In recent years, Wireless Rechargeable Sensor Networks (WRSNs) have adopted wireless energy transfer technology, which has emerged as a promising solution to the limited energy issues in traditional Wireless Sensor Networks (WSNs). Since the sensor’s lifetime is decided by the charging scheme of the Mobile Charger (MC), designing an effective charging algorithm is challenging. Although many efforts have been devoted to optimizing charging schemes in WRSNs, the studies still face several critical issues. Firstly, most previous studies assumed that the MC’s battery capacity is sufficient or unlimited, resulting in the MC can move and charges all sensors in a charging cycle. That may cause a long waiting time for energy-hurry sensors and significant exhaustion of the MC’s energy. Secondly, existing works often optimize the MC’s charging path, whereas the charging time has not been thoroughly considered. This work aims to solve the limitations above by optimizing both the charging path and charging time simultaneously under the MC ’s limited-energy constraint. Our objective is to minimize the energy depletion of sensor nodes. To this end, we leverage the advantage of the bi-level optimization approach and propose a charging algorithm with two levels: the charging path optimization at the upper level and the charging time optimization at the lower level. The proposed charging scheme combines Genetic Algorithm (GA) and Differential Evolutionary (DE) to identify the optimal charging path and time. We conducted extensive experiments to demonstrate the effectiveness of our charging scheme in comparison to the related studies.
The Inter-Domain Path Computation problem under Node-defined Domain Uniqueness constraint (IDPC-NDU) is a recently investigated topic for finding the effective routing paths on the multi-domain network topology as well as transportation. The objective of the IDPC-NDU is to find the shortest path in the multi-domain directed graph that traverses every domain at most once. Since the IDPC-NDU belongs to NP-Hard class, this paper proposes a novel two-level approach based on an Evolutionary Algorithm (EA) to solve it. The first level aims to determine the sequence of crossed domains using an improved Genetic Algorithm (GA), while the second one aims to locate the minimally costly path between two nodes among the entire domains. Furthermore, we devise an approach to represent a chromosome, which reduces the chromosome length to the number of domains. Experiments on numerous sets of instances were implemented to show the effectiveness and characteristics of the proposed algorithm.
In wireless sensor networks, sensors handle the aggregation of data from neighboring nodes to the base station, in addition to their primary sensing task. Networks can minimize energy usage by batching together multiple outbound packets at certain nodes over a data aggregation tree. Constructing optimal data aggregation trees is an NP-hard problem, thus requiring approximation methods for larger instances. In this paper, we propose a new Multifactorial Evolutionary Algorithm to solve multiple Data Aggregation Tree Problem with Minimum Energy Cost instances simultaneously. Our method utilizes a novel operator scheme for Edge-Set Tree Representation enabling the unification of search spaces between instances, which helps us to obtain better results than contemporary approaches.
Barrier coverage problems in wireless camera sensor networks (WCSNs) have drawn the attention by academic community because of their huge potential applications. Various versions of barrier coverage under WCSNs have been studied such as minimal exposure path, strong/weak barrier, 1/k barrier, full view barrier problems. In this paper, based on new (k−ω) coverage model, we study how to achieve (k −ω) barrier coverage problem under uniform random deployment scheme (hereinafter A(k − ω)BC problem). This problem aims to juggle whether any given camera sensor networks is (k − ω) barrier coverage. A camera sensor network is called (k − ω) barrier coverage if any crossing path is (k − ω) coverage. The A(k − ω)BC problem is useful because it can make balance of the number of camera sensors used and the information retrieved by the camera sensors. Furthermore, this problem is vital for design and applications for camera sensor networks when camera sensor nodes were deployed randomly. Thus, we formulate the A(k − ω)BC problem and then proposed an efficient method named Dynamic Partition for solving this problem . An extensive experiments were conducted on random instances, and the results indicated that the proposed algorithm can achieve high quality and stable solutions in real-time execution.
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