Conventional Routing Protocols Research Articles

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Routing protocols, as a crucial component of the internet of things (IoT), play a significant role in data collection and environmental monitoring tasks. However, existing clustering routing protocols suffer from issues such as uneven network energy consumption, high communication delays, and inadequate adaptation to topology changes. To address these issues, this study proposes an adaptive routing algorithm to balance energy consumption and delay using game theory and deep Q-network (DQN) algorithms (EDRP-GTDQN). Specifically, EDRP-GTDQN evaluates the importance of node positions using node centrality and integrates a game-theoretic-based approach to select optimal cluster heads in terms of node centrality and residual energy. Moreover, graph convolutional networks (GCN) and DQN are incorporated to construct transmission paths for cluster heads, adapt to network topology changes, and balance energy consumption and performance. Furthermore, a cluster rotation mechanism is employed to optimize overall network energy consumption and prevent the formation of hotspots. Experimental results demonstrate that EDRP-GTDQN achieves average performance improvements of 19.76%, 30.04%, 44.2%, and 61.42% in average energy consumption, network lifetime, and average end-to-end delay compared to conventional routing protocols such as EECRAIFA, MRP-GTCO, DEEC, and MH-LEACH. Therefore, EDRP-GTDQN is undoubtedly an effective solution to reduce energy consumption and enhance service quality in wireless sensor networks.

Dynamic multipath routing for energy‐efficient and reliable communication in 6G networks with MIMO

AbstractIn the era of 6G networks, Multiple Input Multiple Output (MIMO) technology offers unprecedented opportunities for high‐throughput and low‐latency communication. Existing communication frameworks, however, have difficulty optimizing both energy efficiency and reliability at the same time. In most cases, conventional routing protocols fail to meet the needs of MIMO systems, making them inefficient and prone to reliability problems due to their inability to dynamically adapt to different network conditions. This research addresses the intricate interplay between energy efficiency and reliability within the context of 6G networks with MIMO. The motivation for this research arises from the imperative to unlock the full potential of 6G networks with MIMO for achieving energy‐efficient and reliable communication. With the advancement of communication technology, seamless connectivity, minimal energy consumption, and robust reliability become increasingly critical. Currently, solutions cannot adapt dynamically to the diverse and dynamic conditions of a 6G environment. Through this research, we aim to bridge this gap, enhancing 6G network performance and sustainability with unprecedented gains in energy efficiency and reliability. We have developed the Dynamic Multipath Routing (DMR) algorithm by harnessing the advanced features of MIMO technology. The DMR algorithm strategically chooses paths to minimize the effects of fading, interference, and channel impairments, creating a resilient communication network. This improvement is essential for meeting the demanding connectivity needs of various 6G applications, covering ultra‐reliable low‐latency communication and massive machine‐type communication.

Enhancing Intrusion Detection Systems through Distributed Ledger Technology for Secure Logging

Nodes in Mobile Ad Hoc Networks (MANETs) are limited battery powered. That’s why energy efficient routing has become an important optimization criterion in MANETs. The conventional routing protocols do not consider energy of the nodes while selecting routes which leads to early exhaustion of nodes and partitioning of the network. This paper attempts to provide an energy aware routing algorithm. The proposed algorithm finds the transmission energy between the nodes relative to the distance and the performance of the algorithm is analyzed between two metrics Total Transmission energy of a route and Maximum Number of Hops. The proposed algorithm shows efficient energy utilization and increased network lifetime with total transmission energy metric.

Research on gaussian mixture model and its distributed data mining algorithm in wireless sensor networks

Optimization of the routing represents an important challenge when considering the rapid development of Wireless Sensor Networks (WSN), which involve efficient energy methods. Applying the effectiveness of a Deep Neural Network (DNN) and a Gaussian Mixture Model (GMM), the present article proposes an innovative method for attaining Energy-Efficient Routing (EER) in WSN. When it comes to dealing with dynamic network issues, conventional routing protocols generally conflict, resulting in unsustainable Energy consumption (EC). By applying algorithms based on data mining to adapt routing selections in an effective procedure, the GMM + DNN methodology that has been developed is able to successfully address this problem. The GMM is a fundamental Feature Extraction (FE) method for accurately representing the features of statistical analysis associated with network parameters like signal frequency, the amount of traffic, and channel states. By learning from previous data collection, the DNN, which relies on these FE, provides improved routing selections, resulting in more efficient use of energy. Since routing paths are constantly optimized to ensure dynamic adaptation, where less energy is used, networks last longer and perform more efficiently. Network simulations highlight the GMM + DNN method’s effectiveness and depict how it outperforms conventional routing methods while preserving network connectivity and data throughput. The GMM + DNN’s adaptability to multiple network topologies and traffic patterns and its durability make it an efficient EER technique in the diverse WSN context. The GMM + DNN achieves an EC of 0.561 J, outperforming the existing state-of-the-art techniques.

An energy-efficient cross-layer-based opportunistic routing protocol and partially informed sparse autoencoder for data transfer in wireless sensor network

When sensor nodes in wireless sensor networks (WSNs) have limited energy, data minimization is crucial. Usually, data communications use up energy. A sensor node's lifespan can frequently be increased by sending and receiving the proper quantity of data. The PISAE (Partially Informed Sparse Autoencoder) is a state-of-the-art unmapped neural network architecture designed to reconstruct all sensor inputs from a limited number of sensors. It is presented in this publication. Use this architecture to create a system for selecting sensors. We now propose the cross-layer based opportunistic routing protocol (CORP) as an opportunistic routing mechanism for WSNs. In order to cut down on processing time and energy consumption and increase the dependability of data transfer, the best way is selected utilizing the CORP technique, which is suggested.For energy-efficient routing based combinatorial random sampling bat optimisation (ERFN-CSSBO), we also suggest using fuzzy neural networks. This enables you to conserve energy, increase the lifespan of your wireless sensor network, and keep your energy consumption in check. With CSSBO (Combined Random Sampling Prevosti Bat Optimisation), the best path is identified by integrating features (such as distance, energy, trust, and internode measurement connection stability) from all the bats.The key challenge in WSN is choosing cluster heads (CH). K-Medoid is used to enhance sensor node clustering. Important considerations include the effect on quality of service (QoS), sensor node position, proximity, and energy state needs. This study combines Hybrid BFO (Bacterial Foraging Optimization) and HSA (Harmony Search Algorithm), two well-known optimization techniques, to pick cluster heads in wireless sensor networks that are optimal in terms of distance and energy. On-task completion. The outcomes of the simulation indicate that the proposed technique improves QoS. Endpoints, throughput (1.0 Mbps), (98.5 % of packets are forwarded), and packet loss rate (1.5 %), and other QoS factors are all included in the performance statistics plotted. It performs better than conventional routing protocols in terms of network lifetime (6100 rounds), delay at both ends (1.5 s), and energy usage (30.35 mJ).

Secure and Energy-Based STEERA Routing Protocol for Wireless Sensor Networks

In wireless sensor networks (WSN), the multifunctional low-power sensor nodes are linked to base stations and are highly responsible for sensing various information. However, maintaining the network’s lifespan is a major issue because of limited battery capacity and node failures. Thus, the proposed study introduced a new Scalable Trust-based Energy Efficient Routing Algorithm (STEERA) with Adaptive Grasshopper Algorithm (AGA) to enhance the network lifespan. At first, the trust values (Tv) are generated for each node to eliminate the malicious node with the help of penalty factor and volatilization. To make efficient routing process, the nodes are formed into cluster groups. A suitable cluster head is selected for each cluster to mitigate the energy consumption problem through the AGA approach by optimizing the parameters like Tv, distance and residual energy. Finally, the data packets are effectively transmitted through a multi-hop routing process by selecting an appropriate path and satisfying certain parameters through Fire Hawks Optimization (FHO). To simulate the proposed techniques, NS2 software is employed, and the performance is measured over various metrics like energy consumption, residual energy, the lifespan of a network, packet loss ratio and average end-to-end delay. The performance analysis shows that the proposed protocol design has obtained higher results than conventional routing protocols.

Quality of Service Management for Fast Data Transmission in Industrial Mobile Communications Using PSO-Based Computer Vision Technique

Mobile ad hoc networks (MANETs) have rapidly expanded in recent years, mostly as a result of the mobile devices’ obvious low cost, heterogeneity, and flexibility. When communication networks are down or inaccessible, sensors can quickly create a reliable network that can be used as a rescue data system. Industrial mobile communication has developed into a significant study area for both industry and academia in recent years. The need for data interchange between various smart devices with various latency flows is enormous. Nevertheless, there has not been as much research done in this area. The suggested work suggests a fuzzy-based improved PSO optimized in MANET to alleviate the drawbacks of the conventional routing approach. The suggested study offers a numerical modelling that can be used to carry out adaptive transmission optimization with a variety of programmable module structures and guarantee cost-effective route establishment with greater throughput, goodput, and lowest delay requirements. To find the best route, the proposed approach combines energy-optimized route construction with data-driven cluster head (CH) selection based on swarm intelligence. Particle swarm optimization- (PSO-) based clustering achieves improved delay, goodput, throughput, and path difference degree as compared to other conventional approaches, according to the extensive simulation results. The energy efficiency of a network that is decentralized is more important. The MANET device’s energy efficiency helps to extend battery life and improve network performance. This research demonstrates how the fuzzy-based improved PSO optimized in MANET helps to raise the network’s energy efficiency. As a result, network energy conservation improves network performance and battery life. Additionally, this method enhances the quality of service methodology. End-to-end delay, energy consumption, packet delivery ratio, and normalized routing overhead are measured and compared between the simulation and conventional routing protocols.

An Efficient LEACH Clustering Protocol to Enhance the QoS of WSN

The development of an energy-efficient routing protocol for wireless sensor networks has been a challenging task for academics. It is very different from the conventional routing protocols, which are based on I.P. addresses. These conventional routing protocols are not preferable for WSNs since conventional routing protocols depend heavily on the routing tables, which often require updates. Also, the WSN varies from thousands to ten thousand, making the task of managing routing tables not easy and economical in terms of hardware resources. There is always research going on to develop an efficient routing protocol in terms of energy for WSNs. LEACH protocol is one of them. Energy consumption in WSNs became a vital factor to be focused on in enforcing an efficient routing strategy. Numerous LEACH protocol variations propose improvements to the current Protocol. In our study, we looked at different LEACH protocol iterations and adopted a modified LEACH protocol to extend the lifetime of wireless sensor networks. We adopted various power levels for transmission between the cluster node, cluster head, and base station, as well as a novel method for choosing the cluster head. Our modified Protocol performs better when compared to parameters like network lifetime, dead nodes per round, Cluster heads formed, packets sent to the base station, etc.

Joint Shortest Chain and Fair Transmission Design for Energy-Balanced PEGASIS in WSNs

The conventional routing protocol considers several local transmission factors in a single node for the many-to-one packet transmission. These factors lead to rapid energy consumption in some specific nodes, thereby generating energy holes to reduce network lifetime. In this article, a novel energy-balanced power-efficient gathering in sensor information systems (EB-PEGASISs) is proposed to improve energy efficiency and utilization for wireless sensor networks (WSNs). In the original power-efficient gathering in sensor information system (PEGASIS), the protocol constructs a chain-based network with the locally shortest distance rather than the globally optimal network length. To improve this, two construction algorithms are proposed to achieve the shortest network length in the EB-PEGASIS, including centralized formation and distributed construction schemes. In the centralized algorithm, the chain length of each starting node is computed, and the minimum chain length can be subsequently determined in the following packet transmission phase. In order to reduce formation complexity, a distributed method uses the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering algorithm to partition the network into clusters. In particular, it forms a subchain in each cluster and connects each subchain into the final shortest chain. Additionally, to balance the energy consumption among all nodes in the packet transmission phase, Jain’s fairness index for residual battery capacity is designed to minimize the fluctuation of energy consumption among nodes in a network. As a result, not only energy efficiency is achieved with the shortest chain length but also energy utilization is balanced for packet transmission. Ultimately, simulation results validate that the network lifetime of the EB-PEGASIS is about 2.46 times the original PEGASIS and 3.36 times the random projection-polar coordinate-chain (RPC) protocol for WSNs.

WSO‐T2FSM: War strategy optimization‐based type‐2 fuzzy‐based starling murmuration for addressing the routing problem in mobile ad hoc network

SummaryNowadays, research based on mobile ad hoc network's (MANET) performance improvement has drawn a particular concentration from scientists worldwide. The shortest path selection among destination and source nodes determines the network's throughput in MANET. In addition, power management and energy consumption are also the major difficulties that MANET suffers. The conventional routing protocols are intended for static bonding, but that protocol does not satisfy the stability in ad hoc networks by means of regularly changing connectivity. This shows the routing problem in MANETs. Therefore, to overcome the routing issues and maintain energy efficiently, the Type‐2 fuzzy‐based Starling Murmuration Optimizer (SMO)‐War Strategy Optimization (WSO) (T2FSMO‐WSO) routing protocol is proposed in this paper. MANET takes into account four input parameters: route length, active link ratio to global traffic, network lifetime, and control packet ratio. The SMO algorithm is employed to choose the relay node with low energy consumption. The trapezoidal membership functions are used to describe the fuzzy sets for these parameters. The content‐based routing among source and destination nodes is determined using the WSO algorithm. The simulation outcomes reveal that the proposed method discovers highly stable paths with minimum distance and minimized the consumption of energy.

VORTEX: Network-Driven Opportunistic Routing for Ad Hoc Networks

The potential of ad hoc networks, which enable flexible and dynamic network establishment only by mobile terminals equipped with wireless communication devices, has recently attracted attention for the coming IoT era. Although the nature of ad hoc networks shows the advantages of their autonomous and distributed network management, a manifestation of drawbacks owing to the nature of wireless communication and the mobility of terminals are inevitable. Many routing protocols have already been proposed to address the issues by adapting to nature and achieving a certain level of improvement. However, the routing protocols still suffer from difficulties in information collection for routing and adaptive route management during communication. Moreover, there is another issue that end pair-based routing procedures prevent other end pairs from reusing the routing information effectively. To address the drawbacks of conventional routing protocols, this paper proposes VORTEX, a novel routing protocol that employs an opportunistic routing strategy using hierarchization. One of the characteristic features of VORTEX is its network-driven opportunistic forwarding, in which packets travel toward destination terminals using hierarchy as a guide without conventional route discovery procedures. Moreover, another characteristic feature of VORTEX is that the hierarchical structure also contributes to adapting to communication environment changes in an autonomous manner. In other words, VORTEX enables flexible network-wide information-based routing only with the locally collected information. The simulation results show that the proposed VORTEX could provide better service quality and reliability with improved efficiency compared to the conventional routing protocols. Furthermore, the most significant contribution is not only in the communication performance but also VORTEX omits route discovery or route maintenance from routing protocols, and formed networks themselves have a function to deliver packets toward destination terminals.

ESTEEM – Enhanced stability and throughput for energy efficient multihop routing based on Markov Chain Model in wireless body area networks

Wireless Medical Body Area Sensor Network (WMBASN) monitors vital human biological parameters used for various healthcare and fitness applications. The design and development of a reliable WMBASN routing protocol with improved network performance are significant tasks for researchers and healthcare industries. This work focuses on multi-hop routing to select a relay node to collect data from far away sensor nodes and to Central Coordinator Node (CCN). A novel approach for choosing best fitted Assistive Node (AN) by incorporating Hidden Markov Chain Model (HMCM) called Enhanced Stability and Throughput for Energy Efficient Multihop routing protocol (ESTEEM). Selection Function selects the best-fitted AN based on HMCM relating to maximum residual energy and minimum distance between the sensor node and CCN. The outcome of the proposed ESTEEM achieves increased throughput, better stability with the first dead node and extended network lifetime with the last dead node at 71% and 95.1% of total simulation time, respectively. In addition, the proposed work also concentrates on CCN placed at different positions on the human body to analyze and identify its best position for better routing. Overall, an improved multi-hop, energy efficiency and better quality of service are achieved based on specific network parameters with random postural movements through ESTEEM, and the results are superior to conventional routing protocols.

Hierarchical energy efficient secure routing protocol for optimal route selection in wireless body area networks

Growth in technology has witnessed the comfort of an individual in domestic and professional life. Although, such existence was not able to meet the medical emergencies during the pandemic COVID-19 and during other health monitoring scenarios. This demand is due to the untouched Quality of Service network parameters like throughput, reliability, security etc. Hence, remote health monitoring systems for the patients who have undergone a medical surgery, bed ridden patients, autism affected subjects etc is in need that considers postural change and then forward to the caretaker in hospitals through wireless body area networks (WBAN). Security in these data are very important as it deals with the life of a subject. In this work, a Hierarchical Energy Efficient Secure Routing protocol (HEESR) is proposed that categorizes the deployed body nodes in to direct node and relay node based on the threshold vale. Unlike other conventional protocols the cluster head selection is based on the energy levels and the traffic priority data like critical and non-critical data, followed by an optimal route to forward the acquired data is identified and the data is compressed using Huffman encoding technique and encrypted using asymmetric cryptographic algorithm for secure data transmission. This protocol mainly appends security and routing efficiency in a hierarchical pattern through data prioritization and out performs the other conventional routing protocols by yielding a better energy consumption of 6%, throughput 92% and security of 93%, which has balanced the packet drop rate considerably and deliver the data within the stipulated time period.

UW-GRE: Underwater Greedy Geographic Routing by Network Embedding

This paper presents the novel routing protocol called Underwater Greedy Geographic Routing by Network Embedding (UW-GRE) for underwater wireless networks. Geographic routing is a promising network routing protocol strategy for wireless networks because it is stateless. However, this routing strategy faces various problems in the underwater environment. This is caused by some challenging characteristics of this environment, such as its 3D nature and the no propagation of GPS signal due to high radio frequency signal attenuation. Accurate localization, void regions, and guaranteed delivery are then some of the problems faced by this routing strategy in this environment. In this paper, we solve those problems efficiently by using geographic routing over virtual coordinates obtained through a network embedding in an n-dimensional (n≥ 2) Euclidean virtual space. By properly embedding the network in this virtual Euclidean space, no geographic information is needed. We implemented the protocol on ns-3. Our results show that, when compared to a conventional geographic routing protocol with perfect localization information, our protocol improves the number of transmissions and energy consumption by up to or higher than 30%, end-to-end delay by up to 28%, while achieving similar network throughput.

Determining Optimal Zone Radius of Zone Routing Protocol Based on Deep Recurrent Neural Networks in the Next Generation Wireless Backhaul Networks

Next-generation wireless networks are becoming more popular and rely on reliable backhaul networks to work properly. Wireless backhaul networks also adopt various innovative technologies to improve capacity and provide more flexible deployments to meet networks' high-quality requirements. One of the essential innovations to maintain the wireless backhaul performance is combining the existing routing protocol technology and the deep learning concept. The concept of deep learning is gaining traction as a powerful way to add intelligence to wireless networks with complex topologies and radio environments. This is because conventional routing protocols do not learn from their previous experiences with various network anomalies. This paper proposed a predictive model of zone radius value using the deep recurrent neural network variant, namely the long short-term memory recurrent neural network (LSTM-RNN) algorithm. Determination of zone radius value conducted by measuring the whole of nodes routing zone using various network performance as input parameters such as Routing Overhead, Energy Consumption, Throughput, and User Usage. Performance measurements such as mean square error (MSE), error distribution histogram, training state, regression, correlation, and time series response are gauged and compared for static and mobile node environments. Results showed that the proposed algorithm can accurately predict zone radius for both environments. However, the accuracy of the proposed algorithm is higher when implemented in a static node environment.

Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols

The planet is the most water-rich place because the oceans cover more than 75% of its land area. Because of the unique activities that occur in the depths, we know very little about oceans. Underwater wireless sensors are tools that can continuously transmit data to one of the source sensors while monitoring and recording their surroundings’ physical and environmental parameters. An Underwater Wireless Sensor Network (UWSN) is the name given to the network created by collecting these underwater wireless sensors. This particular technology has a random path loss model due to the time-varying nature of channel parameters. Data transmission between underwater wireless sensor nodes requires a careful selection of routing protocols. By changing the number of nodes in the model and the maximum speed of each node, performance parameters, such as average transmission delay, average jitter, percentage of utilization, and power used in transmit and receive modes, are explored. This paper focuses on UWSN performance analysis, comparing various routing protocols. A network path using the source-tree adaptive routing-least overhead routing approach (STAR-LORA) Protocol exhibits 85.3% lower jitter than conventional routing protocols. Interestingly, the fisheye routing protocol achieves a 91.4% higher utilization percentage than its counterparts. The results obtained using the QualNet 7.1 simulator suggest the suitability of routing protocols in UWSN.

An Efficient Location-Based Forwarding Strategy for Named Data Networking and LEO Satellite Communications

Low Earth orbit (LEO) satellite constellations are increasingly gaining attention as future global Internet providers. At the same time, named data networking (NDN) is a new data-centric architecture that has been recently proposed to replace the classic TCP/IP architecture since it is particularly well suited to the most common usage of the Internet nowadays as a content delivery network. Certainly, the use of NDN is especially convenient in highly dynamic network environments, such as those of next LEO constellations incorporating inter-satellite links (ISL). Among other native facilities, such as inbuilt security, NDN readily supports the mobility of clients, thus helping to overcome one of the main problems raised in LEO satellite networks. Moreover, thanks to a stateful forwarding plane with support for multicast transmission and inbuilt data caches, NDN is also able to provide a more efficient usage of the installed transmission capacity. In this paper, we propose a new location-based forwarding strategy for LEO satellite networks that takes advantage of the knowledge of the relative position of the satellites and the grid structure formed by the ISLs to perform the forwarding of NDN packets. So, forwarding at each node is done using only local information (node and destination locations), without the need of interchanging information between nodes, as is the case with conventional routing protocols. Using simulation, we show that the proposed forwarding strategy is a good candidate to promote the efficient and effective future use of the NDN architecture in LEO satellite networks.

Design of a novel micro‐Zone adaptive Energy‐Trust evaluation Active On‐Demand Vector protocol to optimize communication in challenging and attacked mobile network

SummaryHigh congestion, mobility, and energy restrictions are the critical challenges to mobile networks. The conventional routing protocols are unable to ensure effective and reliable communication in such dynamic and unpredictable situations. In this paper, a novel micro‐Zone adaptive Energy‐Trust evaluation Active On‐Demand Vector (mZ‐ETAODV) protocol is designed to provide routing solutions in extreme conditions. In this work, the complete network is divided into smaller zones of nxn. The extreme zone position‐based evaluation is conducted to handle the stability and to identify the next intermediate node. The zone‐covered neighbors are further computed under energy and trust measures to identify the most reliable neighbor. The proposed protocol is simulated under high mobility, congestion and man‐in‐middle attack situations. The analytical observations are recorded in terms of Packet Delivery Ratio (PDR) ratio, energy consumption, delay and communication loss parameters. The comparative results are generated against AODV, SAODV, PSAODV, PDS‐AODV, NVS‐AODV, iNVS‐AODV, Tripathy et al., DFMCRP, FBSSR, MBDP‐AODV, AGHA, and Singh et al. protocols. The experimentation is conducted in a highly challenging mobile network with high congestion, high mobility, energy restriction, existence of blackhole, grayhole, DDOS, and mixed attacks. These attacks are performed with 0% to 10% density, and analysis is conducted against PDR ratio, communication delay, route switching, dead node count, remaining energy, and attack detection rate measures. The analysis results identified that the proposed protocol reduced the communication delay and energy consumption and achieved significant gain in attack detection rate, network life and PDR ratio.

Design and implementation of event-based multicast AODV routing protocol for ubiquitous network

We designed and implemented the ECA (Event Condition Action) based Multicast Ad hoc On-demand Distance Vector (ECA-MAODV) routing protocol for the ubiquitous network. The ECA-MADOV protocol plays a predominant role in selecting an efficient route for the creation of an efficient multicast tree and selecting a unique group leader in the multicast group to share and exchange information among group members in an efficient way using a Computational Intelligence (CI) approach. The vague set is a CI that is an enhanced fragment of a fuzzy set. Each element in the Fuzzy set is mapped to [0,1], reflecting its membership grade. We estimated ECA-MAODV’s time and spatial complexity. Besides, we assessed ECA-MAODV performance and compared it with conventional routing protocols such as MAODV and AODV. Finally, simulation results indicate that the ECA scheme proposed is both effective and powerful.

Enhanced UAV-aided vehicular delay tolerant network (VDTN) routing for urban environment using a bio-inspired approach

The evolution of VDTN routing came to solve the problem of routing in sparse Vehicular Ad-hoc Networks (VANETs). Recently, the implication of Unmanned Aerial Vehicles (UAVs) in VANET communication has gathered quicker information dissemination than Roadside Units (RSUs) in urban VANETs, where ground vehicles’ mobility is restricted by the topology of city roads. Gradually, UAVs are emerging for VDTNs: UAV-aided VDTN (UVDTN) routing field has been introduced for sparse areas in order to reduce the network gaps between Vehicle-to-Vehicle (V2V) connections. Meanwhile, the conventional UVDTN routing protocols expose the short contact opportunity time in the case of Vehicle-to-UAV (V2U) and UAV-to-Vehicle (U2V) connections. This constraint entails more congested bundles’ flooding rates and a higher number of lost bundle copies compared to V2V-based forwarding. In this paper, a bio-inspired UVDTN routing protocol is conceived to improve the bundles’ forwarding between ground vehicles and UAVs in terms of flooding optimization and Quality-of-Service (QoS) performances for urban VANET environments. For this purpose, a swarm-inspired approach called the Social Spider Optimization (SSO) is proposed. SSO seeks to find a better selection of next Store-Carry-and-Forward (SCF) relay nodes specifically for V2U and U2V connections. In order to control the bundle replication rate and enhance bundles’ delivery ratio and delay, the proposed bio-inspired UVDTN protocol adjusts the SCF selection decision. The latter is based on predefined historical knowledge-based forwarding parameters and urban geographic topology information. The proposed bio-inspired routing protocol is simulated using the Opportunistic Network Environment (ONE) simulator and compared to a few VDTN and UVDTN routing protocols.