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Object handover is one of the fundamental tasks of service robots. This paper focuses on a robot-to-human object handover controller applied to a domestic service robot. People in need often have manual operation constraints caused by different body postures or declined physical functions. The preplanned handover strategy used in previous studies potentially increases their cognitive burden and makes it difficult to meet their needs for flexible real-time control and intuitive interaction. It also remains challenging to deal with sensing and prediction errors, motion planning and coordination. The robot no longer provides assistance after an object is delivered to a preplanned handover location, and the receiver needs to make extra efforts to get the object released. Therefore, inspired by a human study, a handover controller is designed based on a manually guided handover strategy that solve problems from grasp adjustment to object release in dynamic retraction motion. Flexible control and intuitive interaction are enabled through motion with continuous support from the robot. Without additional sensors or modeling learning, changes in motion and energy consumption are taken into account to explore an appropriate release timing. The results of robot-to-human handover experiments and the user studies indicate that the receiver can freely pull an object to a suitable location as desired and smoothly obtain the object's control to complete a subsequent task using the proposed controller with proper release timing. [ABSTRACT FROM AUTHOR]

The rapid evolution of wireless communication systems necessitates advanced handover mechanisms for seamless connectivity and optimal network performance. Traditional algorithms, like 3GPP Event A3, often struggle with fluctuating signal strengths and dynamic user mobility, leading to frequent handovers and suboptimal resource utilization. This study proposes a novel approach combining Federated Learning (FL) and Long Short-Term Memory (LSTM) networks to predict Reference Signal Received Power (RSRP) and the strongest nearby Reference Signal Received Power (RSRP) signals. Our method leverages FL to ensure data privacy and LSTM to capture temporal dependencies in signal data, enhancing prediction accuracy. We develop a dynamic handover algorithm that adapts to real-time conditions, adjusting thresholds based on predicted signal strengths and historical performance. Extensive experiments with real-world data show our dynamic algorithm significantly outperforms the 3GPP Event A3 algorithm, achieving higher prediction accuracy, reducing unnecessary handovers, and improving overall network performance. In conclusion, this study introduces a data-driven, privacy-preserving approach that leverages advanced machine learning techniques, providing a more efficient and reliable handover mechanism for future wireless networks. [ABSTRACT FROM AUTHOR]

A proposed solution to address increasing scarcity of operating room (OR) nurses is the deployment of robotic scrub nurses (RSNs). One of the primary responsibilities of these robots is the handover of surgical instruments to the surgeon. Despite this tasks' apparent simplicity, it requires great precision and timing, while seamless execution is essential for the surgical workflow. This paper outlines the unique challenges of the instrument handover process between a RSN and a surgeon taking place during laparoscopic surgery. After customizing existing solutions from different domains to meet the specific needs of laparoscopic surgery, we introduced these solutions to medical professionals in a simulated OR scenario, and performed a utility analysis to identify the most suitable solutions. [ABSTRACT FROM AUTHOR]

Beamforming using highly directional antennas is inevitable to compensate the propagation losses and enable communication in the millimeter wave (mmWave) band. However, this directivity makes mmWave links fragile and sensitive to environment dynamics and blockage. Consequently, initial access and management of the mmWave links in dynamic environments turns to be a big challenging issue, especially in dense network scenarios. In particular, a massive amount of beamforming training (BT) overhead with high delay is needed to establish the mmWave link between a user device (UD) and the deployed small cells (SCs). The consequences are become more severe in case of mobility, since frequent handovers (HOs) between different beams/SCs is needed to preserve the connection with moving UDs. Inspired by the fact that directionality makes the mmWave communications to be geometry and location driven, this paper proposes a geometry-based schemes for the initial access and management of mmWave links. In the proposed schemes, geometric information of the target area including the UDs locations, SCs coordinates and the target area maps are exploited to alleviate the complexity of the exhaustive-based BT. The high impact of the proposed mmWave link initial access and management schemes over the conventional ones in different terms such as BT complexity and total energy consumption rates is proved via simulation analysis. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) refer to the communication network, consisting of different types of nodes connected through communication networks deploying diverse radio access technologies like LTE, Wi‐Fi, Zigbee, and Z‐wave, and using different communication protocols and operating frequencies. Vertical handover, is the process of switching a mobile device from one network type to another, such as from a cellular network to a Wi‐Fi network, and is critical for ensuring a seamless user experience and optimal network performance, within the handover process handover triggering estimation is one of the crucial step affecting the overall performance. A mathematical analysis is presented for the handover triggering estimation. The performance evaluation shows significant improvement in the probability of successful handover using the proposed handover triggering condition based on speed, distance, and different mobility models. The handover triggering condition is optimised based on the speed of the mobile node, handover completion time, and the coverage range of the current and the target networks of the HetNet node, with due consideration of the mobility model. [ABSTRACT FROM AUTHOR]

In base-station-based underwater wireless acoustic networks (B-UWANs), effective handover mechanisms are necessary to ensure seamless data services for mobile nodes such as autonomous underwater vehicles (AUVs). Unlike terrestrial base stations (BSs), moored buoy BSs in B-UWANs experience motion responses due to wave loads under environmental conditions, posing unique challenges to the handover process. This study examines how BS motion affects handover decision errors, which arise when AUVs incorrectly initiate handovers to unintended BSs due to BS motion. By utilizing the AUV–BS distance as a handover triggering parameter, our analysis reveals a significant increase in decision errors within the overlapping regions when both the current and target BSs are in motion, especially when moving in the same direction. In addition, these errors intensify with the magnitude of BS motion and are exacerbated by smaller BS network radii. Based on these simulation results, we present an analytical framework that not only measures the influence of BS motion on the AUV–BS distance but also provides strategic insights for refining underwater handover protocols, thereby enhancing operational reliability and service continuity in B-UWANs. [ABSTRACT FROM AUTHOR]

Background: Surgical handover is associated with a significant risk of care failures. Existing research displays methodological deficiencies and little consensus on the outcomes that should be used to evaluate interventions in this area. This paper reports a protocol to develop a core outcome set (COS) to support standardisation, comparability, and evidence synthesis in future studies of surgical handover between doctors. Methods: This study adheres to the Core Outcome Measures in Effectiveness Trials (COMET) initiative guidance for COS development, including the COS-Standards for Development (COS-STAD) and Reporting (COS-STAR) recommendations. It has been registered prospectively on the COMET database and will be led by an international steering group that includes surgical healthcare professionals, researchers, and patient and public partners. An initial list of reported outcomes was generated through a systematic review of interventions to improve surgical handover (PROSPERO: CRD42022363198). Findings of a qualitative evidence synthesis of patient and public perspectives on handover will augment this list, followed by a real-time Delphi survey involving all stakeholder groups. Each Delphi participant will then be invited to take part in at least one online consensus meeting to finalise the COS. Ethics and dissemination: This study was approved by the Royal College of Surgeons in Ireland (RCSI) Research Ethics Committee (202309015, 7th November 2023). Results will be presented at surgical scientific meetings and submitted to a peer-reviewed journal. A plain English summary will be disseminated through national websites and social media. The authors aim to integrate the COS into the handover curriculum of the Irish national surgical training body and ensure it is shared internationally with other postgraduate surgical training programmes. Collaborators will be encouraged to share the findings with relevant national health service functions and national bodies. Discussion: This study will represent the first published COS for interventions to improve surgical handover, the first use of a real-time Delphi survey in a surgical context, and will support the generation of better-quality evidence to inform best practice. Trial registration: Core Outcome Measures in Effectiveness Trials (COMET) initiative 2675. http://www.comet-initiative.org/Studies/Details/2675. [ABSTRACT FROM AUTHOR]

In the B5G/6G network, the deployment of small cells increased to keep up with the growth of mobile traffic. This deployment will increase the number of handovers (HOs) between cells. Ping-pong handover (PPHO) and radio link failure (RLF) are considered the two major problems in HO that may occur. So, the challenge is to set the handover control parameters (HCPs) carefully to find out the proper HO decision that should be appropriate to the environmental constraints. Therefore, in our paper, we propose an adaptive HCPs algorithm that adapts to environmental constraints. In addition, the proposed algorithm will have immunity to RLF and will significantly minimize the amount of PPHO compared to other workers. In the simulation results, our proposed model is evaluated using two frequency plans. By using frequency plan 1, the user mean throughput increased from 270 Kbps to 281 Kbps when the serving cell was fully loaded. By using frequency plan 2, the user mean throughput increased from 6 Mbps to 20 Mbps when the serving cell was fully loaded. In addition, the amount of ping-pong handover between overlapped small cells decreased and will not exceed one PPHO compared to another literature model. [ABSTRACT FROM AUTHOR]

3D stratum roaming can visualize the complex geomorphology, underground structure and stratum distribution of geotechnical engineering. Conventional 3D stratum roaming technology is generally aimed at roaming outside the 3D stratum model but rarely roams inside the 3D stratum model, and the efficiency of switching between external and internal roaming is low. In practical engineering, especially in geological and geotechnical engineering, the underground structure and stratum situation are critical. Therefore, focusing on this problem, this paper adopts a three-dimensional roaming engine to connect the inside and outside of a three-dimensional model. Based on an internal and external boundary identification method, the combination of external roaming and internal roaming of the three-dimensional stratum model is implemented by using a stratum virtual surface, and lightweight loading is carried out by controlling the stratum virtual surface to establish the internal and external roaming of the 3D stratum model, and to give full play to the advantages of lightweight loading to provide more intuitive and comprehensive geological information for the project. [ABSTRACT FROM AUTHOR]

Summary: Mobility management allows the networks to track down a mobile user's location for sending datagrams and to retain the connection when the user changes its location points repeatedly. Whenever a node switches between one base station and another, the connectivity is maintained through a process called handoff management. For real‐time traffic implementations in wireless network communications, handover performance is essential. A mobile node is blocked from transmitting and receiving a datagram for a longer time as the handover delay increases. Unacceptable handover delays are common in real‐time communication services. To guarantee smooth connectivity and continuous provision of services, effective handoff methods are required. The handoff efficiency of network‐based distributed mobility management (DMM) is studied extensively in this article, along with a comparison to PMIPv6. We specifically investigate how handover delay, session recovery, and packet loss are influenced by cell radius. From the analytical and simulation findings this paper concludes that network‐based DMM performs better than PMIPv6. [ABSTRACT FROM AUTHOR]

Heterogeneous networks are needed to meet user demands as wireless network demand rises. Network mobility management is crucial. Mobility management challenges are related to handover solutions to decrease call/packet losses in such networks. The handover is one of the most critical parts of mobility management in the Long-Term Evolution of Advanced (LTE-A) system, which relies on handover procedures to improve quality, coverage, and service in the existing network. The LTE-A future wireless communication networks consist of various femtocells, microcells, and macrocells. Therefore, designing the appropriate mechanism to perform handovers among different cells is a challenging research problem. We propose a novel handover mechanism called multi-objective handover using swarm intelligence algorithm (MH-SIA) for the future wireless communication system. MH-SIA is made of two novel features multi-objective handover and SIA for handover process optimization. The multi-objective trust parameters of each User's Equipment are computed to perform the handover decision-making and target cell selection using the SIA. The computed trust parameters are utilized as the modified fitness function in Differential Evolution (DE) optimization technique. Due to the fast convergence of DE, it performs computationally efficient handover operations. The multi-objective trust parameters are utilized in handover decision-making and target cell selection to improve network performances with minimum handover latency. The experimental result of MH-SIA reveals the efficient performance compared to underlying methods. [ABSTRACT FROM AUTHOR]

In recent days, the emerging ideas of hybridized network are used, which isbased on light fidelity (LiFi) and wireless fidelity (WiFi). The merging concept of LiFi and WiFirequires huge coverage area and reduce traffic overload. The classical Load Balancing (LB) technique aids to optimize the network throughput. Considering the handoveroverhead issue, this paper devises an effective access point selection (APS) and LB technique. The hybrid Lifi–Wifi is employed and location prediction is executed with Deep Long Short Term Memory (Deep LSTM). After accomplishing the location prediction, APS is carried out using the proposed Sewing Training Inspired Optimization (STIO), where the objective function considersSignal Noise Ratio and Shannon capacity. Further, the joint optimization of LB and handover is performed using STIO, where the user is handover to the selected AP. Finally, the multipath transmission control protocol is employed to transit the data. The proposed STIO-Deep LSTM outperformed with a smaller delay of 0.113 ms, ahigh energy efficiency of 0.097 Mbits/joules, asmall handover occurrence of 6.100, and ahigh network throughput of 0.905. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been emerging as an essential aspect of smart applications that are presently there in wireless communication. Gateways play an important role in IoT as they are capable of filtering the data that is being communicated to the cloud platforms. The data is sent via intermediary gateways utilizing a variety of wireless technologies to application servers located in the IoT cloud. Using standard routing strategies to link millions of IoT devices to the internet results in gateway congestion and overload resulting in decrease in IoT network throughput. But for cost-effectiveness, optimal gateway placement is required and this can be possible by integrating Solution Specific Gateways (SSGs) thus it creates a multi-technology gateways management scenario. In this paper, an Enhanced Multi-technology Gateway Management (EMTGM) scheme has been proposed based on the efficient selection of SSGs along with context aware handover decision making based on multiple objectives. The proposed scheme is further strengthened as both AHP and TOPSIS concepts are integrated for effective decision making which makes it a more efficient one. The proposed framework is simulated on Mobisim and its effectiveness has been analyzed with respect to existing approaches under the network parameters like End to end Delay, Packet Delivery Ratio, Residual Energy, Energy Consumption and Throughput by varying the arrival tasks, node count and simulation time. From the results, it is inferred that the proposed scheme outperforms existing schemes and thus is a quite effective one. [ABSTRACT FROM AUTHOR]

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions. [ABSTRACT FROM AUTHOR]

To construct a femtocell based ultra-dense network (UDN) is the important way to satisfy the ever-growing data traffic demands in the next generation mobile networks. However, it causes frequent handovers (HOs), thus leading to negative consequences such as increased power consumption and poor quality of service (QoS). This paper represents a research on the uplink handover (UL-HO) being watched recently and proposes a new method to further improve the energy efficient and QoS. First, we confirm the UL-HO mechanism, formulate the power consumption caused by UL-HO and compare downlink handover (DL-HO) with UL-HO in terms of the performance parameters including the power consumption, handover rate (HOR), handover failure rate (HOFR), ping-pong rate (PPR), delay, packet loss rate (PLR) and throughput during handover (HO). Then, it is proved through simulation that the reliable target cell determination scheme considering uplink reference signal received power (UL-RSRP), available bandwidth, direction of user equipment (UE), and admission control is lower in HOR, HOFR, PPR, power consumption, delay and PLR. In particular, we propose a new algorithm to determine the reasonable target cell considering UL-RSRP, available bandwidth and moving direction of UE, thus reducing HOR and PPR. The improved UL-HO scheme can be implemented easily by adding some functions besides the upper-layer protocol, while it can provide a lot of benefits such as reduced power consumption, delay, frequent HOs resulting in reducing HOR and PPR. Accordingly, this can be introduced into 5G as a HO scheme. [ABSTRACT FROM AUTHOR]

We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short‐term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine‐tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root‐mean‐square error of only 2.12%. [ABSTRACT FROM AUTHOR]

The rapidly growing research on the accessibility of digital technologies has focused on blind or visually impaired (BVI) users. However, the field of human–robot interaction has largely neglected the needs of BVI users despite the increasing integration of assistive robots into daily life and their potential benefits for our aging societies. One basic robotic capability is object handover. Robots assisting BVI users should be able to coordinate handovers without eye contact. This study gathered insights on the usability of human–robot handovers, including 20 BVI and 20 sighted participants. In a standardized experiment with a mixed design, a handover robot prototype equipped with a voice user interface and haptic feedback was evaluated. The robot handed over everyday objects (i) by placing them on a table and (ii) by allowing for midair grasping. The usability target was met, and all user groups reported a positive user experience. In total, 97.3% of all handovers were successful. The qualitative feedback showed an appreciation for the clear communication of the robot's actions and the handover reliability. However, the duration of the handover was seen as a critical issue. According to all subjective criteria, the BVI participants showed higher variances compared to the sighted participants. Design recommendations for improving robotic handovers equally supporting both user groups are given. [ABSTRACT FROM AUTHOR]

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN's identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay. [ABSTRACT FROM AUTHOR]

The current trend of people having their multimedia-capable wireless and mobile devices roam within highly urbanized areas (i.e., smart cities) has led to the wide deployment of overlapping heterogeneous wireless network technologies. In this regard, various challenges have emerged, such as the number of mobile devices that connect or disconnect a wireless network domain, the diversity of time of their connections, frequent handovers as mobile devices frequently change their locations, the heterogeneity of wireless network technologies, cooperation challenges between the overlapping heterogeneous wireless network technologies, the increasing volume of multimedia traffic, security and privacy issues, and many more. This paper focuses on the deployment of a routing-based distributed mobility management (DMM) scheme to address the constraints and limitations of centralized wireless architectures for smart cities. The comparative analysis with centralized mobility management solutions shows significant alleviation in performance as to handover latency and packet losses, thus providing seamless handovers to maintain quality of service (QoS) for multimedia services. [ABSTRACT FROM AUTHOR]

The intensive deployment of sixth-generation (6G) base stations is expected to greatly enhance network service capabilities, offering significantly higher throughput and lower latency compared to previous generations. However, this advancement is accompanied by a notable increase in the number of network elements, leading to increased power consumption. This not only worsens carbon emissions but also significantly raises operational costs for network operators. To address the challenges arising from this surge in network energy consumption, there is a growing focus on innovative energy-saving technologies designed for 6G networks. These technologies involve strategies for dynamically adjusting the operational status of base stations, such as activating sleep modes during periods of low demand, to optimize energy use while maintaining network performance and efficiency. Furthermore, integrating artificial intelligence into the network's operational framework is being explored to establish a more energy-efficient, sustainable, and cost-effective 6G network. In this paper, we propose a small base station sleeping control scheme in heterogeneous dense small cell networks based on federated reinforcement learning, which enables the small base stations to dynamically enter appropriate sleep modes, to reduce power consumption while ensuring users' quality-of-service (QoS) requirements. In our scheme, double deep Q-learning is used to solve the complex non-convex base station sleeping control problem. To tackle the dynamic changes in QoS requirements caused by user mobility, small base stations share local models with the macro base station, which acts as the central control unit, via the X2 interface. The macro base station aggregates local models into a global model and then distributes the global model to each base station for the next round of training. By alternately performing model training, aggregation, and updating, each base station in the network can dynamically adapt to changes in QoS requirements brought about by user mobility. Simulations show that compared with methods based on distributed deep Q-learning, our proposed scheme effectively reduces the performance fluctuations caused by user handover and achieves lower network energy consumption while guaranteeing users' QoS requirements. [ABSTRACT FROM AUTHOR]

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Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this article is to analyze traffic levels, aiming to optimize traffic management and handling. This article considers the two most popular CAC mechanisms: the resource reservation mechanism and the threshold mechanism. It presents an analytical approach to occupancy distribution and blocking probability calculation in 5G mobile networks, incorporating connection handoff and CAC mechanisms for managing multiple traffic streams generated by multi-service sources. Due to the fact that the developed analytical model is an approximate model, its accuracy was also examined. For this purpose, the results of analytical calculations of the blocking probability in a group of 5G cells are compared with the simulation data. This paper is an extended version of our paper published in 17th ConTEL 2023. [ABSTRACT FROM AUTHOR]

Massive MIMO networks are a promising technology for achieving ultra-high capacity and meeting future wireless service demand. Massive MIMO networks, on the other hand, consume intensive energy. As a result, energy-efficient operation of massive MMO networks became a requirement rather than a luxury. Many NP-hard concavity search algorithms for optimal base station switching on-off scheme have been developed. This paper demonstrates the formulation of massive MIMO networks energy efficiency as a constrained variational problem. Our proposed method solution's uniqueness and boundedness are demonstrated and proven. The developed system is a total energy optimization problem formulation. Furthermore, the order in which the base stations are switched on and off is specified for minimal handover overhead signaling and fair user capacity sharing. Results showed that variational optimization yielded optimal base station switching on and off with considerable energy saving achieved and maintaining the user capacity demand. Moreover, the proposed base station selection criteria provided suboptimal handover overhead signaling. [ABSTRACT FROM AUTHOR]

The background observation of the polar region is used to develop the requirements with regard to low carbon and no pollution for scientific research. Polar robots have certain advantages over human beings, but their long‐term residence still faces the problems of energy supply and autonomous control. To solve this problem, a hybrid wind‐ and electricity‐driven polar roaming robot is presented and includes the consideration of low carbon and energy savings. It innovatively uses wind energy as the main driving force and is assisted by electric energy, which is used when wind energy is not sufficiently available. In the ideal scenario, compared with traditional pure electric‐driven robots, it can save up to approximately 3/4 of energy. In this paper, the design principle is described, then the sail structure and some innovative mechanisms of the robot are designed, and the theoretical analysis and simulation of the mechanisms are carried out. The innovative mechanism enables good motion control under wind and electric drives, as well as the switching of the drive modes. Of particular importance, the aerodynamic performance of the sail structure is analyzed, and the optimal control scheme for sailing the robot is proposed. Then, a prototype of the polar robot is developed, the structure rationality and principle feasibility of which are verified in experiments, and the energy‐saving effect is confirmed by calculation. [ABSTRACT FROM AUTHOR]

Mobile wireless networks encounter a critical challenge in maintaining uninterrupted connectivity during node movement and network disruptions. Analytical engines are tailored to assess both node-level parameters like movement and communication patterns, as well as network-level aspects such as bandwidth and signal strength indicators. These analyses inform a decision engine that facilitates node handovers between networks, guided by specific parameters and constraints based on prevailing network conditions. This approach optimizes network connectivity, improving essential Quality of Service (QoS) measures like reduced delay, increased throughput, minimized energy consumption, and enhanced packet delivery ratio. Ensuring smooth network transitions requires advance notification to the target network, allowing preparations for incoming nodes and enhancing the likelihood of uninterrupted communication. However, crafting an effective pre-intimation engine often involves complexity, potentially impacting QoS metrics. To address this, our paper introduces a metaheuristic handover model utilizing network augmentation and game theory, deploying incremental learning to streamline handovers' complexities. Across various heterogeneous network scenarios, this model showcases a 10% boost in energy efficiency, 15% higher throughput, an 8% delay reduction, and a 3% improvement in packet delivery ratio compared to standard models. Ongoing evaluations aim to deepen insights into the model's performance across diverse network scenarios. Future research will focus on integrating additional network parameters and optimizing the learning algorithm for further efficiency gains." [ABSTRACT FROM AUTHOR]

The continued increase in several mobile applications forces to replace existing limited spectrum indoor radio frequency wireless connections with high-speed ones. Visible light communications (VLC) technology has gained prominence in the development of high data rate transmission for fifth-generation networks. In optical wireless communications, light-emitting diode (LED) transmitters are used in applications that desire mobility as LED divergence enables larger coverage. Since each VLC access point covers a small area, handovers of mobile users are inevitable. Wavelength division multiplexing (WDM) can be used in VLC systems to tackle the above issue and to meet the increasing demand for indoor connectivity with high bit rates. In this paper, a new system architecture for WDM with coded modulated optical in orthogonal frequency division multiplexing (OFDM) VLC system in conjunction with red, green, blue, and yellow (RGBY) LEDs is proposed to reduce the impact of random receiver orientation of indoor mobile users over VLC downlink channels and improves the system's bit-error-rate (BER) performance. Simulation results show that the proposed method is not affected by the user's mobility and hence it performs better than other approaches, in terms of BER for all scenarios and at all positions. This study reveals that using WDM-OFDM-VLC with RGBY LEDs to construct a VLC system is very promising. [ABSTRACT FROM AUTHOR]

As the popularity of mobile devices and services continues to increase, the demand for more efficient and effective algorithms to manage network utility is increased. Here, we aim to optimize Network Bandwidth Utilization (NBU) by managing user equipments (UEs) and base stations (BSs) association in 5G ORAN architecture. We consider the network total throughput as an objective function and try to find the suitable Cell Individual Offset (CIO) to trigger UE handover with the A3 event such that the network throughput is optimal. We formulate this problem into a Quadratic Unconstrained Binary Optimization (QUBO) problem such that we can explore the power of Quantum Annealing (QA), which is a promising approach to finding the optimal solution for the NP-Hard problem. We also analyze the penalty coefficients setting problem which impacts the outcome of QA significantly. We test our QUBO formulation on a dataset of 7 BSs and 150 UEs with Fujitsu's third generation Digital Annealer (DA3). Using our QUBO formulation with DA3 solver with fine-tuning, we can improve the throughput with 60% hit rate on this dataset. [ABSTRACT FROM AUTHOR]

In vehicular ad hoc network (VANET) with multihoming, each vehicle is configured with multiple care-of addresses with different network prefixes. As a main part of the handover, the care-of address configuration occupies a large proportion of the handover latency, so VANET with multihoming suffers from substantial handover latency. This paper focuses on the handover in VANET with multihoming and aims to reduce the handover latency. In this paper, the architecture of VANET with multihoming is presented and based on this architecture the address separation mechanism is proposed to solve the address configuration issue. In this mechanism, the node ID in one address is separated from the network prefix. In this way, each vehicle is independently configured with a globally unique node ID only once during its lifetime and then combines its node ID with the network prefixes to construct multiple addresses. Based on this mechanism, the handover algorithm is proposed. In this algorithm, a vehicle achieves the handover without performing care-of address configuration, so the handover latency is reduced. The experimental results demonstrate the feasibility and superiority of the proposal. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the findings in the handover (HO) process performance within three routes in Quito, Ecuador. We used the Net-Monitor Software to gather information from one of the three national mobile operators. Then, we used the R tool to analyze the HO performance. We analyze several performance metrics, such as HO types, HO conditions, and the ping-pong process. Analysis of the results of the outdoor drive tests demonstrate that the radio frequency (RF) parameters, such as Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), power margin, times radio frequency measurements repeats, and HO percentage to nearest BS, are extremely important during different HO types and ping-pong processes because there are statistical differences in these measured RF parameters. The main measurement results demonstrate that RSSI difference between inter HO and intra HO is 20 dB, whereas HOs are performed when the mobile device (MS) gets farther from the base station (BS), approximately 50% of total HOs. Operator achieves a high ping-pong rate of approximately 10% of total HOs. [ABSTRACT FROM AUTHOR]

The proposed work offers a concise review of the application of machine learning (ML) to cellular network handovers (HO) via the Systematic Mapping Study (SMS) methodology, emphasizing the problem areas and requirements. The key points include the paramount role of high-quality data, with meticulous data acquisition and preprocessing as vital steps in ML dataset construction. The article identifies prevalent parameters for HO enhancement and underscores the diversity of ML algorithms, aligning them with specific data input and tasks. This study establishes a robust basis for forthcoming research in applying machine learning to cellular network HOs. [ABSTRACT FROM AUTHOR]

Increasing spectrum efficiency in new-generation communication networks is possible by raising the operating frequencies or increasing the number of serving cells. Both of the solutions indirectly lead to a decrease in the cell size. By shrinking the coverage area of the cells, the mobility management becomes a critical issue in order to provide seamless connectivity. The main tool for mobility management in connected mode is the handover procedure, which conventionally is triggered based on the user equipment's (UE's) measurements of serving and neighboring cells' signal quality. The performance of the methods provided in the 3GPP standard for setting up the handover is based on a comparison between the quality of the received signal from the serving cell and the neighbor cells, which is severely sensitive to the appropriate setting of threshold values and waiting time for setting up the handover. The remarkable methods in the literature to improve the handover performance in the 3GPP standard either utilize positional information or are based on complex algorithms. Positional information parameters such as speed, location, direction of movement, etc., need extra measurement modules. Also, complex algorithms like deep learning are not suitable for a wide range of active devices in 5 G networks with limited resources. In this paper, a novel fuzzy logic-based method is proposed to trigger the handover procedure based on estimated serving cell's and neighbor cells' radio link quality (RLQ) values. The proposed system consists of two stages. A second-order regressor beside a simple fuzzy logic system is introduced to predict the serving cell's and neighbor cells' RLQ. The final handover trigger decision is made with the help of another cascade fuzzy logic system, which is responsible for eliminating too-early, too-late, and ping-pong handovers. Considering the uncertainty handling feature of the interval type II fuzzy logic systems versus type I, we implement both methods and compare their results. Finally, we simulate the proposed algorithm using the ns-3 LTE module with a very tight setting for detecting radio link failure to meet 5 G strict standards. Proposed method succeeded in improving the performance of the handover process at high-speed scenarios by 50%, only with the help of radio link quality information. The main advantage of the proposed method is the proper management of the handover procedure, independent of UE's velocity, as well as its simple structure with few rules, which makes it suitable for use in UAV and IoT devices. [ABSTRACT FROM AUTHOR]

A heterogeneous network (HetNet), combining different technologies, is considered a promising solution adopted by several upcoming generations of mobile networks to keep up with the rapid development of mobile users' requirements while improving network performance. In this scenario, a vertical handover (VHO) algorithm is responsible for ensuring the continuity of the ongoing user connection while moving within the coverage of the HetNet. Although various VHO algorithms were proposed, achieving efficient performance from both network and user perspectives remains challenging. This paper proposes an adaptive optimized vertical handover algorithm based on a multi-attribute decision-making (MADM) algorithm integrated with particle swarm optimization and gravitational search algorithm (PSOGSA) as a framework to implement the handover process. The algorithm includes three main ideas. Firstly, a network selection framework is proposed considering the most important criteria, including signal strength and other networks' attributes, along with users' characteristics regarding their mobility and service preferences. Secondly, two new parameters are introduced as control handover parameters named load factor (LF) and score priority (SP) to reduce unnecessary handovers and the overall HetNet power consumption while achieving balanced load distribution. Lastly, the desired aims are formulated as an objective function, then the PSOGSA algorithm is used to reach the optimal values of both LF and SP, which will be considered when executing the handover algorithm. The presented algorithm is simulated in a heterogeneous wireless network where the fifth-generation (5G) wireless technology coexists with other radio access networks to improve the evaluation field of the proposed algorithm. Also, the proposed algorithm's performance is evaluated in the case of using various MADM algorithms. The simulation results show that the proposed adaptive optimized approach attains efficient performance by decreasing unnecessary handovers by more than 40% and achieving much better load distribution by around 20% to 40%, outperforming traditional handover approaches. [ABSTRACT FROM AUTHOR]

With the increase in user density, it is a challenging problem to achieve higher energy efficiency in vehicle-to-everything (V2X) networks, where frequent handovers occur as users continuously pass through dense cell boundaries. In this paper, we investigate this energy efficiency problem in a highly dense V2X network. To alleviate inter-cell interference and reduce unnecessary handovers, we propose a user-centric network architecture and utilize the coordinated multiple point (CoMP) transmission scheme to enable multiple roadside access points (APs) simultaneously to service one mobile vehicle. Moreover, non-orthogonal multiple access (NOMA) is adopted to enable multiple users to multiplex within one sub-channel and improve system spectrum reuse. Accordingly, the energy efficiency optimization problem is formulated as system achievable rate per joule, with the constraints of the AP transmit power budget, vehicle minimum rate requirements, and the maximum number of CoMP APs serving each vehicle. To solve the optimization problem, a semi-dynamic CoMP AP clustering algorithm is proposed to select a group of CoMP APs to serve a vehicle user by considering the vehicle–AP channel coefficients and the vehicle user's achievable energy efficiency. Then, with the selected CoMP AP clusters, a bidirectional selection algorithm is applied via NOMA to select multiple vehicle users multiplexing within one sub-channel and perform sub-channel power allocation. Numerical simulations are presented to verify the effectiveness of the proposed algorithms, and the results show the improvements offered by the proposed algorithms compared to current benchmarks. [ABSTRACT FROM AUTHOR]

Providing uninterrupted wireless connectivity to vehicles has posed a significant challenge in the context of Vehicle–to–Infrastructure (V2I) communications especially in heterogeneous network environments. This limitation primarily arises due to the constrained infrastructure of vehicular networks. Solutions like Wireless Access for Vehicular Environment (WAVE) protocol and On–Board Units (OBUs) have aimed to expand network capacity and enhance communication between vehicles and infrastructure. However, significant network resources are spent on selecting appropriate target networks during handover in heterogeneous networks. To address issues like incorrect handover network selection and handover delays, researchers have proposed various multi–criteria decision making (MCDM) techniques to choose the optimal candidate cell within HetNet environment. Nevertheless, a majority of these solutions do not account for variations in criteria weights during handoff decisions, which impacts the efficient utilization of resources in V2I communications. To mitigate the problem of incorrect network selection during handover, this study introduces a V2I Multicriteria based Handover Algorithm (mV2I–MHA), considering network parameters such as cost, bandwidth, packet loss ratio, and packet latency. The algorithm’s performance was evaluated using MATLAB R2020a. The obtained results were compared with an existing algorithm where it demonstrated a significant improvement of 27.6% in throughput and a reduction in packet loss rates. [ABSTRACT FROM AUTHOR]

Vertical handovers (VH) is said to be a significant effect of user mobility in a diverse wireless network as it causes a major effect on network performances, like call blocking probability, packet delay, or throughput. On the other hand, the analysis of VH meets a lot of complexities when it comes to investigation, including network modeling that was very complex owing to the diversity in topology. This article aims to present a VH method by means of graph theory. At first, the improved k-partite graph is exploited to model the VH issues in HetNets. Second, improved Dijkstra's algorithm (DA) is deployed for choosing the best path. Further, the analytic hierarchy process is used for estimating the adaptive weights among nodes. Accordingly, optimal selection of weights in DA is performed depending on a hybridized model named as grey wolf upgraded CSO. Ultimately, several metrics such as throughput, packet jitter, and delay and packet losses indicate that the created strategy is efficient. [ABSTRACT FROM AUTHOR]

Twenty-first century is an era of cutting-edge technologies. From artificial intelligence to nanotechnology, modern life has gathered the pace to match up with the shrinking time and fast-moving world. Likewise, a Heterogeneous Network finds its own space in this century of inventions. Every individual, industry, and institute needs to be connected to the internet cloud. Mobile applications have changed the dynamics of every individual's life. From personal management to professional proficiency, mobile applications play an important role in almost every aspect. Numerous applications available these days demand continuous bandwidth connection. To remain always connected, there is a need for an effective handoff procedure. In this paper, we have proposed a convex optimization technique for optimizing the call drop probability, remaining energy and handoffs in heterogeneous networks. The proposed algorithm outperformed the already existing M-OPTG and M-WO algorithms. [ABSTRACT FROM AUTHOR]

Blockchain technology has recently emerged as one of the most innovative solutions on the market. It is predicted to become as popular as the internet was in its early days during the 1980s. With the use of a consensus algorithm, the blockchain creates a cryptographic record that is unalterable. The system uses a combination of repeated sequence hashing and failure tolerance to achieve this outcome. Decentralization, immutability, anonymity, and accountability are just a few of the key features of blockchain. Due to these attributes, many businesses in various industries are now exploring the possibility of incorporating blockchain into their IT systems. Consensus algorithms are a vital component of blockchain technology and play a crucial role in ensuring the reliability and efficiency of the blockchain. The Raft Consensus prevents the generation of new blocks until transactions are confirmed. This also saves storage space, particularly during periods of low transaction activity, as unoccupied blocks do not consume storage space. To evaluate the efficiency of the Raft consensus algorithm, a simulation of a roaming services application for mobile network operators (MNOs) was performed. This simulation assessed the blockchain's efficiency in terms of network performance. The results indicate that implementing this technique could significantly increase transaction volumes. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) networks are a popular standard that carries effective skills to conquer the tests of next generation wireless networks. Also, the 5G systems can support high data traffic by rendering high throughput and low latency towards the massively connected nodes. Here, handover is highly significant for data processing, portability and real time data creation in mobile technologies. With the 5G entrance, the cellular network has become a completely heterogeneous network (HetNet). The Software Defined Network (SDN) concept is used in 5G HetNets for better mobility management. Most existing research works have concentrated on handover authentication, but those works are often prone to re-authentication issues and increased handover delay. Therefore, to overcome the reauthentication process and provide users with better services, a novel handover authentication mechanism using deep learning (DHan_Auth) is proposed. Initially, the 5G data attack and normal data are collected, and the malicious and non-malicious users are classified using the Convolution Stacked long short term memory network model (Conv_SLSTM). To improve handover process and resist network attacks, only the non-malicious user data are authenticated through the key generation and the 5G Handover-Authentication and Key Agreement (5G_AKA) protocol. The process of encryption and decryption are performed using Extended Elliptic curve cryptography (Ex_ECC). When simulating with the PYTHON platform, performance such as handover latency, accuracy and precision are analyzed. The handover latency of the proposed model is 11.8 seconds to 200 nodes, while the classification accuracy in categorizing the malicious and non-malicious users reaches 98.98%. [ABSTRACT FROM AUTHOR]

Summary: In recent years, artificial intelligence techniques, such as software‐defined networks (SDNs), machine learning classification (ML classification), and mobility models (MMs), have become vital in developing networks. Furthermore, communication methodologies, such as handover, directly affect network performance. In this paper, we propose a new system named SSHS, SDN Seamless Handover System, that combines SDN with an ML classifier to administer the network connection of mobile nodes. Through the SSHS system, the SDN will centralize the control to enable comprehensive management over the network, coupled with a decision tree (DT) classifier in the RYU controller to bring intelligence to the SDN application by enabling data analysis and prediction among mobile nodes generated by the RSSGM model. We present the SSHS model's effectiveness in providing a seamless communication handover among multiple access points (APs). The results of this study revealed that the SSHS provided a seamless handover among APs by improving the throughput by 26%, and decreasing the delay of arriving packets by 73% to standard SDN handover system. [ABSTRACT FROM AUTHOR]

Vertical handover management plays an essential role in wireless network technologies, mainly due to the rapid development of various radio access technologies (RATs) that require users to connect seamlessly from one RAT to another. However, in multiple RAT environments, vertical handover management encounters different challenges, including unnecessary handovers, handover failures, ping-pong handovers, and unsuitable access network selection. Essential in vertical handover management is maintaining the desired quality of service (QoS) by the mobile device user. The seamless movement of mobile device users as they run various applications depends on a well-performing vertical handover decision-making algorithm. This bears special significance in a heterogeneous network environment. This paper proposes a vertical handover algorithm that considers user preferences (i.e., a vertical handover algorithm that evaluates the application currently running on a user device). The main objective of the algorithm is to determine when it is necessary to perform the handover, depending on the applications running on the mobile device. The proposed algorithm utilizes a fuzzy logic system to assess whether the handover is necessary and a multiattribute decision-making (MADM) method to select the best available radio access network. A simulation scenario involving different applications at various mobile device velocities was developed. The results proved the algorithm's effectiveness compared to some of the earlier proposed vertical handover algorithms. At velocities below 10 m/s and 30 m/s, the proposed algorithm had 0% and 15.02% unnecessary handovers, respectively, while the technique for order preference by similarity to ideal solution (TOPSIS) utility's function-based algorithm obtained 12.38% and 23.24% at the same velocities, respectively. In addition, compared to TOPSIS, the obtained results of the proposed algorithm demonstrated a lower handover failure rate and ping-pong rate for a velocity span of 1–30 m/s for the considered user applications. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc networks (VANETs) are the primary setting for smart driving, providing road information and instantaneous vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication between vehicles. Nevertheless, providing a VANET system with low computational costs and great serving capacities is critical. However, the efficiency of the whole VANET is significantly reduced since vehicles must re-authenticate with the current roadside unit (RSU) whenever they enter a new RSU coverage region. Handover authentication for VANETs has been the subject of many recent studies, notably secure V2V or V2I communications. We examine the security of recently proposed handover authentication methods for VANETs because it is an emerging and developing field. This study attempts to get an in-depth comprehension of handover authentication mechanisms for VANETs. We have considered Son et al., Maria et al., and Xie et al.'s schemes for our study. These protocols improve compatibility and give vehicles and RSUs a more straightforward way to send messages. However, we have discovered a fundamental security flaw in these systems that leaves them open to attacks, including replay and impersonation. Additionally, to develop a trade-off between efficiency and security, we propose a comparison based on execution cost and security qualities. [ABSTRACT FROM AUTHOR]

Summary: New mobile devices offer multiple network interfaces to allow the users to connect to the best available network. The heterogeneous networks can provide better internet connectivity to the users by means of vertical handover. The handover must be triggered at a suitable point of time to avoid mobility issues such as unnecessary handovers and handover ping‐pongs. The network selection during handover is usually done using classical multi‐attribute decision making (MADM) methods. However, ranking abnormality is one of the prominent issues of the classical MADM methods. To address these challenges, a graph theory and matrix approach (GTMA) with Euclidean distance is proposed for vertical handover in wireless networks. GTMA is used for the selection of the appropriate network and Euclidean distance is utilized for the handover triggering. The simulation results reveal that the proposed method has eliminated the ranking abnormality issue. This proposed technique without triggering has also reduced the number of handovers up to 75.61%, 85.71%, and 66.67% as compared to the traditional MADM methods such as AHP, GRA, and TOPSIS respectively. The use of Euclidean distance for handover triggering has further reduced the number of handovers of the proposed technique as well as traditional techniques for all the traffic types. [ABSTRACT FROM AUTHOR]

In the fifth generation (5G), ultra-dense heterogeneous network (UDHN) is considered as a prominent technology to resolve network system problems. It is challenged to provide secure access since the UDHN contains access points (APs), user equipment (UE) which are characterized with the nature of dynamic, temporary as well as autonomy. The coverage of AP is less when compared to that of classical base station and the problem arises with the interaction between APs and UE during the mobility of UE. In order to attain efficient key agreement with fast and subsequent authentication, a new consensus mechanism has been proposed. By integrating the reinforcement learning method with the actor-critic learning based on fuzzy neural network (RLAC-FNN), blockchain-enabled handover authentication is enabled in 5G heterogeneous Networks. In the proposed method, the user can establish a secure and quick connection by excluding re-authentication and handover operators between heterogeneous cells with less delay. In the proposed approach, all the secondary peers have been further divided into several secondary peer groups based on credit value. The credit of all the secondary peers will be assigned based on their probability of successful participation in consensus. The secondary nodes will summarize all their results to return to the local service centre (LSC). Finally, LSC will identify the trusted and malicious peers by confirming the consensus adaptively by integrating the RLAC-FNN. The performance of the proposed method has analyzed by implementing it in Python platform and compared with existing approaches. The simulation outcomes showed that the proposed method could efficiently reduce the authentication frequency, handover delay, consensus delay, etc., than existing approaches. [ABSTRACT FROM AUTHOR]

In order to tackle the explosive growth of data traffic and the number of terminals, 5G heterogeneous network (HetNet) has become an important evolution direction of 5G networking architecture. Densely deployed micro base stations (gNBs) in 5G HetNets will dramatically increase the handover frequency of user equipment (UE), resulting in more handover failures, and seriously reducing the user experience of mobile UE. Aiming at tackling this problem, this paper proposes a beam enhancement handover scheme with an adaptive threshold. Firstly, different beamforming gains are configured for the mobile UE in the overlapping area of two gNBs to improve the signal strength received by the UE at the edge of gNB coverage. Secondly, for mobile UE with different speeds, adaptive handover decision parameters are configured, and reference signal receiving strength (RSRP) as well as reference signal receiving quality (RSRQ) are used for joint handover decisions to achieve reliable handover. The simulation results verify that the proposed scheme can effectively improve the signal strength of the edge area, and the adaptive joint handover decision algorithm based on UE speed can also effectively improve the handover success probability. [ABSTRACT FROM AUTHOR]

Effective mobility management is crucial for efficient operation of next-generation cellular systems in the millimeter wave (mmWave) band. Massive multiple-input–multiple-output (MIMO) systems are seen as necessary to overcome the significant path losses in this band, but the highly directional beam makes the channels more susceptible to radio link failures due to blockages. To meet stringent capacity and reliability requirements, multi-connectivity has attracted significant attention. This paper proposes a multiagent distributed Q learning-based mobility management scheme for multi-connectivity in mmWave cellular systems. A hierarchical structure is adopted to address the model complexity and speed up the learning process. The performance is assessed using a realistic measurement data set collected from Wireless Insite in an urban area and compared with independent Q learning and a heuristic scheme in terms of handover probability and spectral efficiency. [ABSTRACT FROM AUTHOR]

Handover modifies the user equipment using mobility in which base station provides the best one. The repeated handovers may corrupt mobility reliability due to high signaling load and therefore, network capability enhancement is affected. Here, a network management system in a network is difficult one owing to the rising number of complexity issues and base stations. In this paper, Crow Sun Flower Optimization (CSFO)-based handover method is developed for enabling efficient handover in Fifth Generation (5G) network. This handover method mainly consists of four parts, such as User Preference (UP) section, Network Quality of Service (NQ) module, power section, and Decision System (DS) module. The Quality of service (QoS) is controlled by UP section and NQ module, whereas the power module is concentrated on power. Thus, the handover is decided based on three segments and DS module is used to enable the network. The DS module is effectively decided whether to offer handover in 5G network or not. Moreover, the decision is optimally selected based on an optimization technique, named as CSFO algorithm. The developed CSFO technique is newly designed by integrating Crow Search Algorithm (CSA) and Sun Flower Optimization (SFO) technique. Additionally, three performance indicators, including received power, throughput, and user-served ratio, are used to assess how well the created CSFO-based handover model performs. High received power, throughput, and user served ratio of − 9 0. 2 6 dBm, 2 2 1. 4 kbps and 0.071, respectively, are achieved by the developed handover strategy. [ABSTRACT FROM AUTHOR]

Handoff management is the method in which the mobile node maintains its connection active when it shifts from location to other. The devastating success of mobile devices as well as wireless communications is emphasizing the requirement for the expansion of mobility-aware facilities. Moreover, the mobility of devices requires services adapting their behavior to abrupt context variations and being conscious of handoffs, which make an intermittent discontinuities and unpredictable delays. Thus, the heterogeneity of wireless network devices confuses the situation, since a dissimilar treatment of handoffs and context-awareness is essential for every solution. Hence, this paper introduced the Deep Q network-based Firefly Aquila Optimizer (DQN-FAO) for performing the handoff management. In order to establish the handoff management, the process of selecting network is very important. Here, the network is selected based on the devised FAO algorithm, which is the consolidation of Aquila Optimizer (AO) and Firefly algorithm (FA) that considers the metrics, such as Jitter, Handoff latency, and Received Signal Strength Indicator (RSSI) as fitness function. Moreover, the handover decision is taken by the DQN, where the hyper-parameters are tuned by the devised FAO algorithm. According to the hand over decision taken, the context aware video streaming is happened by adjusting the bit rate of the videos using network bandwidth. Besides, the devised scheme attained the superior performance based on the call drop, energy consumption, handover delay, throughput, handoff latency, and PSNR of 0.5122, 7.086 J, 10.54 ms, 13.17 Mbps, 93.80 ms and 46.89 dB. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]