The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

Screening of antibiotic residues in raw milk of cows and buffalos by diffusion assays

Although antibiotics are valuable drugs for treatment of certain infections, their presence in foodstuff derived from animals is a potential public health hazard. They pose a serious threat as they are implicated in direct toxicity; allergic reactions; disturbance of the normal gut microbiota, carcinogenesis, and emergence of antibioticresistant bacteria. This study investigated the occurrence of antibiotic residues in raw milk samples derived from cows and buffaloes. Samples were collected randomly from different retail outlets in Erbil city (Iraq) from January 1st to June 30th, 2019. The residues were detected by two diffusion assays against Bacillus subtilis bacteria on agar plates. The total occurrence of residues ranged from 11.9% to 13.4% of screened milk samples. No significant differences were found between milk type or location of animal rearing (urban or suburban). Regarding the seasonal variations, spring was found to be associated with gradual decrease in antibiotic residues levels in milk. Such occurrence rate of residues is alarming and require authorities to observe the quality of raw milk introduced to markets for consumption. Further evaluation of antibiotic stability period in raw milk is also necessary.

Fog-LAEEBA: Fog-assisted Link aware and energy efficient protocol for wireless body area network

The integration of Wireless Sensor Networks (WSN) and cloud computing brings several advantages. However, one of the main problems with the existing cloud solutions is the latency involved in accessing, storing, and processing data. This limits the use of cloud computing for various types of applications (for instance, patient health monitoring) that require real-time access and processing of data. To address the latency problem, we proposed a fog-assisted Link Aware and Energy E cient Protocol for Wireless Body Area Networks (Fog-LAEEBA). The proposed solution is based on the already developed state-of-the-art protocol called LAEEBA. We implement, test, evaluate and compare the results of Fog-LAEEBA in terms of stability period, end-to-end delay, throughput, residual energy, and path-loss. For the stability period all nodes in the LAEEBA protocol die after 7445 rounds, while in our case the last node dies after 9000 rounds. For the same number of rounds, the end-to-end delay is 2 seconds for LAEEBA and 1.25 seconds for Fog-LAEEBA. In terms of throughput, our proposed solution increases the number of packets received by the sink node from 1.5 packets to 1.8 packets. The residual energy of the nodes in Fog-LAEEBA is also less than the LAEEBA protocol. Finally, our proposed solution improves the path loss by 24 percent.

FLAG: Fuzzy Logic Augmented Game Theoretic Hybrid Hierarchical Clustering Algorithm for Wireless Sensor Networks

Stability of the Wireless Sensor Network(WSN) is the most critical factor in real-time and data-sensitive applications like military and surveillance systems. Many energy optimization techniques and algorithms have been proposed to extend the stability of a wireless sensor network. Clustering is a well regarded method in the research communities among them. Hence, this paper presents hybrid hierarchical artificial intelligence based clustering techniques, named FLAG and I-FLAG. The first phase of these algorithms use game theoretic technique to elect suitable cluster heads(CHs) and later phase of the algorithms use fuzzy inference system to select appropriate super cluster heads(SCHs) among CHs. The I-FLAG is an improved version of FLAG where additional parameters like energy and distance are considered to elect CHs. Simulations are performed to check superiority of the proposed algorithms over the existing protocols like LEACH, CHEF, and CROSS. Simulation results show that the average stability period of WSN is better in FLAG and I-FLAG compared to other protocols, and so is the throughput of WSN during the stability period.

Enhancement of the antibacterial potential of plantaricin by incorporation into silver nanoparticles

Background Bacteriocins are proteinaceous compounds produced from lactic acid bacteria. Bacteriocins are well-known for their antibacterial potential and safety for application in food. However, the commercial availability of bacteriocin is facing several limitations; among them is the low yield and short stability period. That calls for a new strategy for overcoming these hurdles. Among these approaches is incorporating bacteriocin in nanoparticles. So, the aim of this study was to enhance the plantaricin produced from isolated Lactobacillus plantarum strain using nanotechnology. Results In this study, the plnEF genes encoding plantaricin EF have been identified and sequenced (accession number of MN172264.1). The extracted bacteriocin (EX-PL) was obtained by the ammonium sulfate method. Then, it was used for biosynthesizing plantaricin-incorporated silver nanoparticles (PL-SNPs). The synthesized nanoparticles were confirmed by SEM-EDAX analysis. The antibacterial activity of both combined (PL-SNPs) and extracted plantaricin (EX-PL) were tested against some strains of foodborne pathogenic bacteria. The results revealed that the antibacterial activities were increased by 99.2% on the combination of bacteriocin with the silver nanoparticle. The MIC of EX-PL (7.6 mg/mL) has been lowered after incorporating into silver nanoparticles and reached 0.004 mg/mL for PL-SNPs. Despite that extracted plantaricin showed no inhibitory activity towards Listeria monocytogenes, plantaricin-incorporated silver nanoparticles displayed inhibitory activity against this strain. Furthermore, the stability period at 4 °C was increased from 5 days to 60 days for EX-PL and PL-SNPs, respectively. Conclusions Plantaricin-incorporated silver nanoparticles possess higher antibacterial activity and more stability than the free one, which makes it more fitting for combating foodborne pathogens and open more fields for applications in both food and pharmaceutical industries. Graphical abstract

Optimization and Application of Support Scheme for Tunnel with High In-situ Stress in Ningchan

Based on the national highway 569 Mandala Datong highway Ningchan tunnel, the study on the optimization design of high ground stress tunnel support parameters is carried out. The results show that the single-layer primary support with I20b as the main support framework cannot control the large deformation of high ground stress tunnel, mainly manifested as arch frame failure and concrete spalling; adopting “double-layer initial support” can control large deformation to a certain extent; increasing the stiffness of inner layer support can reduce the “double-layer” to a certain extent The results show that the cumulative deformation of “support”, but cannot significantly shorten the deformation stability period; appropriately increasing the reserved deformation between the inner and outer layers of the initial support has the best supporting effect, the cumulative deformation is small, and the deformation stability period is shortened. The research results provide a basis for similar high stress tunnel support measures.

Enhanced Adaptive Distributed Energy-Efficient Clustering (EADEEC) for Wireless Sensor Networks

Background:Recently Wireless Sensor Network (WSN) is a composed of a full number of arbitrarily dispensed energy-constrained sensor nodes. The sensor nodes help in sensing the data and then it will transmit it to sink. The Base station will produce a significant amount of energy while accessing the sensing data and transmitting data. High energy is required to move towards base station when sensing and transmitting data. WSN possesses significant challenges like saving energy and extending network lifetime. In WSN the most research goals in routing protocols such as robustness, energy efficiency, high reliability, network lifetime, fault tolerance, deployment of nodes and latency. Most of the routing protocols are based upon clustering has been proposed using heterogeneity. For optimizing energy consumption in WSN, a vital technique referred to as clustering.Methods:To improve the lifetime of network and stability we have proposed an Enhanced Adaptive Distributed Energy-Efficient Clustering (EADEEC).Results:In simulation results describes the protocol performs better regarding network lifetime and packet delivery capacity compared to EEDEC and DEEC algorithm. Stability period and network lifetime are improved in EADEEC compare to DEEC and EDEEC.Conclusion:The EADEEC is overall Lifetime of a cluster is improved to perform the network operation: Data transfer, Node Lifetime and stability period of the cluster. EADEEC protocol evidently tells that it improved the throughput, extended the lifetime of network, longevity, and stability compared with DEEC and EDEEC.