Optimization of Delay and Energy in Wireless Body Area Networks Using Swarm Intelligence Based Dynamic Bandwidth Allocation Algorithm

Today for continuous monitoring of patients, medical devices are to be monitored using the Wireless Body Area Network (WBAN). The devices are connected with the Internet of Things Enabled WBAN. Optical networks in recent years have dominated the communication field around the globe due to higher medical data transmission in real time. The bandwidth provided by this network are higher. But due to higher demands in spectrum, the available bandwidth should be used efficiently. Especially the unused bandwidth should be efficiently utilized. In this paper the bandwidth allocation strategy for the IoT enabled WBAN for optical network is presented which is based on Swarm intelligence. The dynamic bandwidth allocation (DBA) was done for the Ethernet passive optical network (NG-EPON). The existing pooling and machine learning approaches found in literature provide DBA management. But the swarm-based method enhances and optimizes the allocation process. This algorithm has the capacity to deal with the bandwidth demands of NGEPON. The novel DBA algorithm outperforms the existing algorithms in terms of average packet delay and average completion time. The method utilizes the ant colony optimization method along with dynamic bandwidth allocation. Implementation was done in MATLAB and NS2.