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Top1. Introduction
ATMA, Queen-MAC and advertisement MAC are simulated in (Swain et al., 2017) and obtained better performance in advertisement MAC with PDR about 90%. Spatial reuse of the channel is improved by (3-29)% in distributed MAC compared to multi-channel MAC (Wu, C. M. et al., 2017), throughput improved by 8.9% and outage probability of primary users improved from 55% to 41%. Energy efficiency is 84% with optimizing the link quality and energy efficiency is 90% by optimizing BER, data packet size (L. T. et al., 2011). Unscented summation in information consensus filter (ICF) performs better than the scented ICF and gives a chance of 99.7% for the existence of true state for the node (Yao, P. et al., 2019).
In harsh environments, the possibility of change in node positions and link quality are more, a link aware and score based protocol is proposed to increase lifetime (Manikandan, A. et al., 2023). A hill climbing algorithm with distributed and environment driven conditions is proposed to have trade-off between energy and lifetime in the network (Yaman, A. et al., 2023). Cryptosystem with public key and homomorphism encryption controls the data security and authentication in wireless networks (Kalyani, G. et al., 2022). In vehicular ad hoc networks voice over internet plays major role. It also faces challenge to transmit voice with high quality. Packet loss, jitter and delays are to be addressed for high QoS (Dafalla, M. E. M . et al., 2022). Q learning algorithm which is distributed in satellite networks and improving its learning efficiency to improve delay, throughput and sum rate (Rwandamuriye, F. X. et al., 2023). Speed of transmission is always a concern in underwater optical networks for which TDMA with distributed nature and cross layer cluster approach with multi-slots are enforced (Huang, J. et al., 2023). Bio medical sensor nodes demand allocated or dedicated time slots as they are heterogeneous themselves and it is difficult to sense and monitor a patient information in the long run to capture information in contention free phases (Masud, F. et al., 2023).
Wireless traffic and the abnormality are addressed by using wireless sniffers which are assigned for each channel in a multi-channel network and hence capture data packets in spite of unreliability in channel conditions (Bahrami, E. et al., 2023). Wireless sensor networks are made of sensor nodes which are self configured and preferable to have smart computing for various applications and in various fields. Node energy had been always a challenge for which density and scalability of nodes play major role (Santhosh, G. et al., 2023). Channel collisions are resolved using two coding techniques in two layers namely LDPC codes and algebraic codes. Low complexity in the algorithm and smaller gap to achieve the expected capacity using these codes (Macintosh, J. et al., 2023). Aerial base stations draw attraction in recent times for cellular networks. These aerial networks are drone carried and must be maintained and monitored accordingly which need additional effort like taking care of line of sight problems are introducing more number of drones (Arribas, E. et al., 2023). Cloud technology with IoT connected devices like detectors, RFID, controllers and 3G networks by improving processing and capacity to improve data skew rate and decrease complexity of the interconnections (Raghavendar, K. et al., 2023).
Solutions for techo-economic analysis is given using present value, traffic intensity for fibre optic wireless networks (Breskovic, D. et al., 2019). A discount of (10-15) % is given for corporate projects, (25-30) % discount for high start-ups and 10% discount for remaining projects. Adaptive Q learning shows 41% lesser delay and 51% more residual energy compared to other methods (Alarifi, A. et al., 2019). Faults in network on chip (NoCs) are addressed using distributed and adaptive routing algorithms (Nehnouh, C. et al., 2019). This method is deadlock free and does not use any virtual channels or routes. Initially selects the route based on state of the link, later based on traffic and congestion, it selects the adjacent routers in second step. Faults are reduced to 0% and latency reduced by 19.4% in this distributed and adaptive routing algorithm.