Upgrading arc median shortest path problem for an urban transportation network

In this paper, we propose an algorithm for an upgrading arc median shortest path problem for a transportation network. The problem is to identify a set of nondominated paths that minimizes both upgrading cost and overall travel time of the entire network. These two objectives are realistic for transportation network problems, but of a conflicting and noncompensatory nature. In addition, unlike upgrading cost which is the sum of the arc costs on the path, overall travel time of the entire network cannot be expressed as a sum of arc travel times on the path. The proposed solution approach to the problem is based on heuristic labeling and exhaustive search techniques, in criteria space and solution space, respectively. The first approach labels each node in terms of upgrading cost, and deletes cyclic and infeasible paths in criteria space. The latter calculates the overall travel time of the entire network for each feasible path, deletes dominated paths on the basis of the objective vector and identifies a set of Pareto optimal paths in the solution space. The computational study, using two small-scale transportation networks, has demonstrated that the algorithm proposed herein is able to efficiently identify a set of nondominated median shortest paths, based on two conflicting and noncompensatory objectives.

An optimal transportation routing approach using GIS-based dynamic traffic flows

This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively.

Solving the median shortest path problem in the planning and design of urban transportation networks using a vector labeling algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.

An algorithm to median shortest path problem (MSPP) in the design of urban transportation networks

This paper proposes an efficient solution algorithm for realistic multi-objective median shortest path problems in the design of urban transportation networks. The proposed problem formulation and solution algorithm to median shortest path problem is based on three realistic objectives via route cost or investment cost, overall travel time of the entire network and total toll revenue. The proposed solution approach to the problem is based on the heuristic labeling and exhaustive search technique in criteria space and solution space of the algorithm respectively. The first labels each node in terms of route cost and deletes cyclic and infeasible paths in criteria space imposing cyclic break and route cost constraint respectively. The latter deletes dominated paths in terms of objectives vector in solution space in order to identify a set of Pareto optimal paths. The approach, thus, proposes a non-inferior solution set of Pareto optimal paths based on non-dominated objective vector and leaves the ultimate decision to decision-makers for purpose specific final decision during applications. A numerical experiment is conducted to test the proposed algorithm using artificial transportation network. Sensitivity analyses have shown that the proposed algorithm is advantageous and efficient over existing algorithms to find a set of Pareto optimal paths to median shortest paths problems.

Real-time traffic flow statistical analysis based on network-constrained moving object trajectories

In this paper, we propose a novel traffic flow analysis method, Network-constrained Moving Objects Database based Traffic Flow Statistical Analysis (NMOD-TFSA) model. By sampling and analyzing the spatial-temporal trajectories of network constrained moving objects, NMOD-TFSA can get the real-time traffic conditions of the transportation network. The experimental results show that, compared with the floating-car methods which are widely used in current traffic flow analyzing systems, NMOD-TFSA provides an improved performance in terms of communication costs and statistical accuracy.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.

An amoeboid algorithm for shortest path in fuzzy weighted networks

Taking the uncertainty existing in edge weights of networks into consideration, finding shortest path in such fuzzy weighted networks has been widely studied in various practical applications. In this paper, an amoeboid algorithm is proposed, combing fuzzy sets theory with a path finding model inspired by an amoeboid organism, Physarum polycephalum. With the help of fuzzy numbers, uncertainty is well represented and handled in our algorithm. What's more, biological intelligence of Physarum polycephalum has been incorporate into the algorithm. A numerical example on a transportation network is demonstrated to show the efficiency and flexibility of our proposed amoeboid algorithm.

Massive data delivery in unstructured peer-to-peer networks with network coding

With more and more multimedia applications on the Internet, such as IPTV, bandwidth becomes a vital bottleneck for the booming of large scale Internet based multimedia applications. Network coding is recently proposed to take advantage to use network bandwidth efficiently. In this paper, we focus on massive multimedia data, e.g. IPTV programs, transportation in peer-to-peer networks with network coding. By through study of networking coding, we pointed out that the prerequisites of bandwidth saving of network coding are: 1) one information source with a number of concurrent receivers, or 2) information pieces cached at intermediate nodes. We further proof that network coding can not gain bandwidth saving at immediate connections to a receiver end; As a result, we propose a novel model for IPTV data transportation in unstructured peer-to-peer networks with network coding. Our preliminary simulations show that the proposed architecture works very well.

Analytical network modeling of heterogeneous large-scale cluster systems

The study of the communication networks for distributed systems is very important, since the overall performance of these systems is often depends on the effectiveness of its communication network. In this paper, we address the problem of networks modeling for heterogeneous large-scale cluster systems. We consider the large-scale cluster systems as a typical cluster of clusters system. Since the heterogeneity is becoming common in such systems, we take into account network as well as cluster size heterogeneity to propose the model. To this end, we present an analytical network model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Towards anomalous diffusion sources detection in a large network

Witnessing the wide spread of malicious information in large networks, we develop an efficient method to detect anomalous diffusion sources and thus protect networks from security and privacy attacks. To date, most existing work on diffusion sources detection are based on the assumption that network snapshots that reflect information diffusion can be obtained continuously. However, obtaining snapshots of an entire network needs to deploy detectors on all network nodes and thus is very expensive. Alternatively, in this article, we study the diffusion sources locating problem by learning from information diffusion data collected from only a small subset of network nodes. Specifically, we present a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay. We theoretically analyze the strength of the model and derive performance bounds. We empirically test and compare the model using both synthetic and real-world networks to demonstrate its performance.

Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems

Machine-to-Machine (M2M) paradigm enables machines (sensors, actuators, robots, and smart meter readers) to communicate with each other with little or no human intervention. M2M is a key enabling technology for the cyber-physical systems (CPSs). This paper explores CPS beyond M2M concept and looks at futuristic applications. Our vision is CPS with distributed actuation and in-network processing. We describe few particular use cases that motivate the development of the M2M communication primitives tailored to large-scale CPS. M2M communications in literature were considered in limited extent so far. The existing work is based on small-scale M2M models and centralized solutions. Different sources discuss different primitives. Few existing decentralized solutions do not scale well. There is a need to design M2M communication primitives that will scale to thousands and trillions of M2M devices, without sacrificing solution quality. The main paradigm shift is to design localized algorithms, where CPS nodes make decisions based on local knowledge. Localized coordination and communication in networked robotics, for matching events and robots, were studied to illustrate new directions.

Home range of the squirrel glider (Petaurus norfolcensis) in a network of remnant linear habitats

Linear strips of natural or semi-natural vegetation are a characteristic feature of rural landscapes throughout the world. Their value for the conservation of fauna in heavily modified landscapes depends on the response of species to the linear shape of the habitat, and the pressures this imposes on population processes and spatial organization. In south-eastern Australia, woodland habitats occupied by the squirrel glider Petaurus norfolcensis, a threatened species of arboreal marsupial, have been preferentially cleared for agriculture leaving only remnants within cleared farmland. In this study, the home range of P. norfolcensis was investigated by radio-tracking 40 gliders within a highly modified landscape where the majority (83%) of remaining wooded habitat occurs as a network of linear strips along roadsides and streams. Individuals were tracked for one to four seasons, resulting in the collection of 4213 independent locational 'fixes'. All fixes of animals were from remnant woodland. Home ranges were elongated and linear, primarily determined by the shape and arrangement of woodland habitat. Seasonal home ranges were small (mean of 1.4–2.8 ha) and ranged between 320 and 840 m long. Small patches of trees in farmland adjacent to the linear habitats were also extensively used. Despite the highly modified landscape structure, home ranges of P. norfolcensis in the linear network were smaller than those estimated from other studies of this species in continuous habitat. The apparent high quality of the linear habitats is attributed to the density of large old trees, which provide foraging and breeding resources, and the productivity of the environment. Linear landscape elements may have a valuable conservation function where they provide resident habitat or enhance landscape connectivity, but their long-term viability is vulnerable to disturbance.

Uncontrolled face recognition by individual stable neural network

There usually exist diverse variations in face images taken under uncontrolled conditions. Most previous work on face recognition focuses on particular variations and usually assume the absence of others. Such work is called controlled face recognition. Instead of the ‘divide and conquer’ strategy adopted by controlled face recognition, this paper presents one of the first attempts directly aiming at uncontrolled face recognition. The solution is based on Individual Stable Neural Network (ISNN) proposed in this paper. ISNN can map a face image into the so-called Individual Stable Space (ISS), the feature space that only expresses personal characteristics, which is the only useful information for recognition. There are no restrictions for the face images fed into ISNN. Moreover, unlike many other robust face recognition methods, ISNN does not require any extra information (such as view angle) other than the personal identities during training. These advantages of ISNN make it a very practical approach for uncontrolled face recognition. In the experiments, ISNN is tested on two large face databases with vast variations and achieves the best performance compared with several popular face recognition techniques.

Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

A performance comparison of three wireless multi hop ad-hoc network routing protocols when streaming MPEG4 traffic

Mobile ad-hoc networks are characterised by constant topology changes, the absence of fixed infrastructure and lack of any centralised control. Traditional routing algorithms prove to be inefficient in such a changing environment. Ad-hoc routing protocols such as dynamic source routing (DSR), ad-hoc on-demand distance vector routing (AODV) and destination-sequence distance vector (DSDV) have been proposed to solve the multi hop routing problem in ad-hoc networks. Performance studies of these routing protocols have assumed constant bit rate (CBR) traffic. Real-time multimedia traffic generated by video-on demand and teleconferencing services are mostly variable bit rate (VBR) traffic. Most of these multimedia traffic is encoded using the MPEG standard. (ISO moving picture expert group). When video traffic is transferred over MANETs a series of performance issues arise. In this paper we present a performance comparison of three ad-hoc routing protocols - DSR, AODV and DSDV when streaming MPEG4 traffic. Simulation studies show that DSDV performs better than AODV and DSR. However all three protocols fail to provide good performance in large, highly mobile network environments. Further study is required to improve the performance of these protocols in mobile ad-hoc networks offering VBR services.