An intrabody nanonetwork (IBNN) is composed of nanoscale (NS) devices, implanted inside the human body for collecting diverse physiological information for diagnostic and treatment purposes. The unique constraints of these NS devices in terms of energy, storage and computational resources are the primary challenges in the effective designing of routing protocols in IBNNs. Our proposed work explicitly considers these limitations and introduces a novel energy-efficient routing scheme based on a fuzzy logic and bio-inspired firefly algorithm. Our proposed fuzzy logic-based correlation region selection and bio-inspired firefly algorithm based nano biosensors (NBSs) nomination jointly contribute to energy conservation by minimizing transmission of correlated spatial data. Our proposed fuzzy logic-based correlation region selection mechanism aims at selecting those correlated regions for data aggregation that are enriched in terms of energy and detected information. While, for the selection of NBSs, we proposed a new bio-inspired firefly algorithm fitness function. The fitness function considers the transmission history and residual energy of NBSs to avoid exhaustion of NBSs in transmitting invaluable information. We conduct extensive simulations using the Nano-SIM tool to validate the in-depth impact of our proposed scheme in saving energy resources, reducing end-to-end delay and improving packet delivery ratio. The detailed comparison of our proposed scheme with different scenarios and flooding scheme confirms the significance of the optimized selection of correlated regions and NBSs in improving network lifetime and packet delivery ratio while reducing the average end-to-end delay.Sensors 2020, 19, 5526 2 of 26 diagnosis 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 c...
The introduction of sensor technology in our daily lives has brought comfort, convenience, and improved health over the past few decades. Technological advances further expanded the use of medical sensors by reducing their size and costs. Medical sensors improve the intelligence and capabilities of healthcare services including, remote health monitoring, surgical procedures, therapy, and rehabilitation. We present a comprehensive review of medical sensors in the last 50 years focusing on their deployment in healthcare applications. The review also discusses the role of Internet of Things (IoT) technology in enhancing the capabilities of sensor technologies for the healthcare domain. Moreover, we also investigate the benefits and challenges of various integrated architectures which have been proposed recently to seamlessly integrate heterogeneous medical sensors with emerging technologies and paradigms that include edge, Mobile Cloud Computing (MCC), fog, and cloud computing technologies. Finally, we identify future challenges that must be addressed to achieve the maximum potential and benefits of medical sensor technologies and ultimately provide robust, scalable, reliable, and cost-effective healthcare delivery.
An Intrabody Nanonetwork (IBNN) is composed of integrated nanoscale devices, implanted inside the human body to collect diagnostic information and tuning medical treatments. The non-invasive continuous monitoring and precision of these nanoscale devices in the diagnostic of diverse diseases is improving advanced monitoring, therapeutic, and telemedicine services. The unique feature constraints of these nanoscale devices (such as inadequate energy, storage, and computational resources) along with the molecular absorption thermal challenges due to Electromagnetic (EM) communication in the Terahertz band (THz) are the primary limitations in enabling efficient routing in IBNNs. Our proposed protocol explicitly addresses these challenges and proposes a routing scheme, which enables temperature-aware energy-efficient data transmission to avoid hotspot formation and controlled energy consumption. Furthermore, our proposed temporal correlation based data decision approach allows only those Nano Bio Sensors (NBSs) to transmit the periodic data packets that have updated information for avoiding unnecessary energy consumption and antenna radiation exposure on biological cells. The presented work also considers the instant data retrieval requirement of the healthcare system and introduces an on-demand data retrieval approach that ensures instant transmission of updated information to the healthcare system. The effectiveness of our proposed scheme is evaluated by comparing it with the flooding scheme and thermal-aware routing algorithm (TARA) using the Nano-SIM tool. The results obtained from extensive simulations validate that our proposed protocol achieves 75%-85 % low temperature rise and improved network lifetime. INDEX TERMS Energy-efficient, intrabody nanonetworks, molecular absorption noise, routing protocol, terahertz, temperature-aware.
An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme. INDEX TERMS Artificial bee colony algorithm, nano router, exponential weighted moving average, intra-body nano-networks, routing protocol.
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