Handoff Decision Research Articles

Intelligent Spectrum Handoff Decision in Cognitive Radio Networks: A Fuzzy System Approach with Adaptive Membership Functions

Intelligent Spectrum Handoff Decision in Cognitive Radio Networks: A Fuzzy System Approach with Adaptive Membership Functions

Context-Aware Vertical Handoff Algorithms for IoT-enabled Environments

-The proliferation of Internet of Things (IoT) devices has ushered in a paradigm shift in the way we interact with our surroundings. As these devices seamlessly exchange data across heterogeneous networks, maintaining uninterrupted connectivity and optimal quality of service (QoS) becomes a paramount concern. The process of vertical handoff, which facilitates the seamless transfer of communication sessions between different networks, is of particular significance in this dynamic IoT landscape. This research paper delves into the realm of "Context-Aware Vertical Handoff Algorithms for IoT-enabled Environments," aiming to harness the power of context-awareness to enhance the efficacy of vertical handoff decisions in IoT scenarios. The introduction highlights the intricate network challenges posed by IoT's diverse devices and applications. These challenges necessitate the development of innovative solutions that can adapt to varying communication technologies, device constraints, and QoS demands. The focal point of this exploration lies in the integration of context-awareness into vertical handoff algorithms. Context-awareness leverages real-time contextual information, encompassing factors such as device location, network conditions, application requirements, and user preferences, to make informed handoff decisions that align with the unique demands of each IoT situation. Drawing from the wellspring of context- aware computing, the paper delves into the mechanics of context-aware vertical handoff algorithms. It explores their attributes and capabilities, shedding light on how these algorithms leverage context to optimize handoff decisions. The paper discusses the diversity of International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 09 | September - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM25730 | Page 2 context-aware algorithms, including fuzzy logic- based, machine learning-driven, and rule-based approaches, and their adaptability to the distinct challenges posed by IoT environments. As the IoT landscape brings forth its own set of challenges, the paper addresses considerations like limited resources, heterogeneous data, and intermittent connectivity. It delves into the spectrum of algorithm types and approaches that align with the IoT's intricacies, ensuring that vertical handoff decisions resonate with the unique characteristics of IoT devices. In the quest to gauge the effectiveness of context-aware vertical handoff algorithms in IoT scenarios, the paper outlines a comprehensive methodology for performance evaluation. This methodology involves defining IoT-centric performance metrics, such as data delivery reliability, energy efficiency, latency, and adaptive decision-making, which accurately capture the impact of context-awareness on QoS enhancement and device efficiency. Through practical case studies, the paper demonstrates how context-aware vertical handoff algorithms find application across various IoT domains, from smart cities to healthcare. It unveils how these algorithms address the dynamic challenges presented by real- world scenarios, where devices navigate between heterogeneous networks and diverse application requirements. Keywords: IoT ,QoS, LPWAN,

Fuzzy Tree Schematic for Spectrum Handoff Reduction in Cognitive Radio Networks

Spectrum is a limited resource, so it must be used effectively. The fixed spectrum allocation method currently in operation may not sustain the growth in the network access users, which continues to increase over time. Many studies have shown that the spectrum deficiency problem can be solved with cognitive radio network (CRN) technology. CRN allows flexible spectrum utilization. Spectrum handoff is one of the fundamental prerequisites for dynamic spectrum access in cognitive radio networks. The ping-pong effect is one of the problems caused by the frequent occurrence of unnecessary handoff spectrum. So, in CRN, the right handoff decision is needed to avoid the ping-pong effect. The proposed fuzzy system carries the concept of a fuzzy tree that uses two fuzzy inference systems (FIS), which work in two stages, namely FIS-1 and FIS-2. In the early stages, the FIS-1 controls the secondary users (SU) power and prevents interference with the primary users (PU). FIS-2 is used to execute precise spectrum handoff decisions. The test results show that the amount of handoff spectrum has decreased by up to 43 % compared to other methods, which are only 60 %. This results in a decrease of 16.67% from the previous method. Thus, the ping-pong effect can be minimized with the right handoff decisions.

Minimization of Handover Decisions with Quality of Service Using Fuzzy Logic Prediction Model

Artificial Intelligence (AI) based network technologies considered best method to enhance the Quality of Service (QoS) of handoff algorithms due to its ability to handle huge data in fast processing. It helps to take effective handoff decision based on Received Signal Strength (RSS), traffic intensity, speed and diversity. In this paper the fuzzy logic prediction model has been developed for handoff decisions. On retrieving the network, the RSS was developed to form a time series data over a period of time. The data is then proceeded with the newly proposed fuzzy logic prediction model for estimation and prediction coefficients, while the predicted values of RSS are organized as fuzzy sets and in conjunction with other measured parameters of network. Moreover, the Received Signal Strength Indicator (RSSI), traffic load in the network, channel capacity, network load (NL), Bit Error Rate (BER), received signal power level has been estimated throughput the Signal to Noise Ratio (SNR), In addition, to user preferences such as the security and cost of the network. The overall performance of proposed fuzzy logic prediction model is capable to predict the handover decision ahead then the available RSS method and other handover necessity estimation techniques. This model also reduces the ping-pong effect associated with other techniques of handover.

Multi-criteria Handoff Decision making Algorithm for Seamless Mobility in Heterogenous Wireless Networks

Wireless networks have been through many transformations to provide Quality of Service (QoS) and seamless mobility. Handoff management plays a vital role in maintaining quality of service while roaming. The focus of the paper is to develop a handoff technique for optimized handoff decision making. Multi-Criteria Decision Making (MCDM) techniques are utilized for making decision where more than one contradicting parameter are involved. Fuzzy Analytic Hierarchy Process (FAHP) is used for weight calculation and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) decides the rank of available networks. The proposed technique utilizes six network parameters i.e., received signal strength, bandwidth, signal to interference & noise ratio, delay, packet loss, and bit error rate for weights calculation under different traffic classes and three decision makers i.e., network, mobile node, and user preference for optimized handoff. The results show that the proposed technique effectively classify the best available network in terms of their rank and reduces the number of unnecessary handoffs.

A novel spectrum handoff switching decision scheme for improved performance of secondary users in cognitive radio network

SummarySpectrum handoff (SH) in the cognitive radio network (CRN) is considered as a key challenging area to enhance the performance of secondary users (SUs) in CRN. If the primary user is detected, the SU may pause and stay on the same channel or may perform SH to another idle channel. An accurate and precise handoff decision improves the overall throughput and quality of experience of end‐users. In this paper, we introduce a new SH algorithm and continuous short‐sensing strategy to improve the overall throughput of SUs. In addition, we have derived the minimum length of the target channel sequence based on network‐specific parameters like desired call dropping probability. Further, an optimum channel search time is obtained to minimize the handoff delay. The simulation result shows that the proposed scheme improves the overall throughput of CRN, and the mean opinion score of different video applications increases by 10%, 4.6%, and 1% for rapid motion, gentle walk, and slight motion types of video applications. In the case of VoIP applications, the maximum simultaneous call is improved by 2 times in the case of G.711, 1.72 times in the case of G.729, and 1.66 times in the case of iLBC.

Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution

Heterogeneous networks (HetNets) with end-to-end (E2E) network slicing are regarded as effective approaches to meet diverse service requirements from vertical industries. Due to the dense deployment of base stations (BSs) and the complicated associations between BSs and E2E network slices (NSs) in the scenario, the handoff problem faces challenges of the huge system state space and handoff action space and the considerable communication overhead. In this paper, we take these issues into account and consider a distributed E2E NS handoff decision framework in the HetNet. A decentralized Markov decision process (DEC-MDP)-based model is formulated for the distributed E2E NS handoff problem, and the jointly observable and random characteristics of the DEC-MDP are analyzed. To obtain a theoretical performance reference, the original distributed E2E NS handoff problem is simplified, and a Nash equilibrium-based performance bound is given. More practically, the multi-agent double deep Q-network-based distributed handoff (MA-DDQN-DH) algorithm with the centralized training and decentralized executing framework is proposed. Simulation results show that the Nash equilibrium-based performance bound is reasonable, and the proposed MA-DDQN-DH algorithm performs well in the comparison.

A vertical handoff decision scheme using subjective-objective weighting and grey relational analysis in cognitive heterogeneous networks

With the limitation and low utilization rate of spectrum resources, how to maintain the best continuous network connection for user while reducing the total number of handoff has become an urgent problem to be solved in the handoff decision of cognitive heterogeneous networks . Therefore, a cognitive heterogeneous network vertical handoff decision scheme combining subjective-objective weighting and grey relational analysis (GRA) is proposed. Firstly, an agent-based decision-making framework is proposed based on the review of relevant literature; secondly, the attributes weighting algorithm combining subjective-objective weighting is proposed; thirdly, the steps of analytic hierarchy process (AHP), entropy weighting method, and GRA method are analyzed in detail; then an example is given to analyze the proposed algorithm in single decision point, and the performance of the proposed algorithm is compared with that of three similar multi-attribute decision making (MADM) algorithms; lastly, some problems and challenges of the existing vertical handoff MADM decision-making algorithm are discussed, and the future research directions are proposed. Simulation results show that the proposed method is effective in reducing the total handoff number and ranking anomaly rate.

Intelligent Process of Spectrum Handoff in Cognitive Radio Network

Spectrum handoff is a crucial function of Cognitive Radio (CR) which is the change of operating frequency. The main problem in spectrum handoff is the time taken in the searching, selection, and switching to a new available channel which can cause a significant amount of delay during spectrum handoff. This research aims to minimize the delay that occurs during spectrum handoff. A Proactive Fuzzy-Based Backup Channel Selection Scheme (PFBBCSS) was proposed where the Secondary User (SU) gathers backup channels in advance before the return of the Primary User (PU), then fuzzy logic would be used for the selection of the best channel out of the available backup channels. The proposed scheme was simulated and evaluated using the MATLAB Simulation tool and the result was compared with a Pure Proactive Spectrum Handoff Scheme. Results showed, in terms of throughput and efficient time utilization under a varying number of licensed channels, that the proposed scheme performed better, making it a good mechanism to be used for handoff decisions by the Secondary User (SU).

Energy efficiency in cognitive radio network using cooperative spectrum sensing based on hybrid spectrum handoff

Rapid spectrum usage in wireless networks may reduce energy efficiency, requiring cognitive radio networks to be more efficient than conventional ones. Due to increased data transmission demand, cognitive radio networks arose from a lack of spectrum bandwidth. Spectrum sensing and handoff decision are two cognitive radio network strategies that help avoid interference, channel access, and cohabitation between primary and secondary users. Current research focuses on handoff decision and cooperative spectrum sensing to improve sensing efficiency and system throughput while neglecting energy efficiency and handoff latency. Spectrum mobility and sensing are essential for energy-efficient cognitive radio networks. This study provides a second priority user transmission system using cooperative spectrum sensing to sense available channels. An energy detection technique optimizes the sensing process's energy usage, leading to energy efficiency. A primary user traffic pattern-based threshold approach is presented for spectrum mobility management. A threshold approach is utilized to calculate probabilistic stay-and-wait and QoS handoff values. The transmission channel is selected using a hybrid handoff strategy based on dynamic spectrum aggregation. Moreover, a cooperative spectrum sensing algorithm is described and simulated to identify the optimum channel with the greatest throughput and minimum energy consumption. The proposed approach increases energy efficiency and throughput while maintaining handoff delay and avoids miss-detection and false alarm. This technique improves energy efficiency, sensing performance, throughput, and handoff time. miss-detection.

Retraction Note to: Trust and privacy based vertical handoff decision algorithm for telecardiology application in heterogeneous wireless networks

Retraction Note to: Trust and privacy based vertical handoff decision algorithm for telecardiology application in heterogeneous wireless networks

Joint Security and Train Control Design in Blockchain-Empowered CBTC System

The communication-based train control (CBTC) system ensures the high efficiency and orderliness of trains and is widely used in urban rail transit networks. The adoption of wireless communication and network techniques makes the CBTC systems more vulnerable to cyber attacks. Identity authentication is an effective approach to improve system security. The existing identity authentication mechanisms in CBTC adopt a centralized key management system sensitive to single-point failures. To improve system security, in this article, we deploy a blockchain in CBTC systems. The client that runs the blockchain program not only acts as blockchain nodes to provide distributed key management for the CBTC system but they also work as a relay node to authenticate the communication between train control nodes in CBTC systems. Based on the blockchain-empowered distributed security scheme, the block producer selection and onboard blockchain client handoff decision problem are studied. With the objective to minimize the impact of the key updating process on CBTC system performance and keep the system security under a reasonable level, we formulate the block producer selection and onboard blockchain client handoff decision problem using the deep reinforcement learning approach. Extensive simulation results illustrate that the proposed blockchain-empowered security scheme can significantly improve the CBTC system security, and CBTC systems need to sacrifice part performance to ensure system security.

An Improved Handoff Algorithm for Heterogeneous Wireless Networks

Heterogeneous Wireless Network is currently a major area of focus in communication engineering. But the important issue in recent communication is the approachability to the wireless networks while maintaining the quality of service. Today, all the wireless access networks are working in tandem to keep the users always connected to the internet cloud that matches the price affordability and performance goals. In order to achieve seamless connectivity, due consideration has to be given to handoff precision and a smaller number of handoffs. Several researchers have used heuristic approaches to solve this issue. In the present work, a hybrid intelligent algorithm has been suggested for vertical handoff decisions. This hybrid intelligent algorithm is based on dual optimization approach which uses “Particle Swarm Optimization (PSO)” and “Mobile Robustness Optimization (MRO)” techniques for improving the quality of services. This approach performs well even in the failure network conditions and gives the best results in terms of connectivity. The results at the last has been compared with the conventional techniques and it has been observed that the proposed methodology outperforms the existing one.

Effective Resource Allocation and Load Balancing in Hierarchical HetNets: Toward QoS-Aware Multi-Access Edge Computing

AbstractMobile edge computing (MEC) is a key feature of next-generation heterogeneous networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. In this research, we investigated on connection management approaches in multi-access edge computing systems. This paper presents joint radio resource allocation and MEC optimization in a multi-layer NOMA HetNet in order to maximize system’s energy efficiency. The continues carrier allocation and handoff decision variables, in addition to the interference incorporated in the goal function, modifies the primary optimization problem to a mixed integer nonlinear programming. Network selection is done statically based on the Analytic Hierarchy Process, and station selection is done dynamically based on the Data Envelope Analysis method. Also, an effective feedback mechanism has been designed in collaboration with the server resource manager to solve a global optimization problem in order to load balancing and meet the users quality of service constraints simultaneously. To reduce the computational complexity and to achieve a locally optimal solution, we applied variable relaxation and majorization minimization approach in which offloading decision and multi-part Markov noise analysis have been developed to model users’ preferences without the need for explicit information from the users. Based on the simulations, the proposed approach not only results in a significant increase of system’s energy efficiency but also enhances the system throughput in multiple-source scenarios.

Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff

The sensitivity to blockages is a key challenge for millimeter wave and terahertz networks in 5G and beyond. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction allows the network to anticipate future blockages, especially dynamic blockages, and initiate user hand-off beforehand. This article presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by red-green-blue (RGB) cameras deployed at the base stations. In particular, the article proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. This is mainly achieved via a deep learning algorithm that learns from visual and wireless data how to predict incoming blockages. The predictions of this algorithm are used by the wireless network to proactively initiate hand-off decisions and avoid any unnecessary latency. The algorithm is developed on a vision-wireless dataset generated using the ViWi data-generation framework. Experimental results on two basestations with different cameras indicate that the algorithm is capable of accurately detecting incoming blockages more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\sim} 90\%$</tex-math></inline-formula> of the time. Such blockage prediction ability is directly reflected in the accuracy of proactive hand-off, which also approaches 87%. This highlights a promising direction for enabling high reliability and low latency in future wireless networks.

An SDN‐based technique for reducing handoff times in WiFi networks

SummaryThe handoff process in WiFi networks has a very important role to provide continuity of the service to wireless users where the access points (APs) are in numerous numbers. In traditional WiFi networks, the handoff process takes a few seconds and is initiated by the wireless devices, but these few seconds could be a reason for information loss when dealing with delay sensitive applications. In this paper, we propose a reduced detection and discovery time approach (DeRy), which generates a solution based on software defined WiFi networks (SD‐WiFi). A simple network management protocol (SNMP) manager and received signal strength indicator (RSSI) manager are employed to report the AP traffic conditions and RSSI values to the SDN controller and instead of the wireless devices, the handoff decisions are taken at the controller end. When to initiate the handoff (detection phase) and which destination AP to associate to (discovery phase) are handled by the centralized SDN controller. To implement DeRy, extensive simulation runs are carried out on Mininet‐NS3‐WiFi network simulator. The simulation results show that the DeRy significantly reduces the handoff times by 60–70% and reduces the average number of retransmission by 4–49%, hence maintaining the throughput for delay sensitive applications such as VoIP, when compared to the standard RSSI‐based handoff scheme, channel measurement based access selection scheme (CMAS) and (DL‐SINR) downlink‐signal to interference plus noise ratio AP selection scheme (DASA).

Computational complexity reduction algorithms for Markov decision process based vertical handoff in mobile networks

SummaryVertical handoff is a major concern in the operation of mobile connections. Multiple wireless networks collate to provide smooth and quality service to the users over mobile connections. This paper formulates a Markov decision process for handoff decisions with a sample space that includes a union of parameters that are important for making a handoff decision. The major contribution of this paper is to propose three different yet closely related algorithms for reducing the computational complexity of the original problem. In particular, we propose a feature‐wise assessment algorithm, a network‐wise assessment algorithm, and a hybrid approach for computational complexity reduction. Discussed algorithms give pseudo‐optimal solutions with a significant reduction in computational complexity. Results indicate that different complexity reduction algorithms perform best under different circumstances. This provides a guideline for the selection of complexity reduction algorithms based on real scenarios.

Design, Implementation, and Analysis of Vertical Handoff Decision Algorithm

The increasing use of mobile communication and computing has made accurate and efficient methods of executing vertical handoff a necessity. This research paper captures the design and implementation of an effectual algorithm for making decision for vertical handoff based on fuzzy logic. The input parameters considered are bandwidth, battery status, and cost. The authors present analysis of the handover value, which gives the handoff decision, in relation to the input attributes, using regression analysis, correlation analysis, and ANOVA.

Two-Phase Access Network Selection Scheme Based on Weighted Sum and Game Theoretical Approaches for Vehicular Ad Hoc Networks

The Vehicular Ad hoc NETworks (VANETs) are of high mobile and dynamic in nature. They are highly prone to network discontinuity, thereby the applications running on the vehicles get degraded in its service. The network selection for VANETs is done based on network attributes and user Quality of Service (QoS) demands. In this work, a two-phase access network selection scheme is proposed for VANETs. In the first phase, the available networks are ranked based on QoS demands using Weighted Sum Method (WSM). During the second phase, based on availability of resources, handoff decision and access network selection are carried out using game theory approach. Here, the vehicle and network play the game by adopting different strategies, thereby the optimum Nash Equilibrium (NE) is obtained. The simulation results validate that the proposed scheme outperforms the conventional Multiple Attribute Decision Making (MADM) schemes.

Dynamic handoff policy for RAN slicing by exploiting deep reinforcement learning

It has been widely acknowledged that network slicing is a key architectural technology to accommodate diversified services for the next generation network (5G). By partitioning the underlying network into multiple dedicated logical networks, 5G can support a variety of extreme business service needs. As network slicing is implemented in radio access networks (RAN), user handoff becomes much more complicated than that in traditional mobile networks. As both physical resource constraints of base stations and logical connection constraints of network slices should be considered in handoff decision, an intelligent handoff policy becomes imperative. In this paper, we model the handoff in RAN slicing as a Markov decision process and resort to deep reinforcement learning to pursue long-term performance improvement in terms of user quality of service and network throughput. The effectiveness of our proposed handoff policy is validated via simulation experiments.