Mixed integer formulations for the probabilistic minimum energy broadcast problem in wireless networks

In this paper we study a new variant of the minimum energy broadcast (MEB) problem, namely the probabilistic MEB (PMEB). The objective of the classic MEB problem is to assign transmission powers to the nodes of a wireless network is such a way that the total energy dissipated on the network is minimized, while a connected broadcasting structure is guaranteed by the assigned transmission powers. In the new variant of the problem treated in this paper, node failure is taken into account, aiming at providing solutions with a chosen reliability level for the broadcasting structure. Three mixed integer linear programming formulations for the new problem are presented, together with efficient formulation-dependent methods for their solution. Computational results are proposed and discussed. One method emerges as the most promising one under realistic settings. It is able to handle problems with up to fifty nodes.     

Multi-period traffic routing in satellite networks

We introduce a traffic routing problem over an extended planning horizon that appears in geosynchronous satellite networks. Unlike terrestrial (e.g., fiber optic) networks, routing on a satellite network is not transparent to the customers. As a result, a route change is associated with significant monetary penalties that are usually in the form of discounts (up to 40%) offered by the satellite provider to the customer that is affected. The notion of these rerouting penalties requires the network planners to explicitly consider these penalties in their routing decisions over multiple time periods and introduces novel challenges that have not been considered previously in the literature. We develop a branch-and-price-and-cut procedure to solve this problem and describe an algorithm for the associated pricing problem. Our computational work demonstrates that the use of a multi-period optimization procedure as opposed to a myopic period-by-period approach can result in cost reductions up to 13% depending on problem characteristics and network size considered. These cost reductions correspond to potential savings of several hundred million dollars for large satellite providers.     

Exact and heuristic methods to maximize network lifetime in wireless sensor networks with adjustable sensing ranges

Wireless sensor networks involve many different real-world contexts, such as monitoring and control tasks for traffic, surveillance, military and environmental applications, among others. Usually, these applications consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. We want to individuate a set of covers (that is, subsets of sensors that can cover the whole set of targets) and appropriate activation times for each of them in order to maximize the total amount of time in which the monitoring activity can be performed (network lifetime), under the constraint given by the limited power of the battery contained in each sensor. A variant of this problem considers that each sensor can be activated in a certain number of alternative power levels, which determine different sensing ranges and power consumptions. We present some heuristic approaches and an exact approach based on the column generation technique. An extensive experimental phase proves the advantage in terms of solution quality of using adjustable sensing ranges with respect to the classical single range scheme.     

A cooperative differential game model based on transmission rate in wireless networks

Network throughput and energy efficiency are paramount for network performance in an energy-constrained wireless network. However, it is difficult to achieve optimal objectives simultaneously. Therefore, it is necessary to find a rate control solution based on tradeoff between network throughput and energy efficiency. In this paper, we propose a cooperative differential game model and find an optimal rate control of each player to get the total minimal cost with tradeoff between network throughput and energy efficiency of the networks.     

Application of generalized diagonal dominance in wireless sensor network optimization problems

The recent application of the diagonal dominance in the development of the optimization algorithms in the wireless sensor networks design, has been done by Yuan and Yu (2006) [14], extended in Yu et al. (2006) [9], and surveyed in Machado and Tekinay [11]. In this paper, we will use the concept of generalized diagonal dominance, to improve the obtained results regarding the power control game, in three directions. We also discuss the applicability of such improvements.     

Lifetime maximization in wireless directional sensor network

This paper addresses two versions of a lifetime maximization problem for target coverage with wireless directional sensor networks. The sensors used in these networks have a maximum sensing range and a limited sensing angle. In the first problem version, predefined sensing directions are assumed to be given, whereas sensing directions can be freely devised in the second problem version. In that case, a polynomial-time algorithm is provided for building sensing directions that allow to maximize the network lifetime. A column generation algorithm is proposed for both problem versions, the subproblem being addressed with a hybrid approach based on a genetic algorithm, and an integer linear programming formulation. Numerical results show that addressing the second problem version allows for significant improvements in terms of network lifetime while the computational effort is comparable for both problem versions.     

Exact approaches for lifetime maximization in connectivity constrained wireless multi-role sensor networks

In this paper, we consider the duty scheduling of sensor activities in wireless sensor networks to maximize the lifetime. We address full target coverage problems contemplating sensors used for sensing data and transmit it to the base station through multi-hop communication as well as sensors used only for communication purposes. Subsets of sensors (also called covers) are generated. Those covers are able to satisfy the coverage requirements as well as the connection to the base station. Thus, maximum lifetime can be obtained by identifying the optimal covers and allocate them an operation time. The problem is solved through a column generation approach decomposed in a master problem used to allocate the optimal time interval during which covers are used and in a pricing subproblem used to identify the covers leading to maximum lifetime. Additionally, Branch-and-Cut based on Benders’ decomposition and constraint programming approaches are used to solve the pricing subproblem. The approach is tested on randomly generated instances. The computational results demonstrate the efficiency of the proposed approach to solve the maximum network lifetime problem in wireless sensor networks with up to 500 sensors.     

Models and algorithms to improve earthwork operations in road design using mixed integer linear programming

In road construction, earthwork operations account for about 25% of the construction costs. Existing linear programming models for earthwork optimization are designed to minimize the hauling costs and to balance the earth across the construction site. However, these models do not consider the removal of physical blocks that may influence the earthwork process. As such, current models may result in inaccurate estimates of optimal earthwork costs, leading to poor choices in road design. In this research, we extend the classical linear program model of earthwork operations to a mixed integer linear program model that accounts for blocks. We examine the economic impact of incorporating blocks via mixed integer linear programming, and find significant savings for most road designs in our test-set. However, the resulting model is considerably harder to solve than the original linear program. Based on structural observations, we introduce a set of algorithms that theoretically reduce the solving time of the model. We confirm this reduction in solve time with numerical experiments.     

Routing and wavelength assignment in optical networks using bin packing based algorithms

This paper addresses the problem of routing and wavelength assignment (RWA) of static lightpath requests in wavelength routed optical networks. The objective is to minimize the number of wavelengths used. This problem has been shown to be NP-complete and several heuristic algorithms have been developed to solve it. We suggest very efficient, yet simple, heuristic algorithms for the RWA problem developed by applying classical bin packing algorithms. The heuristics were tested on a series of large random networks and compared with an efficient existing algorithm for the same problem. Results indicate that the proposed algorithms yield solutions significantly superior in quality, not only with respect to the number of wavelength used, but also with respect to the physical length of the established lightpaths. Comparison with lower bounds shows that the proposed heuristics obtain optimal or near optimal solutions in many cases.     

Efficient calculation of the most reliable pair of link disjoint paths in telecommunication networks

In transmission networks an important routing problem is to find a pair of link disjoint paths which optimises some performance measure. In this paper the problem of obtaining the most reliable pair of link disjoint paths, assuming the reliability of the links are known, is considered. This is a non-linear optimisation problem. It is further introduced the constraint that the length of the paths should not exceed a certain number of links, which makes the efficient resolution of the problem more complex.     

A column generation based heuristic for sensor placement,activity scheduling and data routing in wireless sensor networks

A wireless sensor network is a network consisting of distributed autonomous electronic devices called sensors. In this work, we develop a mixed-integer linear programming model to maximize the network lifetime by optimally determining locations of sensors and sinks, sensor-to-sink data flows, and activity schedules of the deployed sensors subject to coverage, flow conservation, energy consumption and budget constraints. Since solving this model is difficult except for very small instances, we propose a heuristic method which works on a reformulation of the problem. In the first phase of this heuristic, the linear programming relaxation of the reformulation is solved by column generation. The second phase consists of constructing a feasible solution for the original problem using the columns obtained in the first phase. Computational experiments conducted on a set of test instances indicate that both the accuracy and the efficiency of the proposed heuristic is quite promising.     

A biobjective optimization model for routing in mobile ad hoc networks

In this paper, the problem of finding optimal paths in mobile ad hoc networks is addressed. More specifically, a novel bicriteria optimization model, which allows the energy consumption and the link stability of mobile nodes to be taken into account simultaneously, is presented. In order to evaluate the validity of the proposed model, a greedy approach is devised. Some preliminary computational experiments have been carried out, in a simulation environment. The numerical results are very encouraging, showing the correctness of the proposed model. Indeed, the selection of a shorter route leads to a more stable route, but to a greater energy consumption. On the other hand, if longer routes are selected the route fragility is increased, but the average energy consumption is reduced.     

Metaheuristic hybridizations for the regenerator placement and dimensioning problem in sub-wavelength switching optical networks

Physical layer impairments severely limit the reach and capacity of optical systems, thereby hampering the deployment of transparent optical networks (i.e., no electrical signal regenerators are required). Besides, the high cost and power-consumption of regeneration devices makes it unaffordable for network operators to consider the opaque architecture (i.e., regeneration is available at every network node). In this context, translucent architectures (i.e., regeneration is only available at selected nodes) have emerged as the most promising short term solution to decrease costs and energy consumption in optical backbone networks. Concurrently, the coarse granularity and inflexibility of legacy optical technologies have re-fostered great interest in sub-wavelength switching optical networks, which introduce optical switching in the time domain so as to further improve resources utilization. In these networks, the complex regenerator placement and dimensioning problem emerges. In short, this problem aims at minimizing the number of electrical regenerators deployed in the network. To tackle it, in this paper both a greedy randomized adaptive search procedure and a biased random-key genetic algorithm are developed. Further, we enhance their performance by introducing both path-relinking and variable neighborhood descent as effective intensification procedures. The resulting hybridizations are compared among each other as well as against results from optimal and heuristic mixed integer linear programming formulations. Illustrative results over a broad range of network scenarios show that the biased random-key genetic algorithm working in conjunction with these two intensification mechanisms represents a compelling network planning algorithm for the design of future sub-wavelength optical networks.     

Optimal infrastructure design and expansion of broadband wireless access networks

This paper investigates the important infrastructure design and expansion problem for broadband wireless access networks subject to user demand constraints and system capacity constraints. For the problem, an integer program is derived and a heuristic solution procedure is proposed based on Lagrangean relaxation. In the computational experiments, our Lagrangean relaxation based algorithm can solve this complex design and expansion problem quickly and near optimally. Based on the test results, it is suggested that the proposed algorithm may be practically used for the infrastructure design and expansion problem for broadband wireless access networks.     

Rerouting tunnels for MPLS network resource optimization

In Multi-Protocol Label Switching (MPLS) networks, traffic demands can be routed along tunnels called Label Switched Paths (LSPs). A tunnel is characterized by a path in the network and a reserved bandwidth. These tunnels can be created and deleted dynamically, depending on traffic demand arrivals or departures. After several operations of this type, the network resource utilization can be unsatisfactory, with congestion or too long routing paths for instance. One way to improve it is to reroute tunnels; the rerouting process depends on the LSP Quality of Service (QoS) requirements.     

Wavelength assignment for reducing in-band crosstalk attack propagation in optical networks: ILP formulations and heuristic algorithms

Today’s Transparent Optical Networks (TONs) are highly vulnerable to various physical-layer attacks, such as high-power jamming, which can cause severe service disruption or even service denial. The transparency of TONs enables certain attacks to propagate through the network, not only increasing their damage proportions, but also making source identification and attack localization more difficult. High-power jamming attacks causing in-band crosstalk in switches are amongst the most malicious of such attacks. In this paper, we propose a wavelength assignment scheme to reduce their damage assuming limited attack propagation capabilities. This complements our previous work in Furdek et al. (M. Furdek, N. Skorin-Kapov, M. Grbac, Attack-aware wavelength assignment for localization of in-band crosstalk attack propagation, IEEE/OSA Journal of Optical Communications and Networking 2 (11) (2010) 1000–1009) where we investigated infinite jamming attack propagation to find an upper bound on the network vulnerability to such attacks. Here, we consider a more realistic scenario where crosstalk attacks can spread only via primary and/or secondary attackers and define new objective criteria for wavelength assignment, called the PAR (Primary Attack Radius) and SAR (Secondary Attack Radius), accordingly. We formulate the problem variants as integer linear programs (ILPs) with the objectives of minimizing the PAR and SAR values. Due to the intractability of the ILP formulations, for larger instances we propose GRASP (Greedy Randomized Adaptive Search Procedure) heuristic algorithms to find suboptimal solutions in reasonable time. Results show that these approaches can obtain solutions using the same number of wavelengths as classical wavelength assignment, while significantly reducing jamming attack damage proportions in optical networks.     

A new linear programming approach and genetic algorithm for solving airline boarding problem

The airline industry is under intense competition to simultaneously increase efficiency and satisfaction for passengers and profitability and internal system benefit for itself. The boarding process is one way to achieve these objectives as it tends itself to adaptive changes. In order to increase the flying time of a plane, commercial airlines try to minimize the boarding time, which is one of the most lengthy parts of a plane’s turn time. To reduce boarding time, it is thus necessary to minimize the number of interferences between passengers by controlling the order in which they get onto the plane through a boarding policy. Here, we determine the passenger boarding problem and examine the different kinds of passenger boarding strategies and boarding interferences in a single aisle aircraft. We offer a new integer linear programming approach to reduce the passenger boarding time. A genetic algorithm is used to solve this problem. Numerical results show effectiveness of the proposed algorithm.