Optimal design and augmentation of strongly attack-tolerant two-hop clusters in directed networks

We consider the problems of minimum-cost design and augmentation of directed network clusters that have diameter 2 and maintain the same diameter after the deletion of up to R elements (nodes or arcs) anywhere in the cluster. The property of a network to maintain not only the overall connectivity, but also the same diameter after the deletion of multiple nodes/arcs is referred to as strong attack tolerance. This paper presents the proof of NP-completeness of the decision version of the problem, derives tight theoretical bounds, as well as develops a heuristic algorithm for the considered problems, which are extremely challenging to solve to optimality even for small networks. Computational experiments suggest that the proposed heuristic algorithm does identify high-quality near-optimal solutions; moreover, in the special case of undirected networks with identical arc construction costs, the algorithm provably produces an exact optimal solution to strongly attack-tolerant two-hop network design problem, regardless of the network size.     

Relay node placement in two-tiered wireless sensor networks with base stations

This paper addresses the relay node placement problem in two-tiered wireless sensor networks with base stations, which aims to deploy a minimum number of relay nodes to achieve certain coverage and connectivity requirement. Under the assumption that the communication range of the sensor nodes is no more than that of the relay node, we present a polynomial time (5+?)-approximation algorithm for the 1-coverage 1-connected problem. Furthermore, we consider the fault tolerant problem in the network, we present a polynomial time (20+?)-approximation algorithm for the 2-coverage 2-connected problem, where ? is any given positive constant. For the k-coverage 2-connected situation, we present a polynomial time (15k?10+?)-approximation algorithm.     

On dual power assignment optimization for biconnectivity

Topology control is an important technology of wireless ad hoc networks to achieve energy efficiency and fault tolerance. In this paper, we study the dual power assignment problem for 2-edge connectivity and 2-vertex connectivity in the symmetric graphical model which is a combinatorial optimization problem from topology control technology.The problem is arisen from the following origin. In a wireless ad hoc network where each node can switch its transmission power between high-level and low-level, how can we establish a fault-tolerantly connected network topology in the most energy-efficient way? Specifically, the objective is to minimize the number of nodes assigned with high power and yet achieve 2-edge connectivity or 2-vertex connectivity.We addressed these optimization problems (2-edge connectivity and 2-vertex connectivity version) under the general graph model in (Wang et al. in Theor. Comput. Sci., 2008). In this paper, we propose a novel approximation algorithm, called Candidate Set Filtering algorithm, to compute nearly-optimal solutions. Specifically, our algorithm can achieve 3.67-approximation ratio for both 2-edge connectivity and 2-vertex connectivity, which improves the existing 4-approximation algorithms for these two cases.     

On optimal placement of relay nodes for reliable connectivity in wireless sensor networks

The paper addresses the relay node placement problem in two-tiered wireless sensor networks. Given a set of sensor nodes in Euclidean plane, our objective is to place minimum number of relay nodes to forward data packets from sensor nodes to the sink, such that: 1) the network is connected, 2) the network is 2-connected. For case one, we propose a (6+ε)-approximation algorithm for any ε > 0 with polynomial running time when ε is fixed. For case two, we propose two approximation algorithms with (24+ε) and (6/T+12+ε), respectively, where T is the ratio of the number of relay nodes placed in case one to the number of sensors. We further extend the results to the cases where communication radiuses of sensor nodes and relay nodes are different from each other.     

A novel approach for detecting multiple rumor sources in networks with partial observations

Locating source of information diffusion in networks has very important applications such as locating the sources of epidemics, news/rumors in social networks or online computer virus. In this paper, we consider detecting multiple rumor sources from a deterministic point of view by modeling it as the set resolving set (SRS) problem. Let G be a network on n nodes. A node subset K is an SRS of G if all detectable node sets are distinguishable by K. The problem of multiple rumor source detection (MRSD) in the network can be modeled as finding an SRS K with the smallest cardinality. In this paper, we propose a polynomial-time greedy algorithm for finding a minimum SRS in a general network, achieving performance ratio \(O(\ln n)\). This is the first work establishing a relation between the MRSD problem and a deterministic concept of SRS, and a first work to give the minimum SRS problem a polynomial-time performance-guaranteed approximation algorithm. Our framework suggests a robust and efficient approach for estimating multiple rumor sources independent of diffusion models in networks.     

Exact and heuristic algorithms for solving the generalized minimum filter placement problem

We consider a problem of placing route-based filters in a communication network to limit the number of forged address attacks to a prescribed level. Nodes in the network communicate by exchanging packets along arcs, and the originating node embeds the origin and destination addresses within each packet that it sends. In the absence of a validation mechanism, one node can send packets to another node using a forged origin address to launch an attack against that node. Route-based filters can be established at various nodes on the communication network to protect against these attacks. A route-based filter examines each packet arriving at a node, and determines whether or not the origin address could be legitimate, based on the arc on which the packet arrives, the routing information, and possibly the destination. The problem we consider seeks to find a minimum cardinality subset of nodes to filter so that the prescribed level of security is achieved. We formulate a mixed-integer programming model for the problem and derive valid inequalities for this model by identifying polynomially-solvable subgraphs of the communication network. We also present three heuristics for solving the filter placement problem and evaluate their performance against the optimal solution provided by the mixed-integer programming model. The authors gratefully acknowledge the comments of two anonymous referees, whose input led to an improved version of this paper. Dr. Smith gratefully acknowledges the support of the Office of Naval Research under Grant #N00014-03-1-0510 and the Defense Advanced Research Projects Agency under Grant #N66001-01-1-8925.     

Energy conservation through resource-aware movement in heterogeneous mobile ad hoc networks

Energy conservation in mobile ad hoc networks is of paramount importance because most mobile nodes usually have very limited energy supply. Previous research on this issue focused on the design at the network or MAC or physical layer. In this paper, we study this problem from the new perspective of node mobility, i.e., analyzing the impact of node movement on energy conservation. In particular, armed with the inherent resource heterogeneity in mobile ad hoc networks, we propose a novel resource-aware movement strategy to make better use of some powerful nodes to achieve energy conservation. We also formulate the resource-aware movement as a NP-complete distance-constrained least-cost (DCLC) routing problem and propose an efficient heuristic solution. Extensive simulations have been used to demonstrate the effectiveness of the proposed schemes.     

Complexity of determining the most vital elements for the p-median and p-center location problems

We consider the k most vital edges (nodes) and min edge (node) blocker versions of the p-median and p-center location problems. Given a weighted connected graph with distances on edges and weights on nodes, the k most vital edges (nodes) p-median (respectively p-center) problem consists of finding a subset of k edges (nodes) whose removal from the graph leads to an optimal solution for the p-median (respectively p-center) problem with the largest total weighted distance (respectively maximum weighted distance). The complementary problem, min edge (node) blocker p-median (respectively p-center), consists of removing a subset of edges (nodes) of minimum cardinality such that an optimal solution for the p-median (respectively p-center) problem has a total weighted distance (respectively a maximum weighted distance) at least as large as a specified threshold. We show that k most vital edges p-median and k most vital edges p-center are NP-hard to approximate within a factor $\frac{7}{5}-\epsilon$ and $\frac{4}{3}-\epsilon$ respectively, for any ?>0, while k most vital nodes p-median and k most vital nodes p-center are NP-hard to approximate within a factor $\frac{3}{2}-\epsilon$ , for any ?>0. We also show that the complementary versions of these four problems are NP-hard to approximate within a factor 1.36.     

An effective algorithm for obtaining the whole set of minimal cost pairs of disjoint paths with dual arc costs

In telecommunication networks design the problem of obtaining optimal (arc or node) disjoint paths, for increasing network reliability, is extremely important. The problem of calculating k _c disjoint paths from s to t (two distinct nodes), in a network with k _c different (arbitrary) costs on every arc such that the total cost of the paths is minimised, is NP-complete even for k _c =2. When k _c =2 these networks are usually designated as dual arc cost networks.     

Online tree node assignment with resource augmentation

Given a complete binary tree of height h, the online tree node assignment problem is to serve a sequence of assignment/release requests, where an assignment request, with an integer parameter 0≤i≤h, is served by assigning a (tree) node of level (or height) i and a release request is served by releasing a specified assigned node. The node assignments have to guarantee that no node is assigned to two assignment requests unreleased, and every leaf-to-root path of the tree contains at most one assigned node. With assigned node reassignments allowed, the target of the problem is to minimize the number of assignments/reassignments, i.e., the cost, to serve the whole sequence of requests. This online tree node assignment problem is fundamental to many applications, including OVSF code assignment in WCDMA networks, buddy memory allocation and hypercube subcube allocation.     

Critical edges/nodes for the minimum spanning tree problem: complexity and approximation

In this paper, we study the complexity and the approximation of the k most vital edges (nodes) and min edge (node) blocker versions for the minimum spanning tree problem (MST). We show that the k most vital edges MST problem is NP-hard even for complete graphs with weights 0 or 1 and 3-approximable for graphs with weights 0 or 1. We also prove that the k most vital nodes MST problem is not approximable within a factor n ^1?? , for any ?>0, unless NP=ZPP, even for complete graphs of order n with weights 0 or 1. Furthermore, we show that the min edge blocker MST problem is NP-hard even for complete graphs with weights 0 or 1 and that the min node blocker MST problem is NP-hard to approximate within a factor 1.36 even for graphs with weights 0 or 1.     

The meaning of leader integrity: A comparative study across Anglo,Asian, and Germanic cultures

The current study explores and compares the meaning of leader integrity in six societies representing three culture clusters, including Ireland and the U.S. (Anglo cluster), Germany and Austria (Germanic Europe cluster), as well as China (PRC) and Hong Kong (Confucian Asia cluster). Reponses were obtained from 189 managers using an on-line, open-response questionnaire and analyzed through a data-driven thematic analysis of the manifest and latent content of the responses. Looking within cultures, findings provide initial evidence of the culture-specific attributes and behaviors that leaders with integrity are expected to possess and convey toward others. Looking across cultures, comparative analysis revealed nine common themes that were endorsed in all or a majority of the societies: these include Guided by Strong Personal Moral Code/Values, Value–Behavior Consistency, Word–Action Consistency, Honest, Fair and Just, Openness and Transparency, Consideration and Respect for Others, Sense of Responsibility for/toward Others, and Abiding by Rules and Regulations. Implications of the findings are discussed.     

An improved distributed data aggregation scheduling in wireless sensor networks

This paper focuses on the distributed data aggregation collision-free scheduling problem, which is one of very important issues in wireless sensor networks. Bo et al. (Proc. IEEE INFOCOM, 2009) proposed an approximate distributed algorithm for the problem and Xu et al. (Proc. ACM FOWANC, 2009) proposed a centralized algorithm and its distributed implementation to generate a collision-free scheduling for the problem, which are the only two existing distributed algorithms. Unfortunately, there are a few mistakes in their performance analysis in Bo et al. (Proc. IEEE INFOCOM, 2009), and the distributed algorithm can not get the same latency as the centralized algorithm because the distributed implementation was not an accurate implementation of the centralized algorithm (Xu et al. in Proc. ACM FOWANC, 2009). According to those, we propose an improved distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks. Not an arbitrary tree in Bo et al. (Proc. IEEE INFOCOM, 2009) but a breadth first search tree (BFS) rooted at the sink node is adopted, the bounded latency 61R+5Δ?67 of the schedule is obtained, where R is the radius of the network with respect to the sink node and Δ is the maximum node degree. We also correct the latency bound of the schedule in Bo et al. (Proc. IEEE INFOCOM, 2009) as 61D+5Δ?67, where D is a diameter of the network and prove that our algorithm is more efficient than the algorithm (Bo et al. in Proc. IEEE INFOCOM, 2009). We also give a latency bound for the distributed implementation in Xu et al. (Proc. ACM FOWANC, 2009).     

Why locally-fair maximal flows in client-server networks perform well

Maximal flows reach at least a 1/2 approximation of the maximum flow in client-server networks. By adding 1 additional time round to any distributed maximal flow algorithm we show how this 1/2-approximation can be improved on bounded-degree networks. We call these modified maximal flows ??locally fair?? since there is a measure of fairness prescribed to each client and server in the network. Let N=(U,V,E,b) represent a client-server network with clients U, servers V, network links E, and node capacities b, where we assume that each capacity is at least one unit. Let d(u) denote the b-weighted degree of any node u??U??V, ??=max?{d(u)|u??U} and ??=min?{d(v)|v??V}. We show that a locally-fair maximal flow f achieves an approximation to the maximum flow of $\min\{1,\frac{\varDelta^{2}-\delta}{2\varDelta^{2}-\delta\varDelta-\varDelta}$ }, and this result is sharp for any given integers ?? and ??. This results are of practical importance since local-fairness loosely models the steady-state behavior of TCP/IP and these types of degree-bounds often occur naturally (or are easy to enforce) in real client-server systems.     

A note on anti-coordination and social interactions

This note confirms a conjecture of (Bramoullé in Games Econ Behav 58:30–49, 2007). The problem, which we name the maximum independent cut problem, is a restricted version of the MAX-CUT problem, requiring one side of the cut to be an independent set. We show that the maximum independent cut problem does not admit any polynomial time algorithm with approximation ratio better than n ^1?? , where n is the number of nodes, and ? arbitrarily small, unless $\mathrm{P} = \mathrm{NP}$ . For the rather special case where each node has a degree of at most four, the problem is still APX-hard.     

The k-Canadian Travelers Problem with communication

This paper studies a variation of the online k-Canadian Traveler Problem (k-CTP), in which there are multiple travelers who can communicate with each other, to share real-time blockage information of the edges. We study two different communication levels for the problem, complete communication (where all travelers can receive and send blockage information with each other) and limited communication (where only some travelers can both receive and send information while the others can only receive information). The objective is that at least one traveler finds a feasible route from the origin to the destination with as small cost as possible. We give lower bounds on the competitive ratio for both the two communication levels. Considering the urban traffic environment, we propose the Retrace-Alternating strategy and Greedy strategy for both the two communication levels, and prove that increasing the number of travelers with complete communication ability may not always improve the competitive ratio of online strategies.     

A hybrid knowledge discovery model using decision tree and neural network for selecting dispatching rules of a semiconductor final testing factory

One of the most challenging production decisions in the semiconductor testing industry is to select the most appropriate dispatching rule which can be employed on the shop floor to achieve high manufacturing performance against a changing environment. Job dispatching in the semiconductor final testing industry is severely constrained by many resources conflicts and has to fulfil a changing performance required by customers and plant managers. In this study we have developed a hybrid knowledge discovery model, using a combination of a decision tree and a back-propagation neural network, to determine an appropriate dispatching rule using production data with noise information, and to predict its performance. We built an object-oriented simulation model to mimic shop floor activities of a semiconductor testing plant and collected system status and resultant performances of several typical dispatching rules, earliest-due-date (EDD) rule, first-come-first-served rule, and a practical dispatching heuristic taking set-up reduction into consideration. Performances such as work-in-process, set-up overhead, completion time, and tardiness are examined. Experiments have shown that the proposed decision tree found the most suitable dispatching rule given a specific performance measure and system status, and the back propagation neural network then predicted precisely the performance of the selected rule.     

On the minimum routing cost clustered tree problem

For an edge-weighted graph \(G=(V,E,w)\), in which the vertices are partitioned into k clusters \(\mathcal {R}=\{R_1,R_2,\ldots ,R_k\}\), a spanning tree T of G is a clustered spanning tree if T can be cut into k subtrees by removing \(k-1\) edges such that each subtree is a spanning tree for one cluster. In this paper, we show the inapproximability of finding a clustered spanning tree with minimum routing cost, where the routing cost is the total distance summed over all pairs of vertices. We present a 2-approximation for the case that the input is a complete weighted graph whose edge weights obey the triangle inequality. We also study a variant in which the objective function is the total distance summed over all pairs of vertices of different clusters. We show that the problem is polynomial-time solvable when the number of clusters k is 2 and NP-hard for \(k=3\). Finally, we propose a polynomial-time 2-approximation algorithm for the case of three clusters.     

Parameterized complexity of k-anonymity: hardness and tractability

The problem of publishing personal data without giving up privacy is becoming increasingly important. A precise formalization that has been recently proposed is the k-anonymity, where the rows of a table are partitioned into clusters of sizes at least k and all rows in a cluster become the same tuple after the suppression of some entries. The natural optimization problem, where the goal is to minimize the number of suppressed entries, is hard even when the stored values are over a binary alphabet or the table consists of a bounded number of columns. In this paper we study how the complexity of the problem is influenced by different parameters. First we show that the problem is W[1]-hard when parameterized by the value of the solution (and k). Then we exhibit a fixed-parameter algorithm when the problem is parameterized by the number of columns and the number of different values in any column. Finally, we prove that k-anonymity is still APX-hard even when restricting to instances with 3 columns and k=3.     

Clustering with or without the approximation

We study algorithms for clustering data that were recently proposed by Balcan et al. (SODA’09: 19th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1068–1077, 2009a) and that have already given rise to several follow-up papers. The input for the clustering problem consists of points in a metric space and a number k, specifying the desired number of clusters. The algorithms find a clustering that is provably close to a target clustering, provided that the instance has the “(1+α,ε)-property”, which means that the instance is such that all solutions to the k-median problem for which the objective value is at most (1+α) times the optimal objective value correspond to clusterings that misclassify at most an ε fraction of the points with respect to the target clustering. We investigate the theoretical and practical implications of their results. Our main contributions are as follows. First, we show that instances that have the (1+α,ε)-property and for which, additionally, the clusters in the target clustering are large, are easier than general instances: the algorithm proposed in Balcan et al. (SODA’09: 19th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1068–1077, 2009a) is a constant factor approximation algorithm with an approximation guarantee that is better than the known hardness of approximation for general instances. Further, we show that it is NP-hard to check if an instance satisfies the (1+α,ε)-property for a given (α,ε); the algorithms in Balcan et al. (SODA’09: 19th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1068–1077, 2009a) need such α and ε as input parameters, however. We propose ways to use their algorithms even if we do not know values of α and ε for which the assumption holds. Finally, we implement these methods and other popular methods, and test them on real world data sets. We find that on these data sets there are no α and ε so that the dataset has both (1+α,ε)-property and sufficiently large clusters in the target solution. For the general case where there are no assumptions about the cluster sizes, we show that on our data sets the performance guarantee proved by Balcan et a. (SODA’09: 19th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1068–1077, 2009a) is meaningless for the values of α,ε for which the data set has the (1+α,ε)-property. The algorithm nonetheless gives reasonable results, although it is outperformed by other methods.