Two-hop Neighbors Research Articles

Cross-layer UAV network routing protocol for spectrum denial environments

Unmanned Aerial Vehicles (UAVs), which connect to one another over wireless networks, are being used in warfare more frequently. Nevertheless, adversarial interference has the potential to disrupt wireless communication, and the UAV routing methods in use today struggle to handle interference. In this paper, we propose a Cross-Layer UAV Link State Routing protocol, CLUN-LSR, to combat against jamming attacks. CLUN-LSR features three designs. First, it obtains real-time spectrum status from the link layer. Such capabilities are provided by many existing radios, especially the ones in military applications, but are ignored by traditional routing protocols. Second, based on the cross-layer information, CLUN-LSR adds efficient routing functions during routing, including the use of the number of two-hop neighbor nodes as a metric for route selection. Third, CLUN-LSR selects nodes that are not in the interference area, thereby reducing network interruptions and improving data transmission efficiency. All table-driven routing protocols can apply CLUN-LSR for better performance. We apply CLUN-LSR to the existing routing protocol MP-OLSR and simulate it using a commercial network simulator. Simulation results show that our innovative routing protocol demonstrates superior performance compared to existing table-driven routing methods, particularly in terms of packet transmission rate and overall throughput.

Joint routing and computation offloading based deep reinforcement learning for Flying Ad hoc Networks

Flying ad-hoc networks (FANETs) consisting of multiple Unmanned Aerial Vehicles (UAVs) are widely used due to their flexibility and low cost. In scenarios such as crowdsensing and data collection, data collected by UAVs are transmitted to base stations for processing and then sent to data centers. Still, the deployment of base stations is costly and inflexible. To address this issue, this paper introduces a position-based Computing First Routing (CFR) protocol designed for efficient task transmission and computation offloading in FANETs. This protocol facilitates task processing during data transfer and ensures the delivery of fully processed results to the data center. Considering the dynamically changing topology of the FANETs and the uneven distribution of the UAVs’ computation power, deep reinforcement learning is used to make multi-objective decisions based on the Q-values computed by the model. FANETs are centerless clusters, and two-hop neighbor tables containing position and computing power information are used to make less costly decisions. Simulation experiments demonstrate that CFR outperforms other benchmark schemes with an approximately 6% higher packet delivery rate, an approximately 21% reduction in end-to-end delay, and about a 34% decrease in total cost. Furthermore, it effectively ensures the completion of task offloading before reaching the destination node. This occurs due to the design of a hierarchical reward function that takes into account dynamic changes in delay and energy consumption, as well as the injection of neighbor computing power information into the two-hop neighbor table.

QQAR: A Q-learning-based QoS-aware routing for IoMT-enabled wireless body area networks for smart healthcare

Recently, the Internet of Medical Things (IoMT) has gained broad acclaim in the academic and industrial sectors due to its use in integrating medical devices and healthcare systems. Wireless body area networks (WBANs) are a promising technology in smart healthcare for real-time patient monitoring and health data analysis by medical professionals. Efficient data transmission is essential in ensuring reliable and timely delivery of healthcare facilities. Healthcare services require reliable, delay-sensitive, energy- and congestion-aware communication, which makes it challenging to design a routing protocol. To address quality of services (QoS) in IoMT-enabled WBANs for healthcare, we propose Q-learning-based quality of services (QoS) aware routing (QQAR). Firstly, we used two-hop-based link reliability estimation to ensure link reliability from sources to sink nodes. Routing decisions using two-hop neighbor information guarantees successful packet delivery. Secondly, we differentiated the packets based on traffic priority, which assures the delivery of critical packets even in a congested network. Particularly, considering two-hop node velocity with energy balancing enhanced the guarantee of timely packet delivery before deadlines. In addition, the multi-sink approach enhanced network reliability. Finally, we utilized Q-learning to select suitable neighbor nodes for packet forwarding in a decentralized fashion. A multi-sink load balance and congestion control mechanism adjusts the routing decisions in the QQAR to avoid congestion. Our simulation and comparison results showed that QQAR outperforms the existing routing protocols across various performance metrics under distinct network conditions.

Variety-aware GAN and online learning augmented self-training model for knowledge graph entity alignment

Recently, self-training strategies are adopted in some entity alignment methods, which address the scarcity of training data by selecting newly-aligned pairs from the predicted alignment of unlabeled data. However, the boundary between positive and negative pairs in predicted alignment is difficult to determine, which may lead to inappropriate alignment in newly-aligned pairs. Besides, some pre-aligned pairs have been fitted by the method during training iteration, and combining all pre-aligned pairs with newly-aligned pairs to retrain the method may result in overfitting problems. To address these problems, a Self-training Entity Alignment Framework based on Variety-aware GAN and Online Learning Algorithm named SEAGAN is proposed in this paper. To select reliable newly-aligned pairs from the predicted alignment, a variety-aware GAN with a metric of match variety that eliminates negative pairs differing significantly from positive pairs is designed. It leverages the distribution of entity pairs to determine the boundary between different types of pairs. Moreover, SEAGAN designs an online learning algorithm that combines newly-aligned pairs with their one-hop and two-hop neighbor entities in pre-aligned pairs to update model parameters, which alleviates overfitting problems. We conduct extensive experiments on four real-world datasets to compare SEAGAN with fifteen state-of-the-art methods. Experiment results show that SEAGAN has better performance than state-of-the-art methods on metrics of Prec@1, Prec@5, and MRR.

A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.

Network epidemiology plays a fundamental role in understanding the relationship between network structure and epidemic dynamics, among which identifying influential spreaders is especially important. Most previous studies aim to propose a centrality measure based on network topology to reflect the influence of spreaders, which manifest limited universality. Machine learning enhances the identification of influential spreaders by combining multiple centralities. However, several centrality measures utilized in machine learning methods, such as closeness centrality, exhibit high computational complexity when confronted with large network sizes. Here, we propose a two-phase feature selection method for identifying influential spreaders with a reduced feature dimension. Depending on the definition of influential spreaders, we obtain the optimal feature combination for different synthetic networks. Our results demonstrate that when the datasets are mildly or moderately imbalanced, for Barabasi-Albert (BA) scale-free networks, the centralities' combination with the two-hop neighborhood is fundamental, and for Erdős-Rényi (ER) random graphs, the centralities' combination with the degree centrality is essential. Meanwhile, for Watts-Strogatz (WS) small world networks, feature selection is unnecessary. We also conduct experiments on real-world networks, and the features selected display a high similarity with synthetic networks. Our method provides a new path for identifying superspreaders for the control of epidemics.

Q-learning-based routing inspired by adaptive flocking control for collaborative unmanned aerial vehicle swarms

A collaborative unmanned aerial vehicle (UAV) swarm can support various applications, including aerial surveillance and emergency communication. Owing to the high mobility, limited energy of UAVs, and frequent link breakages, data routing from remote UAVs to a base station may produce retransmissions, loops, energy holes, and long delay. In a UAV swarm network, therefore, relative mobility, path stability, and delay should be jointly taken into consideration to improve routing performance because they are highly coupled with each other. However, they are not properly exploited in the existing literature. To address these issues, in this paper, a Q-learning (QL)-based routing protocol inspired by adaptive flocking control (QRIFC) is proposed. The proposed adaptive flocking control algorithm generates optimal mobility with fairness in travel distance for each UAV to control the optimal node density. It also addresses the trade-off between aerial coverage and quality of service in connectivity by imposing constraints on the minimum separation distance and maximum allowable inter-UAV spacing using two-hop neighbor information. Additionally, it provides a stable link duration (LD) between neighboring UAVs and optimizes the control overhead. Furthermore, QL performs multi-objective optimization by utilizing a new state exploration and exploitation strategy to select an optimal routing path in terms of delay, stable path selection defined by predictive LD, and energy consumption. According to an extensive performance study, the proposed QRIFC outperforms existing routing protocols by 21-40 percent of average end-to-end delay and 9-23 percent of average packet delivery ratio, with less retransmissions.

Identification of Malicious Activity in Distributed Average Consensus via Non-Concurrent Checking

We consider the problem of average consensus in a network system under a fixed, undirected communication topology, when there are malicious nodes present that may try to influence the average calculation. In the setting considered, the average consensus is performed by the nodes in a distributed fashion using a linear iterative algorithm. We assume malicious nodes can manipulate, in an arbitrary manner, the value of their state in the aforementioned algorithm; the problem is then to check whether or not each node is correctly performing the updates of its state. To address this problem, we propose a distributed algorithm whereby each node is in charge of checking the updates performed by its neighboring nodes based on information that it receives from them and also from the neighbors of its neighbors. The algorithm leverages ideas from non-concurrent error detection schemes and its main advantage is that information from two-hop neighbors is only needed infrequently—a relaxation that significantly reduces the communication overhead associated with the requirement to make such information available

Secure gossip against intermittently malicious agents

Security of distributed consensus algorithms is rapidly becoming a matter of paramount importance. Existing approaches address it by identifying corrupted nodes and deleting them from the network, but this results in the permanent loss of the contribution of these agents even if they return to their proper functionality. In this paper, we consider the case when legitimate nodes might exhibit malicious behavior that occurs intermittently and only for certain time instants. In particular, we provide a strategy to ensure that corrupted values never get incorporated in consensus computations, but without having to permanently exclude any nodes from the network and allowing them to participate to the consensus calculations once they have regained proper behavior. This approach allows the network to use all available information to achieve consensus and preserves the graph connectivity. Moreover, the proposed protocol relaxes several of the assumptions traditionally required by alternative state of the art methodologies. Our approach is based on reiterated exchange and comparison among each node and its one- and two-hop neighbors, and amounts to a variation of the standard gossip algorithm in which three nodes are involved at each round. After analyzing the convergence of the proposed algorithm, simulations are carried out to illustrate its effectiveness.

TGRV: A trust-based geographic routing protocol for VANETs

Vehicular ad-hoc networks (VANETs) consist of highly mobile and self-organized nodes that wirelessly communicate with one another and transmit a variety of information, some of which may be critical information. Due to the rapidly changing topology of VANETs and the high-speed mobility of the participating vehicles, the routing of packets in these networks is a challenging task. Moreover, the presence of malicious vehicles in VANETs makes the correct routing of packets more challenging. In this paper, we propose a trust-based geographic routing protocol for VANETs (TGRV) that limits the participation of malicious vehicles in routing. In TGRV, to select the next-hop, a sender not only considers the distance, speed, and direction of its neighbors but also evaluates their direct trust and recommendation trust. For this purpose, TGRV uses a monitoring system that allows each vehicle to monitor the correct packet forwarding rate of its next-hop. In this way, the vehicle updates its direct trust to the next-hop and retransmits the packets if the packets are lost. The vehicle also sends its observations of the next-hop to its neighbors with a push-based notification, and based on that, the neighbors can update their recommendation trust about the next-hop. The monitoring system applies distance prediction in a modified promiscuous mode to better estimate the correct packet forwarding rate of the next-hop. To enhance the accuracy of trust management, the trust values of interactions are reduced over time by using a decay factor. TGRV uses the number and trust of two-hop neighbors, which helps in selecting the next-hop that is in a more trusted zone. Our extensive simulations on OMNeT++ show that when the percentage of malicious vehicles in an urban scenario increases from 0 to 25%, the packet delivery ratio of TGRV decreases from 95.1 to 88.7%, which performs very well compared to the GPSR and PGRP protocols. Also, the end-to-end delay of TGRV increases from 3.79 to 7.07 s and the average hop count of TGRV increases from 6.7 to 9.9, which are significant increases compared to the other two protocols. These increases are acceptable because, in the other two protocols, there is no monitoring and retransmission, and the probability of packet delivery failure on longer routes is higher. The cost and security analysis also show that the memory, communication, and computational overheads of TGRV are acceptable, and TGRV is resistant and resilient to some important trust-based attacks.

Adaptively prioritizing candidate forwarding set in opportunistic routing in VANETs

Opportunistic routing (OR) paradigms in vehicular networks via opportunistic candidate set selection have a better performance over the pre-defined next-hop node in terms of packet delivery rate. Nevertheless, uneven vehicle distribution, highly dynamic topology, varying link interference, and bandwidth restrictions impose drastic challenges for satisfying applications requirements in static prioritizing candidate set selection policies. Besides, fulfilling quality of service (QoS) requirements of heterogeneous applications, due to the limited local topology perspective and many involving conflicting routing criteria, will be further compromised. Considering the candidate set selection policy as a Multi-Criteria Decision Making (MCDM) task, an adaptive two-hop cooperative forwarding set selection strategy called APFS is proposed in this paper. By developing a local topology perspective to the two-hop neighbor information, the APFS scheme jointly considers indicators of link stability, delay assessment, and link good-put as routing criteria. Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods are employed in two different scenarios in prioritizing candidate forwarding sets. First, classical AHP-TOPSIS technique with a mutable pair-wise comparison matrix is applied, and then Neutrosophic Set (NS) is combined with the classical AHP-TOPSIS method to handle ambiguous vehicular environments. The simulation results show that the APFS strategy using both prioritization policies have significantly improved the performance of packet delivery ratio, average end-to-end delay, throughput, and hop counts as compared to other schemes.

SATMAC: Self-Adaptive TDMA-Based MAC Protocol for VANETs

Rapid development and deployment of vehicular ad-hoc networks (VANETs) require an efficient and scalable media access control (MAC) protocol to support high-priority safety applications and infotainment requirements. This paper proposes SATMAC, a self-adaptive time division multiple access (TDMA)-based MAC protocol for VANETs. In order to improve the stability of the time slot scheduling in VANETs, a slot status updating strategy is carefully designed, which utilizes accurate information of the two-hop neighbors and the rough information of the three-hop neighbors to detect the potential packet collisions and avoids potential collisions by adjusting the occupied time slot. Besides, an adaptive frame length (the number of time slots contained in a frame) approach is proposed on the basis of the slot adjustment to support various densities of vehicles, where the frame length between neighbors can be inconsistent. We conduct theoretical analysis and extensive simulations in a realistic VANET environment to evaluate SATMAC. Simulation results show that compared with IEEE 802.11p and LTE-V2X PC5 Mode 4, SATMAC significantly improves PDR of beacons over 60%. Moreover, our SATMAC design is further implemented and validated on our FPGA-based testbed.

A Simple Yet Effective Layered Loss for Pre-Training of Network Embedding

Pre-training of network embedding aims to encode unlabeled node proximity into a low-dimensional space, where nodes are close to their neighbors while being far from negative samples. In recent years, Graph Neural Networks have shown groundbreaking performance in semi-supervised learning on the node classification and link prediction tasks. However, because of their inherent information aggregation pattern, almost all these methods can only obtain inferior embedding results in the pre-training of the unlabeled nodes. The margins between a target node and its multi-hop neighbors become hard distinguishable during node message aggregation. To address this problem, we propose a simple yet effective layered loss to combine with a graph attention network, dubbed as LlossNet, for pre-training. We regard the proximity of a target node and its two-hop neighbors as a unit (called a unit graph), where a target node is needed to be more closer to its direct neighbor than its two-hop neighbors. As such, LlossNet would be able to preserve the margins of nodes in the learned embedding space. Experimental results of various downstream tasks including classification and clustering demonstrate the effectiveness of our method on learning discriminative node representations.

Maximum and top-k diversified biclique search at scale

Maximum biclique search, which finds the biclique with the maximum number of edges in a bipartite graph, is a fundamental problem with a wide spectrum of applications in different domains, such as E-Commerce, social analysis, web services, and bioinformatics. Unfortunately, due to the difficulty of the problem in graph theory, no practical solution has been proposed to solve the issue in large-scale real-world datasets. Existing techniques for maximum clique search on a general graph cannot be applied because the search objective of maximum biclique search is two-dimensional, i.e., we have to consider the size of both parts of the biclique simultaneously. In this paper, we divide the problem into several subproblems each of which is specified using two parameters. These subproblems are derived in a progressive manner, and in each subproblem, we can restrict the search in a very small part of the original bipartite graph. We prove that a logarithmic number of subproblems is enough to guarantee the algorithm correctness. To minimize the computational cost, we show how to reduce significantly the bipartite graph size for each subproblem while preserving the maximum biclique satisfying certain constraints by exploring the properties of one-hop and two-hop neighbors for each vertex. Furthermore, we study the diversified top-k biclique search problem which aims to find k maximal bicliques that cover the most edges in total. The basic idea is to repeatedly find the maximum biclique in the bipartite graph and remove it from the bipartite graph k times. We design an efficient algorithm that considers to share the computation cost among the k results, based on the idea of deriving the same subproblems of different results. We further propose two optimizations to accelerate the computation by pruning the search space with size constraint and refining the candidates in a lazy manner. We use several real datasets from various application domains, one of which contains over 300 million vertices and 1.3 billion edges, to demonstrate the high efficiency and scalability of our proposed solution. It is reported that 50% improvement on recall can be achieved after applying our method in Alibaba Group to identify the fraudulent transactions in their e-commerce networks. This further demonstrates the usefulness of our techniques in practice.

GAR: Gradient assisted routing for topology self-organization in dynamic mesh networks

Modern mobile handheld devices, such as smartphones and tablets, feature multiple wireless interfaces, some of which can support device-to-device communications, which enable mesh networks on even when the infrastructure is unavailable. One of the key technological challenges hampering the use of multi-hop mesh networks is the extremely high communication overhead of route discovery and maintenance algorithms. The problem is especially pronounced under dynamic network conditions caused by user mobility and nodes joining and leaving the network. In this paper, we propose a fully distributed algorithm for constructing a virtual coordinate system used for geo-like routing by approximating the physical network nodes coordinate. The proposed algorithm, called gradient assisted routing (GAR), builds upon two-hop neighbors’ information exchanged in beacons in contrast to conventional geographic routing protocols which rely on external positioning information. We evaluate the proposed solution using algorithmic, topological, and routing-related metrics of interest. We further numerically quantify how the node mobility increases the time needed for topology stabilization, and how network size affects the route discovery success rate. Our comparison also shows that for small to mid-size mesh networks (up to 60 nodes), the performance of the proposed routing procedure is similar to the conventional geographic routing protocols that exploit external positioning information. The proposed solution may efficiently supplement the traditional on-demand routing in small to mid-size mesh systems by independently establishing 50 to 70% of paths and thereby reducing the discovery overheads.

A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks

Flying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> networks (FANETs) have emanated over the last few years for numerous civil and military applications. Owing to underlying attributes, such as a dynamic topology, node mobility in 3-D space, and the limited energy of unmanned aerial vehicles (UAVs), a routing protocol for FANETs is challenging to design. Exiting topology-based routing is unsuitable for highly dynamic FANETs. Location-based routing protocols can be preferred for FANETs owing to their scalability, but are based on one-hop neighbor information and do not contemplate the reachability of further appropriate nodes for forwarding. Owing to the rapid mobility of UAVs, the topology frequently changes; thus, some route entries in the routing table can become invalid and the next-hop nodes may be unavailable before a timeout. That is, the routing decision based on one-hop neighbors cannot assure a successful delivery. In this study, we propose a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning-based topology-aware routing (QTAR) protocol for FANETs to provide reliable combinations between the source and destination. The proposed QTAR improves the routing decision by considering two-hop neighbor nodes, extending the local view of the network topology. With the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning technique, QTAR adaptively adjusts the routing decision according to the network condition. Our simulation results reveal that QTAR outstrips the existing routing protocols in respect of various performance metrics under distinct scenarios.

MSM: A Method of Multi-Neighborhood Sampling Matching for Entity Alignment

The heterogeneity of knowledge graphs brings great challenges to entity alignment. In particular, the attributes of network entities in the real world are complex and changeable. The key to solving this problem is to expand the neighborhoods in different ranges and extract the neighborhood information efficiently. Based on this idea, we propose Multi-neighborhood Sampling Matching Network (MSM), a new KG alignment network, aiming at the structural heterogeneity challenge. MSM constructs a multi-neighborhood network representation learning method to learn the KG structure embedding. It then adopts a unique sampling and cosine cross-matching method to solve different sizes of neighborhoods and distinct topological structures in two entities. To choose the right neighbors, we apply a down-sampling process to select the most informative entities towards the central target entity from its one-hop and two-hop neighbors. To verify the effectiveness of matching this neighborhood with any neighborhood in the corresponding node, we give a cosine cross-graph neighborhood matching method and conduct detailed research and analysis on three entity matching datasets, which proves the effectiveness of MSM.

Adaptive Time Difference of Time of Arrival in Wireless Sensor Network Routing for Enhancing Quality of Service

Underwater wireless communications are critical in military and corporate operations such as environmental monitoring, underwater exploration, and scientific data collection. Existing protocols for terrestrial wireless sensor networks (TWSNs) perform poorly in terms of energy efficiency, dependability, and transmission. Because they have separate qualities, they cannot be used directly in the UWSN. The present challenges include developing an EDVR algorithm for determining the distance to each node and the variance in node depth in order to estimate energy consumption reductions. This technique takes the depth of the two-hop neighbors into account and calculates the time aid from the Adaptive Time Difference of Arrival (ATDoA), which is avoided by broadcasting information to its neighboring node, with farther forward nodes. To determine the time difference between the reception of two signals at a node, the adaptive time Difference of time of arrival (ATDoA) is easier to measure than the time at which the signal arrives. In the UWSN, the following transmission assigns higher node energy if the node is lower. It increases system performance, saves lives, and minimizes packet wait time at the destination. The results show that nodes have a longer lifetime, fewer dead nodes, use less energy, and take less time to propagate than techniques.

LTHS: A heuristic algorithm based on local two-hop search strategy for influence maximization in social networks

Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy.

Joint Optimization of Multi-Hop Broadcast Protocol and MAC Protocol in Vehicular Ad Hoc Networks.

Beacon messages and emergency messages in vehicular ad hoc networks (VANETs) require a lower delay and higher reliability. The optimal MAC protocol can effectively reduce data collision in VANETs communication, thus minimizing delay and improving reliability. In this paper, we propose a Q-learning MAC protocol based on detecting the number of two-hop neighbors. The number of two-hop neighbors in highway scenarios is calculated with very little overhead using the beacon messages and neighbor locations to reduce the impact of hidden nodes. Vehicle nodes are regarded as agents, using Q-learning and beacon messages to train the near-optimal contention window value of the MAC layer under different vehicle densities to reduce the collision probability of beacon messages. Furthermore, based on the contention window value after training, a multi-hop broadcast protocol combined with contention window adjustment for emergency messages in highway scenarios is proposed to reduce forwarding delay and improve forwarding reliability. We use the trained contention window value and the state information of neighboring vehicles to assign an appropriate forwarding waiting time to the forwarding node. Simulation experiments are conducted to evaluate the proposed MAC protocol and multi-hop broadcast protocol and compare them with other related protocols. The results show that our proposed protocols outperform the other related protocols on several different evaluation metrics.

A Distributed Multi-Hop Intra-Clustering Approach Based on Neighbors Two-Hop Connectivity for IoT Networks.

Under a dense and large IoT network, a star topology where each device is directly connected to the Internet gateway may cause serious waste of energy and congestion issues. Grouping network devices into clusters provides a suitable architecture to reduce the energy consumption and allows an effective management of communication channels. Although several clustering approaches were proposed in the literature, most of them use the single-hop intra-clustering model. In a large network, the number of clusters increases and the energy draining remains almost the same as in un-clustered architecture. To solve the problem, several approaches use the k-hop intra-clustering to generate a reduced number of large clusters. However, k-hop proposed schemes are, generally, centralized and only assume the node direct neighbors information which lack of robustness. In this regard, the present work proposes a distributed approach for the k-hop intra-clustering called Distributed Clustering based 2-Hop Connectivity (DC2HC). The algorithm uses the two-hop neighbors connectivity to elect the appropriate set of cluster heads and strengthen the clusters connectivity. The objective is to optimize the set of representative cluster heads to minimize the number of long range communication channels and expand the network lifetime. The paper provides the convergence proof of the proposed solution. Simulation results show that our proposed protocol outperforms similar approaches available in the literature by reducing the number of generated cluster heads and achieving longer network lifetime.