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The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU’s parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods.

Handoff management is an indispensable component in supporting network mobility. The handoff situation raises while the Mobile Router (MR) or Mobile Node (MN) crosses the different wireless communication access technologies. At the time of inter technology handoff the multiple interface based MR can accomplish multihoming features such as enhanced availability, traffic load balancing with seamless flow distribution. These multihoming topographies greatly responsible reducing network delays during inter technology handoff. This article proposes a multihoming based Mobility management in Proxy NEMO (MM-PNEMO) scheme that considers benefits of using multiple interfaces. To support the proposed scheme design a numerical framework is developed that will be used to assess the performance of the proposed MM-PNEMO scheme. The performance is evaluated in the state-of-art numerical simulation approach focusing the key success metrics of signalling cost and packet delivery cost, that eventually scaling the total handoff cost. The numerical simulation result shows that the proposed MM-PENMO delightedly reduces the average handoff cost to 60% compared to existing NEMO Basic support protocol (NEMO-BSP) and PNEMO.

The transfer of critically ill patients from the operating room (OR) to the surgical intensive care unit (SICU) involves handoffs between multiple providers. Incomplete handoffs lead to poor communication, a major contributor to sentinel events. Our aim was to determine whether handoff standardization led to improvements in caregiver involvement and communication. A prospective intervention study was designed to observe thirty one patient handoffs from OR to SICU for 49 critical parameters including caregiver presence, peri-operative details, and time required to complete key steps. Following a six month implementation period, thirty one handoffs were observed to determine improvement. A significant improvement in presence of physician providers including intensivists and surgeons was observed (p = 0.0004 and p < 0.0001, respectively). Critical details were communicated more consistently, including procedure performed (p = 0.0048), complications (p < 0.0001), difficult airways (p < 0.0001), ventilator settings (p < 0.0001) and pressor requirements (p = 0.0134). Conversely, handoff duration did not increase significantly (p = 0.22). Implementation of a standardized protocol for handoffs between OR and SICU significantly improved caregiver involvement and reduced information omission without affecting provider time commitment.

Next-generation wireless networks (NGWN) consist of the integration of various technologies, such as Mobile ad-hoc networks (MANET), Wi-Fi, WiMAX, and LTE which are connected to the internet. Switching off the nodes among networks with same or different technology is handled by mobile IP. The determination of hand-off is not solely reliant on received signal strength, as relying solely on this metric could result in unnecessary hand-offs. Various factors, such as power consumption in communication, delay, traffic load, and network bandwidth, also play crucial roles in ensuring successful transmission. This paper introduces a seamless hand-off technique based on Markov processes (S-MSH), which takes into account different network properties that impact the Quality of Experience (QoE) for mobile terminals (MT) during communication. The proposed approach focuses on creating a Markov Decision Process (MDP) model for the system, considering user traffic requirements. The Q-learning algorithm is applied to the model to predict whether a hand-off is beneficial. An integrated similarity index-based approach, termed S-MSH, has been introduced to expedite the convergence rate of MSH. Simulation and numerical results demonstrate that the proposed approach surpasses the performance of the Network Priority Multicriteria Vertical Handover Decision Algorithm (NPMH) and the Simple Additive Weighing Algorithm (SAW) in terms of total reward and the number of handoffs.

In this paper, we have demoralized the transmission processing concerns of fog nodes and IoT device layer attack during the handoff (mobility) of IoT devices in the fog environment. A secure routed and handoff mechanism is proposed in order to avoid the attack by exploring the trust value and rating of each fog IoT and fog nodes/devices based on their communication behaviour. A trust manager is established between fog layer and IoT layer that keeps the record of all fog nodes in its look-up table and detects the malicious fog and IoT nodes. Further, the fog nodes computes rating of their service requested IoT layer and routed the services through highly trusted possible path. The proposed mechanism is validated against conventional security approaches over certain networking parameters.

Cognitive Radio-based Internet of Things (CR-IoT) is an emerging paradigm that combines the strengths of CR and the IoT to address spectrum scarcity and enhance IoT connectivity. This paper introduces a new Priority-Based Channel Allocation (PBCA) scheme with integrated queueing and dynamic retrial mechanisms for CR-IoT applications. The scheme effectively mitigates connection drops for low priority IoT (IoT-LP) data traffic in the network, where heterogeneous real-time and non-real time nodes opportunistically share licensed spectrum with primary users (PUs). Unlike conventional approaches that often sacrifice low-priority traffic under heavy load, the proposed scheme ensures fair and reliable access while preserving priority for delay-sensitive traffic. Spectrum handoff, connection request queueing, and a new retrial process are jointly employed to enhance quality of service. The system is modeled using a multi-dimensional continuous-time Markov chain and validated through extensive simulations. Results show that the proposed PBCA scheme eliminates connection drops for IoT-LP nodes, increases total channel utilization by up to 4.65%, and reduces connection handoff probability by 50.05% compared with the baseline Random DSA scheme.

Handoff events, in which mobile nodes switch between various network access points, frequently result in performance degradation in wireless networks. TCP-PRN (Path Recovery Notification), a method designed to lessen the negative effects of temporary link disconnections, was developed in response to this challenge. The study presents an improved PRN mechanism for wireless networks with the goal of reducing the performance degradation brought on by handoff events. The proposed approach uses Deep Q-Networks (DQN), Stacked Autoencoders (SAEs), and Convolutional Neural Networks (CNNs) to efficiently handle link disconnections and accurately predict handoff events. The mechanism is able to predict handoff events accurately by using CNNs, which makes proactive packet recovery strategies possible. Prediction accuracy is increased through the use of SAEs in feature extraction from network data. Sender adaptation based on anticipated handoff events and current network congestion conditions is made possible by DQN implementation. Furthermore, the decisions made for TCP-PRN congestion control are improved by hybrid optimization techniques, particularly the combination of Tuna Swarm and Whale Optimization of Tuna customized whale optimization (TCWO). Simulation results show notable improvements in network adaptability and reliability. With accurate handoff prediction and effective link disconnect management, the improved TCP-PRN mechanism performs better than existing approaches. Through effective handoff event management and decreased performance degradation, the approach greatly increases network reliability.

In order to enhance spectrum competence for successful data rates, future generation vehicle networks will be able to provide the method for effective data dissemination via diverse radio technologies. Higher mobility networks cause unnecessary handoffs since a vehicle regularly moves between various diverse networks. In this case, a subpar handoff algorithm exacerbates this issue by causing significant packet loss during frequent and erratic handoffs, commonly referred to as ping-pong handoffs. The proposed model Hybrid Cat Swarm optimization–TOPSIS Algorithm (HCSTA) is used to execute the vertical handoffs in urban VANET. Both mobility and network simulator are employed to demonstrate the effectiveness of the proposed techniques. The effectiveness of this work is demonstrated through the combined utilization of mobility and network simulators. Handoff occurrences are affected by the direction of moving vehicles and usage technology. In the Same direction, 150 vehicles experienced 6 handoffs due to longer dwell times, while those in the opposite direction encountered 11 handoffs. In the utility comparison, UMTS (Universal Mobile Telecommunications System) to Wi-Fi transitions face low handoffs compared to DSRC (Dedicated Short Range Communication) to WiMAX (Worldwide Interoperability for Microwave Access), in opposite directions. Utility rates of technologies like Wi-Fi (Wirelesss Fidelity), WiMAX, UMTS, and DSRC vary with speed as high, moderate and low, with WiMAX providing the most consistent performance. The packet delivery rate (0.78) decreases with higher vehicle densities 150 and speeds 85 Kmph due to increased network load and high bandwidth utilization compared to low speed scenarios. The overall network throughput increases from 38.9 to 39.9 Kbps with moderate vehicle densities, with a level noted at 85 and 115 vehicles for the 50–60 km/h speed category, indicating optimal load and moderate bandwidth efficiency.

Vehicular Ad-hoc Networks is a interesting and key research area which have more number of issues which needs immediate attention when moving towards intelligent transportation system. In this research work, we have proposed a new mechanism of IP scheme which highly helps in handover of vehicle from one RSU to RSU. RSU is playing a key role in delivering of one vehicle from its range to other RSU. Here, how Quality of service can be handled when unexpected handover happens between RSU. So, A new mechanism to predict the vehicles which may enter into handoff process based on its mobility and GPS location. This proposed IP scheme and the predicted handoff is tested through two protocols called Weighted Clustering algorithm and also with Mutated K-Means algorithm to test the performance. The results shows better results to maintain the QoS demands of the vehicle when the handoff is predicted in prior.

The co‐channel interference and frequent handoff are two important issues that have a great impact on the spectrum and energy efficiency in the non‐orthogonal multiple access (NOMA) with successive interference cancellation (SIC) in the small cell networks (SCN). This paper investigates the performance of SIC decoding order based on the users’ channel state information (CSI) and random SIC decoding order under the perfect and imperfect conditions for the handoff optimization in SCNs with NOMA. The effect of SIC decoding order in the joint base station association and power control problem is studied by considering the motion of mobile users (MUs). Two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs, under the maximum allowable transmit power and minimum average‐rate constraints. Two objectives are combined into a single‐stage optimization problem through the weighted sum method. The formulated problem is solved using a game theory‐based algorithm and primal decomposition theory. The simulation results show that changing the SIC decoding order can lead to performance improvement in the uplink NOMA‐ based SCNs. Also, it is shown that the proposed algorithm can significantly reduce the frequent handoffs in small cell networks. This paper investigates the effect of successive interference cancellation decoding order in the joint base station association and power control problem by considering the motion of mobile users in the in the small cell networks with non‐orthogonal multiple access.