Mobile sinks (MSs) are capable of collecting data along specified paths in wireless sensor networks (WSNs). They are deployed as a popular alternative for data loggers, to which all nodes have to send sensory data. If MSs' paths (or cycles) are not well determined, it might take a relatively long time for the MSs to make a round trip. Recent research works have proposed methods to determine rendezvous points (RPs) that the MSs must pass by to collect data, with the aim of reducing the data-collection time. Determination of the number of RPs is important, and it is challenging to make ensure that there are sufficient RPs widely located throughout a sensor network, forming a circle along which the MSs can spend limited time traveling. This research presents a method for designing paths and pinpointing RPs for MSs to collect data, as well as determining the next hop to relay data for each sensor node. Instead of reducing the MSs' travel time, the focus of this research is to preserve the energy of all sensor nodes in WSNs. Our method determines the maximum number of RPs such that the MSs can run through each RP's communication range (within a time constraint) without depleting their own energy. The method comprises three main steps. First, we calculate the number of RPs and design the MS path. Second, the exact data-collection points are determined. The last step is to specify the path along which sensory data are relayed to the MS. In our experiments, we simulate two WSNs of different sizes. The results show that our method outperforms the others by 70%-80% in terms of the sensor node uptime, power consumption, MS traveling time and the number of RPs.

Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors: frequency, variety, and repetition. HITS-3 identifies distinctive POIs based on user- and POI-visit patterns, ranks them accordingly, and recommends them to cold-start users. For existing users, CF-3 utilizes collaborative filtering based on their previous check-in history and POI distinctive aspects. Our experimental results conducted on a Foursquare dataset demonstrate that LACF-Rec3 outperforms prior methods in terms of recommendation accuracy, ranking precision, and matching ratio. In addition, LACF-Rec3 effectively solves the challenges of data sparsity, the cold-start issue, and tedium problems for cold-start and existing users. These findings highlight the potential of LACF-Rec3 as a promising solution to the challenges encountered by LBRS.

Effective crossover methods are essential for genetic algorithms because they enhance population diversity, resulting in an increase in the search space to be explored and the mitigation of trapping at local optima. Ring-based crossover methods are designed to address the shortcomings of traditional crossover techniques; for example, the ends of binary-encoded chromosomes tend to remain unaltered, even when their fitness values are low. By conjoining the leftmost and rightmost parts of a chromosome to form a ring, the ring-based crossovers allow offspring bits to be inherited from parent bits at the front and rear positions. Such a ring-based technique helps increase population diversity while requiring a relatively low number of fitness evaluations. To the best of our knowledge, there has been no comparative study or comprehensive analysis of ring-based crossover techniques. In this paper, we study and compare the characteristics of various existing ring-based crossover techniques (i.e., circle-ring (CRC), annular (AC), ring (RC) and front-rear crossover (FRC)). Although they each have distinct characteristics, they share a common crossover concept: bits at different positions can be swapped. We propose a generalized ring-based crossover (GRC) as an umbrella of the ring-based crossovers, embodying all of their beneficial characteristics. Chromosome shifting, ring forming, chromosome exchange and offspring creation are integrated into the proposed model. The experimental results show that GRC outperforms the other ring-based crossover methods in all experiments, and these results are consistent with the results of behavioral analysis. In a trap problem, GRC outperformed the state-of-the-art BOA method and require 40-times fewer fitness evaluations. GRC tends to maintain a variety of candidates in populations and to preserve the building blocks of solution, and it requires 93% and 57% fewer fitness evaluations (on average) compared to traditional crossover and other ring-based crossover methods.