Optimal Hop Research Articles

Hybrid Tasmanian Devil and Elephant Herding Model-Based Optimal Cluster Head Selection and Routing in MANET

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes.

Probability-based mapping approach for an application-aware networks-on-chip architectures

In a digital and automation era, on-chip multi-core architecture plays a vital role in effective communication in the field of very large-scale integrated circuits (VLSI). In this paper, we propose a unique mapping approach in which a probability-based core selection from the application benchmark into the center to eccentric way of placement of cores in the standard network architecture improves the performance of networks-on-chip (NoC). The proposed approach utilizes a structured mapping strategy, in contrast to the random mapping. This characteristic renders the proposed method a robust solution for a diverse range of NoC architectures irrespective of scale. The proposed approach provides better quality of service (QoS) with optimal total communication bandwidth and average hop count. The performance of the proposed mapping approach is validated with various experiments over standard and real-time benchmarks. The investigation results indicate that the total communication cost over real-time NoC benchmarks for the proposed mapping approach offers 43.06%, 22.75%, and 16.69% average improvement over CastNet, NMAP, and mapGtoM respectively. Furthermore, we adopt uniform geometric and shuffled traffic patterns to identify the latency and throughput of the proposed probability-based mapping approach. The investigation results indicate that the proposed mapping approach outperforms existing mapping procedures.

AFHO‐based link reliable routing and power‐assisted channel allocation protocol in cognitive radio networks for healthcare applications

AbstractIn the realm of cognitive networks, accommodating the increasing need for radio spectrum to facilitate both secondary and primary users stands as an intricate and demanding task. This study introduces a tailored framework for healthcare applications in Cognitive Radio (CR) networks for link reliable routing and power‐assisted channel allocation. Through the integration of Adaptive Fire Hawk Optimization for routing, the strategy addresses optimization complexities while prioritizing robust link quality via optimal hop selection for effective communication. A new routing optimization challenge is formulated, aiming to establish reliable communication pathways and extend link reliability. Moreover, the framework incorporates power‐assisted channel allocation to strategically enhance resource management and overall network performance. The rigorous evaluation demonstrates significant enhancements across key performance metrics, which showcase percentage improvements of 9.86%, 14.56%, 3.85%, 5.06%, 6.94%, and 17.80% for active nodes, delay, Packet Delivery Ratio (PDR), residual energy, and throughput, respectively. These advancements underscore the proposed approach's efficacy in enhancing CR network performance, particularly for healthcare applications.

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks.

With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network's route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments.

An adaptive congestion and energy aware multipath routing scheme for mobile ad-hoc networks through stable link prediction

Advancement in mobile ad-hoc network (MANET's) has offered solutions to wide range of real-time applications. MANET's consist of group of battery operated wireless mobile nodes that forms a dynamic topology and functions autonomously without infrastructure. Mobile nodes are limited to resource, bandwidth, energy and memory. Due to mobile nature of the nodes, mobility and resource limitations are the two major factors that affect MANET's performance. Frequent topology changes and mobility affects the link stability between two nodes, frequent link interruption results in network congestion, lead to packet drop and increase retransmission delays. During multihop data transmissions, the wireless channel is shared among neighbour nodes. Determining the channel status and predicting the quality of link is still a challenging issue. Existing MANET's routing scheme considers optimal hop count as a metric for data transmission and selects the shortest path. Due to nodes movement, frequent link failure occurs, resulting in packet loss and retransmission of packet, which restablished path avoids packets loss, and alternate paths can be discovered within time such that congestion is controlled and delay is minimised. Therefore, it is required to design routing scheme for MANET's that can adapt to network changes and conditions to support quality of service (QoS). In this paper, we propose an Adaptive Congestion and Energy Aware Multipath Routing Scheme (ACEAMR) to provision QoS requirements in MANET's. ACEAMR discovers stable, energy aware, and congestion free paths towards destination. The congestion level at node is determined by computing queue utilization through adaptive feedback mechanism. ACEAMR integrates stable link prediction (SLP) to estimate the link quality and channel availability for reliable data transmission. Energy aware nodes are computed using fitness function of nodes having sufficient energy to extend network lifetime. Simulation results show that proposed ACEAMR achieves 30 % of higher throughput, 25 % more in packet delivery ratio, 27 % less delay and 28 % energy efficient compared to LLECP-AOMDV, OLEADM and AOMDV schemes.

Enhancing Security in Mobile Ad Hoc Networks: Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm

Wireless technologies have grown in popularity and are used in many applications. Transient Mobile Ad hoc Networks (MANETs) serve specific goals without infrastructure. The dynamism of these networks makes them useful for ubiquitous computing. However, high mobility, the lack of a centralized authority, and open media make MANETs vulnerable to various security risks. Thus, an Intrusion Detection System (IDS) should be used to monitor and detect system security issues. To prevent and improve security against unauthorized access, intrusion screening is crucial. The depletion of a mobile node's power supply can impact its capacity to transmit packets, as this functionality is contingent upon the system's overall lifespan. Computational Optimization-driven solutions have been prevalent in the context of IDS and secure routing inside MANETs. This research employs the Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm (EPSO-IDSRA). Enhanced Particle Swarm Optimization technique (EPSO) has provided confidence-based, secure, and energy-efficient routing in MANETs. The EPSO technique is applied to identify optimal hops for enhancing the routing process. The initial step involves the activation of the fuzzy clustering algorithm, followed by selecting Cluster Heads (CHs) based on assessing their indirect, direct, and recent confidence values. Furthermore, the identification of value nodes was contingent upon assessing confidence levels. Also, the CHs are involved in multi-hop routing, and determining the optimal route depends on the anticipated protocol, which chooses the most favorable paths considering factors such as latency, throughput, and connectivity within the designated area. The EPSO method, presented for secure routing (at time 50ms), yielded an optimal energy consumption of 0.15 millijoules, a minimal delay of 0.008 milliseconds, a maximum throughput of 0.8 bits per second, and an 89% detection rate.

Energy efficient optimal hop transmission using minimum power least cost algorithm in cooperative routing for wireless sensor network

Cooperative communication has gained a lot of popularity recently. Through a variety of shortest path methods, this article's paradigm may efficiently reduce the amount of power consumed and hop transmission. In this research, we construct the Minimum Power Least Cost Routing (MPLCR) algorithm and evaluate its performance. The design of the proposed algorithm took into account link computation, sequential scanning algorithm approach, and balance (residual) energy. To prevent connection failures and lessen network traffic, the link calculation is used to choose the best route (relay node). In order to reduce network power consumption, a sequential scanning technique was used to find the shortest path with the fewest hops. And also discuss relay nodes and their characteristics in order to improve the transmission stream's quality of service. An ideal path is one that ensures end-to-end transmission while using the least amount of transmitted power. The minimum power least cost routing algorithm uses cooperative communications to help build the smallest power route. When compared to the current algorithms, the proposed approach uses less energy by more than 30%.

Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks

Low energy consumption has always been one of the core issues in the routing design of underwater sensor networks. Due to the high cost and difficulty of deployment and replacement of current underwater nodes, many underwater applications require the routing protocol design to consider the network lifetime extension problem. Based on this, we designed a new routing protocol that takes into account both low energy consumption and balanced energy consumption, and achieves effective extension of the network lifetime, called adaptive power-controlled depth-based routing protocol for underwater wireless sensor networks (APCDBRP). The protocol consists of two phases: (1) the route establishment phase and (2) the data transmission phase. In the route establishment phase, the initial path is established by the sink node broadcasting beacon packets at the maximum transmission power. The receiving nodes update their routing tables based on the beacon information and forward the beacon packets. In the data transmission phase, APCDBRP introduces a novel forwarding factor that considers both energy efficiency and energy balance. It selects the optimal next hop based on high energy efficiency and relatively abundant energy, thus extending the network’s lifetime. Additionally, APCDBRP proposes a new data protection and route reconstruction mechanism to address issues such as network topology changes due to node mobility and data transmission failures. Our simulation is based on AquaSim–Next Generation, which is a specialized tool built on the NS3 platform for researching underwater networks. Simulation results demonstrate that, compared to other typical routing protocols, APCDBRP exhibits superior performance in reducing network energy consumption and extending the network’s lifetime. It also achieves a high packet delivery rate with lower energy consumption.

Adaptive Data Aggregation Scheme with Optimal Hop Selection Using Optimized Distributed Voronoi-Based Cooperation with Energy-Aware Dual-Path Geographic Routing Protocol

Adaptive Data Aggregation Scheme with Optimal Hop Selection Using Optimized Distributed Voronoi-Based Cooperation with Energy-Aware Dual-Path Geographic Routing Protocol

PARouting: Prediction-supported adaptive routing protocol for FANETs with deep reinforcement learning

Flying Ad-hoc Networks (FANETs) are becoming increasingly popular for various applications. Effective routing protocols for FANETs are essential yet challenging due to the high dynamic nature of Unmanned Aerial Vehicles (UAVs). Most existing routing protocols require the periodic broadcast of Hello packets to maintain neighbor tables that store the locations of neighbors, mobility patterns, etc. However, the frequent exchange of Hello packets leads to a large routing overhead in FANETs. This paper proposes PARouting, a prediction-supported adaptive routing protocol with Deep Reinforcement Learning, which introduces a novel UAV mobility prediction algorithm using Deep Learning (DL-UMP) to estimate the locations of UAVs. Based on DL-UMP, we design an adaptive Hello packet mechanism to realize on-demand broadcasting of Hello packets, which reduces routing overhead. The routing process is formulated as a Partially Observable Markov Decision Process, and a new Q-network structure is proposed to select the optimal next hop. Simulation results confirm the accuracy of the DL-UMP and show that PARouting outperforms benchmark routing protocols in terms of packet delivery rate, end-to-end delay, and routing overhead.

LPLL-LEACH: A study of a low-power and low-delay routing protocol based on LEACH

For a series of problems, such as uneven energy consumption and long packet forwarding time of a low-energy adaptive clustering hierarchy (LEACH), an efficient routing protocol with low power and low latency based on LEACH (LPLL-LEACH) is proposed in this paper. In terms of energy, first, the optimal number of cluster heads (CHs) is calculated by quantitative analysis. Second, multiple factors are considered to redefine the threshold for electing CHs. In addition, ordinary nodes decide which cluster to join according to the cost function. Moreover, a forwarding function is utilized to determine the optimal next hop CH to forward the data. Last, the ordinary nodes are allowed to directly communicate with the base station (BS). In regard to latency, a hybrid mechanism of carrier sense multiple access and time division multiple access (CSMA_TDMA) is applied to minimize the time delay of data transmission. MATLAB is used to analyze the latency and energy of LPLL-LEACH and to compare it with LEACH, LEACH-C, ECH and EE-LEACH. The experimental results show that the time of all node deaths extended by 107%, 45%, 31.8%, and 8.8%. Similarly, the latency is greatly reduced, which improves the efficiency of data transmission.

Throughput of Hybrid UAV Networks With Scale-Free Topology

Unmanned Aerial Vehicles (UAVs) hold great potential to support a wide range of applications due to the high maneuverability and flexibility. Compared with single UAV, UAV swarm carries out tasks efficiently in harsh environment, where the network resilience is of vital importance to UAV swarm. The network topology has a fundamental impact on the resilience of UAV network. It is discovered that scale-free network topology, as a topology that exists widely in nature, has the ability to enhance the network resilience. Besides, increasing network throughput can enhance the efficiency of information interaction, improving the network resilience. Facing these facts, this paper studies the throughput of UAV network with scale-free topology. Introducing the hybrid network structure combining both ad hoc transmission mode and cellular transmission mode into UAV network, the throughput of UAV network is improved compared with that of pure ad hoc UAV network. Furthermore, this work also investigates the optimal setting of the hop threshold for the selection of ad hoc or cellular transmission mode. It is discovered that the optimal hop threshold is related with the number of UAVs and the parameters of scale-free topology. This paper may motivate the application of hybrid network structure into UAV network.

Optimal Positioning of Hovering UAV Relays for Mitigation of Pointing Error in Free-Space Optical Communications

The relay positions or hop distances in a multi-hop relaying scheme is an important parameter that can be optimized in order to mitigate the angle-of-arrival variance or pointing error in a free-space optical (FSO) backhaul link. In this study, we have optimized the relay positions for amplify-and-forward and decode-and-forward relays in a multi-hop unmanned aerial vehicle-based relaying scheme for FSO. Particularly, we have shown that a significant performance improvement may be achieved by optimizing the outage probability as a function of the hop distance for amplify-and-forward relays when the relays are constrained by a finite power gain. Additionally, we have discovered that for a low signal-to-noise ratio channel, the optimal hop distance of a particular hop for decode-and-forward relays is inversely proportional to angle-of-arrival variance in that hop.

Algorithms for optimal min hop and foremost paths in interval temporal graphs

Path problems are fundamental to the study of graphs. Temporal graphs are graphs in which the edges connecting the vertices change with time. Min hop paths problem in a temporal graph is the problem of finding time respecting paths from source vertex to every destination vertex such that the path goes through minimum number of edges. Foremost paths problem in a temporal graph requires to find time respecting paths that arrive at the destination vertices at earliest possible time. In this paper we present algorithms to find min hop paths and foremost paths in interval temporal graphs. These algorithms are benchmarked against the fastest algorithms known for foremost and min-hop paths by Wu et al. (IEEE Trans Knowl Data Eng 28(11):2927–2942, 2016a. https://doi.org/10.1109/TKDE.2016.2594065) that work on contact sequence temporal graph model. On the available test data, our foremost path algorithm provides a speedup of up to 1800 over the fastest algorithm for contact sequence graphs; the speedup for our min-hop algorithm is up to 6700. We also demonstrate that interval temporal graph model on which our algorithms work represents a superset of contact sequence temporal graphs. We show that path problems exist that are NP-hard in interval temporal graph model but polynomial in the contact sequence temporal graph model in terms of the number of vertices and edges in the input graph. This is due to the fact that the contact sequence graph model may require much larger number of edges than the corresponding interval temporal graph to represent a given temporal graph.

Reinforcement learning based dynamic distributed routing scheme for mega LEO satellite networks

Recently, mega Low Earth Orbit (LEO) Satellite Network (LSN) systems have gained more and more attention due to low latency, broadband communications and global coverage for ground users. One of the primary challenges for LSN systems with inter-satellite links is the routing strategy calculation and maintenance, due to LSN constellation scale and dynamic network topology feature. In order to seek an efficient routing strategy, a Q-learning-based dynamic distributed Routing scheme for LSNs (QRLSN) is proposed in this paper. To achieve low end-to-end delay and low network traffic overhead load in LSNs, QRLSN adopts a multi-objective optimization method to find the optimal next hop for forwarding data packets. Experimental results demonstrate that the proposed scheme can effectively discover the initial routing strategy and provide long-term Quality of Service (QoS) optimization during the routing maintenance process. In addition, comparison results demonstrate that QRLSN is superior to the virtual-topology-based shortest path routing algorithm.

An Enhanced DV-Hop Localization Scheme Based on Weighted Iteration and Optimal Beacon Set

Node localization technology has become a research hotspot for wireless sensor networks (WSN) in recent years. The standard distance vector hop (DV-Hop) is a remarkable range-free positioning algorithm, but the low positioning accuracy limits its application in certain scenarios. To improve the positioning performance of the standard DV-Hop, an enhanced DV-Hop based on weighted iteration and optimal beacon set is presented in this paper. Firstly, different weights are assigned to beacons based on the per-hop error, and the weighted minimum mean square error (MMSE) is performed iteratively to find the optimal average hop size (AHS) of beacon nodes. After that, the approach of estimating the distance between unknown nodes and beacons is redefined. Finally, considering the influence of beacon nodes with different distances to the unknown node, the nearest beacon nodes are given priority to compute the node position. The optimal coordinates of the unknown nodes are determined by the best beacon set derived from a grouping strategy, rather than all beacons directly participating in localization. Simulation results demonstrate that the average localization error of our proposed DV-Hop reaches about 3.96 m, which is significantly lower than the 9.05 m, 7.25 m, and 5.62 m of the standard DV-Hop, PSO DV-Hop, and Selective 3-Anchor DV-Hop.

FLOC: Hesitant Fuzzy Linguistic Term Set Analysis in Energy Harvesting Opportunistic Clustering Using Relative Thermal Entropy and RF Energy Transfer

Limited energy resources and sensor nodes’ adaptability with the surrounding environment play a significant role in the sustainable Wireless Sensor Networks. This paper proposes a novel, dynamic, selforganizing opportunistic clustering using Hesitant Fuzzy Linguistic Term Analysis- based Multi-Criteria Decision Modeling methodology in order to overcome the CH decision-making problems and network lifetime bottlenecks. The asynchronous sleep/awake cycle strategy could be exploited to make an opportunistic connection between sensor nodes using opportunistic connection random graph. Every node in the network observe the node gain degree, energy welfare, relative thermal entropy, link connectivity, expected optimal hop, link quality factor etc. to form the criteria for Hesitant Fuzzy Linguistic Term Set. It makes the node to evaluate its current state and make the decision about the required action (‘CH’, ‘CM’ or ‘relay’). Our proposed scheme leads to an improvement in network lifetime, packet delivery ratio and overall energy consumption against existing benchmarks.

Deep-Learning-Aided Packet Routing in Aeronautical Ad Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multiobjective Optimization

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

J-RCA: A Joint Routing and Charging Algorithm With WCE Assisted Data Gathering in Wireless Rechargeable Sensor Networks

With the development of wireless charging technology, the wireless energy replenishment using mobile wireless charging equipment (WCE) has been a promising way to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). However, many existing researches have neglected some dynamic factors (e.g. topology changes in routing selection) which lead to low energy efficiency of charging tour. In this paper, a joint routing and charging algorithm (J-RCA) with WCE assisted data gathering is proposed to increase the network lifetime (NL). For routing, an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is presented to balance energy consumption of nodes, which can fully consider the difference between indexes to select the optimal next hop. In terms of charging, we present a two-step charging scheme that includes calculating the shortest Hamilton cycle of original charging nodes and selecting the other nodes called passer-by nodes. That is, when WCE moves along the shortest Hamilton cycle, it considers both routing requirements and spatial location to pick up some passer-by nodes for energy replenishment with the extra energy. In this way, the energy carried by WCE can be fully utilized and NL can be effectively extended. The simulation results demonstrate that the proposed algorithm has certain advantages in prolonging NL and improving transmission reliability.

DS Evidence Theory-Based Energy Balanced Routing Algorithm for Network Lifetime Enhancement in WSN-Assisted IOT

Wireless sensor networks (WSNs) can provide data acquisition for long-term environment monitoring, which are important parts of Internet of Things (IoT). In the WSN-assisted IoT, energy efficient routing algorithms are required to maintain a long network lifetime. In this paper, a DS evidence theory-based energy balanced routing algorithm for network lifetime enhancement (EBRA-NLE) in WSN-assisted IOT is proposed. From the perspective of energy balance and minimization of routing path energy consumption, three attribute indexes are established to evaluate the forward neighboring nodes. Then a route selection method based on DS evidence theory is developed to comprehensively evaluate the nodes and select the optimal next hop. In order to avoid missing the ideal solution because of the excessive difference between the index values, the sine function is used to adjust this difference. The simulation results show that the proposed EBRA-NLE has certain advantages in prolonging network lifetime and balancing energy between nodes.