Abstract
Data transmission has witnessed a new wave of emerging technologies such as IoT. This new way of communication could be done through smart communication such as smart sensors and actuators. Thus, data traffic keeps traversing to the main servers in order to accomplish the tasks at the sensors side. However, this way of communication has encountered certain issues related to network due to the nature of routing forth and back from the end users to the main servers. Subsequently, this incurs high delay and packet loss which successively degrades the overall Quality of Service (QoS). On the other hand, the new way of data transmission, which is called “edge IoT network”, has not only helped on reducing the load over the network but also made the nodes to be more self-manage at the edge. However, this approach has some limitations due to the power consumption and efficiency, which would lead to node failure and data loss. Therefore, this paper presents a new model of combining network science and computer network in order to enhance the edge IoT efficiency. Simulation results have shown a clear evidence in improving the efficiency, communicability, degree, and overall closeness.
Highlights
In the past few decades, the advances in technology is increasing very rapidly where end terminals have become wellspread over the network
This paper looks at the efficiency and load distribution issues at the edge Internet of things (IoT) network
In contrast to the previous reviewed studies, this paper looks at the edge IoT efficiency and load distribution from network science perspective
Summary
In the past few decades, the advances in technology is increasing very rapidly where end terminals have become wellspread over the network. The massive development in technologies, such as wireless communications and mobiles have created a platform for people to exchange information . Wireless sensors network (WSN) is one of the most promising and convenient for data gathering, mainly at the era of Internet of things (IoT) [2]. Such heterogeneous in data communication and gathering shall bring more burden over the network. Big data is more generated which needs careful consideration in terms of data classification and analysis [3]
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