Link Stability Research Articles

HFLFO: Hybrid fuzzy levy flight optimization for improving QoS in wireless sensor network

In IoT-based WSN applications, energy is a vital parameter since limited residual energy of a node often leads to packet loss, diminished network lifetime, and end-to-end delay. IoT nodes are often resource-constrained, with limited computational and storage capabilities, communication range, and energy-saving potential. To offer an improved Quality of Service (QoS) guarantee, the network lifetime and energy consumption of the WSN-based IoT applications need to be improved. QoS is an important parameter in time-sensitive IoT applications. Hence this paper introduces a novel QoS improvement framework for the WSN-IoT networks, and the objective of this work is twofold. In the first phase, we utilize an Aquila optimizer (AO) for an efficient and reliable cluster head selection which is based on different factors such as node level, distance to the sink node, energy level, etc. The AO algorithm enhances the lifetime of the WSN network by electing the QoS-aware relay node as the cluster head. In the next phase, the Hybrid Fuzzy Levy Flight Particle swarm optimization algorithm (HFLPSO) is presented to enhance the QoS of the network even further by identifying a set of optimal paths to route the packets and measure the reliability of each path via different metrics (packet loss rate, residual energy, end-to-end delay, link stability, and the number of hops). The simulation experiments were conducted using different metrics such as End to End delay, Packet loss rate, and Packet delivery ratio to prove that the proposed model is capable of improving the QoS of the WSN-IoT networks when compared to the different state-of-art methodologies. When compared to the existing techniques, the proposed model offers a 10% improvement in the packet loss rate and a 17% improvement in the packet delivery rate.

NOMA-Assisted Routing Algorithm Design for UAV Ad Hoc Relay Networks

Unmanned aerial vehicle (UAV) ad hoc networks can be deployed flexibly and rapidly without any infrastructure, so as to play an irreplaceable role in postdisaster relief. However, the dynamic topology and energy constraints of UAVs always bring new challenges to the design of effective network architectures and routing strategies. In this article, a three-layer UAV network architecture is constructed, and the UAV channel models for the different layers are discussed categorically. A greedy perimeter comprehensive evaluation routing (GCER) algorithm is designed on the basis of geographic routing greedy perimeter stateless routing (GPSR). By estimating the energy, link stability, and data throughput of the UAV neighboring nodes, the optimal next-hop UAV node is synthetically evaluated using the analytic hierarchy process (AHP). In particular, the use of nonorthogonal multiple access (NOMA) technology in ad hoc networks is considered, and a nonconvex optimization problem for maximizing the throughput estimation of UAV relay nodes based on NOMA is proposed to solve for the optimal allocation of UAV transmission power in routing. Numerical results show that the proposed GCER routing algorithm has significant advantages over GPSR in improving link stability. The use of NOMA access technology in UAV ad hoc networks has a significant increase in the throughput of the data links.

Coalition Game Based Distributed Clustering Approach for Group Oriented Unmanned Aerial Vehicle Networks

Clustering is an effective solution to improve the management efficiency of large-scale systems. One one hand, UAVs performing the same task have a similar moving tendency. One the other hand, the network topology of the UAV swarms is dynamically changing. Considering the above two aspects, our main contributrons are designing a group-oriented distributed clustering algorithm based on the coalition game that couples task attributes and communication attributes. The clustering goal is to divide UAVs performing the same task into one cluster and make each cluster have more UAVs under the cluster size limits, thus improving communication efficiency. The proposed algorithm comprehensively considers the task group information and communication link stability as the coalition value. UAVs decide whether to leave their current coalition based on coalition values. Through periodic parallel switch operations selection, UAVs are divided into the desired clustering structure. Simulations verify that our clustering algorithm is effective and better than the existing ones, especially in communication link stability, cluster numbers, and load balance.

GNSS PPP with added QZSS: Positioning and time-frequency transfer

Regional navigation system Quasi-Zenith Satellite System (QZSS) play an important role in positioning, time and frequency transfer for the Asia-Pacific region under high cut-off angle. This paper combines QZSS and the major navigation systems for Precise Point Positioning (PPP) positioning and time–frequency transfer. The time–frequency transfer experimental results show that with the addition of QZSS, the clock difference accuracy and the frequency stability of the corresponding time link are significantly improved for the case of cut-off above 15°. The corresponding range is between 5% and 30% of accuracy, and between 1% and 90% of frequency stability.

Distributed Beam Tracking for Vehicular Communications via UAV-Assisted Cellular Network

In this paper, we propose a distributed beam tracking scheme for millimeter-wave unmanned aerial vehicle (UAV)-assisted communication systems. In the considered systems, aerial node base (aNB) connects the ground vehicles (GV) through directive beams to mitigate the path loss. However, line-of-sight (LoS) blockage may result in the link disconnection, which leads to the corruption of observations. To solve this issue, we extend the one-to-one beam tracking into the multi-node cooperative beam tracking scheme. For the multi-node cooperative problem, we develop the distributed extended Kalman filter (EKF)-based beam tracking to overcome the high nonlinearity and field-of-view (FoV) limitation of the observation model. While the multi-node cooperation scheme alleviates the corruptions of the observations, it may cause the error propagation between neighbor nodes. Thus, we incorporate the resilient network control into the proposed scheme. Consequently, a distributed EKF-based multi-node cooperative beam tracking protocol is proposed for the correction of corrupted observations and link stability. From our comprehensive simulation, the result of the proposed scheme shows 87.07% error reduction compared to the single node method. This improvement is comparable to the result of the centralized method (91.28% error reduction), and the proposed scheme also has the advantage of scalability.

LSTDA: Link Stability and Transmission Delay Aware Routing Mechanism for Flying Ad-Hoc Network (FANET)

LSTDA: Link Stability and Transmission Delay Aware Routing Mechanism for Flying Ad-Hoc Network (FANET)

Stochastic Bat Optimization Model for Secured WSN with Energy‐Aware Quantized Indexive Clustering

The wireless sensor networks (WSNs) with dynamic topology communication among the sensor nodes is vulnerable to numerous attacks. As they have limited power, there arises a conflict between the complex security scheme and the consumption of energy which are inversely proportional to each other. Hence, a trade‐off should be accomplished between the implemented scheme and energy. A novel secure and energy‐aware routing technique quantized indexive energy‐aware clustering‐based combinatorial stochastic sampled bat optimization (QIEAC‐CSSBO) is proposed which consists of clustering, optimal route path identification, and route maintenance. The clustering process and selection of cluster head (CH) with high residual energy is done using the quantized Schutz indexive Linde–Buzo–Gray algorithm (QIEAC). Optimal route identification is done using CSSBO (combinatorial stochastic sampled Prevosti’s bat optimization), and fitness of every bat is measured on combinatorial functions, namely, distance, energy, trust, and link stability among nodes. Stochastic universal sampling selection procedure is applied to select the global best optimal path for secure data transmission. Lastly, route maintenance process is performed to identify alternative route while link failure occurs among nodes. Experimental assessment is performed using various performance metrics, namely, energy consumption, packet delivery ratio, packet drop rate, throughput, and delay. The proposed method QIEAC‐CSSBO enhances the performance of packet delivery ratio by 4%, throughput by 26%, and packet drop rate by 27% and reduces energy consumption by 11%, as well as delay by 16% as compared to existing techniques.

Mobility Aware Zone-Based Routing in Vehicle Ad hoc Networks Using Hybrid Metaheuristic Algorithm

Vehicle Ad hoc Networks (VANETs) have high mobility and a randomized connection structure, resulting in extremely dynamic behavior. Several challenges, such as frequent connection failures, sustainability, multi-hop data transfer, and data loss, affect the effectiveness of Transmission Control Protocols (TCP) on such wireless ad hoc networks. To avoid the problem, in this paper, mobility-aware zone-based routing in VANET is proposed. To achieve this concept, in this paper hybrid optimization algorithm is presented. The hybrid algorithm is a combination of Ant colony optimization (ACO) and artificial bee colony optimization (ABC). The proposed hybrid algorithm is designed for the routing process which is transmitting the information from one place to another. The optimal routing process is used to avoid traffic and link failure. The fitness function is designed based on Link stability and Residual energy. The validation of the proposed algorithm takes solution encoding, fitness calculation, and updating functions. To perform simulation experiments, NS2 simulator software is used. The performance of the proposed approach is analyzed based on different metrics namely, delivery ratio, delay time, throughput, and overhead. The effectiveness of the proposed method compared with different algorithms. Compared to other existing VANET algorithms, the hybrid algorithm has proven to be very efficient in terms of packet delivery ratio and delay.

TAGKA: Threshold Authenticated Group Key Agreement Protocol Against Member Disconnect for UANET

The development of unmanned aerial vehicle (UAV) technology has been advancing rapidly and is widely applied in various domains. Compared to a single UAV, the multi-UAV system, known as the UAV Ad Hoc Network (UANET), can collaborate to accomplish complex tasks more efficiently. Due to the UAVs communicating through open wireless channels, they are vulnerable to various attacks. Group key agreement protocols are often used to guarantee the security of UANET communication. However, the high dynamics of UAVs and the instability of the wireless links often result in certain UAVs temporarily losing connection with the group, rendering them incapable of participating in the group key agreement process. Regrettably, existing literature on group key agreement does not consider this issue, which makes them not applicable to UANET. To address this issue, we propose a threshold authenticated group key agreement protocol (TAGKA) that can resist the disconnection of group members. Then, considering that the temporarily disconnected UAVs need to obtain the latest group key after the communication links are recovered, we design a group key recovery protocol. The security of the scheme is proved under the random oracle model. Moreover, formal security verification using the Tamarin and rigorous informal security analysis is also performed. Finally, we use NS-3 to simulate the protocol, and the experimental results show that our protocol can be effectively applied to UANET.

Enhanced Probabilistic Route Stability (EPRS) Protocol for Healthcare Applications of WBAN

Wireless Body Area Network (WBAN) is among the most promising technologies for enhancing life quality. WBANs enable constant monitoring of physiological processes by implanting or wearing small, low-power, intelligent sensor nodes in or on the human body. These sensor nodes may be placed either invasively or non-invasively. Patient data must be disseminated reliably and promptly for WBAN’s healthcare apps to function. For this reason, medical teams may use real-time apps for sharing vital information like blood pressure, an ECG, and an EEG. Critical data packets are delay-sensitive and must arrive at sink nodes within the time constraints that satisfy QoS for WBAN. However, networks’ unpredictable and dynamic nature (node mobility, link partitioning) makes reliable data transfer a challenging task. Additionally, postural mobility and ultra-short wireless range cause rapid topology changes, resulting in network partitioning. The network partitioning causes failure of data delivery to the sink or coordinator and causes a delay as well. In the case of normal data, it is not a big issue, but it is not tolerable for emergency data because it may be life-threatening. Consequently, compromising the link reliability and stability results in higher delays, increased packet re-transmissions, and decreased throughput performance. Therefore, we propose an Enhanced Probabilistic Route Stability (EPRS) scheme to address these issues. The proposed EPRS scheme introduces a cost function called Link Assessment Cost (LAC) that makes coherent decisions regarding route reliability in determining whether an active route is a good candidate for routing and satisfying QoS requirements. The LAC is based on two critical factors about link status, i.e., Route Stability Factor (RSF) and Expected Probability of link <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E(p)$ </tex-math></inline-formula> . Based on these factors, a score is assigned to a link that determines the status (likelihood) of a link, either connected or disconnected. In this way, the multi-facet EPRS selects the most stable and reliable routes despite the disconnection in the networks, thereby improving the route stability and throughput, minimizing the end-to-end delay, route discovery calls, and re-transmissions as depicted by simulation results.

A Geographic Routing Protocol Based on Node Scoring and Intersection Evaluation

VANET has a good application prospect in the current vehicle communication. As one of MANET, it has the characteristics of dynamic network topology and indirect connection, which makes the wireless connection between nodes unstable. Aiming at this problem, we propose a routing protocol combining link stability, node density and distance scale. When data packets are transmitted to the intersection, the optimal adjacent intersection is selected to determine the forwarding path, which improves the geographical forwarding of data packets located at the intersection. Through simulation, we find that the proposed geographic routing protocol based on node scoring and intersection evaluation (GRNSIE) has good routing performance in PDR and latency when compared with GPSR, GyTAR and GROOV.

Multi-hop data dissemination using a multi-metric contention-based broadcast suppression strategy in VANETs

Vehicular Ad-hoc Networks (VANETs) are highly dynamic and self-organized ad hoc wireless networks that enable vehicles to set up and maintain communication without any central base station. In multi-hop-based VANETs, messages are disseminated throughout the network through a relaying. However, the dissemination of time-critical emergency warning messages remains challenging due to the broadcast storm problem. The widely proposed broadcast suppression strategies are either beacon-based, which demands extra bandwidth or do not scale well under different traffic conditions. Thus, this paper proposes a multi-criteria-based broadcast suppression scheme for multi-hop VANETS. The proposed scheme utilizes multiple node attributes, including relative velocity, traffic density, lane weight (moving lane), link stability, movement angle and distance to sender, to estimate a node's quality. Upon receiving a new message, each node can contend for the next-hop relay position using its quality value such that a high-quality node is granted the earliest channel access to broadcast the data packet. The performance of the proposed scheme is validated using data generated from traffic simulation in the OMNET++ environment under different traffic conditions. The results from the simulation claim that the proposed strategy alleviates broadcast overhead by 30% and significantly improves the overall dissemination efficiency by 48.97% compared to the bench-marked methods. The results showed that the objectives are met by significantly improving dissemination efficiency with increased reachability, reduced broadcast overhead and end-to-end delay. Moreover, the scheme enables each receiving node to evaluate its broadcasting suitability autonomously, making the relay selection process dynamic and adaptive to any traffic condition.

A Deep Reinforcement Learning-Based QoS Routing Protocol Exploiting Cross-Layer Design in Cognitive Radio Mobile Ad Hoc Networks

In this paper, we propose a novel deep reinforcement learning-based quality-of-service (QoS) routing protocol, namely DRQR, exploiting cross-layer design to establish efficient QoS (EQS) routes in cognitive radio mobile ad hoc networks. An EQS route is a route with minimum end-to-end (E2E) queuing delay subject to QoS constraints such as link stability, residual energy, number of hops and avoiding licensed channels of primary users. Particularly, we propose an NP-complete optimization problem which has a feasible solution as an EQS route. To tackle this problem, we design a new deep reinforcement learning model which supports the DRQR protocol to establish EQS routes in real time by offline training instead of online training like most of literature studies. Moreover, the DRQR protocol guarantees to have high system performance. A mathematical analysis of the E2E queuing delay with random waypoint mobility model also provides to verify simulation results. Numerical results show that the DRQR protocol outperforms state-of-the-art routing protocols in terms of routing delay, queuing delay, control overhead, PDR and energy consumption.

Adaptively prioritizing candidate forwarding set in opportunistic routing in VANETs

Opportunistic routing (OR) paradigms in vehicular networks via opportunistic candidate set selection have a better performance over the pre-defined next-hop node in terms of packet delivery rate. Nevertheless, uneven vehicle distribution, highly dynamic topology, varying link interference, and bandwidth restrictions impose drastic challenges for satisfying applications requirements in static prioritizing candidate set selection policies. Besides, fulfilling quality of service (QoS) requirements of heterogeneous applications, due to the limited local topology perspective and many involving conflicting routing criteria, will be further compromised. Considering the candidate set selection policy as a Multi-Criteria Decision Making (MCDM) task, an adaptive two-hop cooperative forwarding set selection strategy called APFS is proposed in this paper. By developing a local topology perspective to the two-hop neighbor information, the APFS scheme jointly considers indicators of link stability, delay assessment, and link good-put as routing criteria. Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods are employed in two different scenarios in prioritizing candidate forwarding sets. First, classical AHP-TOPSIS technique with a mutable pair-wise comparison matrix is applied, and then Neutrosophic Set (NS) is combined with the classical AHP-TOPSIS method to handle ambiguous vehicular environments. The simulation results show that the APFS strategy using both prioritization policies have significantly improved the performance of packet delivery ratio, average end-to-end delay, throughput, and hop counts as compared to other schemes.

A fuzzy based approach to enhance the lifespan of sensor network

The use of wireless sensor networks (WSNs) for data collection is widespread. The resource constraint is an important factor in WSN communications design. The issue arises naturally in WSNs as a result of uneven energy consumption caused by multi-hop routing and dynamic network models, which substantially affects network lifetime. The nodes are dispersed over distant sensing areas and are powered by finite or limited energy batteries that are difficult to replace. The energy of nodes is reduced as a result of changes in network topology or the network’s lifespan and the main intention of this research is to figure out how to make sensor networks last longer. The suggested study work focuses on a specific routing strategy for WSNs that employs the AO-star algorithm with a Fuzzy approach and link stability for extending the network lifetime. The technique chooses the optimum routing path by the sensing point to the receiving node based on how much energy is consumed, the smallest number of nodes with the shortest latency, and lower transmission loads with higher throughput. To compare the proposed strategy’s efficiency in energy consumption balancing and network lifespan enhancement, the proposed technique may achieve a 30% longer average network lifetime than the A-star algorithm.

Displacement-Aware Service Endowment Scheme for Improving Intelligent Transportation Systems Data Exchange

Intelligent Transportation Systems (ITS) is a smart-transportation system for road-side assistance and data exchange support by integrating cloud and wireless networks. ITS facilitates vehicle-to-vehicle and vehicle-to-anything (V2X) data exchanges for satisfying user demands. The rate of big data granting to the vehicular users is interrupted by the fundamental attributes such as mobility and link instability of the vehicles. To address the issues in vehicular data exchange big data, this article introduces displacement-aware service endowment scheme with the benefits of data offloading. Displacement-aware big data endowment ensures responsive availability of vehicle request information despite unfavorable location and density factors. The time congruency in V2V and V2X data exchanges are adopted for minimizing data exchange dropouts. In the data offloading phase, extraneous information and big data responses are detained based on data exchange relevance to improve congestion free big data endowment. The distinct methods work in a co-operative manner to improve big data quality of fast configuring smart vehicles to provide reliable big data in smart city environments.

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

LoRa (Long Range), a wireless communication technology for low power wide area networks (LPWANs), enables a wide range of IoT applications and inter-device communication, due to its openness and flexible network deployment. In the actual deployment and operation of LoRa networks, the static link transmission scheme does not make full use of the channel resources in the time-varying channel environment, resulting in a poor network performance. In this paper, we propose a more effective adaptive data rate (ADR) algorithm for low-cost gateways, we firstly analyze the impact of the different hardware parameters (RSSI, SNR) on the link quality and classify the link quality using the fuzzy support vector machine (FSVM). Secondly, we establish an end device (ED) throughput model and energy consumption model and design different adaptive rate algorithms, according to the different link quality considering both the link-level performance and the MAC layer performance. The proposed algorithm uses machine learning to classify the link quality, which can accurately classify the link quality using a small amount of data, compared to other adaptive rate algorithms, and the link parameter adaptation algorithm can maximize the throughput while ensuring the link stability, by considering the link-level performance and the MAC layer performance, compared to other algorithms. The results show that it outperforms the standard LoRaWAN ADR algorithm in both the single ED and the multi ED scenarios, in terms of the packets reception rate (PRR) and the network throughput. Compared to the LoRaWAN ADR in 32 multi-ED scenarios, the proposed algorithm improves the throughput by 34.12% and packets the reception rate by 26%, significantly improving the network throughput and the packets reception rate.

Research and optimization of GPSR routing protocol for vehicular ad-hoc network

Security is one of the most critical issues to Vehicular Ad-hoc Networks (VANETs) since the information transmitted is asynchronous and distributed. Vulnerability and instability are two of the challenges remain to be addressed by the research community and the industry. In this paper, we first proposed a trust reliability based model and extended the GPSR protocol to TM-GPSR protocol. Then, we improved the LET-GPSR protocol based on the link connection time prediction. On this basis, combined the decision index of the TM-GPSR and LET-GPSR protocols, we proposed the RC-GPSR routing protocol. We built the standard testing platform on the NS2 and SUMO, the average end-to-end delay and packet delivery rate of GPSR protocol and the three updates protocols under different node density, node speed, and malicious node ratio are simulated and evaluated. The results showed that under the same conditions, compared with GPSR protocol, RC-GPSR protocol has a lower average end-to-end delay and a higher packet delivery rate, which effectively improves the link stability and security.

Mobility Management UAV‐based Grouping routing protocol in flying ad hoc networks for biomedical applications

SummaryWireless body area networks play a vital role in the medical field by saving human lives. It senses the human body condition and transmits it to the respective doctors. The faster the communication, the sensed data can be transmitted faster and the patients in emergency can be treated on time. Therefore, to increase the speed of communication, Unmanned Aerial Vehicles (UAV) are used in biomedical applications. In this flying ad hoc network, there is a possibility of link failure due to the high mobility of UAV. To overcome this issue, a new routing protocol, Mobility Management UAV‐based Grouping (MMUG), for biomedical data transmission from one ground station to another via UAV is suggested in this research. The UAV creates the grouping system based on the coverage range of the group head. Group head selects the node that lies closer to the ground station. Group head continuously monitors the mobility of the unmanned aerial vehicles that lie in the coverage range and thus helps to maintain the link stability. The mobility issue will be overcome by using this proposed scheme, and it manages the mobility. The mobility management helps to increase the network performance by increasing the delivery rate and throughput and reducing the drop rate and overhead. Simulation results show that this routing protocol achieves 86% delivery rate and 1.95 Mbps throughput and reduces the drop rate by 14%, control overhead by 23%, and routing overhead by 20%. Moreover, this model achieves higher network performance compared to the existing techniques.

An improved multipath video data communication in a vehicular delay-tolerant network.

A vehicular network offers diverse beneficial services related to video streaming in different types of setups, including rural and urban. Some of the recent issues in vehicular communication include prospect of leveraging machine learning and blockchain for privacy and security enhancement, and resource allocation for video streaming coupled with integration of 6G networks for high data rate. Considering the extreme mobility and dynamic structure of vehicular networks and the high data rates of video streams, a unitary route may not support the required quality of a video stream. To achieve load balancing, connectivity among vehicles, path diversity, and low delay, the multipath transmission with a delay-tolerant network (DTN) concept based on a node disjoint algorithm is considered. In this proposed study, video frames are categorized in accordance with priority and forwarded via two graded paths. The first path carries the video reference frame, which is the most important frame for video decoding. The second path carries neighboring frames during video transmission. For the efficient selection of an optimal relay vehicle, a communication cost function is introduced into the existing DTN. This communication cost function is based on three key enhancement parameters: link stability rate, accessible bandwidth estimation, and transmission delay. The improvement in this study, is the integration of store-carry-forward strategy to the existing multipath data forwarding strategy. On the basis of the simulation outcomes, the proposed multipath video data communication in a vehicular DTN (MVDTN) scheme can enhance video data delivery in terms of packet loss ratio, end-to-end delay, structural similarity index measure, and peak signal-to-noise ratio. Considering the aforementioned metrics, our proposed schemes outperform the baseline schemes, namely, road-based multi-metrics forwarder selection evaluation for multipath video streaming and quality of service-aware multipath video streaming for an urban vehicular ad hoc network by using ant colony optimization.