Multi-hop Paths Research Articles

Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential Modeling

In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the development of a dialog, leading to potential sequential dependencies among them. However, most of existing CRSs neglect these potential sequential dependencies. In this article, we first propose a Transformer-based sequential conversational recommendation method, named TSCR, to model the sequential dependencies in the conversations to improve CRS. In TSCR, we represent conversations by items and the item-related entities, and construct user sequences to discover user preferences by considering both the mentioned items and item-related entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Meanwhile, in certain domains, knowledge graphs formed by the items and their related entities are readily available, which provide various different kinds of associations among them. Given that TSCR does not benefit from such knowledge graphs, we then propose a knowledge graph enhanced version of TSCR, called TSCRKG. In specific, we leverage the knowledge graph to offline initialize our model TSCRKG, and augment the user sequence of conversations (i.e., sequence of the mentioned items and item-related entities in the conversation) with multi-hop paths in the knowledge graph. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines, and the enhanced version TSCRKG further improves recommendation performance on top of TSCR.

MHEC: One-shot relational learning of knowledge graphs completion based on multi-hop information enhancement

With the wide application of knowledge graphs, knowledge graph completion has garnered increasing attention in recent years. However, we find that the long tail relation is more common in the KG. These relations typically do not have a large number of triples for training and are referred to as few-shot relations. The knowledge graph completion in the few-shot scenario is a major challenge currently. The current mainstream knowledge graph completion algorithms have the following drawbacks. The metric-based methods lack interpretability of results, while the algorithms based on path interaction are not suitable for few-shot scenarios and the availability of the model is limited in sparse knowledge graphs. In this paper, we propose a one-shot relational learning of knowledge graphs completion based on multi-hop information enhancement(MHEC). Firstly, MHEC extracts entity concepts from multi-hop paths to obtain task related entity concepts and filters out noisy neighbor attributes. Then, MHEC combines multi-hop path information between head and tail to represent entity pairs. Compared to previous completion methods that only consider structural features of entities, MHEC considers the reasoning logic between entity pairs, which not only includes structural features but also possesses rich semantic features. Next, MHEC introduces a reasoning process in the completion task to address the issues of lack of interpretability in the one-shot scenario. In addition, to improve completion and reasoning quality in sparse knowledge graphs, MHEC utilizes contrastive learning to enhance pre-training vector representations of entities and relations and proposes a matching processor that leverages the semantic information of pre-training vectors to assist the reasoning model in expanding the multi-hop paths. Experiments demonstrate that MHEC outperforms the state-of-the-art completion techniques on real-world datasets NELL-One and FB15k237-One.

Subgraph Structure Membership Inference Attacks against Graph Neural Networks

Graph Neural Networks (GNNs) have been widely applied to various applications across different domains. However, recent studies have shown that GNNs are susceptible to the membership inference attacks (MIAs) which aim to infer if some particular data samples were included in the model’s training data. While most previous MIAs have focused on inferring the membership of individual nodes and edges within the training graph, we introduce a novel form of membership inference attack called the Structure Membership Inference Attack (SMIA) which aims to determine whether a given set of nodes corresponds to a particular target structure, such as a clique or a multi-hop path, within the original training graph. To address this issue, we present novel black-box SMIA attacks that leverage the prediction outputs generated by the target GNN model for inference. Our approach involves training a three-label classifier, which, in combination with shadow training, aids in enabling the inference attack. Our extensive experimental evaluation of three representative GNN models and three real-world graph datasets demonstrates that our proposed attacks consistently outperform three baseline methods, including the one that employs the conventional link membership inference attacks to infer the subgraph structure. Additionally, we design a defense mechanism that introduces perturbations to the node embeddings thus influencing the corresponding prediction outputs by the target model. Our defense selectively perturbs dimensions within the node embeddings that have the least impact on the model's accuracy. Our empirical results demonstrate that the defense effectiveness of our approach is comparable with two established defense techniques that employ differential privacy. Moreover, our method achieves a better trade-off between defense strength and the accuracy of the target model compared to the two existing defense methods.

Knowledge Graph Reasoning via Dynamic Subgraph Attention with Low Resource Computation

Knowledge graphs (KGs) suffer from inherent incompleteness, which has spurred research into knowledge graph reasoning (KGR), i.e., ways to infer missing facts based on existing triples. The most prevalent approaches employ a static mechanism for entity representation across the entire graph, sharing entity embeddings for different query relations. Nevertheless, these static entity embeddings fail to capture specific semantics for various query scenarios, resulting in inaccurate entity representations and susceptibility to reasoning errors. Furthermore, the whole graph learning style requires substantial computational resources, especially since KGs are often of large-scale. Consequently, these methods are impractical in low-resource environments. To address these issues, we devise a framework called dynamic subgraph attention (DSA), which learns dynamic entity embeddings for different query relations across subgraphs. In our approach, by utilizing multi-hop path history information obtained through path-based learning, we guide a dynamic attention mechanism to generate dynamic entity embeddings for different query relations. To ensure the semantic information of entities, the embedding-based and path-based learning are jointly trained for KGR. Additionally, the proposed dynamic aggregation mechanism operates on subgraphs, resulting in a remarkable 6–7 times resource conservation compared to GPU computations. Empirical experiments further demonstrate that DSA outperforms the current methods significantly on three benchmark datasets.

Multi-hop temporal knowledge graph reasoning with multi-agent reinforcement learning

Knowledge graph (KG) is a key component of artificial intelligence. In recent years, many large-scale knowledge graphs have been produced and put into practical applications. At present, researchers have proposed many methods to reason facts that do not exist in knowledge graphs using the existing information. However, most traditional reasoning methods lack interpretability, and cannot get the reasoning paths. Therefore, the multi-hop path reasoning method of knowledge graph has gradually become a hot spot. This method finds the next relations through the reinforcement learning agent, gets the whole path, and scores the path. Although these multi-hop path reasoning methods make the reasoning method interpretable, these multi-hop path reasoning methods focus on the choice of relations, ignoring the importance of entities in the path reasoning process. Moreover, with the development of the Temporal Knowledge Graph (TKG), traditional multi-hop path reasoning methods cannot effectively process time information. To solve these problems, a multi-hop path reasoning method of temporal knowledge graph based on multi-agent reinforcement learning is proposed, which is named MA-TPath. MA-TPath uses two agents to perform relation selection and entity selection iteratively. Meanwhile, considering the diversity of temporal reasoning paths, we propose a new type of reward function. In MA-TPath, two agents employ the Long Short-Term Memory Networks (LSTM) to capture current results from the environment and output the corresponding action vectors to the environment through activation functions. Experimentally, MA-TPath outperforms all existing models in 4 out of 4 indicators on the YAGO dataset, 3 out of 4 indicators on the GDETL-5 dataset and the ICEWS-250 dataset, and 1 out of 4 indicators on the ICEWS05–15 dataset. Analysis of the temporal reasoning path indicates that MA-TPath not only surpasses other state-of-the-art reasoning methods over four public temporal knowledge graph datasets but provides rationality for results.

Factor graph-based deep reinforcement learning for path selection scheme in full-duplex wireless multihop networks

A wireless multihop network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any packets via intermediate nodes by a multihop fashion. In the WMN, there are still some issues that need to be resolved, like due to any source node may choose an uncertainty path to send their packets through the multihop fashion and this leads to the performance of network capacity can degrade drastically. To solve this problem, in this research, we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from any source node to a destination node with highest end-to-end (E2E) throughput. Besides that, a factor graph (FG) approach and a nested lattice code (NLC) representation are used to reduce the computation time. According to the numerical studies with the NLC is applied, our simulation results reveal that the proposed NLPS and INLPS algorithms can improve the overall average network capacity up to 3.1 times and 10.5 times compared to FG, respectively. However, the overall average computation time are highly increased for NLPS and INLPS, i.e., about 0.627 s and 1.221 s, respectively compared to FG, which is about 0.006 s. In other words, both NLPS and INLPS algorithms can achieve high network capacity and moderate computation time.

Energy-Efficient Computation Peer Offloading in Satellite Edge Computing Networks

Recently, MEC has been integrated with satellite networks to process remote terrestrial computation tasks with superior coverage and delay. Since single satellite computation is hard to tackle spatially uneven computation workloads, computation peer offloading among multiple satellites is urgently needed to further improve service quality and resource utilization. However, considering limited resources, deficient energy, and costly overheads of communication and computation, how to enable efficient offloading cooperation in the time-varying satellite networks is a significant challenge. In this paper, we first design a satellite peer offloading scheme, where offloading is performed along multi-hop paths to explore collaborative computing capabilities. Second, we formulate the Multi-Hop Satellite Peer offloading (MHSPO) problem, aiming to jointly minimize the delay and energy consumption under system resources and backlog constraints. Then, to adapt to the network dynamics, the decision-making process with uncertain future workloads is optimized by leveraging the delayed online learning method under the Lyapunov framework. Finally, we develop a practical online distributed algorithm to solve the MHSPO problem, which is proven to achieve close-to-optimal performance. Extensive simulations show that multi-hop peer offloading among satellites improves edge computing performance efficiently.

TCV-D: An Approach for Path Selection in Vehicular Task Offloading

The vehicular task offloading technology enhances the transportation system by offloading tasks of task-requesting vehicles (TRV) to either vehicular fog nodes (VFN) or road-side units (RSU) using single-hop or multi-hop communication. Due to the limited availability of VFN/RSU within a single-hop of TRV, a multi-hop path is employed for offloading tasks from TRV to VFN/RSU. However, the multi-hop path selection approaches have several issues, such as frequent re-connections due to varying vehicle speed, lower successful offloading when task deadline is missed, increased outage time when no VFN/RSU is within communication range of TRV, and inefficient vehicle selection to offload task leading to longer path lifetime. To address these issues, we have proposed a novel approach called Time-Computation-Variance based deadline sensitive path selection (TCV-D), which considers contextual information from k-hop neighbors. The approach offers four offloading modes: Direct mode, RSU mode, VFN mode, and Search mode, depending on the availability of RSUs/VFNs in single and multi-hop scenarios. To ensure tasks are delivered within deadlines, the proposed approach executes tasks by task forwarding vehicle or neighbor of task forwarding vehicle instead of designated VFN/RSU if delivering the task directly to the destination would exceed the deadline constraint. Extensive result analysis demonstrates substantial improvements compared to the existing k-hop-limited offloading time-based path selection (k-hop-limited-OTS) approach, including a 60% reduction in re-connections, a 35% decrease in path life time, a 30% decrease in outage time, and an 84% increase in successful offloading ratio, approximately.

HITLinQ: Improving path capacity through hybrid information-theoretic link scheduling in multi-hop wireless networks

By taking advantage of full-duplex radios, a one-sided interference channel model in the case of co-directional transmission on two links having a common node is introduced. Analysis of this model shows that asymmetric increase of transmitter power or decrease of distance will change the path capacity. When the signal noise ratio is high in this channel, using full-duplex radios for simultaneous co-directional transmission will worsen the path capacity, while direct transmission is better. On this basis, a scheduling strategy, called the hybrid information-theoretic link scheduling (HITLinQ) algorithm, which combines self-interference and inter-link interference management with link reconstruction, is proposed. The simulation results show that HITLinQ can provide capacity of not less than several baseline algorithms on a long multi-hop path under given setting.

Document-level relation extraction with global and path dependencies

Document-level relation extraction (RE) focuses on extracting relations for each entity pair in the same sentence or across different sentences of a document. Several existing methodologies aim to capture the intricate interactions among entities across a document by constructing diverse document graphs. However, these graphs frequently cannot sufficiently model the intricate global interactions and concurrent explicit path reasoning. Therefore, we introduce a distinctive graph-based model designed to assimilate global and path dependencies within a document for document-level RE, termed graph-based global and path dependencies (GGP). Specifically, the global dependency component captures interactions between mentions, entities, sentences and, the document through two interconnected graphs: the mention-level graph and the entity-level graph (ELG). To integrate relevant paths essential for the designated entity pair, the path dependency component consolidates information from various multi-hop paths of the target entity pair through an attention mechanism on the ELG. In addition, we devised an innovative method for learning path representation, which encapsulates relations and intermediate entities within the multi-hop path in the ELG. Comprehensive experiments conducted on standard document-level RE and CDR datasets reveal the following key findings: (i) GGP achieves an Ign F1 score of 59.98%, surpassing baselines by 0.61% on the test set; and (ii) the integration of various features derived from entities, sentences, documents, and paths enhances GGP's performance in document-level RE.

A collaborative learning framework for knowledge graph embedding and reasoning

Knowledge graph embedding (KGE) and knowledge graph reasoning (KGR) aim to automatic completion of knowledge graph (KG). The difference is that most KGE models learn the embedded representation at a triple level. In contrast, KGR models focus more on optimizing decision-making and enhancing the interpretability of reasoning processes with multi-hop paths. As a result, KGE models are better at learning triplet embeddings, whereas KGR models can capture the multihop information between entity pairs. However, KGE and KGR models only focus on one aspect that affects the completion performance. This paper proposes a plug-and-play collaborative learning framework (CLF) for jointly enhancing knowledge graph embedding and reasoning, which can accommodate existing KGR and KGE models. The two models exchange training experiences in this framework to realize mutual learning through a collaborative learning module. In this module, a new distance function is designed to maintain the independence of candidate entities’ probabilities and avoid information loss. Furthermore, a knowledge augmentation module is designed to identify missing key triples to assist in the further iterative training of the framework. Extensive experiments on the benchmark datasets demonstrate that our framework significantly improves the performance of existing models.

WOAD3QN-RP: An intelligent routing protocol in wireless sensor networks — A swarm intelligence and deep reinforcement learning based approach

Wireless Sensor Networks (WSN) are a crucial part of the Internet of Things (IoT), and research on WSN routing protocols has always been a hot topic in academia. However, traditional WSN routing protocols have limited utilization of available information during the routing decision process, leading to challenges such as insufficient adaptability to network topology changes, high communication delays, and short network lifetimes. To address these issues, this paper proposes an innovative intelligent routing algorithm WOAD3QN-RP, which cleverly integrates swarm intelligence algorithms and deep reinforcement learning. The WOAD3QN-RP not only effectively reduces delay but also balances energy consumption and flexibly adapts to changes in network topology, while simultaneously determining the optimal multi-hop path, effectively extending the lifetime of the network. Firstly, the WOAD3QN-RP algorithm employs the Whale Optimization Algorithm (WOA) to determine the optimal cluster heads (CHs). In the process of selecting CHs, the algorithm comprehensively considers key factors such as the residual energy of nodes, node distance, and communication delay, thereby significantly improving the accuracy and efficiency of CH selection, which contributes to better energy distribution and performance of the network. Secondly, in terms of multi-hop path selection, WOAD3QN-RP uses a dueling double deep Q-network (D3QN) to determine the optimal multi-hop path. Through utilizing neural networks to interact with the environment, intelligent agents are trained to learn routing policies to adapt to dynamic changes in the network topology and ensure the balance between energy consumption and multi-hop routing performance. Experimental results show that WOAD3QN-RP exhibits significant advantages over existing routing protocols in terms of network lifetime, energy efficiency, and communication delay.

Applying SMART-Based Power Gating Approach in NoC System

SMART is a low-latency solution that transmits nonconflicting flits to remote routers, enabling traversal of multi-hop paths within a single cycle. It reduces packet latency but ignores high power consumption in idle components. Additionally, SMART’s low latency is highly dependent on [Formula: see text], which represents the maximum number of hops traversed within a cycle. Therefore, this paper presents SMART-PG, a power gating method based on SMART that reduces network power consumption by shutting down idle components such as router buffers under low-load conditions. We also extend the router architecture to enable the continuous establishment of single-cycle multi-hop bypass paths. Compared to SMART routers, our method eliminates packet buffering even when a packet reaches the last router of a single-cycle multi-hop path, reducing packet latency and sensitivity to [Formula: see text]. Finally, we propose a routing algorithm that enables efficient packet transmission in one-dimensional and two-dimensional networks even when routers are powered off. Experimental results show that our method reduces packet latency and static power consumption by an average of 7.5% and 46.4%, respectively, compared to the baseline router. Compared to SMART routers, our method demonstrates significant superiority in terms of packet latency and the impact of multi-hop length on packet latency.

Multi-hop path reasoning over sparse temporal knowledge graphs based on path completion and reward shaping

Multi-hop path reasoning plays a crucial role in temporal knowledge graph reasoning, aiming to infer deep complex relationships and obtain interpretable reasoning results. Previous reasoning models primarily focus on designing temporal knowledge graphs with rich paths between entities but struggle to handle path sparsity, known as sparse temporal knowledge graphs. Sparse temporal knowledge graphs store only essential knowledge information, resulting in missing connection paths between certain entities and encountering issues of sparse rewards and information scarcity. To tackle these challenges, this paper proposes a multi-hop path reasoning model over sparse temporal knowledge graph based on path completion and reward shaping (STKGR-PR). STKGR-PR dynamically completes missing paths using a temporal embedding model to alleviate path sparsity. To tackle the issue of sparse rewards caused by reduced hit rates, we propose semantic composition-based reasoning path embeddings derived from relation embeddings and timestamp embeddings for reward shaping. Considering the limited information contained in sparse temporal knowledge graphs, we incorporate time vectors into the embedding and multi-hop path reasoning models to enhance the accuracy of reasoning paths and results. Experimental results conducted on six sparse temporal datasets sampled from ICEWS14 and ICEWS515–05 demonstrate that STKGR-PR outperforms state-of-the-art multi-hop path reasoning models over temporal knowledge graphs across all evaluation metrics, while ensuring interpretability.

Event causality identification via graph contrast-based knowledge augmented networks

Identifying causality between events is a crucial research task in natural language processing. However, existing methods either ignore background knowledge of events or do not consider interference of knowledge graph noise on event representations. In this paper, we propose a novel Graph Contrast-based Knowledge Augmented Network (GCKAN) for event causality identification task, which integrates comprehensive background knowledge of events from knowledge graphs and alleviates the knowledge graph noise problem. First, a descriptive knowledge augmentation module is proposed to aggregate one-hop neighbor information in knowledge graphs and learn meaningful descriptive knowledge of events. Then, a relational knowledge augmentation module is designed to encode multi-hop path information and learn latent reasoning knowledge between event pairs. In addition, trustworthiness-based and degree-based graph contrastive learning schemas are devised in the two modules respectively, which suppress knowledge graph noise during information aggregation and derive more robust knowledge-aware event representations. Extensive experiments on three public datasets demonstrate the consistent superiority of GCKAN over state-of-the-art knowledge-based techniques. Noise interference experiments and cross-topic adaptation experiments further verify the robustness and generalization of GCKAN.

Fuzzy Enhanced Black Widow Spider with Secure Encryption Random Permutation Pseudo Algorithm for Energy Efficient Cluster Communication in WSN

Devices connected to the Internet of Things (IoT) are increasingly being used. Modern mobile apps may be incorporated onto low-cost, low-power devices with the help of these extensions. This integration is made possible by the utilization of low-cost, low-power sensor nodes. All sensor nodes transmit data either directly or via a multi-hop path to other receivers or nodes. In order to enable effective inter-cluster communication through ideal cluster head selection, we build a fuzzy-enhanced black widow spider based on the Secure Encrypted Random Permutation Pseudo Algorithm (FEBWS-SERPPA) in this study. By taking energy, latency, and distance characteristics into account, the suggested FEBWS-SERPPA algorithm enhances the black widow spider optimization method by choosing the optimal cluster head in the cluster. To ensure its effectiveness, the suggested FEBWS-SERPPA algorithm's performance is compared to the state-of-the-art. The suggested FEBWS-SERPPA algorithm delivers enhanced performance speed with very low energy consumption and extended network lifetime when compared to existing cutting-edge methods.

Hybrid FFBAT optimized multi-hop routing in Internet of Nano-Things

Wireless Nano-Sensor Networks (WNSNs) are molecular-level networks consisting of nano-machines that have very limited energy capacity. Due to the high energy consumption of the nodes in WNSNs during the data transmission, energy-efficient routing algorithms may help to reduce overall energy consumption. On the other hand, metaheuristics are useful for solving problems such as routing, energy, security and connectivity for traditional sensor networks. Therefore, this study proposes a hybrid Firefly and BAT Optimization based energy-efficient multi-hop routing algorithm, namely nanoFFBAT, between nano-sensor nodes and randomly placed convenient nano-routers for WNSNs-supported Internet of Nano-Things (IoNT) applications. In this hybrid approach, Firefly optimization is used for selecting the most energy-efficient neighbor by a nano-sensor node based on the flashing behavior of fireflies on the way for forming multi-hop paths and the BAT algorithm is adapted for building energy-efficient routing path discovery in WNSNs and also finding the optimal solution in solution space. In addition, nanoFFBAT detects redundant nodes on the energy-efficient multi-hop routing paths and shortens these paths if there exists any neighbor node already has been selected. The results of nanoFFBAT are compared with the shortest path from a nano-node to the convenient nano-router, a genetic algorithm-based energy-efficient routing algorithm for sensor networks and a WNSN routing protocol in the literature. According to the experimental simulation results, nanoFFBAT saves 306.929 nJ on a path on average and prolongs network lifetime 16.298 times more on average compared to the mentioned algorithms.

Optimal Antenna Locations for Coverage Extension in Sub-Terahertz Vehicle-to-Vehicle Communications

Sub-terahertz (sub-THz, 100-300 GHz) communications promise to bring extraordinary rates in future 6G systems. High path loss and blockage effects will limit the coverage of base stations (BS) to a few hundred meters making deployment of such systems along the roads expensive. As a way to decrease the BS density, relaying has been proposed. However, vehicle-to-vehicle (V2V) propagation is characterized by different sets of communications paths depending on the antenna locations raising the question of their optimal positions. In this paper, by utilizing IEEE 802.15.3d parameters and 300 GHz propagation measurements, we develop a mathematical framework for comparison of multi-hop relaying systems with different antenna locations. We utilize coverage, BS availability, and the data rate over a multi-hop path as metrics of interest. Our results show that the windshield location is characterized by lower data rates and larger coverage while bumper and engine levels are similar in terms of these metrics. For the windshield location, the coverage is extended by 50% with BS availability 0.95. The windshield location is recommended as it is less sensitive to the technology penetration rate and is characterized by larger coverage. The proposed approach shows gains of up to 32% in terms of required BS density for the range of vehicles density (10-40 units/km).

A Multi-hop Path Query Answering Model for Knowledge Graph based on Neighborhood Aggregation and Transformer

Multi-hop path query answering is a complex task in which a path needs to be inferred from a knowledge graph that contains a head entity and multi-hop relations. The objective is to identify the corresponding tail entity accurately. The representation of the path is a critical factor in this task. However, existing methods do not adequately consider the context information of the entities and relations in the path. To address this issue, this paper proposes a novel multi-hop path query answering model that utilizes an enhanced reasoning path feature representation to incorporate intermediate entity information and improve the accuracy of path query answering. The proposed model utilizes the neighborhood to aggregate the entity representation in the reasoning path. It employs a recurrent skipping network to splice the embedding of the relationship and the entity in the reasoning path based on their weight. Additionally, the model adds the position representation to obtain the reasoning path representation. Moreover, the model uses Bi-GRU and Transformer to obtain the local and global context features of each entity in the reasoning path. Finally, the reasoning path feature representation is input into the feedforward neural network and predicted through the softmax layer. The effectiveness of the proposed model in addressing the multi-hop path query answering task is demonstrated by experimental results on the WN18RR and FB15K-237 datasets. Specifically, the proposed model outperforms existing methods in terms of accuracy.

Dingo‐optimization‐based task‐offloading algorithm in multihop V2V/V2I‐enabled networks

AbstractIn the Vehicle‐to‐Vehicle‐ (V2V) and Vehicle‐to‐Infrastructure‐ (V2I) enabled edge computing networks, the task vehicle may fail to offload tasks to the nodes outside the one‐hop communication with high computing resources, resulting in longer offloading time. Therefore, in this paper, we first construct a multihop V2V/V2I communications‐enabled edge computing architecture, which innovatively adopts vehicles and roadside units (RSUs) task offloading jointly to expand the communication resources of the task vehicle. Then, for the offloading node selection strategy, we create a vehicle adjacency table and propose a quickly depth‐first search‐based scheme to search the optimal multihop path. Finally, we develop an optimal offloading scheme based on the discrete dingo optimization algorithm (DDOA) to solve the task‐processing‐time minimization problem, which can converge quickly and achieve lower task offloading latency. Each dingo in DDOA is designed to choose one of the group attack, persecution, and scavenger strategies to search the solution space and accelerate the convergence of the DDOA by a survival rule. The numerical experimental results reveal that our proposed algorithm can outperform the other algorithms and reduce the delay time by 80% compared with the local scheme.