Optimal Routing Research Articles

ARZSEP: Angle-Based Routing Optimization in ZSEP Protocol for Heterogeneous WSNs

ARZSEP: Angle-Based Routing Optimization in ZSEP Protocol for Heterogeneous WSNs

Hybrid secure routing and monitoring mechanisms in IoT-based wireless sensor networks using Egret-Harris optimization

ABSTRACT Security is the primary concern when creating security protocols for wireless sensor networks (WSN), which motivates many academics to find security solutions that effectively provide a few benefits, such as low consumption of electricity, flexible communication, and low cost. A few restrictions still remain, including the inability to exactly select the expected cluster, the sensor nodes’ constrained functionality, and their poor efficiency. Thus, Egret-Harris optimization for hybrid secure routing and monitoring mechanisms in IoT-based WSNs (EHO optimized routing protocol in WSN) was introduced in this research. The created EHO algorithm combines the search, fitness function, and hunting phase features from Harris hawks and egrets to determine the best solution among all practical solutions while ensuring safe data transfer from the cluster heads (CHs) to the Base station (BS). Specifically, the EHO-enabled clustering is applied to the suggested model to efficiently choose the ideal group of CHs. Additionally, the EHO algorithm assists in choosing the best possible routes with minimal distance and less delay for facilitating energy-efficient transmission. With 100 nodes analyzed, the suggested EHO-WSN approach without any attacks achieved 22 alive nodes, a delay of 0.10 ms, a normalized energy of 0.346J, and a throughput of 0.64 bps, respectively. Additionally, in the presence of a Sybil attack, the suggested EHO-WSN technique achieves 14, 0.214 J, 0.010 ms, and 0.512 bps for an analysis involving 100 nodes. Compared to previous methods, the suggested EHO-WSN model without attack achieves a delay of 0.07 ms, a throughput of 0.30 bps, an energy of 0.374 J, and 37 alive nodes for 200 node evaluation. For 200 nodes under examination, the EHO-WSN technique yields superior results of attaining 12 alive nodes, a delay of 0.072 ms, a throughput of 0.181 bps, and a normalized energy of 0.330 J even in the presence of the Sybil attack and exceeded other traditional techniques.

Data Aggregation Scheme Using Differential Evolution with Sailfish Optimization for Clustering and Routing in IoT

Data Aggregation Scheme Using Differential Evolution with Sailfish Optimization for Clustering and Routing in IoT

Multi-Depot Electric Vehicle–Drone Collaborative-Delivery Routing Optimization with Time-Varying Vehicle Travel Time

This paper presents an electric vehicle-drone (EV–drone) collaborative-delivery routing optimization model that leverages the time-varying characteristics of electric vehicles and drones across multiple distribution centers (i.e., central depots) to address the logistics industry’s low-carbon transformation in the last-mile delivery. The model aims to minimize total delivery costs by formulating a mixed-integer programming (MIP) model that accounts for essential constraints such as nonlinear charging time, time-varying EV travel time, delivery time window, payload capacity, and maximum range. An improved adaptive large-neighborhood search (ALNS) algorithm is developed to solve the model. Experimental results validate the effectiveness of the proposed algorithm and highlight the impact of EV and drone technology parameters, along with the time-varying EV travel times, on the economic efficiency of delivery distribution and route planning.

Electricity arbitrage for mobile energy storage in marginal pricing mechanism via bi-level programming

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

Optimizing Multi-Tier Scheduling and Secure Routing in Edge-Assisted Software-Defined Wireless Sensor Network Environment Using Moving Target Defense and AI Techniques

Software Defined Wireless Sensor Networks (SDWSN) enable flexibility in Wireless Sensor Network (WSN) environments by defining the controllable functions to WSN nodes by the Software Defined Network (SDN) controller. Due to the rapid evolution of SDWSNs, adverse effects also have occurred in terms of interference, energy consumption, and security issues. Several state-of-the-art works lend their utmost best to the SDWSN environment. However, the complete picture (i.e., relatability and security in SDWSN) poses severe challenges. The state-of-the-art issues is addressed in this research by proposing interference-aware Multi-Tier Scheduling for the SDWSN environment (MTS-SDWSN). First, we perform network construction in which the proposed network is constructed in a 2D hexagonal grid structure to resolve the connectivity issue. Upon constructing the network, the SDWSN nodes are clustered and managed to reduce the energy consumption using the Divide Well To Merge Better (DWTMB) algorithm in which the optimal Cluster Leader (CL) is selected based on adequate constraint. The data from the clustered nodes are sent to the Local Base Station (LBS) via CL in which they are scheduled in multi-tier format to diminish the complexity and interference issues. The first tier involved in scheduling among Cluster Members (CMs) and CL using adequate metrics, whereas the successive tiers (i.e., second and third) involved in scheduling among CLs to LBSs and LBSs to Sink Node (SN) are done using the Non-Cooperative Fuzzy Theory (NCFT) method. Last, the scheduled nodes are routed to appropriate destinations using Secure and Optimal Routing Protocol (SORP). The proposed SORP includes the Alibaba and Forty Thieves (AFT) and Multi Criteria Decision Making (MCDM) algorithms for selecting and ranking the optimal routes. Further, the security of the routes is enabled by adopting trust and Moving Target Defense (MTD) mechanisms. The MTD includes route switching among the SDWSN devices and active switch handling using Cycle Generative Adversarial Networks (CGAN) among the switches. The proposed work is implemented using a NS-3.26 simulation tool, and performance of the proposed model and existing works shows that the proposed work outperforms the existing works.

Multiobjective Gannet Dung Beetle Optimization for routing in IoT-WSN

Multiobjective Gannet Dung Beetle Optimization for routing in IoT-WSN

A 3D multiobjective multi-item eco-routing problem for refrigerated fresh products delivery using NSGA-II with hybrid chromosome

In a developing countries, say India, about 40 percent fresh foods are wasted during transportation and $14 billion of post harvest products are lost due to inefficiency of cold supply chain such as using non-refrigerated vehicle during last mile transport. Hence, temperature control through refrigerated transportation is very essential. But, the by-product of this process i.e., carbon emission (CE) due to transportation and refrigeration is detrimental for the world. Only logistic transportation contributes about 25 percent of global CE. Due to the world wide infrastructural development, there are several available routes for the road transportation among different cities. For the maintenance of these routes, there are some toll plazas for collection of money. Some routes also cross the rail lines. For maximum profit, a supplier distributes the products at the earliest as the retailers fix the product’s price with respect to its freshness. Again, this process generates more CE. With these realities, a 3D multiobjective multi-item eco-routing problems for refrigerated fresh products (3DMOMIERPsfRFPs) are developed, where a refrigerated vehicle starts from a depot (refrigerated) and comes back after distributing the fresh products to retailers as per their demands. The optimal routing plan, vehicle’s velocity and preservation rate are determined so that total profit and CE are respectively maximized and minimized simultaneously. For solution, an NSGA-II with different modified operators (NSGA-IIwDOs) is developed with hybrid chromosomes having discrete and continuous variables together, probabilistic selection, problem-specific crossover and generation-dependent mutation. Some performance metrics are presented through NSGA-IIwDOs on the standard TSPLIB problems. Results of 3DMOMIERPsfRFPs without and with specific delivery time slots, road-dependent vehicle velocity and time constraints are evaluated. Pareto front for multiobjective are depicted. As a particular case, results of a previous investigation are obtained. Some of the managerial decisions are observed.

AI-Driven DRC Routing Convergence in IC Design : A Paradigm Shift in Semiconductor Development

This article explores the transformative impact of Artificial Intelligence (AI) on Integrated Circuit (IC) design, particularly in Design Rule Checking (DRC) and routing convergence. As semiconductor technology advances to smaller nodes, traditional design methodologies struggle with increasing complexity, time-to-market pressures, and stringent manufacturing constraints. Integrating AI-driven approaches offers promising solutions, dramatically reducing design cycles, improving yield, and enabling continued scaling. The article discusses the challenges of modern IC design, the critical role of DRC, and the AI revolution in routing optimization. It examines the benefits of AI-driven DRC routing convergence, including reduced time-to-market, improved design quality, cost reduction, enhanced scalability, and adaptability to new processes. Finally, it addresses current challenges and future research directions in this rapidly evolving field.

UAV 2D Path Planning Considering Ground Suitability for Flying Safety in Digital Twin

Abstract. The utilization of various UAVs such as facility inspection and delivery is increasing. Ensuring safety in carrying out various UAV missions is an important issue related to human and property damage. Safe operation of UAVs flying autonomously along pre-located routes requires safe route planning to prevent collisions between aircraft in consideration of ground and air obstacles. Pass flight is also essential to minimize damage in the event of a crash. This paper deals with the optimal UAV 2D routing plan considering ground compatibility for flight safety. High ground compatibility here means that the damage caused by the crash is less than when it is not. In this paper, we first defined dynamic and static obstacles. Next, we derived ground compatibility considering dynamic and static obstacles. We used estimation algorithms and land use data to calculate ground compatibility values for flight safety, and through experiments, we calculated an optimal 2DUAV route plan for flight safety.

Optimization for container truck routing in container terminal with multi quay cranes considering emissions policy

In container terminal, the container trucks should go by a “quay crane - container trucks -import and export block” routing, and the optimization of this routing has become the focus of this paper. This study divided the problem of routing optimization into two levels: one is a routing optimization model of single container truck which takes the lowest total cost as the target, without considering the distribution of overall transportation task; the other is a routing optimization model concerning the allocation of no-load trucks, aiming at the lowest cost of transportation task. We designed an improved Particle Swarm Optimization (PSO) algorithm to solve the two-level optimization model, and the influence of emission policy on the two-level optimization model and the influence of the number of container trucks on the transportation distance of level II in optimization model are analyzed. The results show that the increase of carbon tax will lead to the decrease of emissions in level I, but has no effect on the emissions in level II. The increase in the number of container trucks will lead to a reduction of the no-load transportation distance in level II.

Passive unmanned maritime search and rescue routing method

Addressing the challenges inherent in passive unmanned search and rescue missions at sea, including difficulties in target identification, broad search areas, and slow route planning, a strategic process was introduced for maritime search and rescue area planning and a route planning model specifically designed for passive unmanned missions. By thoroughly understanding the emergency response operations at sea and the specific needs for route planning, an optimal routing model have been developed considering factors such as the efficiency of search and rescue area coverage and the cost of rescue routes. The objective function is constructed within these constraints and solved using the Whale Optimization Algorithm. The validity of the model is confirmed through designated scenario experiments, indicating that our proposed model for maritime passive unmanned search and rescue route planning is capable of swiftly identifying the search and rescue area and efficiently discovering a route with reduced costs. This innovation offers valuable decision-making support for the planning of future emergency maritime search and rescue operations in both military and civilian sectors.

Retraction Note: A hybrid fuzzy logic based ant colony routing optimization system for wireless communications

Retraction Note: A hybrid fuzzy logic based ant colony routing optimization system for wireless communications

Data-driven vehicle rental and routing optimization: An application in online retailing

Due to limited self-owned vehicles, online retailers often struggle to meet high demands for deliveries, especially during large promotions. This study employs machine learning to tackle this challenge by shipping products and renting vehicles in advance. We explore a large amount of historical demand data, enabling accurate forecasting of demand information. It is then combined with an improved meta-heuristic algorithm named the Improved Discrete Whale Optimization Algorithm (IDWOA) to help online retailers make optimal decisions. The algorithm involves a discretization method and an effective perturbation strategy, along with information sharing, Cauchy mutation, and an elimination strategy. Experimental results demonstrate that our method can reduce costs by 14.78% compared to temporary vehicle rentals, and it significantly outperforms other comparative algorithms. Therefore, our study effectively integrates machine learning algorithms with an improved meta-heuristic approach, allowing for increased utilization of data-driven advantages to enhance the precision and efficiency of vehicle rental and routing optimization.

Timeliness-oriented rush repair optimization of workforce scheduling and routing for logically complex systems under uncertainty

The optimization of workforce scheduling and routing problems (WSRPs) with a focus on timeliness-oriented rush repair in logically complex systems has received extensive attention. As there are many characteristics of complex system rush-repair, such as limited repair time and complex correlations, conventional approaches exhibit shortcomings in terms of timeliness (i.e., a comprehensive indicator that integrates both timely and mission reliability). To improve the overall emergency response efficiency under uncertain disaster scenarios, we develop a general decision-making framework for rush-repair WSRP. An optimization model is then formulated based on integer linear programming to maximize the overall timeliness while satisfying the skill-matching requirements and system-level recovery constraints. Furthermore, a novel matheuristic algorithm is proposed as a general solution approach for rush-repair decision-making. Finally, diverse benchmark groups, including different scales and sparsity levels, are simulated to conduct computational experiments. Compared to several representative algorithms, the results show that the proposed method has strong robustness and high efficiency.

Secure and efficient trust enabled routing in mobile ad hoc network using game theory and grey wolf optimisation techniques

AbstractMobile Ad hoc Networks (MANETs) are crucial wireless networks for military, corporate, and emergency use, yet they are vulnerable to disruptions from malicious nodes. The presence of malicious nodes can lead to message transmission and routing disorganisation, and network performance is effectively compromised. Game theory‐based fuzzy secure clustering (GTFSC) improves performance metrics in low‐scale and high‐scale networks. This protocol's novel ability to dynamically scale performance measures as nodes expand improves efficiency and adaptability. While improving performance metrics, the proposed algorithm also efficiently identifies malicious nodes and re‐routes the transmission, excluding the found malicious nodes. For any MANET system, secure and successful data transmission is paramount. The proposed protocol integrates various algorithms to fulfil its aim of customised EGT, GWO, and fuzzy clustering. Black hole attacks, grey hole attacks, Sybil attacks, and data tampering attacks are all considered to provide comprehensive attacks on MANET. Every node is assigned trust values, which get updated on data transmission. Fuzzy Clustering is employed to identify malicious nodes. Evolutionary Game Theory (EGT) optimises network organisation by designating cluster heads and clusters as nodes. Additionally, the proposed protocol leverages the Grey Wolf Optimisation Routing Algorithm (GWO), which establishes efficient routes from the source to the sink node. The analysis result shows maximum performance with a packet delivery ratio of around 98%, throughput of 90% end‐to‐end delay reduced by 15%, and energy consumption reduced by 18%, respectively, compared to an existing protocol.

Electric Vehicle Distribution Route Optimisation and Charging Strategy Considering Dynamic Loads

Electric Vehicle Distribution Route Optimisation and Charging Strategy Considering Dynamic Loads

Ship model-based route optimisation for decision support in deep sea shipping

Abstract We present a new approach and route optimization methodology to support analysis and planning of vessel and fleet performance in deep sea shipping for green, energy efficient, and safe navigation. The developed methodology combines the ship technical characteristics based on a ship model developed for a particular vessel type and energy-saving technology options and an optimization algorithm taking into account weather conditions. Optimization involves two stages: graph construction and Dynamic Programming labelling algorithm implemented to solve the shortest path problem with variable speed. The new approach is implemented as part of a decision-support tool EcoRouter enabling the user to conduct analysis of safe and Pareto optimal solutions. Several applications to support real fleet planning and ship performance analysis have been identified including project engineering for future energy-saving ship technologies, for example, wind-assisted propulsion.

ADRP-DQL: An adaptive distributed routing protocol for underwater acoustic sensor networks using deep Q-learning

Underwater Wireless Sensor Networks (UWSNs) face unique constraints due to their unstructured and dynamic underwater environment. Data gathering from these networks is crucial as energy resources are limited. In this regard, efficient routing protocols are needed to optimize energy consumption, increase the network lifetime, and enhance data delivery in these networks. In this work, we develop an Adaptive Distributed Routing Protocol for UWSNs using Deep Q-Learning (ADRP-DQL). This protocol employs the ability of reinforcement learning to dynamically learn the best routing decisions based on the network’s state and action-value estimates. It allows nodes to make intelligent routing decisions, considering residual energy, depth and node degree. A Deep Q-Network (DQN) is employed as the function approximator to estimate action values and choose the optimal routing decisions. The DQN is trained using off-policy and on-policy strategies and the neural network model. Simulation results demonstrate that ADRP-DQL performs well regarding energy efficiency (EE), data delivery ratio, and network lifetime. The results highlight the proposed protocol’s effectiveness and adaptability to UWSNs. The ADRP-DQL protocol contributes to intelligent routing for UWSNs, offering a promising approach to enhance performance and optimize energy utilization in these demanding environments.

Distributionally Robust Optimal Routing for Integrated Satellite-Terrestrial Networks Under Uncertainty

Distributionally Robust Optimal Routing for Integrated Satellite-Terrestrial Networks Under Uncertainty