An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks

The green industrial Internet of things (IIoT) is emerging as a new paradigm, which envisions the concept of connecting different devices and reducing energy consumption. In multi-hop low power and lossy network, a resource-constrained node should aware of its energy consumption while routing the data packets. As part of IoT, the routing protocol for low power and lossy network (RPL) is considered to be a default routing standard. Recently, RPL has gained a significant maturity, but still, energy optimization is one of the main issues, because the default objective function (OF), which makes routing decision mainly based on a single parameter, such as link quality, and ignores the energy cost. Therefore, this paper aims to consider the concept of green IIoT concerning how a routing approach can achieve energy efficiency in resource-constrained IoT networks. For this, we propose a resource aware and reliable OF (RAROF), which constructs an optimum routing path by exploiting the information regarding the duty cycle, link quality, energy condition, and resource availability of a node. In addition, we propose node vulnerability index (NVI), a new routing metric that identifies the vulnerable nodes in terms of energy. To deal with the diverse data traffic of the IIoT network, we implement a multi-queuing based traffic differentiation approach that ensures the application requirements. The extensive simulation results show that the proposed RAROF can effectively extend the lifetime of the network, enhance the energy efficiency, and achieve higher reliability than that of other OFs. In this way, RAROF makes a routing decision with the purpose of extending network lifetime and minimizing energy depletion, paving the way towards green IIoT.