Cluster-based Wireless Sensor Networks Research Articles

Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks

In Wireless Sensor Networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes a considerable amount of sensor node resources. Data redundancy occurs due to the spatial and temporal correlation among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates the knowledge extraction by eliminating the correlated data. However, most of the data aggregation techniques have lower accuracy as they do not cater for erroneous data from faulty nodes and pose an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels, i.e., at the node level and at the cluster head level. At the node level, the data aggregation is performed using Exponential Moving Average (EMA) and a threshold-based mechanism is adopted to detect any outliers for improving the accuracy of aggregated data. At the cluster head level, we have employed a modified version of Euclidean distance function to provide highly-refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, energy consumption at the nodes and cluster heads, and delivers highly-refined and fused data to the base station.

Energy efficient fuzzy clustering and routing using BAT algorithm

Clustering of sensor nodes is one of the prominent methods applied to Wireless Sensor Networks (WSN). In the cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes are composed of limited battery power. Therefore, energy efficiency in WSN is crucial. A load of sensor node and its distance from base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSN. In this paper, we propose a load-balanced clustering algorithm using Fuzzy C means (FCM) algorithm and an energy-efficient routing approach using BAT-algorithm (FC-RBAT). The cluster heads (CHs) are selected according to the score of the sensor node from each cluster. After selection of the CHs, the BAT-inspired routing algorithm is applied on the CHs. The best routing path from each CH to the BS is obtained from the proposed approach. The simulations are conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network and the standard deviation of a load of the sensor node. It is observed that FC-RBAT outperforms compared algorithms, namely EAUCF, DUCF and SGA, under the evaluation factors.

A Novel Cross-Layer Cross-Domain Routing Model and It’s Optimization for Cluster-Based Dense WSN

Wireless Sensor Networks (WSNs) plays its adorable performance in the current day communication as it could sense different environmental and physical parameters by utilizing low-cost sensor devices. The network's growth due to scientific enrichment has altogether made it feasible to design a cross-layer protocol based on the energy-efficient network. This obviously concerns the prolonging of network lifetime. This research work attempts to introduce a novel Cross-Layer Design Routing model under the clustering approach. The implemented work depends on a cross-layer mechanism via diverse layers (comprising physical layer and network layer). A cluster-based routing is introduced, where the optimal cluster head is selected using a new hybrid algorithm named Alpha Wolf-assisted Whale Optimization Algorithm (AW-WOA). Thereby, the shortest path is defined and ensures the prolonging of network lifetime. The proposed hybrid algorithm is the hybridized form of the Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Moreover, the optimal cluster head selection is purely based on certain constraints like energy consumption, delay, and distance, respectively. In the end, the performance of the implemented technique is proved over other conventional approaches with regards to the alive node and network lifetime. In the alive node analysis of supernode, on considering the 1st test case, the presented AW-WOA model at 2000 rounds accomplishes 100% alive node than other existing models, wherein GWO and ATEER attain 91.67% and 75%, the other WOA, PSO, and AWOA ATEER attain 83.33% of alive nodes respectively.

A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks.

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

Lightweight load-balanced and authentication scheme for a cluster-based wireless sensor network

Clustering technology is one of the crucial technologies to prolong the lifetime in wireless sensor networks. However, most cluster schemes choose cluster head randomly to send data without considering load balancing and security. In addition, some cluster heads in the highly active area may be overloaded, while others in the low active area may be overloaded, which may easily lead to extreme imbalance in task allocation. Our research on relevant literature shows that the existing authentication schemes do not fully consider the load balancing of cluster heads, while the load balancing schemes ignore the security authentication of cluster heads. Therefore, this article effectively combines load balancing and security verification, and proposes a lightweight load balancing and verification scheme (secure load and energy balancing) based on clustered wireless sensor networks. Secure load and energy balancing implements cluster head’s authentication and confidentiality and integrity of all messages in load balancing. This scheme not only effectively maintains the energy balance of the whole network but also successfully improves the security overhead, thus prolonging the network lifetime. The simulation results show that compared with other similar schemes, this scheme has higher packet forwarding rate, longer network life, and lower overhead. This further proves that the scheme is energy-saving, safe, dynamic, stable, and sustainable.

An Efficient Index-Modulation-Based Data Gathering Scheme for Wireless Sensor Networks

Decision gathering in cluster-based Wireless Sensor Networks (WSNs) plays an important role in the overall performance of WSNs employed for target detection applications. Recently, a novel decision gathering scheme has been proposed that relies on the Index Modulation (IM) concept, called IM-based WSN (IM-WSN). It achieves a significant improvement in the decision error rate accompanied with a high computational complexity as compared to the literature. In this letter, following the same principle of the IM-WSN scheme, an efficient decision gathering scheme is proposed, which is able to significantly reduce the computational complexity and improve the decision error rate. The performance of the proposed scheme is analyzed through a solid mathematical framework that is validated by simulation results.

Network Lifetime Enhancement in Wireless Sensor Networks Using Energy Aware Clustering with Fuzzy System

The lifetime of the network states that the failure time of the initial network nodes or if the building blocks of WSN modules in the network runs out of power. The successive exchange of messages leads to power loss and burdening of sensor nodes. It reduces the network lifetime. This paper presents the energy aware clustering algorithm along with fuzzy to contemplate the residual energy. In cluster based wireless sensor networks, the cluster head gathers all kind of information from each node and has the responsibility of passing of the collected data to the various destination nodes. It diminishes the message overhead, delay, increasing packet delivery ratio and enhancing the lifetime of network.

A COMPREHENSIVE REVIEW ON SECURE AND ENERGY AWARE MULTI-HOP ROUTING IN CLUSTERING BASED WIRELESS SENSOR NETWORK

A COMPREHENSIVE REVIEW ON SECURE AND ENERGY AWARE MULTI-HOP ROUTING IN CLUSTERING BASED WIRELESS SENSOR NETWORK

An Efficient Mobile Sink based Data Collection Model for Clustered based Wireless Sensor Network

The Wireless Sensor Network (WSN) is considered to be a core component of tomorrow's real-time data communication networks, such as the Internet of Things (IoT). Modern networks need low-latency and high-throughputs in real-time due to a heterogeneous network. The availability of low-latency real-time data access incurs energy costs from the sensor systems. Clustering helped in maintaining the scalability and energy usage of sensors. However, it incurs overhead of the independent cluster head and sensor device within the close range of the sump pump. Since it would take longer transmission and recovery time. This Mine Research Paper introduces an Accessible Mobile Sensor Dependent Data Collection (EMSDC) Model for Cluster Based WSN (CWSN). Experiments are carried out to verify the efficiency of EMSDC and to equate it with the existing versions. The findings of the Latency and Overhead benchmarks demonstrated a lot of progress over the state-of-the-art versions.

A Framework for Energy-Efficient Clustering With Utilizing Wireless Energy Balancer

Clustering has been accepted as one of the most efficient techniques for conserving the energy of wireless sensor networks (WSNs). In cluster-based WSN, the cluster heads (CH) consume more energy compared with normal nodes, which results in rising their energy consumption and reducing the network lifetime. There are several energy-efficient routing schemes which have been presented to reduce the energy consumption of CHs. In such schemes, further clustering of CHs will result in reducing the energy consumption of CHs. However, this rule has not been considered in previous related works. Likewise, unbalanced energy consumption of CHs is one the main design challenges which leads to increase the wasted energy and premature network death. A number of recent studies have sought to improve the balanced energy via utilizing devices equipped with large and expensive energy harvesters, which leads to extra cost Therefore, a feasible solution is needed to achieve balanced energy consumption of CHs. Consequently, to overcome the aforementioned limitations, we proposed a framework for energy-efficient clustering by utilizing a wireless energy balancer. Firstly, an n-level clustering is presented which results in fully leveraging and reducing the energy consumption of CHs. Secondly, an energy balancer is utilized in order to reduce the wasted energy to the fullest possible extent by zeroing the variance between the remaining energy of CHs. The performance of the proposed scheme has been compared against CMS2TO and DGOB mechanisms. Simulation results show that the proposed scheme achieved improved performance in terms of network lifetime by 20%, total energy consumption by 52%, network overhead by 20%, computation time by 46%, and wasted energy by 86% compared to existing schemes. In conclusion, the proposed framework proves to be a viable solution for enhancing the network lifetime and energy efficiency.

Energy-Efficient Cluster-Based Wireless Sensor Networks Using Adaptive Modulation: Performance Analysis

Wireless Sensor Networks (WSNs) play a vital role in modern technology since they have recently emerged into enormous essential applications of the Internet of Things (IoT). However, WSNs encounter a shortage in the lifetime due to limitations in the power supply. Accordingly, many solutions are reported in literature to deal with energy saving problem in the WSNs. In this paper, a novel method is presented to minimize energy consumption using adaptive modulation that is jointly integrated with clustering technique . This method is considered as a promising solution for dense and sparse cluster-based WSNs to improve energy efficiency. In the suggested solution, the adaptive modulation is implemented in the communication link between cluster members (CMs) and cluster head (CH). Besides, distance-based adaptive modulation step function is proposed in which the optimum modulation order is selected to achieve the minimum energy consumption between CMs and CH. The proposed method is evaluated extensively in order to investigate the impact of adaptive modulation in cluster-based WSNs using M-ary Quadrature Amplitude Modulation (MQAM) system. The performance evaluation addresses both energy consumption and throughput by using two metrics: cluster density, and cluster size. Regarding simulation results, by varying cluster density and cluster size, the adaptive modulation shows significant saving in energy consumption where it constitutes a lower bound for energy consumption. Also, it shows a great impact on throughput where it constitutes an upper bound for the throughput. Moreover, the adaptive modulation shows a considerable leverage on energy saving for small number of clusters, conversely, the energy saving decreases as the number of clusters increase. Finally, it is concluded that these findings can provide a remarkable guidance for designing an energy efficient WSNs.

Cluster-based wireless sensor network for structural health monitoring system

The wireless sensor network for structural health analysis system reduces the cost of installation and further maintenance compared to wired sensor system. These sensors are deployed to detect dama...

Cluster-based wireless sensor network for structural health monitoring system

Cluster-based wireless sensor network for structural health monitoring system

Lightweight route adjustment strategy for mobile sink wireless sensor networks

<span>As a result of numerous applications and low installation costs, wireless sensor networks (WSNs) have expanded excessively. The main concern in the WSN environment is to lower energy consumption amidst nodes while preserving an acceptable level of service quality. Using multi-mobile sinks to reduce the nodes' energy consumption have been considered as an efficient strategy. In such networks, the dynamic network topology created by the sinks mobility makes it a challenging task to deliver the data to the sinks. Thus, in order to provide efficient data dissemination, the sensor nodes will have to readjust the routes to the current position of the mobile sinks. The route re-adjustment process could result in a significant maximization in the communication cost, which boosts the total energy depletion. This paper proposes a lightweight routes re-adjustment strategy for mobile sink wireless sensor networks (LRAS-MS) aimed at minimizing communication cost and energy consumption by reducing route re-adjustment in a cluster-based WSN environment. The simulation results show a significant reduction in communication costs and extending the network lifetime while maintaining comparable low data delivery delay. </span>

Cluster-based WSN protocol that improves network lifetime through CH replacement

Cluster-based WSN protocol that improves network lifetime through CH replacement

Cluster-based WSN protocol that improves network lifetime through CH replacement

Sensor nodes in wireless sensor networks are wirelessly configured, so they are free to install, but there is a problem with limited energy. Since these nodes are no longer usable when they are exh...

An energy-efficient data aggregation approach for cluster-based wireless sensor networks

In wireless sensor networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes considerable sensor node resources. Data redundancy occurs due to the spatial and temporal correlations among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates knowledge extraction by eliminating the correlated data. However, most data aggregation techniques have low accuracy because they do not consider the presence of erroneous data from faulty nodes, which represents an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels: the node level and the cluster head level. At the node level, the data aggregation is performed using the exponential moving average (EMA), and a threshold-based mechanism is adopted to detect any outliers to improve the accuracy of data aggregation. At the cluster head level, we have employed a modified version of the Euclidean distance function to provide highly refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, and energy consumption at the nodes and cluster heads and delivers highly refined, fused data to the base station.

Lightweight multi-agent framework for a cluster-based wireless sensor network

Sensor applications and wireless sensor networks (WSNs) are becoming a part of our everyday life. A number of network arrangements are used in WSN. In this paper, we focus on the cluster based network to help identify the issues associated with communication within such networks. We present a lightweight multi-agent routing framework for a cluster based WSN to resolve some issues associated with such networks. By using state- of-art protocol in a unique combination and categorizing cluster layers, we take full advantage of the properties of the selected protocols. The simulation results illustrate that the proposed method is light-weight in terms of energy consumption by the sensor nodes communicating information within a cluster based network. Nevertheless, high network throughput and robust data communication are also achieved.

Trust-based energy-efficient routing protocol for Internet of things–based sensor networks

Internet of things grew swiftly and many services, software, sensors-embedded electronic devices and related protocols were developed and still in progress with full swing. Internet of things enabling physically existing things to see, hear, think and perform a notable task by allowing them to talk to each other and share useful information while making decision and caring-on/out their important tasks. Internet of things is greatly promoted by wireless sensor network as it becomes a perpetual layer for it. Wireless sensor network works as a base-stone for most of the Internet of things applications. There are severe general and specific threats and technical challenges to Internet of things–based sensor networks which must overcome to ensure adaptation and diffusion of it. Most of the limitations of wireless sensor networks are due to its resource constraint objects nature. The specified open research challenges in Internet of things–based sensor network are power consumption, network lifespan, network throughput, routing and network security. To overcome aforementioned problems, this work aimed to prolong network lifetime, improve throughput, decrease packet latency/packet loss and further improvise in encountering malicious nodes. To further tune the network lifetime in terms of energy, wireless harvesting energy is suggested in proposed three-layer cluster-based wireless sensor network routing protocol. The proposed mechanism is a three-tier clustering technique with implanted security mechanism to encounter malicious activities of sensor nodes and to slant them into blacklist. It is a centred-based clustering protocol, where selection of cluster head and grid head is carried out by sink node based on the value of its cost function. Moreover, hardware-based link quality estimators are used to check link effectiveness and to further improve routing efficiency. At the end, excessive experiments have been carried out to check efficacy of the proposed protocol. It outperforms most of its counterpart protocols such as fuzzy logic–based unequal clustering and ant colony optimization–based routing hybrid, Artificial Bee Colony-SD, enhanced three-layer hybrid clustering mechanism and energy aware multi-hop routing in terms of network lifetime, network throughput, average energy consumption and packet latency.

Ambient self-powered cluster-based wireless sensor networks for industry 4.0 applications

Smart grid is one of the major prospective candidates in the Industrial Internet of Things family that ensures smooth and efficient power distribution, restoration in times of emergency, and usage control for the consumers. Electric power generators contribute at the core of smart grid along with the transmission lines and transformers. Extensive research works are conducted to optimize different parameters such as efficient energy usage, automated demand response, and emergency grid failure recovery. However, the component status analysis of the electric generators within a smart grid is still in the nascent stage. In this paper, we propose a novel routing protocol for supervised device-data transfer from smart grid generators to the command and control center using wireless ad hoc and sensor networks. Our protocol assumes various sensor devices (temperature sensors, oil level sensor, turbine status sensors, etc.) to be employed on each generator due to their mechanical sophistication. Additionally, we introduce the ambient energy harvesting for the sensors energy replenishment to accommodate tolerable node outage. Our simulation results demonstrate promising outcome with respect to different key parameters such as message flow, energy consumption, outage frequency, remaining energy, and harvested energy.