Clustered Sensor Networks Research Articles

Reducing Energy Consumption and Clustering in Wireless Sensor Networks using an Improved Discrete Gorilla Troops Optimization Algorithm with Fuzzy Rules

Among the most common and extensively operated methods in wireless sensor networks (WSN) to increase the efficiency and performance of WSN is network clustering because, through this solution, data is transmitted through the closest possible path. In the clustering method, a portion of the nodes in the network become cluster heads, then a cluster is formed by joining the nodes close to the cluster head. However, the main and important problem in this issue is preventing the creation of unbalanced clusters since the spreading of cluster heads in the network can be unequal. In this paper, we have presented an algorithm based on the discrete Gorilla Troops Optimizer (DGTOA) algorithm and K-Means with a fuzzy clustering approach. In this model, first, several cluster heads are chosen by applying the discrete DGTOA algorithm, and then the output of the DGTOA algorithm is given as initial points and coordinates in K-means. Finally, the cluster head is utilized to wield the fuzzy system. Also, different criteria and graphs were used to compare the proposed model, and the obtained results were measured with other methods, and the obtained outcomes indicate the high performance of the proposed model.

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.

EE-TLT: Energy-efficient routing protocol using two-level tree-based clustering in wireless sensor network

EE-TLT: Energy-efficient routing protocol using two-level tree-based clustering in wireless sensor network

Adaptive traffic aware clustering and routing model for wireless sensor networks

<span>Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models.</span>

A Novel Particle Swarm Optimization-Based Clustering and Routing Protocol for Wireless Sensor Networks

A Novel Particle Swarm Optimization-Based Clustering and Routing Protocol for Wireless Sensor Networks

Dingo optimization influenced arithmetic optimization – Clustering and localization algorithm for underwater acoustic sensor networks

Underwater acoustic sensor networks (UASNs) are quickly being used in environmental monitoring, marine life observation, and underwater resource research. This paper introduces the Dingo Optimisation Influenced-Arithmetic Clustering and Localisation Algorithm. The proposed method was developed due to the above considerations. DOIAO-CLA combines Dingo Optimisation (DO) and Arithmetic Optimisation (AO), two powerful optimisation methods. DOIAO-CLA uses DO's robust exploration and exploitation features to group sensor nodes with similar propagation characteristics. It also optimises cluster formation and reduces distance discrepancies using AO. DOIAO-CLA also enables reliable localization by merging distance- and angle-based methods. DO optimises node localization by using distance and angle of arrival data. This integration enhances UASN node localization accuracy and reliability. The DOIAO-CLA was tested against other clustering and localization algorithms like Archimedes Optimisation Algorithm (AOA), Squirrel Search Algorithm (SSA), Hybrid Cat Cheetah Optimisation Algorithm (HCCOA), Ant Colony Optimisation Algorithm (ACOA), Grey Wolf Optimisation Algorithm (GWOA), and Particle Swarm Optimisation Algorithm. According to the results, DOIAO-CLA taps alternatives for network robustness, data aggregation, and localization precision. Finally, DOIAO-CLA solves the UASN clustering and localization problem in a novel and effective way.

A hybrid approach of multi-cast routing and clustering in underwater sensor networks

A hybrid approach of multi-cast routing and clustering in underwater sensor networks

Energy efficient multi-criterion binary grey wolf optimizer based clustering for heterogeneous wireless sensor networks

Energy efficient multi-criterion binary grey wolf optimizer based clustering for heterogeneous wireless sensor networks

Fuzzy clustering and optimal routing in wireless sensor network

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively.

New key management scheme lattice-based for clustered wireless sensor networks.

Aiming at the quantum algorithm which can solve the problem of large integer decomposition and discrete logarithm in polynomial time, an anti-quantum computing key management scheme for clustered sensor networks is proposed in this paper. The lattice-based cryptosystem is used to achieve the anti-quantum performance of the key management scheme, and the security of the network is further improved through the mutual authentication of sensor network nodes. Due to the limited storage space of sensor nodes, this paper adopts the cluster management of wireless sensor networks, and most sensor nodes only need a small amount of storage space, thus reducing the deployment cost. Cluster management is suitable for medium and large-scale deployment of sensor networks. Because the data traffic is much larger than that of mutual authentication, the sensor nodes in wireless sensor networks use symmetric keys to communicate with each other after mutual authentication, which can effectively improve the communication efficiency in the case of frequent data communication. Experiments show that the authentication scheme based on lattice cryptosystem proposed in this paper will not improve with the continuous improvement of the security level, and its authentication scale will maintain a relatively stable state, while the algorithm scheme based on RSA will increase the authentication cost with the continuous improvement of the security level, so the scheme proposed in this paper is more suitable for application in the environment with high security level. This scheme can effectively reduce the cost of mutual authentication of sensor nodes, is conducive to the expansion of the network, and can ensure the security of authentication between sensor nodes even in the post-quantum era.

Enhancing energy utilization for high power node multicasting in wireless sensor networks

In multicasting packets of data from a node will be sent to a group of receiver nodes at the same time. Multicasting lowers transmission costs. Energy conservation is critical to a sensor network’s long-term viability. Sensor networks have limited and non-replenishable energy supplies, maximizing network lifetime is crucial in sensor nodes. As a result, clustering has become one of the popular methods for extending the lifetime of an entire system by integrating information at the cluster head. Cluster head (CH) selection is the important serving node in each cluster in the Wireless sensor networks (WSN). This paper introduces a High Power Node (HPN) multicasting approach which embeds a cluster of sink node data in packet headers to allow receiver for utilizing a approach for transferring multicast packet data via the shortest paths. The proposed Energy efficient multicasting cluster based routing (EEMCR) protocol utilized high power nodes, which shall play a critical role in minimal energy usage. The implementation findings demonstrate that, when compared with the previous methodologies, the suggested algorithm has enhanced in terms of packet delivery ratio (PDR), End to end delivery rate, efficiency and achieves low energy consumption. The proposed EEMCR obtain 95% efficiency. The results are then compared to other existing algorithms to determine the superiority of the proposed methodology.

Ant Colony Optimization with Levy-Based Unequal Clustering and Routing (ACO-UCR) Technique for Wireless Sensor Networks

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters.

Proposed energy efficient clustering and routing for wireless sensor network

Wireless sensor network (WSN) is considered a growing research field that includes numerous sensor nodes used to gather, process, and broadcast information. Energy efficiency is considered one of the challenging tasks in the WSN. The clustering and routing are considered capable approaches to solve the issues of energy efficiency and enhance the network’s lifetime. In this research, the multi-objective-energy based black widow optimization algorithm (M-EBWOA) is proposed to perform the cluster-based routing over the WSN. The M-EBWOA-based optimal cluster head discovery is used to assure an energy-aware routing over the WSN. The main goal of this M-EBWOA is to minimize the energy consumed by the nodes while improving the data delivery of the WSN. The performance of the M-EBWOA is analyzed as alive and dead nodes, dissipated energy, packets sent to base station, and life expectancy. The existing research such as low-energy adaptive clustering hierarchy (LEACH), hybrid grey wolf optimizer-based sunflower optimization (HGWSFO), genetic algorithm-particle swarm optimization (GA-PSO), and energy-centric multi-objective Salp Swarm algorithm (ECMOSSA) are used to evaluate the efficiency of M-EBWOA. The alive nodes of the M-EBWOA are 100 for 2,500 rounds, which is higher than the LEACH, HGWSFO, GA-PSO, and ECMOSSA.

Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks

Traditional machine learning techniques have been widely used to establish the trust management systems. However, the scale of training dataset can significantly affect the security performances of the systems, while it is a great challenge to detect malicious nodes due to the absence of labeled data regarding novel attacks. To address this issue, this paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs). First, type-2 fuzzy logic is adopted to evaluate the reputation of sensor nodes while alleviating the uncertainty problem. Then, trust vectors are collected to train a GAN-based codec structure, which is used for further malicious node detection. Moreover, to avoid normal nodes being isolated from the network permanently due to error detections, a GAN-based trust redemption model is constructed to enhance the resilience of trust management. Based on the latest detection results, a trust model update method is developed to adapt to the dynamic industrial environment. The proposed trust management mechanism is finally applied to secure clustering for reliable and real-time data transmission, and simulation results show that it achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.

Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions

SummaryA wireless sensor network (WSN) is a network of tiny sensors deployed to collect data. These sensors are powered with batteries that have limited power. Recharging and/or replacement of these batteries, however, are not always feasible. Over the past few years, WSN applications are being deployed in diverse fields such as military, manufacturing, healthcare, agriculture, and so on. With the ever‐increasing applications of WSNs, improving the energy efficiency of the WSNs still remains to be a challenge. Applying fuzzy logic to the problem of clustering exploits the uncertainty associated with the factors that affect the lifetime of these sensors and enables the development of models that would improve their performance in real‐world applications. We present a comprehensive review of various fuzzy‐based techniques for clustering in WSNs whose main goal is to optimize energy usage in WSNs while simultaneously improving their overall performance.

THSI-RP: A two-tier hybrid swarm intelligence based node clustering and multi-hop routing protocol optimization for wireless sensor networks

Wireless sensor networks exploit clustering and routing techniques to improve energy efficiency, but these methods are generally considered as a non-deterministic polynomial (NP-hard) problem. To tackle this problem, we propose a novel two-tier hybrid swarm intelligence-based hierarchical routing protocol (THSI-RP). In the first tier, THIS-RP incorporates a hybrid swarm intelligence algorithm that combines Grey Wolf Optimization (GWO) and Marine Predators Algorithm (MPA) for the clustering algorithm. By incorporating the wolf factors from GWO into MPA as elite predators, we enhance the network search efficiency. This algorithm achieves adaptive optimal clustering with a controllable scale, considering the residual energy of nodes, relative distance between nodes, and node centrality. In the second tier, the routing algorithm uses a hybrid SI algorithm based on GWO and the graph model. First, we integrate distance and energy factors to dynamically select forwarding nodes using GWO. Then, based on the distance and energy balance principle, a weight cost function is established and combined with the minimum spanning tree method to construct a communication routing tree between the forwarding node and the base station to achieve optimized inter-cluster multi-hop routing. Simulation results demonstrate that THSI-RP outperforms several typical routing protocols in various metrics over EEUC, LPSO, LGWO, and LACO. For instance, THSI-RP achieves percentage improvement total packets by 290%, 10.9%, 8.9%, 19.9%, respectively. It also performs better in FND by 88.7%, 10.9%, 8.6%, and 16.3%, in HDN by 86.6%, 8.1%, 6.3%, and 17.1%, and in ADN by 41.7%, 8.2%, 6.4%, 18.3%.

Energy centric reputation index and fuzzy-based clustering for wireless sensor networks

Energy conservation is essential for a wireless sensor network to enhance its network lifetime. Clustering is one of the most important energy conservation techniques that prolong the WSN by providing balanced energy depletion across the network. Various clustering strategies have been proposed in the past to enhance the efficiency of the network till now. In the last two decades, fuzzy inference system based clustering solution has been proven to provide promising solutions. The existing fuzzy-based clustering algorithms consider the naïve network parameters as input that saves only a limited amount of energy in the network. However, in wireless sensor networks, the requirement is to make them highly energy efficient through an effective clustering strategy. To achieve effective clustering there is a need for energy-centric network parameters for the clustering instead of naïve network parameters. In this article, a novel energy-centric reputation index is proposed in which the naïve network parameters are unified with energy parameters in the background. Further, the reputation index scores are passed into a fuzzy-based approach to obtain the fitness value of the node chosen as the cluster head. Thus, in this paper, an energy-centric reputation index and fuzzy-based clustering(ECRIF) model is proposed for wireless sensor networks. The proposed model provides a highly energy-efficient wireless sensor network and is designed to work in two phases. In phase 1, three reputation indexes concerning internodal distance, distance to the sink, and the degree of the node is calculated while considering their energy consumption pattern in the background. In phase 2, the three reputation indexes are passed onto the fuzzy system as fuzzy input parameters to obtain the fitness value of the nodes for cluster head selection. Simulation results confirm that the proposed protocol enhances the overall lifetime of the network on an average by 70% in terms of the first node dies, 61% in terms of half node dies, and 78% in terms of the last node dies when compared with two other existing clustering models.

ACRA: Adaptive meta-heuristic based Clustering and Routing Algorithm for IoT-assisted wireless sensor network

ACRA: Adaptive meta-heuristic based Clustering and Routing Algorithm for IoT-assisted wireless sensor network

Energy balanced reliable and effective clustering for underwater wireless sensor networks

While sending data from a submerged wireless sensor network, more power is necessary due to the additional distance the signal must travel as well as the higher amount of energy lost along the route. Also, the acoustic medium generated by the shadow and the acoustic animals alters the transmission between the source and destination nodes. Multipath fading, significant path loss, and various interferences are some of the difficulties that come with communication at larger depths. The connection strength was variable, there was a low bandwidth, a high salt content, high pressure, high temperature, a sinkhole, and the route curved. These variables influence a variety of network aspects, including energy efficiency, link quality, and packet delivery ratio (PDR). The Energy Balanced Reliable and Effective Clustering (EBREC) method was created to ensure that data packets reach at their destination intact. This was done in order to avoid the complications caused by these considerations. PDR and transmission loss reduction are other targets of the presented effort. The anticipated EBREC’s performance is assessed by comparing it to many already defined benchmarks. According to the evaluation report, the simulation results reveal that EBREC performs very well when compared to the other methods in terms of Residual Energy of 0.615 J, Consumed Energy of 2.3 mJ, Throughput of 10Mbps, PDR of 97.6 %, Network Lifetime of 1750 sec, Number of Alive Nodes of 91%. Hence, the simulation results reveal that the EBREC technique beats competing approaches.

Joint Clustering and Routing Optimisation for Low-power Wireless Sensor Networks

Wireless sensor networks (WSNs) have been one of the fields that have attracted a lot of attentions from many scientific researchers in recent years. The sensor nodes of the network are fixed or moved to detect the environment and impart the data accumulated from the remote monitored regions via wireless connections. It is indicated in complex environments such as forests, deep seas, urban areas, etc., the sensor nodes in WSNs are usually tiny and battery-driven devices. Thus, energy-effective data accumulation methods required to improve the network’s lifetime are very necessary. In this paper, we propose a joint technique of fuzzy clustering and heuristic ant routing (FCHAR) to save the energy for low-power WSNs. Simulation results are shown to demonstrate the benefits of the proposed FCHAR compared to other conventional ones.