Finding shortest safari routes in simple polygons

Let P be a simple polygon, and let be a set of disjoint convex polygons inside P, each sharing one edge with P. The safari route problem asks for a shortest route inside P that visits each polygon in . In this paper, we first present a dynamic programming algorithm with running time O(n³) for computing the shortest safari route in the case that a starting point on the route is given, where n is the total number of vertices of P and polygons in . (Ntafos in [Comput. Geom. 1 (1992) 149-170] claimed a more efficient solution, but as shown in Appendix A of this paper, the time analysis of Ntafos' algorithm is erroneous and no time bound is guaranteed for his algorithm.) The restriction of giving a starting point is then removed by a brute-force algorithm, which requires O(n⁴) time. The solution of the safari route problem finds applications in watchman routes under limited visibility.     

Finding the shortest path in stochastic networks

Common network parameters, such as number of nodes and arc lengths are frequently subjected to ambiguity as a result of probability law. A number of authors have discussed the calculation of the shortest path in networks with random variable arc lengths. Generally, only a subset of intermediate nodes chosen in accordance with a given probability law can be used to transition from source node to sink node. The determination of a priori path of the minimal length in an incomplete network is defined as a probabilistic shortest path problem. When arc lengths between nodes are randomly assigned variables in an incomplete network the resulting network is known as an incomplete stochastic network. In this paper, the computation of minimal length in incomplete stochastic networks, when travel times between nodes are allowed to be exponentially distributed random variables, is formulated as a linear programming problem. A practical application of the methodology is demonstrated and the results and process compared to the Kulkarni’s [V.G. Kulkarni, Shortest paths in networks with exponentially distributed arc lengths, Networks 16 (1986) 255–274] method.     

Self-healing in transparent optical packet switching mesh networks: A reinforcement learning perspective

While transparent optical networks become more and more popular as the basis of the Next Generation Internet (NGI) infrastructure, such networks raise many security issues because they lack the massive use of optoelectronic monitoring. To increase these networks’ security, they will need to be equipped with proactive and reactive mechanisms to protect themselves not only from failures and attacks but also from ordinary reliability problems. This work presents a novel self-healing framework to deal with attacks on Transparent Optical Packet Switching (TOPS) mesh networks. Contrary to traditional approaches which deal with attacks at the fiber level, our framework allows to overcome attacks at the wavelength level as well as to understand how they impact the network’s performance. The framework has two phases: the dimensioning phase (DP) dynamically determines the optical resources for a given mesh network topology whereas the learning phase (LP) generates an intelligent policy to gracefully overcome attacks in the network. DP uses heuristic reasoning to engineer the network while LP relies on a reinforcement learning algorithm that yields a self-healing policy within the network. We use a Monte Carlo simulation to analyze the performance of the aforementioned framework not only under different types of attacks but also using three realistically sized mesh topologies with up to 40 nodes. We compare our framework against shortest path (SP) and multiple path routing (MPR) showing that the self-organized routing outperforms both, leading to a reduction in packet loss of up to $88\%$ with average packet loss rates of $1\times {10}^{-3}$. Finally, some conclusions are presented as well as future research lines.     

Optimal partitioning of groups in selecting the best choice

This article deals with the group interview problem, in which each group contains several alternatives and each group of alternatives is presented and evaluated sequentially over time. We derive the optimal selection strategy for the group interview problem with a general utility function. Among the various types of utility function, we focus on the best choice problem, in which our utility is one if we successfully select the best choice and zero otherwise. We derive a simple selection rule called the optimal partitioning strategy in which the decision-maker divides the entire groups into two disjoint sets and, after evaluating the choices in the first set, chooses the relatively best choice available for the first time in the second set. Because the selected choice is not necessarily the absolutely best choice, we also consider the probability distribution of the actual rank of the choice selected under the partitioning strategy.

Scope and purpose

In many managerial decision situations such as buying a car, selling a house, or searching for a job, several alternatives are presented sequentially and an accept-or-reject decision is made immediately after evaluating each alternative. The classical secretary problem and its extensions have been successfully applied to such a sequential search and selection problem. This article deals with a more generalized version of the secretary problem, called the group interview problem, in which several groups of alternatives are presented and evaluated sequentially over time. Based on a stochastic dynamic programming approach, we propose the optimal selection strategy for the group interview problem with various types of the decision-maker's utility function. There are many potential applications of the group interview problem, including consumer search and purchase process, job search problem, sequential assignment of batch jobs, and so on.     

Finding routes in anonymous sensor networks

We consider networks of anonymous sensors and address the problem of constructing routes for the delivery of information from a group of sensors in response to a query by a sink. In order to circumvent the restrictions imposed by anonymity, we rely on using the power level perceived by the sensors in the query from the sink. We introduce a simple distributed algorithm to achieve the building of routes to the sink and evaluate its performance by means of simulations.     

The best keying protocol for sensor networks

Many sensor networks (especially networks of mobile sensors or networks that are deployed to monitor crisis situations) are deployed in an arbitrary and unplanned fashion. Thus, any sensor in such a network can end up being adjacent to any other sensor in the network. To secure the communications between every pair of adjacent sensors in such a network, each sensor $x$ in the network needs to store $n?1$ symmetric keys that sensor $x$ shares with all the other sensors, where $n$ is an upper bound on the number of sensors in the network. This storage requirement of the keying protocol is rather severe, especially when $n$ is large and the available storage in each sensor is modest. Earlier efforts to redesign this keying protocol and reduce the number of keys to be stored in each sensor have produced protocols that are vulnerable to impersonation, eavesdropping, and collusion attacks. In this paper, we present a fully secure keying protocol where each sensor needs to store $(n+1)/2$ keys, which is much less than the $n?1$ keys that need to be stored in each sensor in the original keying protocol. We also show that in any fully secure keying protocol, each sensor needs to store at least $(n?1)/2$ keys.     

Best routes selection in international intermodal networks

This study focuses on one of the intermodal operational issues: how to select best routes for shipments through the international intermodal network. International intermodal routing is complicated by three important characteristics: (1) multiple objectives; (2) scheduled transportation modes and demanded delivery times; and (3) transportation economies of scale. In this paper, the international intermodal routing problem is formulated as a multiobjective multimodal multicommodity flow problem (MMMFP) with time windows and concave costs. The objectives of this paper are to develop a mathematical model encompassing all three essential characteristics, and to propose an algorithm that can effectively provide answers to the model. The problem is NP-hard. It follows that the proposed algorithm is a heuristic. Based on relaxation and decomposition techniques, the original problem is broken into a set of smaller and easier subproblems. The case studies show that it is important to incorporate the three characteristics into the international intermodal routing problem, and our proposed algorithm can effectively and efficiently solve the MMMFP with time windows and concave costs.     

L 1 shortest paths among polygonal obstacles in the plane

We present an algorithm for computingL ₁ shortest paths among polygonal obstacles in the plane. Our algorithm employs the “continuous Dijkstra” technique of propagating a “wavefront” and runs in timeO(E logn) and spaceO(E), wheren is the number of vertices of the obstacles andE is the number of “events.” By using bounds on the density of certain sparse binary matrices, we show thatE =O(n logn), implying that our algorithm is nearly optimal. We conjecture thatE =O(n), which would imply our algorithm to be optimal. Previous bounds for our problem were quadratic in time and space. Our algorithm generalizes to the case of fixed orientation metrics, yielding anO(n?^?1/2 log² n) time andO(n?^?1/2) space approximation algorithm for finding Euclidean shortest paths among obstacles. The algorithm further generalizes to the case of many sources, allowing us to compute anL ₁ Voronoi diagram for source points that lie among a collection of polygonal obstacles.     

有向赋权网络中任意节点对的最短路径集求解方法

有向赋权网络任意节点对之间的最短路径可能多于一条,运用Floyd算法对已知加权交互网络的最短路径进行求解,对获得最短路径后的每一个节点对,向其中插入已知交互网络中的其余所有节点,并计算此时的节点对之间的路径,通过与前次Floyd算法计算出的最短路径进行比较,筛选出构成最短路径的所有中间节点,并构建路径支撑树,基于路径支撑树确定任意节点对的最短路径集.     

A new algorithm for shortest paths among obstacles in the plane

We introduce a new algorithm for computing Euclidean shortest paths in the plane in the presence of polygonal obstacles. In particular, for a given start points, we build a planar subdivision (ashortest path map) that supports efficient queries for shortest paths froms to any destination pointt. The worst-case time complexity of our algorithm isO(kn log² n), wheren is the number of vertices describing the polygonal obstacles, andk is a parameter we call the illumination depth of the obstacle space. Our algorithm usesO(n) space, avoiding the possibly quadratic space complexity of methods that rely on visibility graphs. The quantityk is frequently significantly smaller thann, especially in some of the cases in which the visibility graph has quadratic size. In particular,k is bounded above by the number of different obstacles that touch any shortest path froms.Partially supported by NSF Grants IRI-8710858 and ECSE-8857642 and by a grant from Hughes Research Laboratories, Malibu, CA.     

A model of best choice under incomplete information

This paper suggests two approaches to the construction of a two-player game of best choice under incomplete information with the choice priority of one player and the equal weights of both players. We consider a sequence of independent identically distributed random variables (x _i , y _i ), i = 1..., n, which represent the quality of incoming objects. The first component is announced to the players and the second component is hidden. Each player chooses an object based on the information available. The winner is the player whose object has a greater sum of the quality components than the opponent’s object. We derive the optimal threshold strategies and compare them for both approaches.     

The fuzzy shortest path length and the corresponding shortest path in a network

The fuzzy shortest path (SP) problem aims at providing decision makers with the fuzzy shortest path length (FSPL) and the SP in a network with fuzzy arc lengths. In this paper, each arc length is represented as a triangular fuzzy set and a new algorithm is proposed to deal with the fuzzy SP problem. First, we proposed a heuristic procedure to find the FSPL among all possible paths in a network. It is based on the idea that a crisp number is a minimum number if and only if any other number is larger than or equal to it. It owns a firm theoretic base in fuzzy sets theory and can be implemented effectively. Second, we propose a way to measure the similarity degree between the FSPL and each fuzzy path lengths. The path with the highest similarity degree is the SP. An illustrative example is given to demonstrate our proposed approach.     

双光路光纤液位测量系统的研制

介绍了一种用于光纤液位传感器的电路测量系统。根据光纤光强调制的特点,设计了一种采用LD激光器以及能够进行对称补偿的双光路接收电路,消除了外界环境非测量因素的干扰。经现场试验,得到了理想的测量结果,证明可用于石化工业等危险环境的液位高效在线连续测量。     

Finding the shortest path in the shortest time using PCNN's

A pulse coupled neural network (PCNN) can run mazes nondeterministically (taking all possible paths) with constant time per step. Thus, when a signal emerges, it has taken the shortest path in the shortest time.     

A cost matrix agent for shortest path routing in ad hoc networks

Wireless ad hoc networks do not rely on an existing infrastructure. They are organized as a network with nodes that act as hosts and routers to treat packets. With their frequent changes in topology, ad hoc networks do not rely on the same routing methods as for pre-established wired networks; they require routing methods for mobile wireless networks. To select a path from a source to a destination in dynamic ad hoc networks, an efficient and reliable routing method is very important. In this paper, we introduce a cost-matrix-based routing algorithm. An agent node creates topology information in the form of the adjacency-cost matrix which shows link costs of the network.Based on the adjacency-cost matrix, the minimum-cost matrix and the next-node matrices can be calculated. Based on the minimum-cost matrix and the next-node matrices, the minimum cost between source and destination nodes and between intermediate nodes on the minimum-cost paths can be calculated.The matrices are periodically distributed by the agent to the other nodes. Based on the minimum-cost matrix and the next-node matrices, each node decides the minimum-cost path to its destination. Because none of the nodes except the agent needs to gather network topology information, the control overhead of the proposed method is small compared with those of the general table-driven routing protocols.     

General theory of best variants choice: Some aspects

The conventional mechanisms of making the best choice, in particular, optimization mechanisms, make explicit or implicit use of the structural principle of "pair comparability" for variants and of the specific logic of the "dominant" rule for choice. This paper argues for going beyond the framework of "pairness-dominance" on the structures and rules of choice and for using "nonconventional" structures and rules. This is shown to be possible if the notions of "multitudinal influences of variants" is formalized in determining the best choices.     

Multi-layer network architecture in support of end-to-end transparent optical connectivities

In order to effectively accommodate a wide variety of emerging and future applications with diverse requirements, an intelligent optical network capable of the rapid and resilient delivery of on-demand transparent transmission capacity is required. This paper investigates a multi-layer network architecture where a generalized multiple protocol label switching (GMPLS) controlled transparent wavelength switched optical network (WSON), as the transport core, collaborates with multiple client optical burst swit...     

Context-aware handoff middleware for transparent service continuity in wireless networks

Advances in wireless networking and content delivery are enabling new challenging provisioning scenarios where a growing number of users access continuous services, e.g., audio/video streaming, while moving among different points of attachment to the Internet, possibly with different connectivity technologies, e.g., Wi-Fi, Bluetooth, and cellular 3G. That calls for novel middlewares capable of dynamically personalizing service provisioning to the characteristics of client environments, in particular to discontinuities in wireless resource availability due to handoffs. The paper proposes a novel middleware solution, called Mobile agent-based Ubiquitous multimedia Middleware (MUM), that performs effective and context-aware handoff management to transparently avoid service interruptions during both horizontal and vertical handoffs. MUM exploits the full visibility of wireless connections available and their handoff implementations (handoff awareness), of service quality requirements and handoff-related quality degradations (QoS awareness), and of network topology and local resource availability (location awareness); that visibility enables MUM to provide original solutions for handoff prediction, multimedia continuity via adaptive data buffering/pre-fetching, and proactive re-addressing/rebinding.     

Enabling powerful GUIs in ISOBUS networks by transparent data compression

As the functionality of ISOBUS compliant agriculture machines increases, demands on the underlying bus network capacity increase as well. Therefore, to prevent potential bottleneck performance of critical applications, bus utilization must be carefully optimized. In this paper, a methodology for transparent compression/decompression of Object Pool files arising from the use of powerful GUIs during network initialization time is presented. Comprehensive simulation experiments developed under CANoe.ISO11783 shows that data compression remarkably reduces bus utilization during ISOBUS network initialization time, thus enabling the use of powerful GUIs. Furthermore, simulation results suggest GZIP as the best performing method for transparent ISOBUS data compression.