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.

Rapid Physarum Algorithm for shortest path problem

As shortest path (SP) problem has been one of the most fundamental network optimization problems for a long time, technologies for this problem are still being studied. In this paper, a new method by integrating a path finding mathematical model, inspired by Physarum polycephalum, with extracted one heuristic rule to solve SP problem has been proposed, which is called Rapid Physarum Algorithm (RPA). Simulation experiments have been carried out on three different network topologies with varying number of nodes. It is noted that the proposed RPA can find the optimal path as the path finding model does for most networks. What is more, experimental results show that the performance of RPA surpasses the path finding model on both iterations and solution time. © 2014 Elsevier B.V.

The simple analytics of optimal growth with illegal migrants

This paper analyses the impact of illegal migration on the optimal path of domestic (resident) consumption. The analysis draws two important conclusions. First, if illegal migrants and domestic labour are perfect substitutes, illegal migration necessarily lowers the long-run per capita consumption of domestic residents. Second, if illegal migrants and domestic labour are imperfect substitutes, the effect on the long-run per capita domestic consumption is ambiguous, however, in the Cobb–Douglas case, the result is clear cut and per capita domestic consumption rises as a result of illegal migration.

Path planning for sensor data collecting mobile robot

A mobile robot employed for data collection is faced with the problem of travelling from an initial location to a final location while maintaining as close a distance as possible to all the sensors at a given time in the journey. Here we employ optimal control ideas in forming the necessary control commands for such a robot resulting not only the necessary acceleration commands for the underlying robot, but also the resulting trajectory. This approach can also be easily extended for the case of producing the optimal trajectory for an ariel vehicle used for data collection from indiscriminately scattered ad-hoc sensors located on the ground. We demonstrate the implementation of our algorithm using a Pioneer 3-AT robot.

GeoConnect : geographic and connectivity-aware routing protocol for wireless sensor network

Sensor nodes are closely tied with their geographic location and their connectivity. In recent years many routing protocols have been developed to provide efficient strategy. But most of them are either focus on the geographic proximity or on connectivity. However in sparse network, Geographic routing would fail at local dead ends where a node has no neighbour closer to destination. In contrast, connectivity-based routing may result in non-optimal path and overhead management. In this paper we designed a scalable and distributed routing protocol, GeoConnect, which considers geographic proximity and connectivity for choosing next hop. In GeoConnecl, we construct a new naming system that integrates geographic and connectivity information into a node identification. We use dissimilarity function to compute the dissimilarity and apply a distributed routing algorithm to route packets. The experimental results show that GeoConnect routing provides robust and better performance than sole geographic routing or connectivity routing.

Co-evolution path model : how enterprises as complex systems survive on the edge of chaos

In this theoretical paper, we introduce and describe a model, and demonstrate its origins from the disciplines of Enterprise Architecture, cybernetics and systems theory. We use cybernetic thinking to develop a ‘Co-evolution Path Model’ that describes how enterprises as complex systems co-evolve with their complex environments. The model re-interprets Stafford Beer’s Viable System Model, and also uses the theorem of the ‘good regulator’ of Conant and Ashby, exemplifying how various complexity management theories could be synthesised into a cybernetic theory of Enterprise Architecture, using concepts from the generalisation of EA frameworks.

Dynamic path determination policy for distributed multimedia content adaptation

Multimedia content adaptation allows the ever increasing variety of handheld devices such as Smartphones to access distributed rich media resources available on the Internet today. Path planning and determination is a fundamental problem in enhancing performance of distributed multimedia content adaptation systems. Most of the existing path determination mechanisms use static path determination criteria based solely on associating a path with a single behavior aggregate score. However, some criteria such as availability are best represented using different functionality rather than being accumulated into the aggregate score. Moreover, since selection criteria have different behavior towards the score, this principle need to be considered. In this paper, we propose a dynamic multi-criteria path determination policy that selects an optimal path to the content adaptation services that best meet the user preferences and QoS requirements. The performance of the proposed approach is studied in terms of score’s fairness and reliability under different variations. The results indicate that the proposed policy performs substantially better than the baseline policy.

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

Design and evaluation of coordinated in-network caching model for content centric networking

In-network caching has been widely adopted in Content Centric Networking (CCN) to accelerate data delivery, mitigate server load and reduce network traffic. However, the line-speed requirement makes the in-network caching space very limited. With the rapid growth of network traffic, it is significant challenging to decide content placement in such limited cache space. To conquer this conflict, coordinated in-network caching schemes are needed so as to maximize the profit of ubiquitous caching capacities. In particular, in-network caching in CCN is deployed as an arbitrary network topology and naturally supports dynamic request routing. Therefore, content placement scheme and dynamic request routing are tightly coupled and should be addressed together. In this paper, we propose a coordinated in-network caching model to decide the optimal content placement and the shortest request routing path under constraints of cache space and link bandwidth in a systematic fashion. Via extensive simulations, the effectiveness and efficiency of our proposed model has been validated.

Modelling of porosity defects in high pressure die casting with a neural network

High Pressure Die Casting (HPDC) is a complex process that results in casting defects if configured improperly. However, finding out the optimal configuration is a non-trivial task as eliminating one of the casting defects (for example, porosity) can result in occurrence of other casting defects. The industry generally tries to eliminate the defects by trial and error which is an expensive and error -prone process. This paper aims to improve current modelling and understanding of defects formation in HPDC machines. We have conducted conventional die casting tests with a neural network model of HPDC machine and compared the obtained results with the current understanding of formation of porosity. While most of our findings correspond well to established knowledge in the field, some of our findings are in conflict with the previous studies of die casting.

Using mobile agents to detect node compromise in path-based DoS attacks on wireless sensor networks

Wireless sensor networks represent a new generation of real-time  embedded systems with significantly different communication constraints from the traditional networked systems. With their development, a new attack called a path-based DoS (PDoS) attack has appeared. In a PDoS attack, an adversary, either inside or outside the network, overwhelms sensor nodes by flooding a multi-hop endto- end communication path with either replayed packets or injected spurious packets. In this article, we propose a solution using mobile agents which can detect PDoS attacks easily.

Using mobile agents to detect and recover from node compromise in path-based DoS attacks in wireless sensor networks

Wireless sensor networks represent a new generation of real-time embedded systems with significantly different communication constraints from the traditional networked systems. With their development, a new attack called a path-based DoS (PDoS) attack has appeared. In a PDoS attack, an adversary, either inside or outside the network, overwhelms sensor nodes by flooding a multi-hop end-to end communication path with either replayed packets or injected spurious packets. Detection and recovery from PDoS attacks have not been given much attention in the literature. In this article, we propose a solution using mobile agents which can detect PDoS attacks easily and efficiently and recover the compromised nodes.