Multi-hop Routing Protocol Research Articles

An Energy-Efficient Multihop Routing Protocol for 3-D Bridge Wireless Sensor Network Based on Secretary Bird Optimization Algorithm

An Energy-Efficient Multihop Routing Protocol for 3-D Bridge Wireless Sensor Network Based on Secretary Bird Optimization Algorithm

High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks.

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

Markov-reward based estimation of the idle-time in vehicular networks to improve multimetric routing protocols

Analyzing vehicular ad hoc networks (VANETs) poses a considerable challenge due to their constantly changing network topology and scarce network resources. Furthermore, defining suitable routing metrics for adaptive algorithms is a particularly hard task since these adaptive decisions should be taken according to the current conditions of the VANET. The literature contains different approaches aimed at optimizing the usage of wireless network resources. In a previous study, we introduced an analytical model based on a straightforward Markov reward chain (MRC) to capture transient measurements of the idle time of the link formed between two VANET nodes, which we denote as Tidle. This current study focuses on modeling and analyzing the influence of Tidle on adaptive decision mechanisms. Leveraging our MRC models, we have derived a concise equation to compute Tidle. This equation provides a quick evaluation of Tidle, facilitating quick adaptive routing decisions that align with the current VANET conditions. We have integrated our Tidle evaluation into multihop routing protocols. We specifically compare performance results of the 3MRP protocol with an enhanced version, I3MRP, which incorporates our Tidle metric. Simulation results demonstrate that integrating Tidle as a decision metric in the routing protocol enhances the performance of VANETs in terms of packet losses, packet delay, and throughput. The findings consistently indicate that I3MRP outperforms 3MRP by up to 50% in various scenarios across high, medium, and low vehicular densities.

Game Theory-Based Multi-Hop Routing Protocol with Metaheuristic Optimization-Based Clustering Process in WSN for Precision Agriculture

Game Theory-Based Multi-Hop Routing Protocol with Metaheuristic Optimization-Based Clustering Process in WSN for Precision Agriculture

Multiple-Junction-Based Traffic-Aware Routing Protocol Using ACO Algorithm in Urban Vehicular Networks.

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average.

Yellow Saddle Goatfish Optimization Algorithm based Energy Aware Multihop Routing Protocol for Wireless Sensor Networks

Yellow Saddle Goatfish Optimization Algorithm based Energy Aware Multihop Routing Protocol for Wireless Sensor Networks

Multi-Hop Clustering and Routing Protocol Based on Enhanced Snake Optimizer and Golden Jackal Optimization in WSNs.

A collection of smaller, less expensive sensor nodes called wireless sensor networks (WSNs) use their sensing range to gather environmental data. Data are sent in a multi-hop manner from the sensing node to the base station (BS). The bulk of these sensor nodes run on batteries, which makes replacement and maintenance somewhat difficult. Preserving the network's energy efficiency is essential to its longevity. In this study, we propose an energy-efficient multi-hop routing protocol called ESO-GJO, which combines the enhanced Snake Optimizer (SO) and Golden Jackal Optimization (GJO). The ESO-GJO method first applies the traditional SO algorithm and then integrates the Brownian motion function in the exploitation stage. The process then integrates multiple parameters, including the energy consumption of the cluster head (CH), node degree of CH, and distance between node and BS to create a fitness function that is used to choose a group of appropriate CHs. Lastly, a multi-hop routing path between CH and BS is created using the GJO optimization technique. According to simulation results, the suggested scheme outperforms LSA, LEACH-IACA, and LEACH-ANT in terms of lowering network energy consumption and extending network lifetime.

Advanced Recursive Best-First Search (RBFS) based Routing Protocol for Multi-hop and Multi-Channel Cognitive Wireless Mesh Networks

Cognitive Wireless Mesh Network (CWMN) is an opportunistic network in which radio channels can be assigned according to their availability to establish connections among nodes. After establishing a radio connection among nodes, it is necessary to find an optimal route from the source node to the destination node in the network. If there remain more channels among nodes, the minimum weighted channel should be taken into account to establish expected routes. The graph theoretic approach fails to model the multi-channel cognitive radio networks due to abrupt failure in finding new successful routes as it can’t figure multi-channel networks. In this paper, a multi-edged graph model is being proposed to overcome the problems of cognitive radio networks, such as flooding problems, channel accessing problems etc. A new channel accessing algorithm has been introduced, and optimal routes have been selected using a heuristic algorithm named RBFS. Simulation results are compared with DJKSTRA based routing protocols.

Parallel Deployment and Performance Analysis of a Multi-Hop Routing Protocol for 5G Backhaul Networks Using Cloud and HPC Platforms

Parallel Deployment and Performance Analysis of a Multi-Hop Routing Protocol for 5G Backhaul Networks Using Cloud and HPC Platforms

HDRA: A Haybird Data Reduction and Routing Algorithm

Presently, wireless sensor networks (WSNs) are emerging as a vibrant field of research due to various challenging aspects such as energy consumption, routing strategies, effectiveness, among others. Despite unresolved issues within WSNs, a substantial array of applications has already been developed. For any application design, a primary objective is to optimize the WSN in terms of its lifecycle and functionality. Recent studies on data reduction methods have shown that sensor nodes often transmit data directly (single hop) to the base station (BS). However, a significant concern is that most existing multi-hop routing protocols do not address data reduction before forwarding data to the BS. Consequently, this study introduces a Hybrid Data Reduction and Routing Algorithm (HDRA). The principal aim of HDRA is to prolong the lifespan of cluster-based WSNs. It strives to decrease the packet transmission by sensor nodes, especially when there's minimal change in sensor readings. The findings indicate that HDRA outperforms the LEACH protocol in terms of energy efficiency in sensor networks, irrespective of network type (T, H, or TH) or deployment scenarios (200x200m or 400x400m). Overall, the proposed algorithm enhances network performance by conserving energy and extending network lifespan.

An energy efficiency routing protocol for UAV-aided WSNs data collection

In recent years, Unmanned Aerial Vehicle (UAV)-aided Wireless Sensor Networks (WSNs) have become a hot research topic. The routing protocol is a crucial technology for data transmission in UAV-WSNs. In this paper, we propose an efficient routing protocol for UAV-WSNs data collection to address the high energy consumption and premature death of some sensor nodes (SNs) caused by existing routing protocols. Firstly, the UAV-WSNs data collection framework is established, and a UAV communication coverage model is derived. Secondly, according to the data transmission link, we preliminarily divide the routing area into air-to-ground routing area and ground-to-ground routing area. Based on this, a multi-hop routing protocol based on unequal-sized clustering is proposed to promote the balance of energy consumption between clusters. Then, we propose a sector dynamic adjustment strategy inspired by lottery turntable wheels, which dynamically adjusts the member nodes in each cluster by simulating a rotation to ensure the balance of energy consumption between SNs in the cluster. Finally, the feasibility and efficiency of the proposed protocol are verified through simulation and comparative experiments.

An optimized multi-hop routing protocol for wireless sensor network using improved honey badger optimization algorithm for efficient and secure QoS

A wireless sensor network (WSN) is a significant technology that might contribute in the industrial revolution. Sensor Nodes (SNs) in WSNs are equipped with a battery as a power source. Energy is the most critical resource for WSNs since the battery cannot be replaced or refilled. Several solutions have been devised and implemented over the years to preserve WSNs, which are a scarce resource. This study suggested a model to solves the energy issue as well as security issue and presents an optimized multi-hop routing in WSNs based on improved Honey Badger Algorithm (I-HBA). This method for multi-hop routing involves two steps: selecting a Cluster Head (CH) and routing the data packets. The I-HBA is used to pick energy-efficient CHs for efficient data transfer. The SNs then send their data through the chosen CH, which forwards it to the base station (BS) using the most efficient number of hops. The suggested I-HBA is used to select the finest hops. The proposed model employs the multi-objective fitness function including eight parameters for effective routing in the WSNs. Furthermore, security conscious multihop routing is carried out by employing a trust model that includes direct and indirect trust, data, integrity and forwarding rate factor. Additionally, the proposed I-HBA based routing protocol compared with the existing protocol such as LEACH, HEED, and PSO; and the proposed protocol outperforms the existing models. The findings show that for the same number of iterations, the whole quantity of data packets receives by the WSN using the proposed I-HBA based multihop routing protocol is substantially larger than for the PSO, LEACH, and HEED-based models.

Stop & Offload: Periodic data offloading in UAV networks

Swarms of Unmanned Aerial Vehicles (UAVs) are a key technology to support communication in many harsh environments where fixed infrastructures (e.g., 5G) are disrupted or not available. However, the fast mobility and highly dynamic network topology pose unique challenges and require the development of novel multi-hop routing protocols. Previous work in this direction extends geographical protocols or adapts approaches designed for Mobile Ad-hoc NETworks (MANETs), rarely taking full advantage of UAV capabilities. In this paper, we introduce a novel data offloading approach, namely Stop & Offload, that exploits the device controllable mobility to facilitate network routing. The swarm of UAVs performs data offloading synchronously and recurrently. At fixed intervals of time, the swarm interrupts the sensing mission (Stop) and moves, as little as possible, to build a connected formation to the base station and offload the data (Offload). We provide both centralized solutions — assuming a long-range control channel — and a distributed solution — working in the absence of a control channel. By means of extensive simulations we show that our proposals outperform state-of-the-art solutions, decreasing the time taken to build a connected formation of about 45% and increasing the time spent on sensing of 10%. Additionally, we compared our protocol with various routing strategies and observe remarkable improvements, including a 50% reduction in average packet delay.

The optimization of nodes clustering and multi-hop routing protocol using hierarchical chimp optimization for sustainable energy efficient underwater wireless sensor networks

The optimization of nodes clustering and multi-hop routing protocol using hierarchical chimp optimization for sustainable energy efficient underwater wireless sensor networks

THSI-RP: A two-tier hybrid swarm intelligence based node clustering and multi-hop routing protocol optimization for wireless sensor networks

Wireless sensor networks exploit clustering and routing techniques to improve energy efficiency, but these methods are generally considered as a non-deterministic polynomial (NP-hard) problem. To tackle this problem, we propose a novel two-tier hybrid swarm intelligence-based hierarchical routing protocol (THSI-RP). In the first tier, THIS-RP incorporates a hybrid swarm intelligence algorithm that combines Grey Wolf Optimization (GWO) and Marine Predators Algorithm (MPA) for the clustering algorithm. By incorporating the wolf factors from GWO into MPA as elite predators, we enhance the network search efficiency. This algorithm achieves adaptive optimal clustering with a controllable scale, considering the residual energy of nodes, relative distance between nodes, and node centrality. In the second tier, the routing algorithm uses a hybrid SI algorithm based on GWO and the graph model. First, we integrate distance and energy factors to dynamically select forwarding nodes using GWO. Then, based on the distance and energy balance principle, a weight cost function is established and combined with the minimum spanning tree method to construct a communication routing tree between the forwarding node and the base station to achieve optimized inter-cluster multi-hop routing. Simulation results demonstrate that THSI-RP outperforms several typical routing protocols in various metrics over EEUC, LPSO, LGWO, and LACO. For instance, THSI-RP achieves percentage improvement total packets by 290%, 10.9%, 8.9%, 19.9%, respectively. It also performs better in FND by 88.7%, 10.9%, 8.6%, and 16.3%, in HDN by 86.6%, 8.1%, 6.3%, and 17.1%, and in ADN by 41.7%, 8.2%, 6.4%, 18.3%.

Design of improved deer hunting optimization enabled multihop routing protocol for wireless sensor networks

A wireless sensor network (WSN) encompasses a huge set of sensor nodes employed to collect data and transmit it to a base station (BS). Due to its compact, inexpensive, and scalable nature of sensors, WSN finds its applicability in diverse real-time applications. The battery-operated sensor nodes necessitate the design of a multi-hop routing protocol for the effective utilization of available energy in the network. Routing can be considered an optimization problem and can be solved by the design of bioinspired algorithms. This study introduces an improved deer hunting optimization-enabled multihop routing (IDHO-MHR) protocol for WSN. The major intention of the IDHO-MHR approach is to optimally find the routes to the destination in WSN. The IDHO algorithm is initially derived by the incorporation of the Nelder Mead (NM) concept into the traditional DHO algorithm. In addition, the IDHO-MHR technique primarily derives a fitness function with the inclusion of two major variables, namely residual energy (RE) and distance. The nodes with higher RE and minimum distance have the probability of becoming optimal routes from the networks. The performance validation of the IDHO-MHR approach is performed, and the outcomes are inspected in various aspects. The experimental outcomes reported the supremacy of the IDHO-MHR protocol over the other recent approaches.

A multi-hop routing protocol for an energy-efficient in wireless sensor network

The low-energy adaptive clustering hierarchy (LEACH) protocol has been developed to be implemented in wireless sensor networks (WSNs) systems such as healthcare and military systems. LEACH protocol depends on clustering the employed sensors and electing one cluster head (CH) for each cluster. The CH nodes are changed periodically to evenly distribute the energy load among sensors. Updating the CH node requires electing different CH and re-clustering sensors. This process consumes sensors’ energy due to sending and receiving many broadcast and unicast messages thus reduces the network lifetime, which is regarded as a significant issue in LEACH. This research develops a new approach based on modifying the LEACH protocol to minimize the need of updating the cluster head. The proposal aims to extend the WSN’s lifetime by maintaining the sensor nodes’ energy. The suggested approach has been evaluated and shown remarkable efficiency in comparison with basic LEACH protocol and not-clustered protocol in terms of extending network lifetime and reducing the required sent messages in the network reflected by 15%, and, in addition, reducing the need to reformatting the clusters frequently and saving network resources.

Network intelligence vs. jamming in underwater networks: how learning can cope with misbehavior

In this paper, we present a machine-learning technique to counteract jamming attacks in underwater networks. Indeed, this is relevant in security applications where sensor devices are located in critical regions, for example, in the case of national border surveillance or for identifying any unauthorized intrusion. To this aim, a multi-hop routing protocol that relies on the exploitation of a Q-learning methodology is presented with a focus on increasing reliability in data communication and network lifetime. Performance results assess the effectiveness of the proposed solution as compared to other efficient state-of-the-art approaches.

Lightweight multi-hop routing protocol for resource optimisation in edge computing networks

Data transmission over power line communication (PLC) infrastructure will proliferate lightweight Internet of Things (IoT) nodes in 5G and 6G networks. Consequently, a corresponding lightweight multi-hop routing protocol (LMRP) with reduced path loss and computational complexities will be required at the edges of PLC networks to connect the cloud sinks. Using a multi-layered system architecture, we present an LMRP for optimal routing and highlight the components of a smart PLC network comprising edge power pool orchestration, edge layer service provisioning, fog latency layer, and cloud resilient backbone. The LMRP reduces path loss and node failure states at the edge while optimising throughputs, minimum cost flow, and signal stability. A multi-hop deterministic testbed is designed and applied in three different locations to estimate path loss leveraging TelosB IoT node, Raspberry Pi (RPI) with NesC, and Java scripted logger application. Three different testbeds of varying path loss characteristics at the Federal University of Technology Owerri (FUTO) are used while the analysis was completed at Manchester Metropolitan University engineering LAB. The result of PL mitigation in Location 1 (sonic FUTO) shows 33.89%, 33.25%, and 32.77% with genetic algorithm (GA), particle swarm optimisation (PSO), and the proposed LMRP, respectively. In Location 2 (Old SEET Complex, FUTO), the PL obtained are 33.81%, 33.57%, and 32.62%, while Location 3 (New SEET Complex, FUTO) yields 33.65%, 33.41%, and 32.74% in PL mitigation for GA, PSO, and LMRP, respectively. Despite improved PL mitigation, the results also show that the proposed scheme offers a lightweight routing performance of at least 76.30% compared to similar schemes.

A consensus-based approach to reputational routing in multi-hop networks

When it comes to the security of the Internet of Things (IoT), securing their communications is paramount. In multi-hop networks, nodes relay information amongst themselves, opening the data up to tampering by an intermediate device. To detect and avoid such malicious entities, we grant nodes the ability to analyse their neighbours behaviour. Through the use of consensus-based validation, based upon the blockchain's miners, all nodes can agree on the trustworthiness of all devices in the network. By expressing this through a node's reputation, it is possible to identify malicious devices and isolate them from network activities. By incorporating this metric into a multi-hop routing protocol such as AODV, we can influence the path selection process. Instead of defining the best route based upon overall length, we can choose the most reputable path available, thus traversing trustworthy devices. By performing extensive analyses through multiple simulated scenarios, we can identify a decrease in packet drop rates compared to AODV by approximately 48% and 38% when subjected to black hole attacks with 30 and 100 node networks respectively. Furthermore, by subjecting our system to varying degrees of grey holes, we can confirm its adaptability to different types of threats.