Fair Data Transmission Research Articles

Data Fairness Transmission and Adaptive Duty Cycle through Machine Learning in wireless Sensor Networks

In this paper, we propose the data fairness transmission and adaptive duty cycle through machine learning in wireless sensor networks. The mechanism of this paper is mainly composed of two parts. The proposed mechanism is based on the sleep-wake structure, which is one of the methods to increase the lifespan of the entire network by efficiently using the energy of the nodes. The first is a mechanism to support priority and data fairness. To this end, data input to the node is divided into priority classes according to transmission urgency and stored. Introduces the concept of cross-layer to rearrange data destined for the same destination. In addition, we propose a fair data transmission mechanism that allows even low-priority data to participate in transmission after a certain period. The second is an adaptive duty cycle mechanism through machine learning. For this purpose, public data related to forest fires are collected. The collected data is refined into data for each forest fire location and data for each forest fire time. For the refined data, an SVM (Support Vector Machine) model of supervised learning is used for machine learning, and a mechanism for adaptively adjusting the duty cycle of each node through the trained model is proposed. The computer language used for machine learning is Python language, and Google's Psychic Learn is used for the machine learning library. It was compared with the existing MAC protocol for evaluation, and it was confirmed that excellent energy efficiency results were obtained.

Be Stable and Fair: Robust Data Scheduling for Vehicular Networks

The stable and fair data transmission of vehicular networks can improve transport efficiency and reduce traffic accident. It is challenging to ensure the stability and fairness of data transmission in dynamic vehicular networks. However, existing works based on opportunistic scheduling cannot support reliable transmission since stability and fairness are difficult to be guaranteed at the same time. In this paper, we propose a stable scheduling scheme for dynamic vehicular networks based on fair allocation of available channel resources. We formulate the problem of stability and fairness as network utility maximization, and propose an algorithm, called SF-NUM, to solve it. SF-NUM uses the stability scheduling method based on back pressure vector for the first time in vehicular networks. In order to achieve the fairness distribution of channel resources of the vehicular networks in the distributed computing way, multiple factors are considered in the SF-NUM algorithm. The experimental results show that the proposed algorithm outperforms other algorithms.

A Fair Data Transmission Strategy in Underwater Acoustic Sensor Networks

When Underwater Acoustic Sensor Networks (UASNs) transmit large amounts of data, the collisions of link layer increase, and it causes that the traffic load of link is heavier. These situations could easily lead to network data loss, unfair channel competition and unfair data transmis - sion. In order to reduce collisions and improve the reliability and fairness of data transmission, Time Distance Ratio (TDR) and underwater fairness transmission are presented, and this paper proposes a Fair Data Transmission Strategy (FDTS) for UASNs, which consists of queue management, transmission control and competition waiting. It focuses on considering the fairness of data transmission and enhancing network performance in case of network congestion. Simulation results show that when network load is heavier, FDTS not only effectively improve the fairness of data transmission and the success rate of delivery but also reduce end-to-end delay, average energy consumption and TDR of packets

A fair non-repudiation framework for data integrity in cloud storage services

Data storage is one of the most profitable applications on the cloud computing platforms. Although a transparent service model provides more flexibility and convenience, it may be subject to the loss of data integrity. Our study revealed vulnerability in some commercial cloud storage services, which potentially will lead to disputation between client and service provide or among multiple uses. In this paper, we propose a framework that supports a fair data transmission procedure without the risk of disputation. A basic two-party non-repudiation (TPNR) protocol and an advanced multi-party non-repudiation protocol (MPNR) are proposed. Rationales behind the new protocol’s design and its working modes are presented in detail. We also discuss its robustness in the face of typical malicious network attacks.

Traffic-aware routing protocol for wireless sensor networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.