Route Maintenance Research Articles

Efficient Routing in VANETs Using MRRP Algorithm

Designing a reliable routing protocol for Vehicular Ad hoc Network (VANET) poses considerable challenges due to certain unique challenges inherently present in Vehicular Ad hoc Network (VANET) topology. Some of them are needed for vehicles acting as nodes having to abide by traffic rules, uncertain inter-vehicular speed variations that may affect link stability etc. Designing a routing protocol capable of dealing with multiple limiting conditions such as long congestion periods, link failures and handoffs is a challenging task, where most of the existing multipath routing protocol shows poor performance. In this paper, the proposed Multipath Route Restoration Protocol (MRRP)is aimed at providing a robust communication channel in case of link failure between nodes. This is realized by focusing on better route maintenance for the protocol. In a wireless network, a routing protocol determines the particular ways in which routers connect. In a wireless network, as the number of hops in a wireless communication path increases, various signal factors such as interference and path loss degrade the network performance. however, sending data over a longer distance will reduce throughput. Furthermore, link stability is substantially impacted by the unpredictable movement of vehicles. Multipath routing is regarded as a potential solution to improve packet delivery and end-to-end delay in VANETs.

Cross layer protocol architecture for spectrum‐based routing in cognitive radio networks

AbstractNew cell phone services and apps consume more spectrum. Wireless spectrum allows services and apps to communicate with one another. Wi‐Fi quality is improved via smart spectrum usage and new CRT services. The use of spectrum is beneficial. Cross‐layer architecture improves the energy efficiency of wireless networks. System performance is improved by connecting protocol layers. Cross‐layer configuration does not introduce layer functionality into a network. By protecting networks, cross‐layer design increases communication. C‐LNRD uses self‐determined time slots to promote communication. Agents that collect information. At each level, the monitoring agent monitors traffic, time, and topology. Each layer of agents has its own database. Data is received by the network, MAC, and physical layers. Based on its measurements, each node grants trust. Routes were altered. PR ATTACK does not have RTS, CTS, or RREQ to reduce false positives. Spectrum allocation is improved via cognitive radio and learning technologies. Adaptive Cognitive Radio Networks are created using AI, GA, Fuzzy Logic, and Game Theory (ACRN). DSA creates high‐bandwidth MCRNs. This research looks at MCRNs in order to optimise spectrum usage, throughput, routing delay, and overhead. Multihop, the proposed approach by CRN takes into account spectrum awareness, quality route establishment, and route maintenance in the event that a connection fails due to spectrum or a node transfer. New strategies improve the cross‐layer network protocols of MCRN. Learners gain from spectrum models. Sensors and routers are linked by layers. The proposed routing improves both performance and spectrum use.

Design of a Self-Organizing Routing Protocol for Underwater Wireless Sensor Networks Based on Location and Energy Information

Underwater wireless sensor networks (UWSNs) are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, variable topology, limited battery, low processing power and so on. These new features pose many challenges to the design of self-organizing routing protocol for UWSNs. This paper focuses on the application of Ad Hoc On-demand Distance Vector (AODV) routing protocol in UWSNs. In order to solve the problems of packet collision and excessive energy consumption associated with the flooding-based routing discovery method and the periodic hello packet routing maintenance mechanism of AODV, a routing discovery and maintenance method based on location and energy information is proposed, and it is referred to as the route-focusing AODV (RFAODV) routing protocol. In the RFAODV protocol, the routing discovery process is focused on a few nodes through forwarding area control and dynamic delay adjustment. In addition, feedback from a media access control layer and residual energy control are used for routing maintenance. We implement the RFAODV and evaluate its performance according to the sea trial data as parameters in the NS-2. The simulation results show that compared with the other protocols, RFAODV improves the routing discovery success ratio by at least 18%, increases the packet transmission ratio by at least 4%, reduces the protocol overhead by at least 15% and reduces the energy consumption by at least 5% under various simulation scenarios. RFAODV is suitable for large-scale, high-load and dynamic networks underwater wireless sensor networks.

Scheming preventive maintenance assignments for mass rapid transit (MRT): Taoyuan Metro MRT system

Mass rapid transit (MRT) maintenance systems enable owners to make optimal decisions for site selection and preventive maintenance plans while considering the total operating costs. To ensure that the MRT planning system is effective, it is essential to construct a maintenance cost plan. This study implements the mathematical planning method to establish a comprehensive plan for route maintenance and preventive maintenance for the Taoyuan MRT based on the minimum total operating cost as the target, and meets the practical emergency repair time limit, site location distance and other relevant constraints. This study developed and evaluated novel heuristic algorithms with CPLEX software. Sensitivity analysis was also conducted to test the model’s effectiveness. The proposed model establishes the optimum maintenance cost and finds the critical parameters that affect overall cost within 1% error, maintaining service quality. The proposed model is an accurate tool for engineers to reduce time-consuming procedures.

Simultaneous wireless information and power transmission‐based power transfer and energy prediction for efficient communication with golden Taylor sea lion optimization in wireless sensor network

SummaryWireless Sensor Network (WSN) is an emerging lower cost and resourceful solution, which enables controlled observation of the environment. The high amount of energy is required in wireless networks during the transmission of data. Here, Golden Ant Lion Whale Optimization (GALWO) and Golden Taylor Sea Lion Optimization (GTSLnO) techniques are presented for cluster head (CH) selection and prediction of neighbor nodes' age. The six stages performed in this work are setup, steady‐state, prediction, power transfer, communication or route discovery, and route maintenance stages. In the setup level, CH selection is carried out by GALWO, which is the combination of Ant Lion Whale Optimization (ALWO) with Golden Search Optimization (GSO). Moreover, ALWO is an integration of Ant Lion Optimizer (ALO) with the Whale Optimization Algorithm (WOA). In the steady state, the distance, energy, delay, throughput, and trust update are considered as objective functions. In the prediction stage, the Deep Convolutional Neural Network (Deep CNN) is utilized for age prediction of neighbor nodes, wherein Deep CNN is tuned by employing GTSLnO. The GTSLnO is an incorporation of GSO and Taylor series with Sea Lion Optimization (SLnO). Then, the power transfer stage is done utilizing simultaneous wireless information and power transmission (SWIPT). Thereafter, the communication/route discovery stage is conducted for path selection through neighbor node, and lastly, the route maintenance stage is carried out. The GTSLnO–Deep CNN achieved a minimal delay of 0.089 s, maximal residual energy, throughput, and trust of 0.500 J, 98,843 kbps, and 0.452 for DoS attack.

Weight-Based PA-GPSR Protocol Improvement Method in VANET.

Vehicle Ad-hoc network (VANET) can provide technical support and solutions for the construction of intelligent and efficient transportation systems, and the routing protocol directly affects the efficiency of VANET. The rapid movement of nodes and uneven density distribution affect the routing stability and data transmission efficiency in VANET. To improve the local optimality and routing loops of the path-aware greedy perimeter stateless routing protocol (PA-GPSR) in urban sparse networks, a weight-based path-aware greedy perimeter stateless routing protocol (W-PAGPSR) is proposed. The protocol is divided into two stages. Firstly, in the routing establishment stage, the node distance, reliable node density, cumulative communication duration, and node movement direction are integrated to indicate the communication reliability of the node, and the next hop node is selected using the weight greedy forwarding strategy to achieve reliable transmission of data packets. Secondly, in the routing maintenance stage, based on the data packet delivery angle and reliable node density, the next hop node is selected for forwarding using the weight perimeter forwarding strategy to achieve routing repair. The simulation results show that compared to the greedy peripheral stateless routing protocol (GPSR), for the maximum distance-minimum angle greedy peripheral stateless routing (MM-GPSR) and PA-GPSR protocols, the packet loss rate of the protocol is reduced by an average of 24.47%, 25.02%, and 14.12%, respectively; the average end-to-end delay is reduced by an average of 48.34%, 79.96%, and 21.45%, respectively; and the network throughput is increased by an average of 47.68%, 58.39%, and 20.33%, respectively. This protocol improves network throughput while reducing the average end-to-end delay and packet loss rate.

A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks

With the advancement of urban rail transit towards intelligence, the demand for urban rail transit communication has increased significantly, but the traditional urban rail transit vehicle–ground communication system has been unable to meet the future vehicle–ground communication requirements. To improve the performance of vehicle–ground communication, the paper proposes a reliable low-latency multipath routing (RLLMR) algorithm for urban rail transit ad hoc networks. First, RLLMR combines the characteristics of urban rail transit ad hoc networks and uses node location information to configure a proactive multipath to reduce route discovery delay. Second, the number of transmission paths is adaptively adjusted according to the quality of service (QoS) requirements for vehicle–ground communication, and then the optimal path is selected based on the link cost function to improve transmission quality. Third, in order to enhance the reliability of communication, a routing maintenance scheme has been added, and the static node-based local repair scheme is used in routing maintenance to reduce the maintenance cost and time. The simulation results show that compared with traditional AODV and AOMDV protocols, the proposed RLLMR algorithm has good performance in improving latency and is slightly inferior to the AOMDV protocol in improving reliability. However, overall, the throughput of the RLLMR algorithm is better than that of the AOMDV.

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.

Minus HELLO: HELLO Devoid Protocols for Energy Preservation in Mobile Ad-hoc Networks

In mobile ad-hoc networks, nodes have to transmit HELLO or Route Maintenance messages at regular intervals, and all nodes residing within its radio range, reply with an acknowledgment message informing their node identifier, current location, and radio-range. Regular transmitting these messages consume a significant amount of battery power in nodes, especially when the set of down-link neighbors does not change over time and the radio-range of the sender node is large. The present article focuses on this aspect and tries to eliminate the number of HELLO messages in existing state-of-art protocols. Also, it shortens radio-ranges of nodes whenever possible. Moreover, the required number of route re-discovery reduces. Simulation results show that the average lifetime of nodes significantly increases in proposed Minus HELLO devoid routing protocols along with a significant increase in network throughput.

Trust-based co-operative routing for secure communication in mobile ad hoc networks

The working of a Mobile Ad hoc NETwork (MANET) relies on the supportive cooperation among the network nodes. But due to its intrinsic features, a misbehaving node can easily lead to a routing disorder. This paper presents two trust-based routing schemes, namely Trust-based Self-Detection Routing (TSDR) and Trust-based Co-operative Routing (TCOR) designed with an Ad hoc On-demand Distance Vector (AODV) protocol. The proposed work covers a wide range of security challenges, including malicious node identification and prevention, accurate trust quantification, secure trust data sharing, and trusted route maintenance. This brings a prominent solution for mitigating misbehaving nodes and establishing efficient communication in MANET. It is empirically validated based on a performance comparison with the current Evolutionary Self-Cooperative Trust (ESCT) scheme, Generalized Trust Model (GTM), and the conventional AODV protocol. The extensive simulations are conducted against three different varying network scenarios. The results affirm the improved values of eight popular performance metrics overcoming the existing routing schemes. Among the two proposed works, TCOR is more suitable for highly scalable networks; TSDR suits, however, the MANET application better with its small size. This work thus makes a significant contribution to the research community, in contrast to many previous works focusing solely on specific security aspects, and results in a trade-off in the expected values of evaluation parameters and asserts their efficiency.

Landslide Identification Using Optimized Deep Learning Framework Through Data Routing in IoT Application

This paper develops an approach for detecting landslide using IoT. The simulation of IoT is the preliminary step that helps to collect data. The suggested Water Particle Grey Wolf Optimization (WPGWO) is used for the routing. The Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) are combined in the suggested method (WPGWO). The fitness is newly modeled considering energy, link cost, distance, and delay. The maintenance of routes is done to assess the dependability of the network topology. The landslide detection process is carried out at the IoT base station. In feature selection, angular distance is used. Oversampling is used to enrich the data, and Deep Residual Network (DRN) — used for landslide identification — is trained using the proposed Water Cycle Particle Swarm Optimization (WCPSO) method, which combines WCA and PSO. The proposed WCPSO-based DRN offered effective performance with the highest energy of 0.049[Formula: see text]J, throughput of 0.0495, accuracy of 95.7%, sensitivity of 97.2% and specificity of 93.9%. This approach demonstrated improved robustness and produced the global best optimal solution. For the proposed WPGWO, WCA, GWO, and PSO are linked to improve performance in determining the optimum routes. When comparing with existing methods the proposed WCPSO-based DRN offered effective performance.

An Effective Management Model for Data Caching in MANET Environment

A mobile ad-hoc (MANET) network has the main challenge to provide the needed data for the desired mobile nodes. An efficient on request routing protocol for MANET is Ad-hoc on-demand Distance Vector (AODV), which is based on two main methods: route discovery and route maintenance. Route discovery is the process used to detect a route to the destination from the packet source, while route maintenance is the process used to detect a link failure and repair it. Cooperative caching tends improving data availability in mobile ad-hoc networks, the coordination of cache discovery and cache management strategies is very significant in the cooperative caching of MANETs because requests for data and answers to requested data can be reduced simply due to interference, network congestion, or when a forwarding node is out of reach and the route breaks down. Cooperative cache management is much more complicated in cooperative caching because it also depends on neighbouring nodes to decide what to cache. In this paper, three algorithms were proposed: (1) a combination algorithm for cache admission control based on cache data and location of data to save space and reduce data redundancy, (2) a value-based policy for cache placement and replacement instead of the more common least recently used strategy, depending on metrics that describe cached items to increase the local cache hit ratio, and (3) a combined algorithm for cache consistency that includes time-to-live, pull, and push policies to enhance data availability and system scalability. The proposed algorithm implemented by the NS3 simulation program; which used to create a network using the AODV protocol in several parameters and achieve better system performance.

An Effective Management Model for Data Caching in MANET Environment

A mobile ad-hoc (MANET) network has the main challenge to provide the needed data for the desired mobile nodes. An efficient on request routing protocol for MANET is Ad-hoc on-demand Distance Vector (AODV), which is based on two main methods: route discovery and route maintenance. Route discovery is the process used to detect a route to the destination from the packet source, while route maintenance is the process used to detect a link failure and repair it. Cooperative caching tends improving data availability in mobile ad-hoc networks, the coordination of cache discovery and cache management strategies is very significant in the cooperative caching of MANETs because requests for data and answers to requested data can be reduced simply due to interference, network congestion, or when a forwarding node is out of reach and the route breaks down. Cooperative cache management is much more complicated in cooperative caching because it also depends on neighbouring nodes to decide what to cache. In this paper, three algorithms were proposed: (1) a combination algorithm for cache admission control based on cache data and location of data to save space and reduce data redundancy, (2) a value-based policy for cache placement and replacement instead of the more common least recently used strategy, depending on metrics that describe cached items to increase the local cache hit ratio, and (3) a combined algorithm for cache consistency that includes time-to-live, pull, and push policies to enhance data availability and system scalability. The proposed algorithm implemented by the NS3 simulation program; which used to create a network using the AODV protocol in several parameters and achieve better system performance.

An intelligent base link recovery management and path stability in clustered randomly distributed mobile ad hoc networks

MANETs are networks of mobile nodes that are randomly distributed and play a major role in data transmission, route discovery, and route maintenance. Connection recovery and path stability are significant issues in the MANET, making data transfer problematic. When mobile nodes move out of range or when the node lacks adequate energy to maintain connection or path stability, data packet loss happens. We suggest a Clustered Intermediate System-Clustered Intermediate System Local Connection Failure Recovery Algorithm (Clustered Intermediate System-Clustered Intermediate System LLFRA) routing migration technique that builds the node while simultaneously repairing the broken link. Also utilised to look at node energy, energy- drained nodes, and route stability is Dolphin Partner Optimisation (DPO). The suggested approach's primary goal is to guarantee route stability while reducing packet loss. The suggested method is put to the test using the NS-2 simulator. The proposed recovery protocol beats the current technique in charge of PDR, end to end delay, throughput and node failures, according to experimental outputs.

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.

STUDY OF THE PASSENGER TRAFFIC PARAMETERS IN AIR TRANSPORT

The article is an overview of the issue of calculating. The study mainly used the publications of scientists in the chosen field of research, published in recent years, which provides the research with modern scientific achievements in this area. All the publications used are recognized by the scientific community and included in scientometric databases, including Scopus. To obtain the results, the method of system analysis is applied to the methods and approaches used by the authors in solving the problems of calculating the parameters of passenger flows, determining the factors of their formation and modeling such flows. The article formulates the approaches and methods outlined by the authors and reveals certain advantages of the solutions proposed by the authors. The results of the analysis of the development of scientific thought on the issues under consideration are the formation of generalized conclusions regarding the methods of modeling certain transport processes. It is proved that modern scientists pay considerable attention to the modeling of passenger flows in time using mathematical modeling, in particular. Popular methods include neural network methods, computer modeling, and methods for solving problems inherent in engineering networks. As a result, a model was obtained for calculating such parameters as: payback period of the project for the purchase of transport for the route, net and net discounted profit flows, costs of maintaining personnel for route maintenance, passenger correspondence matrix, passenger traffic on the route between network nodes, transferability parameters, and many others. The obtained results make it possible to make systemic decisions on the feasibility of certain actions in the route system and provide probable options for the operation of the route system in its management

CVFP: Energy and trust aware data routing protocol based on Competitive Verse Flower Pollination algorithm in IoT

Internet of Things (IoT) is a network of things that are interconnected to provide a variety of services. These things can include smartphones, actuators, sensors, computers, and wearables. The IoT enables the collection and exchange of data between these objects. The expansion of smart networks is what has given the Internet of Things its global influence. Routing is an important strategy that involves the creation of routes and the transmission of data packets from the source to the intended recipient in the network. Energy and trust are considered to be the two most important parts that provide secure data transmission in a network. However, secure routing in the highly distributed and dynamic environment of IoT still results in a great challenge due to the heterogeneity of devices. Hence, an efficient secure data routing mechanism is designed with the proposed Competitive Verse Flower Pollination (CVFP) algorithm that considers trust, energy, delay, distance, and link lifetime (LLT) parameters to find an optimal routing path. Moreover, the selection of optimal path is made by involving CH nodes such that nodes have higher energy are chosen in the routing strategy. The quality of the link is measured using the LLT constraint in order to decide whether data can be routed or to perform a route maintenance mechanism to ensure reliability in data routing. The performance of the proposed algorithm is analyzed using the simulated data. The proposed algorithm attains a lower delay of 0.204 s, higher residual energy of 0.093 J, a maximum throughput of 94.59%, and a higher trust of 0.713.

A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc networks

Air pollution has changed ecosystem and atmosphere. It is dangerous for environment, human health, and other living creatures. This contamination is due to various industrial and chemical pollutants, which reduce air, water, and soil quality. Therefore, air quality monitoring is essential. Flying ad hoc networks (FANETs) are an effective solution for intelligent air quality monitoring and evaluation. A FANET-based air quality monitoring system uses unmanned aerial vehicles (UAVs) to measure air pollutants. Therefore, these systems have particular features, such as the movement of UAVs in three-dimensional area, high dynamism, quick topological changes, constrained resources, and low density of UAVs in the network. Therefore, the routing issue is a fundamental challenge in these systems. In this paper, we introduce a Q-learning-based routing method called QFAN for intelligent air quality monitoring systems. The proposed method consists of two parts: route discovery and route maintenance. In the part one, a Q-learning-based route discovery mechanism is designed. Also, we propose a filtering parameter to filter some UAVs in the network and restrict the search space. In the route maintenance phase, QFAN seeks to detect and correct the paths near to breakdown. Moreover, QFAN can quickly identify and replace the failed paths. Finally, QFAN is simulated using NS2 to assess its performance. The simulation results show that QFAN surpasses other routing approaches with regard to end-to-end delay, packet delivery ratio, energy consumption, and network lifetime. However, communication overhead has been increased slightly in QFAN.

SAOA: Multi-Objective Fault-Tolerance Based Optimized RPL Routing Protocol in Internet of Things

Several technologies are implemented for serving industrial information and controlling the process in which the increased complexity and allied fault failed to achieve a reliable transmission to activate the timely network. These data must be transferred from one node to another node without any fault or delay in their nodes. To resolve this problem, an algorithm named SAOA is proposed to communicate the messages from various services without any delay in their nodes. IoT with mobile sink node is simulated in the network based on the MoO4RPL objective and the phases involved in this strategy are topology, route discovery, communication, and route maintenance phase. The topology network is generated for network topology and the rank is computed based on the energy, trust, delay, fault tolerance, and link quality in the multi-objective route discovery phase. In the communication phase, the optimal route is selected using proposed SAOA with fitness function factors. At last, the route maintenance phase is performed, and the proposed SAOA has achieved a minimum distance of 53.807 m, maximum energy of 0.415 J, maximum link quality of 2.163, and maximum trust of 0.701, respectively. In the future, another algorithm is implemented in IoT applications with minimum modifications to evaluate the various metric values.

RUBER: Recoverable UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network

This paper is a sequel to a previous article by the authors in which UAV-based energy-efficient reconfigurable routing (UBER) scheme was proposed to address coverage loss and rapid energy depletion issues for smart wireless livestock sensor networks. Sensor node and route failure issues associated with the UBER scheme are therefore addressed in this research by proposing a recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme. RUBER scheme relies on an efficient fault detection and recycling technique, dynamic recovery mechanism, and robust route maintenance technique. Performance of RUBER was analyzed under low, medium and high failure rate network conditions. Performance indices employed for this assessment are failure detection ratio (FDR), failure recovery ratio (FRR), load balancing ratio (LBR), and packet delivery ratio (PDR). Analysis results demonstrated that keeping the failure rate below 10% led to performance improvements of 60.96%, 74.14%, 64.68%, and 60.74% for FDR, FRR, LBR, and PDR, respectively. Performance comparison of RUBER was conducted against UBER and hybrid heterogeneous routing (HYBRID) schemes. Performance metrics utilized for this comparative evaluation are average routing delay (ARD), energy dissipation ratio (EDR), routing overhead (ROH), fault tolerance index (FTI), nodal failure recovered (NFR), route failure recovered (RFR), and cluster survival ratio (CSR). With respect to best-case values, RUBER exhibited gains of 79.67%, 44.98%, 67.88%, 74.90%, 52.20%, 70.56%, and 52.52% over UBER and HYBRID in terms of ARD, EDR, ROH, FTI, NFR, RFR, and CSR respectively. Simulation experiments revealed the relative competitiveness of the RUBER scheme against the benchmarked schemes.