then model the optimal social trust path selection problem with multiple end-to-end QoT constraints as a Multiconstrained Optimal Path (MCOP) selection problem, which is shown to be NP-Complete. To deal with this challenging problem, we propose a novel Multiple Foreseen Path-Based Heuristic algorithm MFPB-HOSTP for the Optimal Social Trust Path selection, where multiple backward local social trust paths (BLPs) are identified and concatenated with one Forward Local Path (FLP), forming multiple foreseen paths. Our strategy could not only help avoid failed feasibility estimation in path selection in certain cases, but also increase the chances of delivering a near-optimal solution with high quality. The results of our experiments conducted on a real data set of online social networks illustrate that MFPB-HOSTP algorithm can efficiently identify the social trust paths with better quality than our previously proposed H_OSTP algorithm that outperforms prior algorithms for the MCOP selection problem.

TPS_DR: A Universal Dimension Reducing Algorithm for Optimal Trust Path Selection in Complex Sensor Network

Energy efficiency, one of the key factors for sensor network, is a challenging work. It is agreed that sink routing is the main influencing factor for energy efficiency and selecting an optimal routing path becomes critical. It is hard for source participant (sink nodes, namely trustor) to select a trustworthy target participant (data collecting sensor nodes, namely trustee), especially when the scale of sensor network becomes increasingly larger. An optimal trust path selection problem (TPS problem) is a Multi-Constrained Optimal Path (MCOP) problem, which is proved to be a NP-Complete problem. In this paper, we first introduce a concept of complex trust network to model the relationships among network nodes in a complex sensor network. In this model, we represent the trust network based on high dimensional vector and matrix. Then, we propose an algorithm TPS DR (Trust Path Selection based on Dimensionality Reduction) to simplify the trust network through reducing the dimensionality, i.e. cluster some similar nodes into a supernode. Our example demonstrates the usage and advantage of our model and algorithm.

Optimal Social Trust Path Selection in Complex Social Networks

Online social networks are becoming increasingly popular and are being used as the means for a variety of rich activities. This demands the evaluation of the trustworthiness between two unknown participants along a certain social trust path between them in the social network. However, there are usually many social trust paths between participants. Thus, a challenging problem is finding which social trust path is the optimal one that can yield the most trustworthy evaluation result.In this paper, we first present a new complex social network structure and a new concept of Quality of Trust (QoT) to illustrate the ability to guarantee a certain level of trustworthiness in trust evaluation. We then model the optimal social trust path selection as a Multi-Constrained Optimal Path (MCOP) selection problem which is NP-Complete. For solving this problem, we propose an efficient approximation algorithm MONTE K based on the Monte Carlo method. The results of our experiments conducted on a real dataset of social networks illustrate that our proposed algorithm significantly outperforms existing approaches in both efficiency and the quality of selected social trust paths.

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.

An adaptive algorithm for QoS-aware service composition in grid environments

Service composition enables users to realize their complex needs as a single request and it has been recognized as a flexible way for resource sharing and application integration since the appearance of Service-Oriented Architecture (SOA). For each of the needed individual services there may be many candidate services available presented by different vendors and with different functional and non-functional properties such as Quality of Service (QoS). Approaches are needed to select candidate services with various QoS levels according to user’s performance requirements meanwhile adapt to dynamic churn in grid environments. This paper mainly focuses on adaptive management of QoS-aware service composition in grid environments and proposes an adaptive algorithm for QoS-aware service composition (AQSC). In AQSC we model this problem as the Multi-Constrained Optimal Path selection problem (MCOP) and use heuristic approach for service selection, then backup services set is introduced as an adaptive mechanism so as to ensure the fulfillment of composite service when some candidate services fail or withdraw. Both theoretical analysis and simulation results indicate that AQSC has high composition success rate, finish rate and low cost.

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.

Two algorithms for multi‐constrained optimal multicast routing

AbstractMultimedia applications, such as video‐conferencing and video‐on‐demand, often require quality of service (QoS) guarantees from the network, typically in the form of minimum bandwidth, maximum delay, jitter and packet loss constraints, among others. The problem of multicast routing subject to various forms of QoS constraints has been studied extensively. However, most previous efforts have focused on special situations where a single or a pair of constraints is considered. In general, routing under multiple constraints, even in the unicast case is an NP‐complete problem. We present in this paper two practical and efficient algorithms, called multi‐constrained QoS dependent multicast routing (M_QDMR) and (multicasting routing with multi‐constrained optimal path selection (M_MCOP)), for QoS‐based multicast routing under multiple constraints with cost optimization. We provide proof in the paper that our algorithms are correct. Furthermore, through extensive simulations, we illustrate the effectiveness and efficiency of our proposals and demonstrate their significant performance improvement in creating multicast trees with lower cost and higher success probability. Copyright © 2003 John Wiley & Sons, Ltd.

An efficient QoS routing algorithm for multi-constrained path selection

An efficient QoS routing algorithm was proposed for multiple constrained path selection. Making use of efficient pruning policy, the algorithm reduces greatly the size of search space and the computing time. Although the proposed algorithm has exponential time complexity in the worst case, it can get the running results quickly in practical application. When the scale of network increases, the algorithm can efficiently control the size of search space by constraint conditions and prior queue. The results of simulation show that successful request ratio (r) of efficient algorithm for multi-constrained optimal path (EAMCOP) is better than that of heuristic algorithm for multi-constrained optimal path (H-MCOP), but average computing time (t) of EAMCOP is far less than that of H-MCOP. And it can be seen that the computing time of EAMCOP is only one fourth of that of H-MCOP in Advanced Research Projects Agency Network (ARPANet) topology.