Clustering of Mobile Ad Hoc Networks: An Adaptive Broadcast Period Approach

Organization, scalability and routing have been identified as key problems hindering viability and commercial success of mobile ad hoc networks. Clustering of mobile nodes among separate domains has been proposed as an efficient approach to address those issues. In this work, we introduce an efficient distributed clustering algorithm that uses both location and energy metrics for cluster formation. Our proposed solution mainly addresses cluster stability, manageability and energy efficiency issues. Also, unlike existing active clustering methods, our algorithm relieves the network from the unnecessary burden of control messages broadcasting, especially for relatively static network topologies. This is achieved through adapting broadcast period according to mobile nodes mobility pattern. The efficiency, scalability and competence of our algorithm against alternative approaches have been demonstrated through simulation results.Comment: 7 pages, 9 figures; IEEE International Conference on Communications, 2006. ICC '0

Parallel Max Cut Approximations

Given a graph with positive integer edge weights one may ask whether there exists an edge cut whose weight is bigger than a given number. This problem is NP-complete. We present here an approximation algorithm in NC which provides tight upper bounds to the proportion of edge cuts whose size is bigger than a given number. Our technique is based on the methods to convert randomized parallel algorithms into deterministic ones introduced by Karp and Wigderson. The basic idea of those methods is to replace an exponentially large sample space by one of polynomial size. In this work, we prove the interesting result that the statistical distance of random variables of the small sample space is bigger than the statistical distance of corresponding variables of the exponentially large space, which is the space of all edge cuts taken equiprobably

On-Line and Dynamic Shortest Paths Through Graph Decompositions

We describe algorithms for finding shortest paths and distances in a planar digraph which exploit the particular topology of the input graph. An important feature of our algorithms is that they can work in a dynamic environment, where the cost of any edge can be changed or the edge can be deleted. For outerplanar digraphs, for instance, the data structures can be updated after any such change in only $O(\log n)$ time, where $n$ is the number of vertices of the digraph. We also describe the first parallel algorithms for solving the dynamic version of the shortest path problem. Our results can be extended to hold for digraphs of genus $o(n)$

Quickest Paths: Faster Algorithms and Dynamization

Given a network $N=(V,E,{c},{l})$, where $G=(V,E)$, $|V|=n$ and $|E|=m$, is a directed graph, ${c}(e) \u3e 0$ is the capacity and ${l}(e) \ge 0$ is the lead time (or delay) for each edge $e\in E$, the quickest path problem is to find a path for a given source--destination pair such that the total lead time plus the inverse of the minimum edge capacity of the path is minimal. The problem has applications to fast data transmissions in communication networks. The best previous algorithm for the single--pair quickest path problem runs in time $O(r m+r n \log n)$, where $r$ is the number of distinct capacities of $N$ \cite{ROS}. In this paper, we present algorithms for general, sparse and planar networks that have significantly lower running times. For general networks, we show that the time complexity can be reduced to $O(r^{\ast} m+r^{\ast} n \log n)$, where $r^{\ast}$ is at most the number of capacities greater than the capacity of the shortest (with respect to lead time) path in $N$. For sparse networks, we present an algorithm with time complexity $O(n \log n + r^{\ast} n + r^{\ast} \tilde{\gamma} \log \tilde{\gamma})$, where $\tilde{\gamma}$ is a topological measure of $N$. Since for sparse networks $\tilde{\gamma}$ ranges from $1$ up to $\Theta(n)$, this constitutes an improvement over the previously known bound of $O(r n \log n)$ in all cases that $\tilde{\gamma}=o(n)$. For planar networks, the complexity becomes $O(n \log n + n\log^3 \tilde{\gamma}+ r^{\ast} \tilde{\gamma})$. Similar improvements are obtained for the all--pairs quickest path problem. We also give the first algorithm for solving the dynamic quickest path problem

Efficient Parallel Algorithms for some Tree Layout Problems

The minimum cut and minimum length linear arrangement problems usually occur in solving wiring problems and have a lot in common with job sequencing questions. Both problems are NP-complete for general graphs and in P for trees. We present here two algorithms in NC. The first solves the minimum length linear arrangement problem for unrooted trees in $O(\log^2 n)$ time and $O(n^2 3^{\log n})$ CREW PRAM processors. The second algorithm solves the minimum cut arrangement for unrooted trees of maximum degree $d$ in $O(d \log^2 n)$ time and $O(n^2 /\log n)$ CREW PRAM processors

A Note on Parallel Algorithms for Optional h-v Drawings of Binary Trees

In this paper we present a method to obtain optimal h-v drawings in parallel. Based on parallel tree contraction, our method computes optimal (with respect to a class of cost functions of the enclosing rectangle) drawings in O(log2 n) parallel time by using a polynomial number of EREW processors. The number of processors reduces substantially when we study minimum area drawings. Our work places the problem of obtaining optimal size h-v drawings in NC, presenting the first algorithm with polylogarithmic time complexity

Cyber Ranges and TestBeds for Education, Training, and Research

In recent years, there has been a growing demand for cybersecurity experts, and, according to predictions, this demand will continue to increase. Cyber Ranges can fill this gap by combining hands-on experience with educational courses, and conducting cybersecurity competitions. In this paper, we conduct a systematic survey of ten Cyber Ranges that were developed in the last decade, with a structured interview. The purpose of the interview is to find details about essential components, and especially the tools used to design, create, implement and operate a Cyber Range platform, and to present the findings

Time-Dependent Bi-Objective Itinerary Planning Algorithm: Application in Sea Transportation

A special case of the Time-Dependent Shortest Path Problem (TDSPP) is the itinerary planning problem where the objective is to find the shortest path between a source and a destination node which passes through a fixed sequence of intermediate nodes. In this paper, we deviate from the common approach for solving this problem, that is, finding first the shortest paths between successive nodes in the above sequence and then synthesizing the final solution from the solutions of these sub-problems. We propose a more direct approach and solve the problem by a label-setting approach which is able to early prune a lot of partial paths that cannot be part of the optimal solution. In addition, we study a different version of the main problem where it is only required that the solution path should pass through a set of specific nodes irrespectively of the particular order in which these nodes are included in the path. As a case study, we have applied the proposed techniques for solving the itinerary planning of a ship with respect to two conflicting criteria, in the area of the Aegean Sea, Greece. Moreover, the algorithm handles the case that the ship speed is not constant throughout the whole voyage. Specifically, it can be set at a different level each time the ship departs from an intermediate port in order to obtain low cost solutions for the itinerary planning. The experimental results confirm the high performance of the proposed algorithms