A novel bio-inspired routing algorithm based on ACO for WSNs

The methods to achieve efficient routing in energy constrained wireless sensor networks (WSNs) is a fundamental issue in networking research. A novel approach of ant colony optimization (ACO) algorithm for discovering the optimum route for information transmission in the WSNs is proposed here for optimization and enhancement. The issue of path selection to reach the nodes and vital correspondence parameters, for example, the versatility of nodes, their constrained vitality, the node residual energy and route length are considered since the communications parameters and imperatives must be taken into account by the imperative systems that mediate in the correspondence procedure, and the focal points of the subterranean insect framework have been utilized furthermore. Utilizing the novel technique and considering both the node mobility and the existing energy of the nodes, an optimal route and best cost from the originating node to the target node can be detected. The proposed algorithm has been simulated and verified using MATLAB and the simulation results demonstrate that new ACO based algorithm achieved improved performance, about 30% improvement compared with the traditional ACO algorithm, and faster convergence to determine the best cost route, and recorded an improvement in the energy consumption of the nodes per transmission

A noble approach of ACO algorithm for WSN

In energy compelled wireless sensor networks (WSNs), the means by which to perform effectual routing is among the main focuses. A noble approach of ant colony optimization (ACO) algorithm for discovering the optimum route in the WSNs for data transmission is proposed here for enhancement and optimization considering the issue of path selection to reach the nodes. Using the proposed ACO algorithm and considering both the node mobility and the existing energy of the nodes, an optimum route and best cost from the originating node to the target node can be detected. The proposed algorithm has been simulated and verified utilizing MATLAB and the simulation results demonstrate that new ant colony optimization based algorithm can achieve better performance and faster convergence to determine the best cost route

Energy-efficient scalable routing protocol based on ACO for WSNS

Efficient routing is an essential requirement for the design of wireless sensor network (WSN) protocols to overcome inherent challenges and to meet hardware and resource constraints. An energy-efficient scalable routing algorithm based on ant colony optimization (ACO) for WSNs is presented here to find the optimal path of data transmission while consuming less energy leading to increase of network’s lifetime. Most of the existing ACO based routing algorithms are designed on the assumption that the sensor nodes and the sinks are stationary and do not consider the overhead of mobility and the current node energy is not considered, which will prompt sudden passing of certain nodes. To overcome the existing problem of accommodating node mobility, reducing initialization time for ant based routing algorithm and to maintain scalability in WSN for time critical applications, an ACO based WSN routing algorithm has been proposed and analyzed in this paper. The proposed algorithm has been simulated and verified utilizing MATLAB. The evaluation results demonstrate that it has reduced energy consumption, almost 50% less consumed energy even with the increasing number of nodes, compared with the traditional ACO and an existing ant-based routing algorithm. Moreover, it increases the nodes’ lifetime and lifetime of the network

Efficient and scalable ant colony optimization based WSN routing protocol for IoT

IoT integrates and connects intelligent devices or objects with varied architectures and resources. The number of IoT devices is growing exponentially. Due to the massive wave of IoT objects, their diversity and heterogeneity among their architectures, the existing communication protocols for wireless networks become ineffective in the context of IoT. Wireless Sensor Network (WSN) has the potential to be integrated to the internet of things (IoT). The issues of the routing of WSNs impose nearly similar prerequisites for IoT routing technique. Most of the traditional routing protocols are not appropriate for WSNs and IoT because of resource constraints, computational overhead and environmental interference and do not take into account the different factors affecting energy parameter and do not accommodate node mobility. Routing algorithms must ensure the data transmission in an efficient way, having proper knowledge of the IoT system. For this reason, many intelligent systems have been utilized to design routing algorithms to handle the network's dynamic state. In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. The proposed routing algorithm is simulated using MATLAB for performance evaluations. The evaluation results have recorded an improvement in conservation of energy, of almost 50% less consumed energy even with an increase in the number of nodes, by comparing with an existing routing technique based on ant system, a current routing protocol for IoT and the conventional ACO algorithm. © 2020 ASTES Publishers. All rights reserved

Genetic divergence in common bean genotypes from the IRAD gene bank: morpho-agronomic characteristics, fungal and bacterial disease resistance, and opportunities for genetic improvement

For successful plant breeding in any crop species, the importance of diversity in the available germplasm population is known and established. Thirty-two common bean (Phaseolus vulgaris) genotypes from the IRAD gene bank in Cameroon were evaluated for divergence in terms of their morpho-agronomic traits, fungal disease resistance, and bacterial disease resistance to assess the opportunity for genetic improvement of the crop. The trait associations were estimated using correlation coefficients and genotypes were classified into groups using cluster and principal component analyses. Seven qualitative and 16 quantitative traits comprising growth, phenological, yield, and disease variables were evaluated in this study. The qualitative markers revealed the degree of polymorphism among the 32 common bean genotypes. The number of phenotypic classes per character (Na) ranged from 2 to 18, with an average of 5.14. The expected gene diversity (He) ranged from 0.37 to 0.93 (mean = 0.56). The number of effective phenotypic classes (Ne) ranged from 1.82 to 14.22, with a mean of 3.85. An extensive range of variation was evident for the majority of traits, highlighting their utility for characterizing common bean germplasm. Many qualitative traits, including seed coat color, seed shape, and seed size, and also some quantitative traits of economic importance including seed yield, were found to be highly variable within the collection, with the MAC55 genotype displaying the highest yield (32.65 g per plant). Four genotypes, namely MAC55, BOA-5-1M6, FEB 192, and Banguem showed resistance to the two main common bean diseases, angular leaf spot and common blight. We detected highly significant correlations among several traits related to yield. A high broad-sense heritability was found for most of the quantitative traits. We carried out two-dimensional principal component analysis and used hierarchical clustering to group the analyzed germplasm according to their phenotypic similitudes. The evidence of agro-morphological diversity in the present collection and the identification of discriminant characters between the available germplasm through the use of PCA analysis have significant implications for establishing breeding schemes in common bean

Assessing biofilm formation by Listeria monocytogenes

Abstract Listeria monocytogenes (L. monocytogenes) is a serious food-borne pathogen for immunocompromised individuals. L. monocytogenes is capable of producing biofilm on the surface of food processing lines and instruments. The biofilm transfers contamination to food products and impose risk to public health. Transfers contamination to food products, and impose risk hazard to public health. The aim of this study was to investigate biofilm producing ability of L. monocytogenes isolates. Microtitre assay was used to measure the amount of biofilm production by ten L. monocytogenes isolates from minced chicken / meat, sausages and burgers. Results showed that all 10 L. monocytogenes isolates were able to form biofilm after 24 h at 20˚C on polystyrene surface (the common surface in food industries). Some strains were capable of forming biofilm more than the others. All strains showed a slight raise in the quantities of attached cells over 48 and 72 h. L. monocytogenes strains isolated from minced chicken, minced meat and burgers were better biofilm-producers comparing to the strains isolated from sausages