Energy Efficient Multi-Path Routing Algorithm Using Gray System Theory for Improvement of Routing and Network Stability in Internet of Things

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routes with separate links. One of such protocols is AOMDV routing protocol. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named RMPGST-IoT is introduced which chooses the routes with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed RMPGST-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to throughput, packet receiving rate, packet loss rate, average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed RMPGST-IoT compared to the ERGID and ADRM-IoT approaches.

A New Energy-Efficient Multipath Routing in Internet of Things Based on Gray Theory

Internet of Things (IoT) is a network of smart things. It indicates the ability that the mentioned physical things transfer information with each other. The characteristics of these networks, such as topology dynamicity and energy constraint, make the routing problem a challenging task in these networks. Traditional routing methods could not achieve the required performance in these networks. Therefore, developers of these networks have to consider specific routing methods in order to satisfy their requirements. One of the routing methods is utilization of the multipath protocols in which data are sent to its destination using multiple routes with separate links. One of such protocols is AOMDV routing protocol. In this paper, AOMDV is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, Ad hoc On-demand Multipath Distance Vector (AOMDV) packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal-to-noise ratio can also be considered during selection of the best route. The proposed method named RMPGST-IoT is introduced which chooses the routes with highest rank for concurrent transmission of data, using a specific method based on the gray system theory. In order to evaluate the results, the proposed Routing Multipath based on Gray System Theory (RMPGST)-IoT method is compared to the Emergency Response IoT based on Global Information Decision (ERGID) and Ad hoc Delay-aware Distributed Routing Model (ADRM)-IoT approaches in terms of throughput, packet receiving rate, packet loss rate, average remaining energy, and network lifetime. The results demonstrate that the performance of the proposed RMPGST-IoT is superior to that of ERGID and ADRM-IoT approaches.

A Talent Cultivation Model for Improving the Innovation Ability of College Students in Scientific Research

The innovation ability of college students in scientific research is constrained by multiple factors. What is worse, the current talent training model in colleges faces many defects. To solve these problems, this paper aims to develop a talent cultivation model that effectively enhances the innovation ability of college students in scientific research. Firstly, the problems with the current talent cultivation model were analyzed, and then the evaluation indicator system was improved for the innovation ability of college students in scientific research. Next, entropy weight method and gray system theory were integrated to create an evaluation model that quantifies the innovation ability of college students in scientific research. On this basis, several strategies were put forward to improve the said ability. The research results help to effectively enhance the innovation ability of college students in scientific research, and optimize the talent cultivation model in colleges and universities.

Reliability Analysis of Key Components of the Pod Based on Grey System Theory

For improving the reliability of key components in an airborne pod, gray system theory is introduced into the virtual design of airborne pod. Based on this theory, key components of airborne pod are analyzed and mathematical models of the gray relational model and absolute and relative gray relational model are established, respectively. The differences and relations of the three methods for evaluating the robustness of products are researched. Considering the influence of relevant factors, the change rate of each evaluation index is calculated, and the reliability analysis of the airborne pod body is realized ultimately. Finally, the No. 2 test pod body is the optimal solution, which provides an effective theoretical basis for determining the optimal structure of the airborne pod body.

A Model for the Influential Factors of the Autonomous Learning Ability of Japanese Majors

The autonomous learning of Japanese majors is affected by various factors and the learning effect is generally undesirable. To solve the problem, this paper aims to develop a model for the influencing factors of the autonomous learning of Japanese majors. Firstly, the authors explored the problems and influencing factors of autonomous learning of Japanese majors. On this basis, several suggestions were put forward to solve these problems. Next, gray system theory (GST) and analytical hierarchy process (AHP) were adopted to establish the indicator system and the assessment model for the autonomous learning effect of Japanese majors. The proposed model can effectively assess the effect of autonomous learning among Japanese majors, providing a good reference for the learning of minor language among college students.

Failure modeling and analysis of composite laminates: Interval-based approaches

Composite materials inherently exhibit scatter in their basic characteristics such as the mechanical properties of constituent materials, fibers orientations, ply thicknesses, and applied loads. The uncertainties present in the basic parameters are available, in most cases, in the form of ranges or intervals. The use of the existing deterministic failure theories, which do not consider the observed variabilities in the input parameters, leads to poor failure assessment of composite materials. Several probabilistic failure models have been proposed in the past few decades. However, the probabilistic methods require a knowledge of the probability distributions of the basic variables which are not available in most practical systems. This work, for the first time, presents an interval-based failure analysis of composite materials using the truncation-based interval analysis and the universal gray system theory. These methods require only the lower and upper bounds of the uncertain parameters which are available in most practical systems. The application of the proposed interval-based failure models is demonstrated by considering two types of graphite/epoxy laminates: [0/±45/90] S and [0/90]2. This work shows that more realistic and meaningful failure assessment can be made using the universal gray system theory and the truncation-based interval analysis compared to the deterministic failure analysis.

Energy Efficient Multi-Path Routing Algorithm Using Gray System Theory for Improvement of Routing and Network Stability in Internet of Things

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routes with separate links. One of such protocols is AOMDV routing protocol. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named RMPGST-IoT is introduced which chooses the routes with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed RMPGST-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to throughput, packet receiving rate, packet loss rate, average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed RMPGST-IoT compared to the ERGID and ADRM-IoT approaches.

A Gray System Theory Based Multi-Path Routing Method for Improving Network Lifetime in Internet of Things Systems

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. IoT has introduced various services and daily human life depends on its reliable and accessible operation. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routs with separate links. One of such protocols is AOMDV routing protocol. AOMDV protocol is a multi-path protocol which uses multiple different paths for sending information in order to maintain the network traffic balance, manage and control node energy, decrease latency, etc. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named GSTMPR-IoT is introduced which chooses the routs with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed GSTMPR-IoT method is compared to the EECRP and AOMDV approaches with regard to throughput, packet delivery rate, end to end delay, average residual energy, and network lifetime. The results demonstrate the superior performance of the proposed GSTMPR-IoT compared to the EECRP and AOMDV approaches.