WHOOPH : Whale Optimization based Optimal Placement of Hub Node within a WBAN

Abstract

The biosensor nodes of a wireless body area network (WBAN) are implanted in distinct parts of a patient's body to monitor the body’s vital signs. Further, the WBAN nodes transmit the measured physiological data to a hub node. Hence, an optimal location for the placement of hub node turns out to be a crucial factor in determining the minimized node power consumption in data transmission. The currently adopted hub placement techniques are time-consuming as they sequentially place the hub at multiple locations on the patient’s body and estimate link reliability for each location. The best-performing location is selected for hub placement. The engagement of other WBAN nodes in the process of hub placement results in an undesirable loss of network energy too. This paper presents the Whale Optimization Algorithm (WOA) based hub placement scheme that directly gives the optimal hub node location and needs minimal involvement of other WBAN nodes during the process of hub placement. The presented scheme incorporates a population of whale search agents where each whale defines the random hub node coordinates. The fitness of whale search agents is estimated in terms of energy spent by the WBAN nodes in data transmission. The WOA-based model converges into an optimized search agent that determines the optimal location for hub placement. The resultant hub location minimizes the overall amount of energy consumed by the WBAN nodes for data transmission, which ultimately results in an elongated lifespan of WBAN operation. The performance results show that the proposed Hub placement scheme attains a significant increase in network stability and lifetime periods in comparison to the state-of-the-art related WBAN protocols. Additionally, the presented Hub placement scheme offers improved network throughput and latency characteristics.

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

“…To resolve this issue and maintain collision-free/non-interruptive services, there is a need to come up with effective routing protocols considering a realistic channel and path loss model, which is lacking in the current literature. 178 In, 179 path loss is generated to a propagating node signal in ISM band caused by the 2.4 GHz on-body communication channel. In, 180 the author mentioned that on-body exchange results show that the path loss between receiver and transmitter elevates with increased gap and frequency.…”

“…Authors in 190 showcased the application of big data in WBAN. Machine learning in WBAN: Machine learning can be applied in WBAN for achieving different purposes such as recognizing and classifying daily human activities 179 and a number of faults and anomalies (abnormal conditions) in the medical sensor network, 191 reducing internetwork interferences using a power controller, and maximizing throughput and minimizing energy dissipation, classification of heterogeneous network traffic, and intrusion detection. At different stages of WBAN processes, machine learning techniques can help in optimizing the network performance and processing of sensed collected data.…”

“…In, 179 path loss is generated to a propagating node signal in ISM band caused by the 2.4 GHz on‐body communication channel. In, 180 the author mentioned that on‐body exchange results show that the path loss between receiver and transmitter elevates with increased gap and frequency.…”