Basic properties of wireless ad-hoc networks, such as connectivity, have traditionally been analyzed and evaluated under the assumption that all nodes have homogeneous communication capabilities. While this assumption usually holds for small networks, it is unlikely to be true in most large scale networks. This paper studies how heterogeneous communication capabilities affect basic properties of ad-hoc networks. More specifically, we consider one type of heterogeneity in which nodes are equipped with two different types of antenna technologies, omni-directional and directional, and evaluate them against four properties: network connectivity, energy consumption, interference tolerance and resilience to failures. Our main results show that even under a very simple network model that does not assume any global knowledge of the network, an ad-hoc network where only 10% to 20% of the nodes are equipped with directional antennas, can outperform a comparable homogeneous network (where all nodes have only omni-directional antennas) in all four metrics.

BACKGROUND: The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest. RESULTS: This paper reports a scalable method for the discovery of protein-protein interactions in Medline abstracts, using a combination of text analytics, statistical and graphical analysis, and a set of easily implemented rules. Applying these techniques to 12,300 abstracts, a precision of 0.61 and a recall of 0.97 were obtained, (f = 0.74) and when allowing for two-hop and three-hop relations discovered by graphical analysis, the precision was 0.74 (f = 0.83). CONCLUSION: This combination of linguistic and statistical approaches appears to provide the highest precision and recall thus far reported in detecting protein-protein relations using text analytic approaches.

In this paper, we present an approach to automatically detect high impact coding errors in large Java applications which use frameworks. These high impact errors cause serious performance degradation and outages in real world production environments, are very time-consuming to detect, and potentially cost businesses thousands of dollars. Based on 3 years experience working with IBM customer production systems, we have identified over 400 high impact coding patterns, from which we have been able to distill a small set of pattern detection algorithms. These algorithms use deep static analysis, thus moving problem detection earlier in the development cycle from production to development. Additionally, we have developed an automatic false positive filtering mechanism based on domain specific knowledge to achieve a level of usability acceptable to IBM field engineers. Our approach also provides necessary contextual information around the sources of the problems to help in problem remediation. We outline how our approach to problem determination can be extended to multiple programming models and domains. We have implemented this problem determination approach in the SABER tool and have used it successfully to detect many serious code defects in several large commercial applications. This paper shows results from four such applications that had over 60 coding defects.

Java 2 has a security architecture that protects systems from unauthorized access by mobile or statically configured code. The problem is in manually determining the set of security access rights required to execute a library or application. The commonly used strategy is to execute the code, note authorization failures, allocate additional access rights, and test again. This process iterates until the code successfully runs for the test cases in hand. Test cases usually do not cover all paths through the code, so failures can occur in deployed systems. Conversely, a broad set of access rights is allocated to the code to prevent authorization failures from occurring. However, this often leads to a violation of the "Principle of Least Privilege"This paper presents a technique for computing the access rights requirements by using a context sensitive, flow sensitive, interprocedural data flow analysis. By using this analysis, we compute at each program point the set of access rights required by the code. We model features such as multi-threading, implicitly defined security policies, the semantics of the Permission.implies method and generation of a security policy description. We implemented the algorithms and present the results of our analysis on a set of programs. While the analysis techniques described in this paper are in the context of Java code, the basic techniques are applicable to access rights analysis issues in non-Java-based systems.

Colin Reeves' feature article provides an insightful survey of many of the applications of genetic algorithms (GAs) to the solution of current problems in operations research. In some cases, GAs were successful, and in others they were not. Reeves correctly does not offer GAs as a panacea, but instead offers considerable insight into when and why GAs work. The difference between a successful application of a GA and an unsuccessful one often lies in the encoding. Five properties of an ideal encoding are listed.

Abstract This paper describes a new approach to finding solutions to the optimal communication spanning tree problem (OCSTP) using a genetic algorithm. The difficulties posed by this problem are reviewed and a genetic algorithm that consistently finds very good, if not optimal, solutions to it is presented. Finally, a comparison of the genetic algorithm's solutions to those produced by a good heuristic is given that demonstrates the genetic algorithm's ability to find solutions at least equivalent to, if not superior to, those found by the heuristic.

The problem of obtaining a minimum cost topology for a mesh network given matrices specifying the cost of links between all pairs of nodes and the internode requirements is considered. A heuristic algorithm which works in terms of general network design principles and uses utilization as a figure of merit is presented. The procedure is applicable to a wide variety of networks, especially to the problem of obtaining starting topologies for other network design procedures. The algorithm's computational complexity is shown to be of order N/sup 2/, a significant improvement over currently used algorithms and fast enough to be embedded in the inner loop of other more general design procedures, e.g., node selection procedures. Computational experience is presented which shows that the procedure is fast and simple and yields solutions of a quality competitive with other much slower procedures.>

An algorithm which produces conflict-free communication schedules in mobility multihop radio networks is presented. These schedules are produced in a completely distributed manner. The algorithm is based on a globally known permutation on the nodes of the network. As a result the only knowledge needed on the part of individual nodes is the number of nodes in the network. This permutation guarantees that conflict-free schedules can be produced in a distributed manner. Two extensions to the basic permutation are discussed. The first enables neighboring nodes to enhance their communication schedules in a fast, robust, distributed manner. The second extension allows the algorithm to operate in the presence of secondary conflicts.>

A parallel protocol verification algorithm, called the two-phase algorithm, is proposed in an attempt to provide a maximum of verification with a minimum of state space. Rather than compose all communicating finite-state machines (FSMs) into one large global reachability tree, the two-phase algorithm constructs a local expanded tree for each FSM augmented with external information. The first phase of the algorithm performs the expanded tree construction and the second phase performs error detection based on the constructed expanded trees. By separating verification into two phases, the algorithm allows verification for all FSMs to be executed in parallel. The algorithm thus requires a shorter run-time. Moreover, by introducing a new method for the construction of the expanded trees, the algorithm requires fewer explored states. The algorithm can verify protocols with any number of processes. Verification for protocols with more than two processes is illustrated.>

The introduction of concentrators in a centralized telecommunication network provides a cost-effective way to connect the network. The star-star (SS) network model is considered, and the star-star concentrator location problem (SSCLP) is then examined. The SSCLP is NP-complete and can be formulated as a 0-1 integer programming problem. A two-phase algorithm is developed to solve the SSCLP. In the first phase, dualizing the side constraints produces a Lagrangian problem that is easy to solve and has an optimal value that is a lower bound (for minimization problems) on the optimal value of the original SSCLP. Heuristics then are applied to produce an upper bound (feasible solution) to the SSCLP. In the second phase, a branch-and-bound method is used to refine the solution space to obtain a tighter lower bound. First, an enumeration heuristic is applied to improve the best feasible solution obtained from the first phase. Then, a procedure for deriving bounding problems is presented and various branching strategies are discussed. Computational examples with up to 100 terminals and 30 potential concentrators are considered. All the network designs obtained are shown to be within 2% of optimal.>

The introduction of concentrators in a centralized telecommunication network is considered. The Star-Star (SS) network model is described, and the Star-Star concentrator location problem (SSCLP) is examined. The SSCLP is NP-complete and can be formulated as a 0-1 integer programming problem. A two-phase algorithm is developed to solve the SSCLP. In the first phase, dualizing the side constraints produces a Lagrangian problem that is easy to solve and whose optimal value is a lower bound (for minimization problems) on the optimal value of the original SSCLP. Then, heuristics are applied to produce an upper bound (feasible solution) to the SSCLP. In the second phase, branch-and-bound is used to refine the solution space to obtain a tighter lower bound. Computational examples with up to 100 terminals and 30 potential concentrators are considered. All the network designs obtained are shown to be within 2% of optimal.>

The problem of analyzing the thoughput of packet radio networks with realistic topologies is considered. We present an algorithm for the solution of this problem and show that both the memory requirements and running time of this algorithm in practice grow polynomially with the size of the problem. Although in theory both can grow exponentially in the worst case, we offer computational experience with the procedure and show that for realistic topologies where connectivity is related to distance, the rate of growth is quadratic in the number of links. Even for regular grids, which are pathological in their symmetry, the rate of growth is only cubic in the number of links. We thus conclude that the procedure is effective for realistic topologies with up to several hundred nodes.

In this paper, we use a Markov model to develop a product form solution to efficiently analyze the throughput of arbitrary topology multihop packet radio networks that employ a carrier sensing multiple access (CSMA) protocol with perfect capture. We consider both exponential and nonexponential packet length distributions. Our method preserves the dependence between nodes, characteristic of CSMA, and determines the joint probability that nodes are transmitting. The product form analysis provides the basis for an automated algorithm that determines the maximum throughput in networks of size up to 100 radio nodes. Numerical examples for several networks are presented. This model has led to many theoretical and practical extensions. These include determination of conditions for product form analysis to hold, extension to other access protocols, and consideration of acknowledgments.

Abstract The problem considered is that of finding an optimal (minimum cost) design for a centralized processing network given a set of locations, traffic magnitudes between these locations, and a single common source or destination. Several heuristics, which are efficient (in terms of their execution time and memory requirements on a digital computer) and which produce seemingly good results, have already been developed and are currently accepted techniques. Some work has also been done on finding optimal solutions to this problem both as a design tool and as a means of verifying the effectiveness of proposed heuristics. We focus on this latter area. Currently known techniques for the optimal solution of this problem via integer programming have fallen short of the desired objectives as they require too much memory and running time to be able to treat problems of realistic size and complexity. We develop an improved technique which is capable of handling more realistic problems.

Abstract Two shortest path algorithms are compared and it is shown that, while one outperforms the other in practice, the former's running time is exponential in the worst case while the latter's is polynomial. A procedure which constructs such worst case examples is given.

We consider the problem of designing a centralized telecommunication network comprised of multipoint lines given a set of terminal locations, traffic requirements, and a common central site. The optimal solution to this problem is a capacitated minimal spanning tree. We develop a class of heuristic algorithms for the solution of this problem by imbedding existing heuristics, referred to as first-order greedy algorithms, inside a loop where small, carefully chosen sets of arcs are alternately forced in and out of the solution. The resultant procedure is shown to be superior to existing techniques, producing solutions typically 2 percent better, while requiring only a modest amount of additional computer time.

Abstract An analysis is made of the computational complexity of a class of heuristic algorithms for the solution of the minimal spanning tree problem subject to a restriction on the maximum number (or weight) of nodes in any subtree rooted at a distinguished node. This is of particular interest in designing networks with branch capacity restrictions. The algorithm is a modification of Kruskal's Algorithm where weights are assigned to the nodes and then used, along with the arc lengths, to select the order in which arcs are considered for inclusion in the spanning tree. Considerations in the efficient implementation of such algorithms are examined and several heuristics for assigning node weights are compared.

The problem of designing minimum-cost multidrop lines which connect remote terminals to a concentrator or a central data-processing computer is studied. In some cases, optimal solutions can be obtained by using either linear integer programming or a branch-bound method. These approaches are not practical, since they lack flexibility and require an enormous amount of computer time for most practical problems. As a consequence, heuristic algorithms have been developed by various authors. In this paper, we point out that all of these algorithms fall into the class of minimum spanning tree (MST) problems, constrained by traffic or response time requirements. The difference between them is mainly the sequential order with which a branch or a line is selected into the tree. Without the constraints, all algorithms converge to a MST. With the constraints, they form different subtrees. Most of the algorithms can be unified into a modified Kruskal's MST algorithm. In the modified algorithm, a weight is associated with each terminal. Let wibe the weight associated with terminali, andd_{i}{j}be the cost for the line directed from terminalito terminalj. When the algorithm fetches the cost for the line, it replaces it withd_{i}{j} - w_{i}. In some cases, wi's need to be readjusted in the middle of the algorithm. The difference between all existing heuristic algorithms is in the way wi's are defined. If wiis zero for alli, the algorithm reduces to the unmodified Kruskal's algorithm; if wiis set to zero whenever a line incident to terminaliis selected as a tree branch, the algorithm reduces to Prim's MST algorithm. An extension of the algorithm to the solution of an associated problem of partitioning the terminals with respect to a predetermined set of concentrators, multiplexers, terminal interface processors, or central computers is also derived. The efficiency of an algorithm depends greatly on how it is implemented. The computational complexity of the unified algorithm is in the order ofN^{2} log Nfor the most general case, whereNis the number of terminals. By using good heuristics, it reduces toK_{1}N log N + K_{2}N, where K1and K2are constants, for many practical applications. The algorithm has been applied to large networks with over 1000 terminals, yielding excellent results and using only 15 seconds of computer time on a CDC 6600 computer. Designs obtained by using different wi's are compared.