Comparative study between metaheuristic algorithms for internet of things wireless nodes localization

<p>Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power <br />consumption.</p>

Computational modeling of chromatin accessibility identified important epigenomic regulators

Chromatin accessibility is essential for transcriptional activation of genomic regions. It is well established that transcription factors (TFs) and histone modifications (HMs) play critical roles in chromatin accessibility regulation. However, there is a lack of studies that quantify these relationships. Here we constructed a two-layer model to predict chromatin accessibility by integrating DNA sequence, TF binding, and HM signals. By applying the model to two human cell lines (GM12878 and HepG2), we found that DNA sequences had limited power for accessibility prediction, while both TF binding and HM signals predicted chromatin accessibility with high accuracy. According to the HM model, HM features determined chromatin accessibility in a cell line shared manner, with the prediction power attributing to five core HM types. Results from the TF model indicated that chromatin accessibility was determined by a subset of informative TFs including both cell line-specific and generic TFs. The combined model of both TF and HM signals did not further improve the prediction accuracy, indicating that they provide redundant information in terms of chromatin accessibility prediction. The TFs and HM models can also distinguish the chromatin accessibility of proximal versus distal transcription start sites with high accuracy.

Probabilistic Cooperative Coded Forwarding for Broadcast Transmissions in Industrial Mobile Edge Communications

Mobile edge computing (MEC) has been considered as a key enabler for the industrial internet of things (IIoT) to cope with the ever-increasing communication and computing demands of nodes. In consideration of the limited power and resource of the IIoT nodes, it is necessary to design cost-effective data sharing mechanisms for MEC-enabled wireless industrial communication networks. In this article, we propose the probabilistic cooperative coded forwarding (PCCF) scheme based on network coding (NC) to minimize the required transmission number in both the data source and relay nodes. The data packets are encoded sparsely in a systematic coding framework so that the decoding process at the receivers can be more efficient. The relationship between the forwarding and coding parameters of the proposed scheme and the successful decoding probability are comprehensively analyzed and the approximations are numerically verified. Throughout the analysis, we find the optimal sparsity of network coding vectors and also the existence of minimum transmission numbers.

Complexity of Tourism Destination Governance

Tourism destinations can be considered as complex systems of interrelated and interdependent stakeholders. The complexity and limited power of influencing the number of stakeholders resulted in network approach to tourism destination governance. This approach is considered both theoretically and practically as a tool for strengthening its sustainable competitiveness, fostering innovation and knowledge sharing. Although the network analysis of tourism destinations has gained a significant attention in recent years, the complex understanding of its contribution to smart development is still missing. The aim of this chapter is to create a framework for smart approach in destination governance using the network science perspective. The chapter provides insights in using network analysis for strengthening the tourism destination governance. The chapter uses a case study methodology on two mature tourism destinations, providing an example of the use of network analysis for destination governance strengthening.

A deep learning framework for predicting human essential genes from population and functional genomic data

Being able to predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve our ability to identify genes associated with genetic disorders. Numerous computational methods have recently been developed to predict human essential genes from population genomic data; however, the existing methods have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome. Here we present an evolution-based deep learning model, DeepLOF, which integrates population and functional genomic data to improve gene essentiality prediction. Compared to previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Furthermore, DeepLOF discovers 109 potentially essential genes that are too short to be identified by previous methods. Altogether, DeepLOF is a powerful computational method to aid in the discovery of essential genes.

MODELING A DC BUS CHARGE CONTROL SYSTEM IGBT INVERTER FOR TWO PHASE LINEAR INDUCTION MACHINE

The problems of modeling in the Matlab environment the modes of the MHD-stirring of aluminum melt in furnaces, taking into account the distribution network. It is noted that the operation of frequency inverters of the power supply system sharply complicates the electromagnetic environment in a network of limited power. It is proposed to apply a complex of models to assess the possibility of reducing the distortion of the network currents by modifying the rectifier control algorithms, while maintaining the stability of the DC bus of the frequency converter.

EV Charging in Case of Limited Power Resource

In the case of the widespread adoption of electric vehicles (EV), it is well known that their use and charging could affect the network distribution system, with possible repercussions including line overload and transformer saturation. In consequence, during periods of peak energy demand, the number of EVs that can be simultaneously charged, or their individual power consumption, should be controlled, particularly if the production of energy relies solely on renewable sources. This requires the adoption of adaptive and/or intelligent charging strategies. This paper focuses on public charging stations and proposes methods of attribution of charging priority based on the level of charge required and premiums. The proposed solution is based on model predictive control (MPC), which maintains total current/power within limits (which can change with time) and imparts real-time priority charge scheduling of multiple charging bays. The priority is defined in the diagonal entry of the quadratic form matrix of the cost function. In all simulations, the order of EV charging operation matched the attributed priorities for the cases of ten cars within the available power. If two or more EVs possess similar or equal diagonal entry values, then the car with the smallest battery capacitance starts to charge its battery first. The method is also shown to readily allow participation in Demand Side Response (DSR) schemes by reducing the current temporarily during the charging operation.

Analysis methodology for the design of start-up rheostats on thermo-dependent polycrystalline resistances

The article provides a brief analysis of the starting processes of electrical devices in autonomous systems of limited power. The existing methods of automatic start-up and regulation of the operation of electrical machines and apparatus are considered, which are a multi-link system, the reliability of which is determined by a number of intermediate links, and the stepping is one of the biggest drawbacks that negatively affect the dynamics of the starting process. In addition, the issues of simplicity, low cost and small dimensions of the automatic control system for electrical installations are of particular importance in the problem of energy saving. The use of low-power thermistors as part of starting devices requires intermediate equipment and various components, which significantly reduces the reliability of the equipment. The increase in currents flowing through the ballasts simplifies the electrical control and regulation circuits. For the use of polycrystalline semiconductor thermistors in circuits with high currents, it is necessary to use special designs in order to prevent overheating of the thermistor material. The article provides algorithms for the synthesis of starting rheostats. A number of restrictions are considered and formulated, on which the nature of the processes of starting electric motors with the help of thermistor rheostats, which ensure the fulfillment of certain restrictions, depends. Recommendations are given for the formation of optimal starting processes using rheostats built on semiconductor polycrystalline thermistors.

Enhancing Wireless Sensor Networks Routing Protocols based on Cross Layer Interaction

Wireless sensor networks aim to develop a smart city based on sensing environment. The routing protocols of wireless sensor networks is important to transfer the data in smart cities since sensor nodes have limited power and transmission range. The aim of this research is to enhance wireless sensor networks routing protocols based on proposed cross-layer interaction between physical layer and network layer also a proposed routing table information of wireless sensor nodes is developed to consider the transmission power of neighbor’s nodes to determine the next hop. Cross-layer interaction provides a useful information and effective adaptation for WSN routing protocols. As a result, the proposed routing protocol shows an improvement in network performance when number of intermediate nodes are minimized.

Binary Tree Based Data Gathering Routing Scheme for Wireless Sensor Networks

Wireless sensor network (WSN) is an emerging area in networking since the era of 21 st century. The major benefits of WSN using sensor nodes make it affordable, scalable, economic and reliable. The limitations of sensor nodes are in terms of fixed and limited power supply, durability, storage and computational facilities which make energy as a vast challenge in deploying sensor nodes in order to prevent them from draining. This paper proposes a novel deployment scheme for connecting the sensor nodes in the form of a 4-sided virtual full binary tree structure. In the proposed scheme, data is expected to reach resource opulence Base Station (BS) via hops as equal to the height of the tree. Also, the stability of the network will increase by an average value of around 82.78% in the range of 49- 98% with existing scheme of the network lifetime with respect to different scenarios. The proposed scheme gives excellent results with a variable number of nodes and changing the size of deployment area of WSN.