Raffaele Cerulli

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.

An Additive Branch-and-Bound Algorithm for the Pickup and Delivery Traveling Salesman Problem with LIFO or FIFO Loading

Abstract This paper introduces an additive branch-and-bound algorithm for two variants of the pickup and delivery traveling salesman problem in which loading and unloading operations have to be performed either in a Last-In-First-Out (LIFO) or in a First-In-First-Out (FIFO) order. Two relaxations are used within the additive approach: the assignment problem and the shortest spanning r-arborescence problem. The quality of the lower bounds is further improved by a set of elimination rules applied at each node of the search tree to remove from the problem arcs that cannot belong to feasible solutions because of precedence relationships. The performance of the algorithm and the effectiveness of the elimination rules are assessed on instances from the literature.

Metaheuristics Comparison for the Minimum Labelling Spanning Tree Problem

We study the Minimum Labelling Spanning Tree Problem: Given a graph G with a color (label) assigned to each edge (not necessarily properly) we look for a spanning tree of G with the minimum number of different colors. The problem has several applications in telecommunication networks, electric networks, multimodal transportation networks, among others, where one aims to ensure connectivity by means of homogeneous connections. For this NP-hard problem very few heuristics are presented in the literature giving good quality solutions. In this paper we apply several metaheuristic approaches to solve the problem. These approaches are able to improve over existing heuristics presented in the literature. Furthermore, a comparison with the results provided by an exact approach existing in the literature shows that we may quite easily obtain optimal or close to optimal solutions.

A branch-and-bound algorithm for the double travelling salesman problem with two stacks

This article studies the double traveling salesman problem with two stacks. A number of requests have to be served where each request consists in the pickup and delivery of an item. All the pickup operations have to be performed before any delivery can take place. A single vehicle is available that starts from a depot, performs all the pickup operations and returns to the depot. Then, it performs all the delivery operations and returns to the depot. The items are loaded in two stacks, each served independently from the other with a last-in-first-out policy. The objective is the minimization of the total cost of the pickup and delivery tours. We propose a branch-and-bound approach to solve the problem. The algorithm uses properties of the problem both to tighten the lower bounds and to avoid the exploration of redundant subtrees. Computational results performed on benchmark instances reveal that the algorithm outperforms the other exact approaches for this problem.

Maximizing lifetime in wireless sensor networks with multiple sensor families

Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.

Bounded-degree spanning tree problems: models and new algorithms

Abstract Given a connected graph G, a vertex v of G is said to be a branch vertex if its degree is greater than 2. We consider two problems arising in the context of optical networks: (i) Finding a spanning tree of G with the minimum number of branch vertices and (ii) Finding a spanning tree of G such that the degree sum of the branch vertices is minimized. For these NP-hard problems, heuristics, that give good quality solutions, do not exist in the literature. In this paper we analyze the relation between the problems, provide a single commodity flow formulation to solve the problems by means of a solver and develop different heuristic strategies to compute feasible solutions that are compared with the exact ones. Our extensive computational results show the algorithms to be very fast and effective.

The labeled maximum matching problem

Given a graph G where a label is associated with each edge, we address the problem of looking for a maximum matching of G using the minimum number of different labels, namely the labeled maximum matching problem. It is a relatively new problem whose application is related to the timetabling problem. We prove it is NP-complete and present four different mathematical formulations. Moreover, we propose an exact algorithm based on a branch-and-bound approach to solve it. We evaluate the performance of our algorithm on a wide set of instances and compare our computational times with the ones required by CPLEX to solve the proposed mathematical formulations. Test results show the effectiveness of our procedure, that hugely outperforms the solver.

A hybrid exact approach for maximizing lifetime in sensor networks with complete and partial coverage constraints

In this paper we face the problem of maximizing the amount of time over which a set of target points, located in a given geographic region, can be monitored by means of a wireless sensor network. The problem is well known in the literature as Maximum Network Lifetime Problem (MLP). In the last few years the problem and a number of variants have been tackled with success by means of different resolution approaches, including exact approaches based on column generation techniques. In this work we propose an exact approach which combines a column generation approach with a genetic algorithm aimed at solving efficiently its separation problem. The genetic algorithm is specifically aimed at the Maximum Network α-Lifetime Problem (α-MLP), a variant of MLP in which a given fraction of targets is allowed to be left uncovered at all times; however, since α-MLP is a generalization of MLP, it can be used to solve the classical problem as well. The computational results, obtained on the benchmark instances, show that our approach overcomes the algorithms, available in the literature, to solve both MLP and α-MLP.

Vehicle-ID sensor location for route flow recognition: Models and algorithms

Abstract We address an important problem in the context of traffic management and control related to the optimum location of vehicle-ID sensors on the links of a network to derive route flow volumes. We consider both the full observability version of the problem, where one seeks for the minimum number of sensors (or minimum cost) such that all the route flow volumes can be derived, and the estimation version of the problem, that arises when there is a limited budget in the location of sensors. Four mathematical formulations are presented. These formulations improve the existing ones in the literature since they better define the feasible region of the problem by taking into account the temporal dimension of the license plate scanning process. The resulting mathematical formulations are solved to optimality and compared with the existing mathematical formulations. The results show that new and better solutions can be achieved with less computational effort. We also present two heuristic approaches: a greedy algorithm and a tabu search algorithm that are able to efficiently solve the analyzed problems and they are a useful tool able to find a very good trade-off between quality of the solution and computational time.

Relations, models and a memetic approach for three degree-dependent spanning tree problems

In this paper we take into account three different spanning tree problems with degree-dependent objective functions. The main application of these problems is in the field of optical network design. In particular, we propose the classical Minimum Leaves Spanning Tree problem as a relevant problem in this field and show its relations with the Minimum Branch Vertices and the Minimum Degree Sum Problems. We present a unified memetic algorithm for the three problems and show its effectiveness on a wide range of test instances.