Energy-efficient Temperature-aware Data Aggregation Scheme For Intrabody Nanonetworks

The emergence of nanotechnology has brought cutting-edge advancements in the development of nanoscale devices.Nanomaterial based nanoscale devices have huge potential for cellular level monitoring,disease diagnosis,damage recovery,and treatment.Intrabody Nanonetwork(Intra-BNN)is composed of these integrated nanoscale devices,which are 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.However,the unique feature constraints of these nanoscale devices(such as inadequate energy,storage,transmission range,and computational resources)are the primary challenges that restrict the direct adaptation of existing Wireless Body Area Networks(WBAN)for Intra-BNNS.In addition to the design constraints,molecular absorption phenomena,triggered by EM waves emission in the Terahertz band(THz)also challenges the Intra-BNNs in terms of temperature rise.In this presented study,we aim to explicitly address the feature and thermal challenges of Intra-BNNs to improve the data collection and routing aspects of Intra-BNNs.The primary motivation of designing an efficient data retrieval mechanism that can collect detected data from resource constraint Nano Sensors(NSs)with minimum resource consumption and temperature rise leads to the major contributions of the proposed schemes.In this study,we design energy-efficient and temperature-aware data aggregation and routing schemes that can improve the lifetime or Intra-BNNs and control temperature rise.Initially,we design two different energy-efficient data aggregation schemes that focus on energy related constraints of Intra-BNNs for improved network lifetime.The first data aggregation approach is based on Feedforward Neural Networks(FFNNs)that integrates the attributes of artificial intelligence to boost the computational intelligence of Intra-BNNs for prolonging the network lifetime.In the proposed FFNN-based data aggregation scheme,data division and labelling are performed to transmit detected information using two different types of packets with different sizes to transmit critical information with minimum energy consumption and reduced redundant data transmission.FFNN-based periodic data transmission exploits the fitness function approximation characteristics of FFNN to improves the activation accuracy of the NSs for increasing the transmission probability of critical information with minimum energy consumption and delay.Whereas our proposed event-driven data transmission also ensures the transmission of high priority data with minimum delay and storage overhead.The detailed evaluation and comparison of our proposed framework with three existing schemes conducted using the Nano-Sim tool highlight that our proposed scheme performs 50%-60%better than state-of-the-art schemes in terms of residual energy,delay,and packet loss.The second data aggregation approach is based on the temporal correlation mechanism.The proposed data aggregation scheme takes advantage of temporal correlation during periodic data transmission for avoiding redundant data transmission overhead.The temporally correlated readings are suppressed and aggregated using the Exponential Weighted Moving Average(EWMA)approach to combine the previous suppressed reading with the fresh readings to inform nanorouter about the past condition of physiological parameters.The simulations carried out using the NanoSIM tool validates that suppressing the transmission of temporally correlated data can save significant energy resources.Later,we propose novel temperature-aware routing protocols that explicitly addresses the thermal related constraints of Intra-BNNs to control temperature rise.The proposed routing schemes aim at stabilizing the temperature in the whole network by avoiding congestion and preventing temperature rise in the heated regions.In the first temperature-aware routing scheme,nanorouters estimate the temperature increase in their region and excludes data collection from the hotspots areas to avoid temperature rise.Moreover,during data collection,NSs selection is also optimized based on data freshness to enable reporting of a more accurate state of physiological parameters with minimized antenna radiation exposure time.The temperature increase analysis provided in this work can also be used for safety health assessment in medical applications.We have evaluated the performance of our proposed scheme by conducting extensive simulations using the Nano-SIM tool.In addition,we compare our proposed temperature-aware routing scheme with the flooding scheme and Thermal Aware Routing Algorithm(TARA)to gain further insights into our temperature-aware routing protocol.The results obtained confirm that our protocol ensures safer intrabody routing and traffic distribution in different regions to normalize temperature rise,avoid congestion,and reduce the communication delay.In the second temperature-aware routing protocol,we introduce the Simulated Annealing(SA)algorithm for designing a temperature-aware routing scheme with minimum complexity.Our proposed SA algorithm-based routing scheme ensures the optimal global selection of NSs for data reporting avoids hotspot formation and temperature rise.The obtained results show that our proposed SA algorithm-based routing scheme outperforms the state-of-the-art schemes with the minimum complexity.Finally,we propose a temperature-aware energy-efficient routing scheme for collaborating controlling excessive energy consumption,and avoiding hotspot formation.In the presented work,we propose a temporal correlation-based data decision approach that allows only those NSs 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 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.