Oil and Gas industry shows great interest in deploying wireless sensor network for monitoring smart wells. The deployed sensors are very expensive and it is important to minimize the deployment cost while satisfying the coverage and connectivity requirements. In this work, we present a coverage-connectivity model for optimal deployment of sensors and relays. The monitored field is presented as a grid of 2D bounded space. We develop an Integer Linear Program (ILP) for minimizing the total cost of the sensors and relays for complete coverage of predetermined critical points in the monitored field and maintain and connectivity of the sensors with a central processing unit. The proposed model lends itself easily to a wide range of industrial applications. A numerical example is presented.

There are basic design elements in Wireless Local Area Networks (WLAN) architecture to provide good service for the users like locations, number and transmission power of the Access Points (APs), also known as Base Stations (BSs). The number of deployed stations has a direct impact on the cost of the WLAN. Moreover, the intercell interference is affected by the number of APs and their locations. It is, therefore, always desirable to minimize the number of stations needed to achieve the required Quality of Service (QoS). Conventionally, it is the network planner's task to manually choose locations and parameters of APs based on prior experience. This manual approach is highly inefficient and time consuming. For this purpose, automatic network planning can substantially reduce the overhead cost of the network, minimize the intercell interference and maximize the QoS. In this work we propose a new placement technique for WLAN APs based on well-known 2-D convolution. The objective of the placement problem is to cover the whole area with minimum number of access points taking into consideration the environmental features of the intended area such as radio propagation model and antenna patterns. The proposed approach shows flexibility and efficiency in this placement problem.

In today's online connected world, almost all corporate networks use some form of perimeter firewalls to manage Internet connections and enforce a security policy at the corporate gateway. Although it can considerably enhance network security and protect business-critical information, a firewall with thousands of rules can become a bottleneck for network performance. The primary goal of this paper is to present a new rule order optimizer based on simulated annealing to find optimal configurations that minimize the average number of rule comparisons while preserving precedence relationships among disjoint rules. The proposed approach is evaluated and its effectiveness is compared with another approximate solution under several firewall configurations and policy profiles.

This paper presents a flexible evolutionary method for minimum-cost multipath constrained routing with load balancing problem in MPLS networks. The proposed solution approach combines genetic algorithms with linear multi-commodity flow to enhance the efficiency of the solution attained. The goal is to determine the distribution of traffic demands over a given capacitated network topology to minimize the routing cost while balancing loads on various links. The constraints that should be satisfied are the maximum hop count, the total number of virtual paths and the link capacities. This problem is a highly constrained multiobjective optimization for which exact optimization methods become helpless to deal with such complexity. Using a case study from the literature, the proposed approach is evaluated and compared with the standard genetic algorithm. We also show how the proposed approach can be used to determine approximate Pareto points and compare them with the exact Pareto front.

The convergence of the fuzzy ISODATA clustering algorithm was proved by Bezdek [3]. Two sets of conditions were derived and it was conjectured that they are necessary and sufficient for a local minimum point. In this paper, we address this conjecture and explore the properties of the underlying optimization problem. The notions of reduced objective function and improving and feasible directions are used to examine this conjecture. Finally, based on the derived properties of the problem, a new stopping criterion for the fuzzy ISODATA algorithm is proposed.

The K-means algorithm is a commonly used technique in cluster analysis. In this paper, several questions about the algorithm are addressed. The clustering problem is first cast as a nonconvex mathematical program. Then, a rigorous proof of the finite convergence of the K-means-type algorithm is given for any metric. It is shown that under certain conditions the algorithm may fail to converge to a local minimum, and that it converges under differentiability conditions to a Kuhn-Tucker point. Finally, a method for obtaining a local-minimum solution is given.