Member Nodes Research Articles

A neuro-fuzzy approach for multipoint data prediction in passive clustered wireless sensor networks

In Passive Clustered Wireless Sensor Networks (WSNs), energy is lost in a sensor node during the data transmission. In order to avoid the energy loss due to data transmission, a data prediction technique is implemented. In this paper, we present a new multi-point data prediction technique, in which the prediction algorithm is initially implemented at both member nodes and cluster heads. The algorithm is updated to cluster head by member nodes by tracking temporal correlation of data. Neuro-Fuzzy model is used as a predictor in both member nodes and cluster heads. The simulation is performed using MATLAB and the overall energy in nodes seems to increase. The mean square error (MSE) value is reduced to greater extend.

A fuzzy logic and DEEC protocol-based clustering routing method for wireless sensor networks

<abstract> <p>Power-efficient wireless sensor network routing techniques (WSN). Clustering is used to extend WSNs' lifetimes. One node act as the cluster head (CH) to represent the others in communications. The member nodes are less important than the cluster hub (CH) in the clustering procedure. Fuzzy techniques based on clustering theory may provide evenly distributed loads. In this study, we provide a fuzzy-logic-based solution that factors in distance to base station (BS), number of nodes, remaining energy, compactness, distance to communicate within a cluster, number of CH, and remaining energy. Fuzzy clustering has a preliminary and final step. First, we select CH based on distance to the base station (BS), remaining node vigor, and node compactness. In the second phase, clusters are created by combining nodes that aren't already in a CH, using density, outstanding vigor, and detachment as limitations. The proposed solution increases load balancing and node longevity. This work provides a unique hybrid routing technique for forming clusters and managing data transfer to the base station. Simulation findings confirm the protocol's functionality and competence. Reduced energy use keeps network sensor nodes online longer. The framework outperforms Stable Election Protocol (SEP), hybrid energy-efficient distributed clustering (HEED), and Low Energy Adaptive Clustering Hierarchy (LEACH). Using the nodes' energy levels to create a grid pattern for the clusters gave four clusters. In addition, the proposed method has a 4347%, 41.46%, 39.26%, 37.57% and 35.67% reduction in average energy consumption when compared with the conventional algorithms. The proposed technologies could increase the network's lifetime, stability interval, packet transfer rate (throughput), and average energy. The suggested protocol is at least 50% better in every statistic that was looked at, such as network lifetime, stability interval, packet transmission rate (throughput), and average energy use.</p> </abstract>

Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR) in WSNs

Sensors are considered as important elements of electronic devices. In many applications and service, Wireless Sensor Networks (WSNs) are involved in significant data sharing that are delivered to the sink node in energy efficient manner using multi-hop communications. But, the major challenge in WSN is the nodes are having limited battery resources, it is important to monitor the consumption rate of energy is very much needed. However, reducing energy consumption can increase the network lifetime in effective manner. For that, clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes. In that concern, this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR), which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes. For providing equal rate of energy consumption among nodes, the dimensions of framed clusters are measured. Moreover, the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station. The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node. Additionally, the model also provides higher throughput and minimal delay in delivering data packets.

Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People

Social networks are an essential component of the Internet of People (IoP) and play an important role in stimulating interactive communication among people. Graph convolutional networks provide methods for social network analysis with its impressive performance in semi-supervised node classification. However, the existing methods are based on the assumption of balanced data distribution and ignore the imbalanced problem of social networks. In order to extract the valuable information from imbalanced data for decision making, a novel method named minority-weighted graph neural network (mGNN) is presented in this article. It extends imbalanced classification ideas in the traditional machine learning field to graph-structured data to improve the classification performance of graph neural networks. In a node feature aggregation stage, the node membership values among nodes are calculated for minority nodes’ feature aggregation enhancement. In an oversampling stage, the cost-sensitive learning is used to improve edge prediction results of synthetic minority nodes, and further raise their importance. In addition, a Gumbel distribution is adopted as an activation function. The proposed mGNN is evaluated on six social network data sets. Experimental results show that it yields promising results for imbalanced node classification.

Detection of Hello Flood Attacks Using Fuzzy-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Clustering is one among the most important strategies to improve the lifetime of wireless sensor networks (WSNs). The frequent occurrence of clustering and the subsequent interchange of data overload the sensor nodes and result in wasting power. WSNs are susceptible to attacks because of their resource-constrained nature and large applications in critical military areas. The objective of the threats to the security of wireless sensor networks is to compromise the network by seizing information for misuse. Security features have become a major concern in these types of networks as it is important to protect sensitive data from unauthorized users. This paper aims to present an enriched clustering strategy to minimize the overhead caused by clustering, by formulating an effective cluster update schedule. It also focuses on the attacks that occur during an exchange of initialization messages with neighbors. Clustering of the network is carried out on the basis of the energy of sensor nodes. The nodes that are the heads of the cluster nodes are determined according to the characteristics of energy factors; hence, the role is frequently switched among the nodes of the cluster. To formulate the next cluster update schedule, a fuzzy inference system is employed, and this uses the energy factor of the node, the distance the node is placed from the sink, and the number of member nodes of the cluster. A mechanism is included during an exchange of initialization messages that detects any malicious node pretending to be a neighbor node. The proposed algorithm is evaluated using simulation, and it is found to produce an improved lifetime of 1700 time units. It is shown to conserve the energy of sensor nodes and protect them from unauthorized nodes posing as legitimate neighbors.

AOC: Assembling overlapping communities

Abstract Through discovery of mesoscale structures, community detection methods contribute to the understanding of complex networks. Many community finding methods, however, rely on disjoint clustering techniques, in which node membership is restricted to one community or cluster. This strict requirement limits the ability to inclusively describe communities because some nodes may reasonably be assigned to multiple communities. We have previously reported Iterative K-core Clustering, a scalable and modular pipeline that discovers disjoint research communities from the scientific literature. We now present Assembling Overlapping Clusters (AOC), a complementary metamethod for overlapping communities, as an option that addresses the disjoint clustering problem. We present findings from the use of AOC on a network of over 13 million nodes that captures recent research in the very rapidly growing field of extracellular vesicles in biology.

Attribute community detection based on latent representation learning and graph regularized non-negative matrix factorization

In recent years, complex networks have become increasingly difficult to detect effectively with the rapid development of networks. On the one hand, the scale of the network has increased sharply, and on the other hand, the nodes in the network contain rich content. The existing algorithms do not take the different richness of attribute information of nodes in the attribute network into account. And the topology information of the node is inconsistent with the attribute information. Thus, this paper proposes an attribute community detection algorithm based on latent representation learning and graph-regularized non-negative matrix factorization (LRL-GNMF). First, the topological information and attribute information in the attribute network is decomposed based on non-negative matrix factorization respectively. Thereby, the member distribution matrix and the attribute distribution matrix are obtained. Secondly, an affinity matrix is constructed for the attribute matrix, and the latent representation of the attribute information is obtained using the latent representation learning method. In addition, a transition matrix is constructed according to the Markov transition probability. The node membership distribution matrix and the attribute distribution matrix are linked. Finally, since the attribute information of nodes in the network is different, a topology-dominated model and an attribute-dominated model are respectively constructed to solve this problem. At the same time, a graph regularization term is introduced to guide the model to obtain more accurate community detection. This paper conducts experimental analysis on 8 real networks. The experimental results show that the proposed algorithm in this paper outperforms the other 11 compared algorithms in the evaluation indicators of community detection accuracy and standard mutual information.

Energy‐aware redundancy‐aware clustering in wireless sensor networks using Spined Loach Searching Optimization

SummaryWireless sensor networks play dominant role in data communication in many engineering applications and faces lot of challenges in its implementation. Energy conservation is also significantly required in these systems as the sensor nodes are battery operated. Hence, the energy efficient‐based technique needs to be implemented in data transmission. The most widely used methods are clustering‐based data transmission and energy efficient routing detection. In clustering, the proper selection of cluster head (CH) node is important to ensure energy balancing. The CH node should have more residual energy as it does many transmissions in the network. Moreover, the location of CH node should be nearer to all member nodes in the cluster. The clustering process must also perform the job of avoiding unnecessary transmissions. By considering these factors, the CH node can be optimally selected using Spined Loach Searching Optimization algorithm. The food searching behavior and environmental suitability for spiny loach fish living are deliberated to develop this bio‐inspired algorithm. In addition, the redundant transmissions inside the clusters can be reduced by employing a new technique called Least Difference Threshold Based Similarity Grouping. This work improves energy efficiency comparing with the conventional low energy adaptive clustering hierarchy protocol, and it is evaluated by setting the network in simulation environment. The network lifetime is increased; energy consumption, delay of transmission and the message cost are reduced.

Distance and energy aware extended LEACH using secondary cluster head for wireless sensor networks

Sensor node's energy consumption is a crucial consideration for designing a wireless sensor network (WSN) as the node is battery powered with limited energy that significantly affects the network lifetime. An efficient design of routing protocol can play an important role to ensure optimum use of node's energy to increase the scalability and lifetime of the network. A clustering-based routing protocol like low energy adaptive clustering hierarchical (LEACH) has attracted much attention in recent years as the network lifetime is effectively increased due to the formation of clusters using sensor nodes. However, the protocol shows the limited performance when the cluster head (CH) selection uses the node having low energy level and far distance from the base station (BS). Here, the selection of secondary cluster head (2CH) is useful, where the member node having the highest residual energy can act as a relay node for data aggregation and ensure the optimum utilization of available energy. In this article, two LEACH protocols namely E-LEACH and DE-LEACH are characterized and further extended by adding the secondary cluster heads, which eventually ensure the optimum choice of CH for sustaining more energy in the network. The proposed Extended E-LEACH and Extended DE-LEACH protocols are analyzed using the network lifetime and total energy consumption and the results show an improved system performance compared to existing 2CH based routing protocols like FLEACH and FLEACH-E. The Extended DE-LEACH shows the best results among all the protocols. In particular, total energy consumption for Extended DE-LEACH is improved by a maximum of 5%, 11% and 21% as compared to Extended E-LEACH, FLEACH-E and FLEACH respectively.

Energy Aware Load Balancing in Cloudlet Federation

With the rapid increase of compute intensive tasks and workload, the need of the hour is to manage the total workload due to resource constraints and increased cost. For complex computations where multiple computer systems are required to execute a single task such as in federated cloudlet environment, load balancing is the main challenge. Load balancing means to divide the total workload between all the member nodes to get the maximum benefits from the federated environment. A cloudlet is a resourceful computer that is coupled to the Internet and is accessible to the mobile devices in the vicinity. Cloudlet Federation is the concept of a cooperative framework to share resources and load balancing among various member cloudlets. Different tasks consume different amount of energy for their execution that results in a large amount of heat dissipation. Due to heat, the performance of the systems is decreased. The more heat, the more performance degradation, so it becomes very vital to keep the energy consumption as minimum as possible. To address this problem, this paper proposes a novel scheduling strategy that will distribute the total load between all the member nodes in a federated cloudlet environment for load balancing. The proposed methodology not only considers the energy level of the system on which the task has to be executed but also considers the energy required by the tasks in queue. The proposed methodology is tested in a Cloudlet Federation environment and the results show an improved load balancing in terms of energy consumption.

RSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks.

In this paper, a new system entropy measure is used to optimize the routing algorithm in power consumption. We introduce the system entropy measure into the problem of industrial wireless sensor networks (iWSNs) routing and propose a high-performance routing algorithm guided by the system entropy measure (rSEM). Based on the cluster iWSNs architecture, the rSEM selects the cluster heads and cluster member nodes successively, according to the system entropy measure, and constructs the iWSNs with the minimum system entropy. The method of the cluster head selection is traversal, while the method of the cluster member selection is a greedy algorithm to reduce the complexity. The experiments show that the power consumption of the iWSNs generated by the rSEM is in the same order of magnitude as that of Dijkstra in both 2D and 3D scenarios. In addition, the delay of the rSEM is slightly higher than that of LEACH. Therefore, the rSEM is suitable for networks that are sensitive to both the delay and power consumption. The rSEM puts forward a new idea for the design of routing for the next-generation iWSNs, which improves the overall network performance according to the network topology, instead of relying on the power consumption or delay performance only.

D2CRP: A Novel Distributed 2-Hop Cluster Routing Protocol for Wireless Sensor Networks

Typically, wireless sensor networks (WSNs) use limited-capacity batteries that cannot be recharged or replaced. In general, designing an energy-efficient routing protocol has a significant impact on prolonging the network lifetime. In this article, a novel distributed 2-hop cluster-routing protocol (D2CRP) is introduced to achieve energy efficiency in WSNs. In the cluster formation phase, each node obtains the information of its neighbor nodes within the 2-hop range to form the 2-hop cluster in a fully distributed manner. The transmission distance and residual energy are jointly considered to determine the energy-efficient cluster head (CH) in each 2-hop cluster. After the CH is generated, each member node can transmit packets to its 1-hop neighbor or the CH directly. To reduce the overall transmission distance for intercluster communication, multiple chains can be formed among CHs via their adjacent CHs that are closer to the base station (BS). As a result, energy-efficient intracluster and intercluster routing can be achieved for packet transmission. In addition, the optimal cluster number of 2-hop clustering is formulated and derived to minimize the energy consumption for both intracluster and intercluster communications. Simulation results show that the optimal cluster number of D2CRP can be achieved and D2CRP is effectively improved on the performances of network lifetime, energy consumption, and packet transmission than the other four state-of-the-art competitive routing protocols, including low-energy adaptive clustering hierarchy (LEACH), R-LEACH, power-efficient gathering in sensor information system (PEGASIS), and two-tier distributed fuzzy logic-based protocol (TTDFP).

An enforced non-negative matrix factorization based approach towards community detection in dynamic networks

Identifying community structures within network dynamics is important for analysing the latent structure of the network, understanding the functions of the network, predicting the evolution of the network as well as detecting unusual events of the network. From various perspectives, a diversity of approaches towards dynamic community detection has been advised. However, owing to the difficulty in parameter adjustment, high temporal complexity and detection accuracy is diminishing as time slice rises; and recognizing the community composition in dynamic networks gets extremely complex. The basic models, principles, qualities, and techniques of latent factor models, as well as their various modifications, generalizations and extensions, are summed up systematically in this study which focuses on both theoretical and experimental research into latent factor models across the latest ten years. Latent factor model like non-negative matrix factorization is considered one of the most successful models for community identification which aims to uncover distributed lower dimension representation so as to reveal community node membership. These models are mostly centred on reconstructing the network from node representations while requiring the representation to have special desirable qualities (non-negativity). The purpose of this work is to provide an experimental as well as theoretical comparative analysis of the latent factor approaches employed to detect communities within dynamic networks. Parallelly we have devised the generic and improved non-negative matrix factorization-based model which will help in producing robust community detection results in dynamic networks. The results have been calculated from the experiments done in Python. Moreover our models methodology focuses on information dynamics so as to quantify the information propagation among the involved nodes unlike existing methods that considers networks first-order topological information described by its adjacency matrix without considering the information propagation between the nodes. In addition, this paper intends to create a unified, state of the art framework meant for non-negative matrix factorization conception which could be useful for future study.

Community detection in weighted networks using probabilistic generative model

Community detection in networks is a useful tool for detecting the behavioral and inclinations of users to a specific topic or title. Weighted, unweighted, directed, and undirected networks can all be used for detecting communities depending on the network structure and content. The proposed model framework for community detection is based on weighted networks. We use two important and effective concepts in graph analysis. The structural density between nodes is the first concept, and the second is the weight of edges between nodes. The proposed model advantage is using a probabilistic generative model that estimates the latent parameters of the probabilistic model and detecting the community based on the probability of the presence or absence of weighted edge. The output of the proposed model is the intensity of belonging each weighted node to the communities. A relationship between the observation of a pair of nodes in multiple communities and the probability of an edge with a high weight between them, is one of the important outputs that interpret the detected communities by finding relevancy between membership of nodes to communities and edge weight. Experiments are performed on real-world weighted networks and synthetic weighted networks to evaluate the performance and accuracy of the proposed algorithm. The results will show that the proposed algorithm is more density and accurate than other algorithms in weighted community detection.

A Novel Cooperative of Cluster Member Nodes with Watchdog for Detecting Attacker Nodes

A Novel Cooperative of Cluster Member Nodes with Watchdog for Detecting Attacker Nodes

Cantor Pairing lightweight key generation for wireless body area networks

Wireless Body Area Networks (WBAN) are getting grandness due to the low-cost digital sensor communication devices. Implantable tiny sensor nodes and wearable sensor nodes are getting membership in a WBAN network. The characteristics of these member nodes are hugely different in terms of processing power and communication technology. Therefore, the nature of a WBAN network comes under heterogeneous category. Combining different types of nodes under one roof with a communication protocol is one of the perplexed tasks of WBAN. Security and Power consumption are the prime concerns in WBAN networks. High security communication protocols consume more power whereas low power consuming protocols lack some security. Equilibrating the balance between power, performance and security is an inevitable chore while designing communication protocols for WBAN. This work named as Cantor Pairing Light Weight Key Generation (CPLKG) is targeted to improve the security without impacting the performance in terms of Throughput, End-to-End delay, Packet Delivery Ratio, Memory and Power consumption. Device ID Hash Function (DHF), Cantor Pairing Key Generation (CPKG) and Cantor Pairing Key Revocation (CPKR) are the contributed functional blocks used to construct the proposed CPLKG method.

Trustworthy Transaction Spreading Using Node Reliability Estimation in IoT Blockchain Networks

Blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically comprise various sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or reliable drone deliveries in smart cities. The wide spread of transactions and shared smart contracts across blockchain networks guarantees ultimate network security. A typical wired blockchain network maintains sufficient redundancy within a stable configuration. However, IoT blockchain networks exhibit unavoidable instability. The dynamic configuration changes caused by flexible node membership make it impossible to achieve the same level of redundancy as a stable network. A trustworthy transaction spreading method provides practical transaction sharing for dynamic IoT networks. We propose a Q-learning framework and a graph convergence network (GCN) to search for the proper spreading path of each transaction. The proposed Q-learning framework determines the next spreading hop using node features. The GCN determines the reliable area based on the Q-learning results. The discovered reliable area guides the proper spreading path of transactions to the destination node. In addition, the proposed trustworthy transaction spreading was implemented over an InterPlanetary File system (IPFS). The IPFS-powered experiments confirmed the practicability of the proposed transaction spreading mechanism.

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

SummaryThe advancement of the Internet of Things (IoT) technologies will play a vital role in the evolution of the smart city, smart healthcare, and smart grid applications. Wireless sensor network (WSN) is one of the futuristic technologies utilized for sensing and data exchange processes in IoT‐enabled applications. However, the hotspot problem, control packet overhead, and inefficient clustering are the most significant challenges to attaining an energy‐efficient IoT‐enabled WSN (IWSN) model. Most of the existing clustering schemes lagged to mitigate these aforesaid constraints since it consumes extra energy for data computation and forwarding tasks under any environmental conditions. In this article, a novel energy‐efficient auto clustering (EEAC) framework has been proposed to develop an effective IWSN model with enhanced quality of service. The EEAC framework comprises three phases such as zone formation, node classification, and auto clustering phases. The objective of the first phase is to significantly form the different zones by alleviating the hotspot problem. Subsequently, the fuzzy logic algorithm is employed in the second phase to classify the nodes as master, sub‐master, and member nodes. Finally, the third phase of the proposed framework will accomplish the auto clustering mechanism based on hop information received from the reported packet. The performance results evident that the proposed EEAC framework obtains a lesser energy consumption of 0.01 J during a dense network and the network lifetime is prolonged up to 52% when compared with existing state‐of‐the‐art clustering models.

GPBFT: A Practical Byzantine Fault-Tolerant Consensus Algorithm Based on Dual Administrator Short Group Signatures

The practical Byzantine fault-tolerant consensus algorithm reduces the operational complexity of Byzantine protocols from an exponential level to a polynomial level, which makes it possible to apply Byzantine protocols in distributed systems. However, it still has some problems, such as high communication overhead, low security, poor scalability, and difficulty in tracking. In this article, we propose a Byzantine fault-tolerant consensus algorithm based on dual administrator short group signatures (GPBFT). Firstly, the certification authority chooses the master node and group administrators based on the credit value. The group administrators organize the nodes into a group, and the members generate the signatures by applying the short group signatures scheme, in which any group member can represent the group during the GroupSign phase. Additionally, the GPBFT algorithm adds the Trace phase. According to member and client authentication information, the group administrator can track the true identity of the malicious node, identify the malicious node, and revoke it. The experimental results show that compared with the PBFT algorithm, the GPBFT algorithm can reduce the network communication overhead, reduce the consensus delay, and greatly improve the security and stability of the system. The algorithm can effectively manage member nodes and enable the tracking of identified malicious nodes while maintaining anonymity in terms of node tracking.

Improvement of Delegated Proof of Stake Consensus Mechanism Based on Vague Set and Node Impact Factor.

The Delegated Proof of Stake (DPoS) consensus mechanism uses the power of stakeholders to not only vote in a fair and democratic way to solve a consensus problem, but also reduce resource waste to a certain extent. However, the fixed number of member nodes and single voting type will affect the security of the whole system. In order to reduce the negative impact of the above problems, a new consensus algorithm based on vague set and node impact factors is proposed. We first use fuzzy values to calculate the ratings of all nodes and initially determine the number of agent nodes according to the preset threshold value. Then, we judge whether a secondary screening is needed. If needed, calculating the nodes’ impact factor based on their neighboring nodes, and combining their impact factors with adjacency votes to further distinguish the nodes with the same fuzzy value. In addition, we analyze the dynamic changes in the composition and scale of the agent node set and give its ideal size through testing. Finally, we compare the proposed algorithm with DPoS algorithm and existing fuzzy set-based algorithms in different scales and network structures. Results show that no matter in what kind of network structures, the effectiveness of the proposed algorithm is improved. Among which, the most noticeable improvement is seen in complex network structures.