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Due to the explosive growth of the Internet of things (IoT) devices and the emergence of diverse new applications, network traffic volume is growing exponentially. The traditional centralized network architecture cannot fulfill IoT devices demand because of the heavy network traffic in industrial IoT. Moreover, IoT devices have limited computational ability and battery power. Energy consumption and time delay problems during computation offloading are fundamental issues. A new architecture known as mobile edge computing (MEC) was introduced to overcome these issues, which brings cloud services and its contents to the edge of the network. IoT devices can offload the data for computation to the cloud server or edge nodes. Different schemes have been proposed to overcome this problem under many scenarios (i.e., single-user, multiuser, and vehicular networks). In this paper, we proposed a modified delay mitigation Levenshtein distance algorithm (MDML). We consider an industrial scenario with multiple IoT devices and multiple servers (edge nodes). Each edge node consists of one MEC server. The proposed algorithm solves the offloading optimization problem of energy and mitigation of time delay with much lower complexity while significantly reducing offloading tasks’ execution time. It works on the basis of dynamic programming, where we break down a complex problem into subproblems. Performance evaluation of our proposed algorithm shows that it can achieve satisfactory energy efficiency and mitigate time delay in the industrial IoT environment.

Mitigation Impact of Energy and Time Delay for Computation Offloading in an Industrial IoT Environment Using Levenshtein Distance Algorithm