# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
## 1 Introduction  
The introduction of wireless technology in the healthcare environment is an effort to provide significant convenience and accessibility. Wireless medical technology in medicine, such as body area networks, has already made a significant impact on hospital systems, where placement of sophisticated wireless devices around the body provides ambient assisted living, remote health monitoring and telemedicine services [1; 2]. Nanotechnology is further boasting the potential of Intrabody Nanonetworks (IBNNs) for a wide range of medical applications such as healing spinal cord injuries [3], detection of cancer bio-markers [4; 5; 6], drug delivery [7] and improved diagnosis [8]. IBNNs are composed of miniature size Nano Biosensors (NBSs) that are capable of obtaining fine-grained data with unprecedented accuracy from specific anatomical areas for monitoring anddiagnosis purposes. Due to their small size and better electronic properties, these NBSs have the potential to operate inside the human body without interrupting cellular biological function. One of the types of these NBSs is surface plasmon resonance sensors, which have already been deployed for effectively diagnosing various types of cancers and cardiovascular diseases [9; 10]. The tremendous potential of IBNNs in revolutionizing healthcare structure is confined by several fundamental limitations including, Nanoscale (NS) communication challenges and inadequate resources of NBSs in terms of energy, storage and computation [9; 11]. In the last few years, research communities focused their attention on addressing these primary challenges for realizing the broader scope of IBNNs. In the context of enabling NS communication, electromagnetic communication in the Terahertz Band (THz) has received significant consideration [10; 12]. The immense opportunities brought by electromagnetic communication in THz band such as extremely high data communication speed are leading to the development of new electromagnetic-based communication schemes [13; 14]. The development of novel schemes for IBNNs also requires an in-depth comprehension of the intense energy constraint of these NBSs for effective outcomes. The extreme energy constraint of these NBSs are elaborated as- a single NBS has a maximum energy of 800 pJ and during single transmission of a 120-bit long data packet it consumes about 10% of its available energy [15]. Thereby NBSs are capable of transmitting only 8 data packets with the maximum energy they may harvest. Therefore, energy efficiency is regarded as the key attribute of the communication protocol for prolonging the lifetime of IBNNs. However, fulfilling the demand for increased network lifetime while ensuring continuous data monitoring and transmission for effective diagnosis and treatment is a laborious challenge for severe energy constraint NS devices. Concerning the requirement of enabling energy-efficient communication in IBNNs past recent years witnessed a few works that explicitly addressed the communication challenges of IBNNs [15; 16; 17; 18]. However, since routing protocols within IBNNs for efficient data collection are at its infancy, therefore, more efforts are required. The presented work is also an effort towards realizing energy-efficient communication for prolonging the lifetime of IBNNs. In this work, we have considered the well-known fact that sensor node consumes their maximum energy during data transmission. Correspondingly, as mentioned before that NBSs consume about 10% of their available energy during one single transmission and accumulating energy consumption of all the NBSs in response to single request messages consequently results in almost 50% consumption of energy resources. The presented work considers this immense decrease in network energy and proposes an alternative approach for the energy-efficient data collection. In our proposed protocol, we have clustered NBSs into spatially correlated regions and instead of collecting data from all implanted NBSs, an optimum number of NBSs are selected.