An adaptive multi-hop routing with IoT abstraction for minimizing delay-node capacity trade-offs in mobile ad-hoc network

<span>Delay and node capacity are incompatible mobile ad hoc constraints because of the network's versatility and self-disciplined design. </span><span>It is a challenging problem to maximize the trade-off between the above mobility correlation factors. </span><span>This manuscript proposes an adaptive multi-hop routing (A.M.R.) for mobile ad-hoc network (MANET) to minimize the trade-off by integrating the internet of things (IoT). IoT nodes' smart computing and offloading abilities are extended to ad-hoc nodes to improve routing and transmission. Dor MANET nodes in route exploration, neighbor selection, and data transmission, the beneficial features of IoT include enhanced decision making. The traditional routing protocols use IoT at the time of the neighbor discovery process in updating the routing table and localization. </span><span>The harmonizing technologies with their extended support improve the performance of MANETs has been estimated. The proposed method achieves better throughput (14.16 Mbps), delay (0.118), packet drop (126), and overhead (36 packets) when compared to existing methods.</span>

Research and Design of Adaptive Networking Method Based on LoRa

An adaptive networking method based on LoRa(Long Range) technology is proposed. The data transmission of wireless sensor networks widely used in the Internet of things is studied. A star network composed of Lora wireless transmission technology is designed to build an adaptive data transmission network. The design process of network topology, hardware and adaptive networking method of adaptive networking are introduced. Aiming at solving the problems of limited node capacity, adjacent frequency interference, and unreasonable channel resource allocation in the LoRa network, an adaptive frequency hopping mechanism is used for networking. The system uses I. MX6ULL as the main control and integrates 8 LoRa modules at the same time, which greatly improves the node capacity of the gateway. The server can realize real-time viewing and monitoring of node equipment, and real-time evaluation of channel communication quality.

Transportation Optimization Models for Intermodal Networks with Fuzzy Node Capacity, Detour Factor, and Vehicle Utilization Constraints

This paper develops a mathematical model for intermodal freight transportation. It focuses on determining the flow of goods, the number of vehicles, and the transferred volume of goods transported from origin points to destination points. The model of this article is to minimize the total cost, which consists of fixed costs, transportation costs, intermodal transfer costs, and CO2 emission costs. It presents a mixed integer linear programming (MILP) model that minimizes total costs, and a fuzzy mixed integer linear programming (FMILP) model that minimizes imprecise total costs under conditions of uncertain data. In the models, node capacity, detour, and vehicle utilization are incorporated to estimate the performance impact. Additionally, a computational experiment is carried out to evaluate the impact of each constraint and to analyze the characteristics of the models under different scenarios. Developed models are tested using real data from a case study in Southern Vietnam in order to demonstrate their effectiveness. The results indicate that, although the objective function (total cost) increased by 20%, the problem became more realistic to address when the model was utilized to solve the constraints of node capacity, detour, and vehicle utilization. In addition, on the basis of the FMILP model, fuzziness is considered in order to investigate the impact of uncertainty in important model parameters. The optimal robust solution shows that the total cost of the FMILP model is enhanced by 4% compared with the total cost of the deterministic model. Another key measurement related to the achievement of global sustainable development goals is considered, reducing the additional intermodal transfer cost and the cost of CO2 emissions in the objective function.

A genetic algorithm to solve the robust design problem for a Flow Network with Node Failure

The robust design problem in a flow network is defined as search optimal node capacity that can be assigned such that the network still survived even under the node’s failure. This problem is considered as an NP-hard. So, this paper proposes a genetic algorithm-based approach to solve it for a flow network with node failure. The proposed based genetic approach is used to assign the optimal capacity for each node to minimize the total capacities and maximize the network reliability. The proposed approach takes the capacity for each critical node should have the maximum capacity (usually equals to the demand value) to alleviate that the reliability to drop to zero. Three network examples are used to show the efficiency of our algorithm. Also, the results obtained by our approach are compared with those obtained by the previous approximate algorithm.

Dynamic Offloading Model for Distributed Collaboration in Edge Computing: A Use Case on Forest Fires Management

With the development of the Internet of Things (IoT), the amount of data is growing and becoming more diverse. There are several problems when transferring data to the cloud, such as limitations on network bandwidth and latency. That has generated considerable interest in the study of edge computing, which processes and analyzes data near the network terminals where data is causing. The edge computing can extract insight data from a large number of data and provide fast essential services through simple analysis. The edge computing has a real-time advantage, but also has disadvantages, such as limited edge node capacity. The edge node for edge computing causes overload and delays in completing the task. In this paper, we proposes an efficient offloading model through collaboration between edge nodes for the prevention of overload and response to potential danger quickly in emergencies. In the proposed offloading model, the functions of edge computing are divided into data-centric and task-centric offloading. The offloading model can reduce the edge node overload based on a centralized, inefficient distribution and trade-off occurring in the edge node. That is the leading cause of edge node overload. So, this paper shows a collaborative offloading model in edge computing that guarantees real-time and prevention overload prevention based on data-centric offloading and task-centric offloading. Also, we present an intelligent offloading model based on several scenarios of forest fire ignition.

Time Reliability of the Maritime Transportation Network for China’s Crude Oil Imports

To evaluate the transportation time reliability of the maritime transportation network for China’s crude oil imports under node capacity variations resulting from extreme events, a framework incorporating bi-level programming and a Monte Carlo simulation is proposed in this paper. Under this framework, the imported crude oil volume from each source country is considered to be a decision variable, and may change in correspondence to node capacity variations. The evaluation results illustrate that when strait or canal nodes were subject to capacity variations, the network transportation time reliability was relatively low. Conversely, the transportation time reliability was relatively high when port nodes were under capacity variations. In addition, the Taiwan Strait, the Strait of Hormuz, and the Strait of Malacca were identified as vulnerable nodes according to the transportation time reliability results. These results can assist government decision-makers and tanker company strategic planners to better plan crude oil import and transportation strategies.

Improved efficient queue resource reallocation strategy for traffic dynamics on scale-free networks

In recent years, the transportation systems have to face the increasing challenges of congestion and inefficiency, and therefore the research on traffic dynamics of complex networks has become a significant area. When the total node capacity of the network is fixed, a reasonable queue resource reallocation strategy is effective in improving the network traffic capacity. In this paper, a new queue resource allocation method is proposed based on the betweenness centrality and the degree centrality of nodes. With the proposed strategy, the node queue length is allocated accurately to enhance the transport efficiency. Simulation results show that the proposed strategy can effectively improve the traffic capacity of the scale-free networks.