Maximum Lifetime Coverage Problem Research Articles

Wireless Sensor Network Coverage Optimization: Comparison of Local Search-Based Heuristics

The Maximum Lifetime Coverage Problem (MLCP) requires heuristic optimization methods due to its complexity. A real-world problem model determines how a solution is represented and the operators applied in these heuristics. Our paper describes adapting a local search scheme and its operators to MLCP optimization. The operators originate from three local search algorithms we proposed earlier: LSHMA, LSCAIA, and LSRFTA. Two steps of the LS scheme’s main loop can be executed in three different ways each. Hence, nine versions of the LS approach can be obtained. In experimental research, we verified their effectiveness. Test cases come from three benchmarks: SCP1, proposed and used in our earlier research on the three LS algorithms mentioned above, and two others found in the literature. The results obtained with SCP1 showed that the algorithm based on the hypergraph model approach (HMA) is the most effective. The remaining results of the other algorithms divide them into two groups: effective ones and weak ones. However, other benchmarks showed that the more redundant the coverage of points of interest (POIs) by sensors, the more effective the perturbation method from the approach inspired by cellular automata (CAIA). The findings expose the strengths and weaknesses of the problem-specific steps applied in the LS algorithms.

Maximum lifetime coverage problem with battery recovery effect

Scheduling sensors to prolong the lifetime of target coverage is one of the central problems faced in wireless sensor networks. This problem, called the maximum lifetime coverage problem (MLCP), can be formulated as a linear program with exponential size and has a polynomial-time approximation scheme (PTAS). In reality, however, sensor batteries are subject to the recovery effect, which means that the deliverable energy in a battery can replenish itself if it is left idle for a sufficient duration. Thanks to this effect, we can obtain much longer sensor lifetime if each sensor is intermittently forced to turn off for some interval. In this study, we introduce two models that extend the MLCP to incorporate the battery recovery effect. The first model, called as duty cycle model, represents the battery recovery effect in a deterministic way. The second one, called as linear recovery model, uses a probabilistic model to imitate this effect. We propose two efficient algorithms that work for both models, adapting greedy and Garg–Könemann-based algorithms for the original MLCP. In our numerical experiments, our greedy algorithm performs best in the duty cycle model, while our Garg–Könemann-based algorithm performs best in the linear recovery model. For each network, we compare the longest lifetime obtained from our algorithms with the longest lifetime obtained from algorithms for the original MLCP. As a result, we found that our lifetime is 10–40% longer.

Enabling Coverage-Preserving Scheduling in Wireless Sensor Networks for Structural Health Monitoring

Wireless sensor networks (WSNs) have been considered to be the next generation paradigm of structural health monitoring (SHM) systems due to the low cost, high scalability and ease of deployment. Due to the intrinsically energy-intensive nature of the sensor nodes in SHM application, it is highly preferable that they can be divided into subsets and take turns to monitor the condition of a structure. This approach is generally called as ‘coverage-preserving scheduling’ and has been widely adopted in existing WSN applications. The problem of partitioning the nodes into subsets is generally called as the ’maximum lifetime coverage problem (MLCP)’. However, existing solutions to the MLCP cannot be directly applied to SHM application. As compared to other WSN applications, we cannot define a specific coverage area independently for each sensor node in SHM, which is however the basic assumption in all existing solutions to the MLCP. In this paper, we proposed two approaches to solve the MLCP in SHM. The performance of the methods is demonstrated through both extensive simulations and real experiments.

Special issue on Simulation with Cellular Automata

Cellular Automata (CA) are discrete dynamical systems. The definition of relatively simple, local interactions results in complex global behaviors of the whole system. This makes it possible to create and study models of many complex systems in different fields such as physics, engineering, environment science, social science, and life sciences. Such an approach is very promising, and during the past few decades CA-based modeling has attracted a growing number of researchers and practitioners interested in studying models of complex phenomena. In this special issue entitled ‘‘Simulation with Cellular Automata,’’ we have proceeded with an open-call announcement targeting novel simulation CA-based studies as well as revised and extended papers presented during the 11 International Conference on Cellular Automata for Research and Industry, held at AGH University of Science and Technology in September 2014. After a thorough and meticulous review process, we finally came up with seven (7) accepted papers dealing with various and, in most cases, interdisciplinary topics under the umbrella of CAbased simulation. More specifically, in the first paper, ‘‘Modelling and Simulation of Complex Cellular Automata Using CellDEVS,’’ Gabriel Wainer and Joaquin Fernandez present a discrete event formalism Cell-DEVS, which enables timing delay constructions. Cell-DEVS is introduced as a formalism supporting a discrete event approach to define cellular models with timing delay constructions and using a simple definition of complex timing. The application of the Cell-DEVS paradigm enables more efficient cellshaped modeling by also supporting the inherent complexity of the proposed models. The second paper is ‘‘Modeling Pedestrian Behavior with Cell-DEVS: Theory and Applications’’ co-authored by Ala’a Al-Habashna and Gabriel Wainer. On the basis of the Cell-DEVS approach, an entity-based modeling and simulation of crowd dynamics, applied in an analysis of evacuation and occupancy levels of multiple-floor buildings, is analytically described. Specifically, the authors present a practical application of Cell-DEVS formalism in the modeling and simulation of pedestrian dynamics and their behavior in different situations. The third paper, entitled ‘‘The Spatial Reproduction Number in a Cellular Automaton Model for Vector-Borne Diseases Applied to the Transmission of Chagas Disease,’’ is co-authored by Baki Cisse, Samira El Yacoubi, and Sebastien Gourbiere. The proposed approach is based on CA and applied to Chagas disease for homogeneous as well as heterogeneous landscapes. It refers to modeling of spatial changes in the basic reproduction number for vector-borne diseases. The main outcome from the performed simulations is an effect of local vector dispersal in a heterogeneous landscape for Chagas disease transmission. The fourth paper, entitled ‘‘Graph Cellular Automata Approach to the Maximum Lifetime Coverage Problem in Wireless Sensor Networks’’ by Antonina Tretyakova, Franciszek Seredynski, and Pascal Bouvry, proposes the generalization of a CA concept called Graph Cellular Automata to solve the maximum lifetime coverage problem in wireless sensor networks (WSN). A new localized algorithm is presented, which uses only some knowledge about the neighboring sensors of WSNs, still able to selfreorganize in such a way to preserve a required coverage ratio and prolong lifetime of the WSN. The fifth paper, ‘‘Cellular Automata Approach for Parallel Machine Scheduling Problem’’ authored by Edyta Kucharska, Katarzyna Grobler-Debska, Krzysztof Rączka, and Lidia Dutkiewicz, deals with scheduling problems with parallel machines using the CA paradigm. An asynchronous non-homogenous cellular automaton is

Graph Cellular Automata approach to the Maximum Lifetime Coverage Problem in wireless sensor networks

In this paper, we propose a distributed algorithm based on a generalization of the Cellular Automata concept called Graph Cellular Automata (GCA) to solve the Maximum Lifetime Coverage Problem (MLCP) in wireless sensor networks (WSNs). In GCA, we adapt life-like state transition functions inspired by Conway’s Game of Life in order to solve the problem. The goal of this paper is to study the quality of state transition functions for an objective provided by the MLCP in WSNs. The proposed algorithm possesses all the advantages of a localized algorithm, i.e., using only some knowledge about neighbors, a WSN is able to self-organize in such a way as to prolong its lifetime, at the same time preserving the required coverage ratio of the target field. Our experimental results show that certain rules are better solvers of the given problem than others. The paper also presents the results of an experimental study of the proposed algorithm and comparison with a centralized Genetic Algorithm.

New approximations for Maximum Lifetime Coverage

In this article dedicated to Professor Yue Minyi on the occasion of his 95th birthday, we present a new polynomial-time approximation scheme for the well-known Maximum Lifetime Coverage problem in wireless sensor networks. For any , this scheme contains an approximation algorithm with performance , which improves the performance ratio of previously known approximation in the literature.