Affinity propagation‐based interference‐free clustering for wireless sensor networks

With the fast development of internet of things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We call these data incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete affinity propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete data set by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.

In view of the characteristics of intermittence and fluctuation of wind power output and the trend of large scale grid connection of wind power and power grid to assume the function of renewable energy consumption, this paper proposes a multi-objective coordinated planning method based on AP (Affinity Propagation) clustering algorithm combined with improved MPGA (Multi-Population Genetic Algorithm) for power grid considering wind power integration. Firstly, the AP clustering algorithm is used to cluster the typical wind farm operation scenarios. Then, the power grid planning optimization model considering wind power consumption and line load balancing rate is established. Finally, the feasibility and validity of the proposed method are verified in the IEEE 18-bus system.

The ensemble of conceivable molecules is referred to as the Chemical Space. In this article we describe a hierarchical version of the Affinity Propagation (AP) clustering algorithm and apply it to analyze the LINGO-based similarity matrix of a 500 000-molecule subset of the PubChem database, which contains more than 19 million compounds. The combination of two highly efficient methods, namely the AP clustering algorithm and LINGO-based molecular similarity calculations, allows the unbiased analysis of large databases. Hierarchical clustering generates a numerical diagonalization of the similarity matrix. The target-independent, intrinsic structure of the database , derived without any previous information on the physical or biological properties of the compounds, maps together molecules experimentally shown to bind the same biological target or to have similar physical properties.

Recent trend to steer Wireless Sensor Networks towards a broad outdoor environment allows to monitor harsh areas and prevent a disaster situation. To guarantee an extensive work of the system and smooth cooperation among involved devices a new clustering approach needs to be found. In this paper, we present a new algorithm for an Event-oriented Focal Weight-based clustering for Wireless Sensor Network which will be able to monitor various parameters (physical, biological, and chemical) of the outdoor environment and signal the current situation further to the network. Shown results demonstrate the applicability of the algorithm for the large amount of nodes with various power levels. The use of a dual-band transceiver and advanced processing elements allow to accommodate new features in the nodes and guarantee a high performance with the low power consumption which is crucial for the outdoor system with no human attendance.

Clustering in wireless sensor networks has been one of the most effective mechanisms for addressing the issues of network lifetime, scalability and bandwidth reusability. In this paper, application of a relatively new meta-heuristic called as quantum inspired genetic algorithm has been proposed for dealing with the problem of multi-hop energy balanced unequal clustering and prolonging the network lifetime. Quantum inspired genetic algorithm conflates the concepts of evolutionary computation with the principles of quantum computing. The quantum computing principles impart huge parallel processing capabilities to the genetic algorithm due to which these algorithms provide better quality solutions in lesser amount of time. Quantum inspired algorithms are capable of providing competitive solutions even with a very small population size which results in reduced computational cost as population size is a factor in complexity calculation for evolutionary and swarm algorithms. The effectiveness of proposed approach has been evaluated by comparing it with traditional genetic algorithm based clustering for wireless sensor networks. Simulation results clearly indicate that quantum inspired genetic algorithm is capable of performing clustering in more efficient way and increasing the network lifetime as compared to traditional genetic algorithm.

Clustering has become a crucial operation in Wireless Sensor Networks (WSNs). Affinity Propagation (AP) is a relatively new clustering technique that has been shown to possess several advantages over long-standing algorithms such as K-means, particularly in terms of quality of clustering and multi-criteria support. However, the original AP algorithm is computationally intensive making it unsuitable for clustering in WSNs. A hierarchical decentralized variation of AP (Hi-WAP) has been recently proposed to reduce the processing cost of AP while minimizing the potentially negative effect of distribution (due to the lack of a global view) on clustering quality. In this paper, we explore the suitability of Hi-WAP for clustering in WSNs. We employ the level of distortion and the processing time as evaluation metrics. We propose an extension to Hi-WAP, termed LAP; Location-aware Affinity Propagation, where clustering is performed while being cognizant of nodes’ locations. Simulation results reveal that LAP, in general, outperforms Hi-WAP. We further study the optimization of LAP parameter values with the objective of minimizing processing time while maintaining a desirable low level of distortion.

By grouping nodes with similar attributes into clusters, the energy efficiency and lifespan of wireless sensor networks(WSNs) can be improved effectively. However, the number of clusters needs to be set in advance, and the optimal cluster heads (CHs) are difficult to determine, which will undoubtedly reduce the network’s overall performance. Therefore, a new clustering method using Affinity Propagation and Chaotic Lion swarm Optimization is proposed in this paper to form optimal clusters, which is called APCLO. In APCLO, Affinity Propagation (AP) is used to construct a cluster topology, forming the initial clustering according to the remaining energies of the nodes and the similarity of the distances between nodes. Moreover, the initial CHs are also determined simultaneously by AP. In order to eliminate the outliers from the initial CHs, Chaotic Lion Optimization(CLO) is presented to find the best CHs, in which a fitness function is set according to residual energy and distance to the BS, and chaotic map is used to speed up the convergence of CLO. Simulation results show that the APCLO protocol is superior to the comparison protocols in terms of energy consumption, network throughput, convergence speed, and lifetime. For network lifespan, it increases by 20.1%, 11.2%, and 13.5% respectively.

In the field of mechanical fault diagnosis, in order to improve the diagnosis accuracy with high feature dimension, a method of features linear fusion method based on AP(Affinity propagation clustering) and AASC(Affinity Aggregation for Spectral Clustering) algorithm was proposed. Low dimension space was obtained by AP clustering, then using AASC algorithm to fuse this selected features, and gained a new feature, meanwhile which can be used independently as a new feature. In the phase of experiment, this method was compared with respectively LPP and PCA, it’s result indicates that this method is excellent in improving diagnosis accuracy.

Wireless sensor networks is a key technology for new ways of interaction between computers and the physical environment. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. Since sensor nodes are densely deployed, redundant data may occur. While cluster-based data gathering is efficient at energy and bandwidth, it's difficult to cluster network efficiently. In this work, a new distributed clustering algorithm for ubiquitous sensor network is presented. Clustering is based on the distance between nodes and the number in a cluster for wireless sensor networks. Simulation results show that the proposed algorithm balances the energy dissipation over the whole network thus increase the amount of data delivery to the sink

In this paper, we propose an efficient centralized clustering algorithm for wireless sensor networks. The algorithm which organizes the sensors into clusters uses memetic algorithm to determine cluster heads and sizes. Memetic algorithms which are similar to genetic algorithms are population-based heuristic search approaches for optimization problems. To assess the efficiency of the proposed clustering technique, we compare the network lifetime with those of other clustering algorithms. The results show that the proposed algorithm considerably increases the network lifetime.

Clustering has been proposed by researches to group a number of nodes to form a cluster that managed by cluster head. The main goal of clustering protocols is to minimize the energy consumption and enhance the network lifetime of wireless sensor networks. We propose DEBC protocol, which is distributed energy balanced clustering for wireless sensor networks. DEBC forms clusters, selects cluster heads, selects cluster senders and determines appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. DEBC compared to well known protocol with two metrics network lifetime and energy dissipation. The simulation results show that DEBC minimizes energy consumption of sensor nodes and enhance the network lifetime.

In addition to the miniaturized sensor nodes, Wireless Sensor and Actor Networks (WSANs) employ significantly more capable actor nodes that can perform application specific actions to deal with events detected and reported by the sensors. Since these actions can be taken at any spot within the monitored region, the actors should strive to provide maximal coverage of the area. In addition, minimizing the time to decide which actor should take what action is of utmost important for responsiveness. In this paper, we propose a distributed actor positioning and clustering algorithm which employs actors as cluster-heads and places them in such a way that the coverage of actors is maximized and the data gathering and acting process times are minimized. Such placement of actors is done by determining the -hop Independent Dominating Set (IDS) of the underlying sensor network. The performance of the approach is validated through simulations.

Wireless Sensor Networks (WSNs) face critical challenges in energy utilization, cluster head distribution, and spatial organization, which collectively degrade network efficiency and operational longevity. To address these issues, we propose a routing algorithm integrating Dijkstra’s clustering technique with an enhanced Grey Wolf Optimization (GWO) framework. The protocol operates on randomly dispersed energy-constrained sensor nodes through two sequential phases: cluster formation and energy-aware routing. In the cluster formation phase, Dijkstra’s algorithm generates spatially balanced clusters by analyzing node positions and distribution density, mitigating the uneven energy depletion observed in conventional protocols like LEACH and KEAC. Within each cluster, a modified GWO algorithm selects optimal cluster heads by prioritizing residual energy and neighborhood connectivity, aligning with dynamic hierarchical optimization principles. For data transmission, a hybrid strategy combines single-hop intra-cluster communication with GWO-optimized multi-hop routing between cluster heads and the sink node. The enhanced GWO dynamically evaluates path quality based on nodal energy levels and depletion trends, improving upon static approaches. Comparative simulations demonstrate the protocol’s effectiveness in extending network lifetime and maintaining reliable data delivery across varying network scales.

Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. There are many algorithms for clustering. One of the cluster based algorithm in wireless sensor networks is LEACH algorithm. In this paper we proposed a new clustering method for increasing of network lifetime. In proposed method Clustering is done symmetrically and the best node with respect to remained energy and distance of other nodes in comparing with each that selected as a cluster head. Although in this protocol we didn’t use GPS but we could find geographical position nodes so easily. However, failures in higher level of hierarchy e.g. cluster-head cause more damage to the system because they also limit accessibility to the nodes that are under their supervision. In this paper we propose an efficient mechanism to recover sensors from a failed cluster. In this performance of the proposed algorithm via computer simulation was evaluated and compared with other clustering algorithms. The simulation results show the high performance of the proposed clustering algorithm.

An integrated multi-criteria decision-making and multi-objective optimization model for socially responsible portfolio selection