Member Nodes Research Articles

UASN‐3D: An energy efficient localization based on LEACH‐BR algorithm (EELBL‐BR)

AbstractUnderwater acoustic sensor networks (UASN) research is gaining popularity as it has various applications such as military communication, oil rig maintenance, animal survival, linking submarines to land, gathering data for water monitoring, and other commercial fields. The two most critical requirements for the application's proper operation are the accurate knowledge of sensor node locations and the efficient transmission of accurate underwater sensor node information to the base station with efficient energy consumption. The proposed energy efficient localization based on the LEACH‐beacon and reinforced node (EELBL‐BR) algorithm satisfies both the requirements in 3D‐UASN. The proposed algorithm considers the deployment and computation of accurate location of sensor nodes in the underwater environment by applying I‐LASP (improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) (Yadav N, Khilar PM. Trans Emerg Telecommun Technol. 2023;34:e4772.) for 3D environment. It performs clustering of sensor nodes for enhancing network lifetime using three different types of nodes such as beacon, reinforced, and member nodes. The proposed clustering LEACH‐BR (low‐energy adaptive clustering hierarchy‐beacon and reinforced nodes) algorithm is based upon the LEACH algorithm which provides accurate location of all the sensor nodes, improves energy consumption and reliability in the underwater environment. The result shows that the proposed algorithm EELBL‐BR, considering both beacon and reinforced nodes, provides the improvement in the number of alive nodes, reduction in the number of dead nodes, reduction in energy consumption and enhances residual energy in the UASN by 68.90%, 51.91%, 51.47%, and 68.12% respectively with respect to the number of rounds as compared to that of the existing algorithm by Rizvi et al. (Wirel Pers Commun. 2022;124(4):3725–3741.) and thus outperforms the existing algorithm (Rizvi HH, Khan SA, Enam RN. Wirel Pers Commun. 2022;124(4):3725–3741.).

Multiusers parallel competitive channel protocol based on unsaturated service

In order to enhance the area throughput of next generation wireless local area network (WLAN) in high density scenarios, orthogonal frequency division multiple access (OFDMA) has been adopted as one of the key technologies in the next generation WLAN communication standards. However, the performance of the existing media access control (MAC) degrades significantly under unsaturated services. Therefore, this paper proposes a multi-user parallel contention channel MAC (MU-MAC) based on unsaturated services, which can effectively reduce the channel access conflict and improve the OFDMA access efficiency of cluster member nodes. On this basis, MU-MAC is enhanced for the spatial clustering group (SCG) formation protocol and support for the unsaturated service characteristics. Further, the optimal access radius when the service is in a non-saturated state is analyzed to make the relevant theoretical analysis more generally, and the expressions for the throughput and area throughput of the proposed protocol are modeled and derived. The simulation results verify the correctness of the theoretical analysis and the efficiency of the protocol performance. The results show that MU-MAC outperforms IEEE 802.11ax and OMAX protocol in area throughput by 40.72% and 104.15%, respectively.

An Energy Efficient Secure routing Scheme using LEACH protocol in WSN for IoT networks

One of the most recent and exciting new applications can be found in the realm of the IoT, which makes it possible to connect various objects or devices over the internet. Many options exist to mitigate the negative effects of communication protocols on WSNs' power consumption. LEACH protocol is a cluster-based one, that uses clusters to divide the network. LEACH uses the random numbers produced by each sensor node to choose a group of nodes to serve as cluster heads (CHs). An enhanced version of the LEACH procedure for stable & energy-efficient CH selection is the subject of the proposed work. The proposed work stabilizes the random number generation by including Rank-based CH selection using Node coverage, Energy consumption rate, and the characteristics of the base station's distance. The node coverage parameter guarantees that the chosen CHs have more neighbors in their coverage circle, it, in turn, lowers the energy required for member nodes to go to the CH. The quantity of energy used rate ensures that the selected CHs have higher energy. According to the findings of the simulations, it is clear that the suggested strategy significantly improves the Energy efficiency performance of WSNs, network longevity, and communication quality.

Cross-Platform UAV Swarm Key Management in Denied Environments

As resources provided by single unmanned aerial vehicles (UAVs) are limited, we propose a cross-platform UAV swarm key management scheme for task scenarios in denied environments. In denied environments where the communication link is open and the UAV nodes may go invalid, secure communication is often at stake. To solve this problem, we propose a key management scheme which, based on the Chinese remainder theorem (CRT) and the Hash function, constructs a swarm key by combining the local key and the session key to reduce the overhead of individual UAV nodes in the swarm. Meanwhile, the swarm head node constructs broadcast messages according to the key update needs, which reduces the overhead of the member nodes, improves the efficiency of key updating, and fulfills the key establishment and updating of the UAV swarm. Experiments show that our proposed scheme has forward and backward security and can defend against collusion attacks and replay attacks; our method was compared with other methods on the MIRACL cryptographic library in Visual Studio 2019, and it was found that our method has a lower computing and communication overhead, provides a solution to cross-platform key management of UAV swarms in denied environments, and ensures safe communication of UAVs in the swarm.

An enhanced energy efficient protocol for large-scale IoT-based heterogeneous WSNs

There is increasing attention, recently, to optimizing energy consumption in IoT-based large-scale networks. Extending the lifetime of battery-powered nodes is a key challenge in such systems and their various application scenarios. This paper proposes a new zone-based and event-driven protocol for saving energy in large-scale heterogeneous WSNs called TESEES (Threshold Enabled Scalable and Energy Efficient Scheme). The proposed protocol is designed to support network scenarios deploying higher levels of heterogeneity with more than three types of sensor nodes (i.e., four, five, and more). TESEES is a reactive version of the proactive SEES protocol, in which we leverage a novel state-of-the-art thresholding model on the zone-based hierarchical deployments of heterogeneous nodes to regulate the data reporting process, avoiding unnecessary frequent data transmission and reducing the amount of energy dissipation of the sensing nodes and the entire system. With this model, we present a general technique for formulating distinct thresholds for network nodes in each established zone. This mechanism allows for individually configuring the nodes with transmission settings tailored to their respective roles, independent of the heterogeneity levels, total node count, or initial energy. This approach ensures that each node operates optimally within the network. In addition, we present an improved hybrid TMCCT (Threshold-based Minimum Cost Cross-layer Transmission) algorithm that operates at the node level and ensures effective data transmission control by considering current sensor values, heterogeneous event thresholds, and previous data records. Instead of periodical data transmission, this hybridization mechanism, integrated with a grid of energy-harvesting relay nodes, keeps the zone member nodes in the energy-saving mode for maximum time and allows for reactive data transmission only when necessary. This results in a reduced data-reporting frequency, less traffic load, minimized energy consumption, and thus a greater extension of the network’s lifetime. Moreover, unlike the traditional cluster-head election in the weighted probability-based protocols, TESEES relies on an efficient mechanism for zone aggregators’ election that runs at the zone level in multiple stages and employs various static and dynamic parameters based on their generated weights of importance. This leads to selecting the best candidate nodes for the aggregation task and, hence, fairly rotating the role among the zones’ alive nodes. The simulation results show significant improvements in the total energy saving, the lifetime extension, and the transmitted data reduction, reaching 29%, 68%, and 26% respectively, compared to the traditional SEES protocol. Also, the average energy consumption per single round has decreased by 36%.

Application of BPNN Optimized by Fireworks Algorithm on Structural Bearing Capacity Analysis of Transmission Tower under Icing Conditions

With the increasing power demand in city development, the construction and application of transmission towers need to meet higher requirements. Icing is an extreme meteorological condition in the world, and icing disasters cause various accidents such as tower collapse and line disconnection of large-area lines every year. Therefore, it is of great significance to study the ultimate bearing capacity of transmission towers under icing conditions. The structural bearing capacity under icing is greatly affected by meteorological conditions and environmental factors, which has strong randomness and complexity, and brings a series of urgent problems to the structural stability of transmission towers. In the paper, the ultimate bearing capacity analysis and failure path research are conducted on the 500 kV linear cathead tower structure, and a structural bearing capacity prediction model based on the fireworks algorithm (FWA) is established. In order to accurately predict the structural bearing capacity, reduce the adverse effects of line icing, and ensure the structural stability of the transmission tower, the FWA is introduced into the neural network model. The self-adjusting mechanism of the local search ability and global search ability of the FWA is used to optimize the optimization process of weights and thresholds in the neural network, and a structural bearing capacity prediction model based on the FWA improved backpropagation neural network (BPNN) is proposed. Through the analysis and calculation of the measured data of the transmission tower in a certain area, and the comparative study with the traditional BPNN and the BPNN optimized by other common algorithms, the analysis and calculation results of the FWA-BPNN model used in this paper are closer to the actual value, the multiple indicators are more superior, and the model is more stable, which can realize the rapid and accurate analysis of the axial stress of tower members and maximum displacement between member nodes of the transmission tower under the icing condition.

Trust-Based Certificate Management for Industrial IoT Networks

The Industrial Internet of Things (IIoT) network is composed of devices that contain sensitive data, which makes them vulnerable to various security threats. Digital Certificates can be used to reinforce the security of the IIoT network, however, their management remains a major issue. Hence, in this paper, we rely on trust management to deal with the whole certificate management process in IIoT networks, from revocation to verification. For this purpose, we organize the IIoT network into a clustering architecture where each cluster head hosts an agent, called CH-UR agent, that renews/revokes the certificates of its cluster member nodes. We apply signaling game theory to build a Certificate Revocation Game modeling the interactions between a member IIoT node and the CH-UR agent. Thus, upon the belief on the member node, updated by using the Bayesian rules, the best response strategy for the CH-UR agent can be obtained. Further, we propose a new efficient certificate verification scheme based on short-lived certificates (SLC) and suitable for IIoT network requirements. The performance evaluation of our framework proves, firstly, the accuracy and convergence speed of our revocation mechanism to detect untrusted devices and on-off attacks. Secondly, the effectiveness of our clustering architecture to reduce the resource consumption resulting from the management of SLCs to 60% even with the increase of network density. Thirdly, the effectiveness of the proposed certificate verification scheme to reduce the time needed to obtain the revocation information as well as the resulting storage and communication overhead to achieve this purpose.

Effective Aggregate Data Collection and Enhanced Network Lifetime Using Energy Efficient Aggregation Data Convening Routing in Wireless Sensor Network

Wireless Technology is an integral part of today's applications. It encompasses commercial and human-oriented environmental monitoring, military applications, robotics, and healthcare monitoring systems, home or factory automation, and logistics. A wireless sensor network is a network system that uses wireless sensor nodes to monitor physical or environmental conditions such as voice, temperature, and spatial dispersive movements. Each node can locally sense its environment, process information and data, and send the data to one or more collection points within the WSN. The existing solution is categorized into member nodes and group/cluster heads (CH). The CH election process increases the overhead of the network and reduces the network lifetime. The processing and energy limitations of the nodes are considered for the CH election process. In this cluster formation methods aiming at the Cluster head selection process and providing trust in hierarchical WSN are proposed. The Energy Efficient Aggregation Data Convening Routing (E2ADCR) is used to estimate the routing path, and aggregate data collection to improve the network lifetime. The major advantage of this technique is to avoid the malicious or selfish node from becoming a dominant cluster in a group of clusters. Initially sink node selection forwards the Configuration Message (CM) to every node on the network to construct the performing node. In this, cluster selection is based on connection density, degree of the node angle, and residual energy (Quality Factor) that is evaluated from the link robustness, energy, and degree of the node. Multi-hop link transmission support path optimization technique is estimated in the path when the obstacle is present in the WSN. To introduce an Aggregated Support-based Data Collection to evaluate each packet flow monitor on the network to if any unrelated packet will eliminate to forward to the sink node. The proposed routing path of computational simplicity is achieved by a simple method. The new routing protocols, which were developed during this research, have better energy efficiency.

RVMF: RELIABLE ROUTING METHOD FOR VEHICULAR AD HOC NETWORKS USING MOTH-FLAME AND FIREFLY OPTIMIZATION ALGORITHMS

With the advancement of wireless communication technology, the intelligent transportation system (ITS) has attracted the attention of vehicle companies and academic researchers. Recently, vehicular ad hoc networks (VANETs) as a leading genuine technology have received serious attention as a kind of mobile ad hoc network (MANET) to ensure the safety of vehicles, drivers, and passengers. However, these networks face many challenges due to the mobility of vehicle nodes, wireless communication, and frequent topology changes. One of the crucial issues of these networks is a cluster-based routing scheme with the ability to provide quality of service (QoS) parameters. A clustering scheme is an appropriate method for increasing the scalability of VANETs. In a cluster-based routing scheme, the cluster head (CH) is responsible for receiving data from its member nodes, and aggregating and transferring data to the next CH node. On the other hand, providing a suitable clustering method is NP-hard problems and meta-heuristic algorithms are suitable for solving these problems. A scalable and reliable routing scheme is necessary and essential in VANETs. In this paper, a routing method based on the clustering technique is presented considering the moth-flame optimization (MFO) algorithm for clustering and the Firefly optimization algorithm (FoA) with a suitable fitness function for routing between CHs. The simulation of the proposed method with MATLAB software shows that the proposed RVMF method improves the parameters of packet delivery rate (PDR), latency, and throughput.

Enhanced security using multiple paths routine scheme in cloud-MANETs

Cloud Mobile Ad-hoc Networks (Cloud-MANETs) is a framework that can access and deliver cloud services to MANET users through their smart devices. MANETs is a pool of self-organized mobile gadgets that can communicate with each other with no support from a central authority or infrastructure. The main advantage of MANETs is its ability to manage mobility while data communication between different users in the system occurs. In MANETs, clustering is an active technique used to manage mobile nodes. The security of MANETs is a key aspect for the fundamental functionality of the network. Addressing the security-related problems ensures that the confidentiality and integrity of the data transmission is secure. MANETs are highly prone to attacks because of their properties.In clustering schemes, the network is broken down to sub-networks called clusters. These clusters can have overlapping nodes or be disjointed. An enhanced node referred to asthe Cluster Head (CH) is chosen from each set to overseetasks related to routing. It decreases the member nodes’ overhead and improvesthe performance of the system. The relationship between the nodes and CH may vary randomly, leading to re-associations and re-clustering in a MANET that is clustered. An efficient and effective routing protocol is required to allow networking and to find the most suitable paths between the nodes. The networking must be spontaneous, infrastructure-less, and provide end-to-end interactions. The aim of routing is the provision of maximum network load distribution and robust networks. This study focused on the creation of a maximal route between a pair of nodes, and to ensure the appropriate and accurate delivery of the packet. The proposed solution ensured that routing can be carried out with the lowest bandwidth consumption. Compared to existing protocols, the proposed solution had a control overhead of 24, packet delivery ratio of 81, the lowest average end-to-end delay of 6, and an improved throughput of 80,000, thereby enhancing the output of the network. Our result shows that multipath routing enables the network to identify alternate paths connecting the destination and source. Routing is required to conserve energy and for optimum bandwidth utilization.

A multi-level generative framework for community detection in attributed networks

AbstractCommunity detection in attributed networks is one of the most important tasks in complex network analysis. Many existing methods propose to integrate the network topology and node attribute from a generative aspect, which models an attributed network as a probabilistic generation process with the community distribution described by hidden variables. Though they can provide good interpretability to the community structure, it is difficult to infer community membership quickly due to their high computational complexity when inferring. Motivated by the multi-level strategy, in this study, we propose a multi-level generative framework to reduce the time cost of generative models for community detection in attributed networks. We first coarsen an attributed network into smaller ones by node matching. Then, we employ the existing generative model on the coarsest network without any modification for community detection, thus efficiently obtaining community memberships of nodes in this small coarsest network. Last, we project the assignments back to the original network through a local refinement mechanism to get communities. Extensive experiments on several real-world and artificial attributed networks show that our multi-level-based method is significantly faster than original generative models and is able to achieve better or more competitive results.

Community Detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational Autoencoder

Community detection is an important research direction in complex network analysis that can help us discover valuable network structures. The community detection algorithms based on multiobjective particle swarm optimization encode community membership of nodes in particles and employ evolutionary strategies to search for the optimal community division. Existing algorithms face two challenges: (1) they are inapplicable to large networks because the evolution process is time-consuming; (2) they are easy to fall into local optima. In this paper, we propose a novel algorithm that combines a multiobjective particle swarm optimization algorithm based on label propagation with a graph attention variational autoencoder to realize community detection. On the one hand, the label propagation strategy is involved in the update of a swarm's particles to speed up its evolution. The optimal solutions found by the particle swarm optimization algorithm are embedded into the objective of the autoencoder to improve the embedding vectors quality. On the other hand, the embedding vectors are used to improve the solutions of the particle swarm optimization algorithm to avoid its early convergence. The experiments on artificial and real-world networks demonstrate the feasibility and effectiveness of our algorithm compared with the state-of-the-art algorithms.

Reliable Data Transmission in Underwater Wireless Sensor Networks Using a Cluster-Based Routing Protocol Endorsed by Member Nodes

Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node’s battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network’s member nodes. As a result, we use clusters, which are produced by dividing the network’s space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle’s focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability.

An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO

Extending the lifetime of wireless sensor networks (WSNs) and minimizing energy costs are the two most significant concerns for data transmission. Sensor nodes are powered by their own battery capacity, allowing them to perform critical tasks and interact with other nodes. The quantity of electricity saved from each sensor together in a WSN has been strongly linked to the network’s longevity. Clustering conserves the most power in wireless transmission, but the absence of a mechanism for selecting the most suitable cluster head (CH) node increases the complexity of data collection and the power usage of the sensor nodes. Additionally, the disparity in energy consumption can lead to the premature demise of nodes, reducing the network’s lifetime. Metaheuristics are used to solve non-deterministic polynomial (NP) lossy clustering problems. The primary purpose of this research is to enhance the energy efficiency and network endurance of WSNs. To address this issue, this work proposes a solution where hybrid particle swarm optimization (HPSO) is paired with improved low-energy adaptive clustering hierarchy (HPSO-ILEACH) for CH selection in cases of data aggregation in order to increase energy efficiency and maximize the network stability of the WSN. In this approach, HPSO determines the CH, the distance between the cluster’s member nodes, and the residual energy of the nodes. Then, ILEACH is used to minimize energy expenditure during the clustering process by adjusting the CH. Finally, the HPSO-ILEACH algorithm was successfully implemented for aggregating data and saving energy, and its performance was compared with three other algorithms: low energy-adaptive clustering hierarchy (LEACH), improved low energy adaptive clustering hierarchy (ILEACH), and enhanced PSO-LEACH (ESO-LEACH). The results of the simulation studies show that HPSO-ILEACH increased the network lifetime, with an average of 55% of nodes staying alive, while reducing energy consumption average by 28% compared to the other mentioned techniques.

A novel Sybil attack detection scheme in mobile IoT based on collaborate edge computing

BackgroundInternet of things (IoT) has been used in many places. IoT make devices connected to the Internet via sensor devices to achieve the interconnection between things and things, people and things. Sybil attacker attacks IoT by imitating the identity of users. Few methods are applicable for mobile IoT in previous Sybil attack detecting methods, while the methods are mainly focus on static IoT.ResultsA distributive and lightweight Sybil attack detection scheme in the mobile IoT is proposed in this paper. This scheme works around received signal strength indications (RSSI). The scheme consists of two rounds. Identity information is sent from member nodes to edge nodes in both of the two rounds. In the first round edge, nodes calculate the possible RSSI interval for each member node; in the second round, they check the RSSI value of member nodes to detect Sybil attacks. Intelligent algorithms are used to predict the position of member nodes, which makes the theoretical interval more accurate. Extensive experimental studies show that in the true and false detection rate, this scheme is superior to many existing schemes.

Deep reinforcement learning-based collaborative routing algorithm for clustered MANETs

Flexible adaptation to differentiated quality of service (QoS) is quite important for future 6G network with a variety of services. Mobile ad hoc networks (MANETs) are able to provide flexible communication services to users through self-configuration and rapid deployment. However, the dynamic wireless environment, the limited resources, and complex QoS requirements have presented great challenges for network routing problems. Motivated by the development of artificial intelligence, a deep reinforcement learning-based collaborative routing (DRLCR) algorithm is proposed. Both routing policy and subchannel allocation are considered jointly, aiming at minimizing the end-to-end (E2E) delay and improving the network capacity. After sufficient training by the cluster head node, the Q-network can be synchronized to each member node to select the next hop based on local observation. Moreover, we improve the performance of training by considering historical observations, which can improve the adaptability of routing policies to dynamic environments. Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion. In addition, the effectiveness of the routing policy in a dynamic environment is verified.

PIRAP: Intelligent Hybrid Approach for Secure Data Transmission in Wireless Sensor Networks

The wireless sensor network (WSN) is a mobile adhoc network which has no support of infrastructure. The complexities in network management are critical to resolve even when choosing the cluster head is more efficient. From clustering procedure, appropriate cluster heads are selected with attention to energy saving among member nodes. When it comes to WSN security, trust-based cluster head selection is critical, assuming the cooperation of all sensor nodes. In light of this assumption, existing approaches were unable to assist in identifying the network’s ideal cluster head. Due to the dynamic topology and mobility of nodes in WSNs, security is a challenge. However, secrecy is often accomplished end-to-end via symmetric keys between two corresponding applications. The symmetric key is incompatible with the WSN environment. Additionally, the WSN nodes vary in their qualification for which portion of the data is accessible in which context. This work proposes an elliptic curve cryptography-enabled ciphertext policy attribute-based encryption (ECC-CP-ABE) algorithm for secure data transmission during intra-cluster communication and inter-cluster communication. In this work, we select a reliable node-based trust value mechanism and this reliable node acts as an attribute authority that inter-cluster provides a decryption private key to cluster members who are involved in intra- and inter-cluster communications. In ECC-CP-ABE, the cluster head node (CHN) and cluster member node (CMN) utilize CP-ABE for the encryption of network messages using an access policy matrix A that is computed from an AND–OR operation-based monotonic tree access structure which is defined over a various set of attributes. To ensure authenticity and integrity, the cluster member and CHN sign each ciphertext using an ECC algorithm. For performance evaluation, we use packet delivery ratio, energy consumption, encryption time, network lifetime, and decryption time as metric measures and compared the results of proposed ECC-CP-ABE with two benchmark methods, Secured WSN and Taylor-based Cat Salp Swarm Algorithm. From the results, we analyze that the proposed ECC-CP-ABE reduces energy consumption by 53.2%; increases packet delivery ratio by 98.6%; increases network lifespan by 97.5%, and reduces encryption time by 20[Formula: see text]s and decryption time by 15[Formula: see text]s.

Artificial Intelligence Machine Learning in Healthcare System for improving Quality of Service

A vital element of widespread patient monitoring is consistency in transmission between the patients and the healthcare professionals not including time and position dependencies. Artificial intelligence (AI) and machine learning (ML) techniques have a vast possibility to proficiently handle the automated function of the mobile nodes distributed in the Mobile ad-hoc network (MANET). ML is a part of AI in that the computer algorithms learn themselves by improving from historical experiences. The main issues in MANET are autonomous operation, maximization of a lifetime, coverage of the network, energy utilization, connectivity issues, quality of service, high bandwidth necessity, communication protocol design, etc. ML is valuable for data aggregation, and it saves the energy of mobile nodes and enhances the network lifetime. In this paper, we propose an Artificial Intelligence Machine Learning Algorithm for improving the Quality of Service in MANET (AIMQ). ML techniques based on artificial neural networks (ANN) algorithm is helpful for data aggregation tasks. This approach formed the clusters by node mobility and connectivity. Glowworm Swarm Optimization (GSO) algorithm is applied in every cluster to choose a proficient Cluster Head (CH). Here, we choose the CH by GSO fitness function based on mobile node degree, node distance, node reliability, and energy. ANN algorithm recognizes and chooses the data aggregator with great energy and more extended stability. It updates the weight of input parameters such as node energy, node degree, packet loss ratio, and node delay to reduce the errors. It minimizes the repetitive CHs selection and member nodes’ re-affiliation in a cluster.

Estimating Mixed Memberships in Directed Networks by Spectral Clustering

Community detection is an important and powerful way to understand the latent structure of complex networks in social network analysis. This paper considers the problem of estimating community memberships of nodes in a directed network, where a node may belong to multiple communities. For such a directed network, existing models either assume that each node belongs solely to one community or ignore variation in node degree. Here, a directed degree corrected mixed membership (DiDCMM) model is proposed by considering degree heterogeneity. An efficient spectral clustering algorithm with a theoretical guarantee of consistent estimation is designed to fit DiDCMM. We apply our algorithm to a small scale of computer-generated directed networks and several real-world directed networks.

A local community detection algorithm based on potential community exploration

Local community detection aims to detect local communities that have expanded from the given node. Because of the convenience of obtaining the local information of the network and nearly linear time complexity, researchers have proposed many local community detection algorithms to discover the community structure of real-world networks and have obtained excellent results. Most existing local community detection algorithms expand from the given node to a community based on an expansion mechanism that can determine the membership of nodes. However, when determining the membership of neighboring nodes of a community, previous algorithms only considered the impact from the current community, but the impact from the potential communities around the node was neglected. As the name implies, a potential community is a community structure hidden in an unexplored network around a node. This paper gives the definition of potential communities of a node for the first time, that is, a series of connected components consisting of the node’s neighbors that are in the unexplored network. We propose a three-stage local expansion algorithm, named LCDPC, that performs Local Community Detection based on Potential Community exploration. First, we search for a suitable node to replace the given node as the seed by calculating the node importance and the node similarity. Second, we form the initial community by combining the seed and its suitable potential community. Finally, the eligible nodes are selected by comparing the similarities between potential communities and the expanding community and nodes and adding them to the initial community for community expansion. The proposed algorithm is compared with eight state-of-the-art algorithms on both real-world networks and artificial networks, and the experimental results show that the performance of the proposed algorithm is better than that of the comparison algorithms and that the application of potential community exploration can help identify the community structure of networks.