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Cluster Computing
Paper

Load-balance scheduling for intelligent sensors deployment in industrial internet of things

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Abstract

The Industrial Internet of Things uses intelligent sensors to collect the physical properties of objects placed on a large area. It provides innovative service and network functioning, such as time-bounded data delivery and efficient sensor sleep cycle management. Intelligent scheduling is crucial to assisting the situation. The periodic scheduling of radio transceivers of sensor nodes into sleep or active mode helps accomplish efficient energy consumption. Such an extensive scale network work on data collection on multihop fashion. Our proposal provides the algorithm to form the node’s backbone, which task is to collect the data from sensors scatted in the area. Further, the heuristic algorithm is proposed to assist the backbone node in balancing the incoming traffic for scheduling. In Time Division Multiple Access (TDMA) driven Medium Access Control (MAC), the nodes are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. Our proposed work is named aggressive scheduling medium access control (AS-MAC). If the owner node does not have data to forward, then the corresponding slot will be aggressively scheduled by other nodes. Therefore, that slot and all consecutive slots if the corresponding owner nodes do not use will go to the needy nodes. The proposed system model presents a unique instance of interference for successfully delivering control and data packets. Therefore, a probabilistic estimation has given for the packet delivery rate for the raised specific condition. Our proposal’s distinctive feature is that the performance is better than Time Division Multiple Access for any given load. Finally, the packet delivery rate, packet drop, energy consumption, and time frame requirement for a different number of sensor node has been calculated and compared with the current state-of-the-art proposals.

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Cluster Computing

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