An Algorithm with Efficiently Collecting and Aggregating Data for Wireless Sensor Networks

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

SummaryEnergy consumption in wireless sensor network (WSN) is one of the important issues as tiny sensor nodes are equipped with a non‐replaceable batteries. Clustering is one of the effective means to prolong the network lifetime in WSN by reducing the number of packets to be sent from cluster heads (CHs) to base station (BS). Recently, game theory techniques have been applied in the clustering process for optimal CH election at each round. However, none of them have considered the energy parameter in their game strategy and multihop routing for data transmission. In this manuscript, an energy‐efficient game‐theoretic clustering and multi‐hop routing scheme (EGCR) has been proposed where the non‐cooperative approach of game theory is used to elect optimal CHs. In the proposed game theoretic framework, each node behaves in a selfish manner while considering the residual energy of nodes and focuses on increasing the overall lifetime of the network. Nash equilibrium is determined among a set of pure and mixed strategies, and then the expected payoff of a node is determined for Nash equilibria. Apart from the energy‐efficient clustering, the EGCR also proposes a multi‐hop routing solution for data transmission that further enhances the lifetime of whole network. In the multi‐hop routing approach, inter‐cluster and intra‐cluster routing techniques are proposed to identify optimal routes from nodes to CHs and from CHs to BS by using various factors such as node centrality, distance from the node to BS, and residual energy. The proposed algorithm is compared with traditional clustering schemes like low‐energy adaptive clustering hierarchy (LEACH) and efficient energy‐aware game theory‐based clustering (EEGC) and also with existing game theory‐based algorithms such as clustered routing for selfish sensors (CROSS), localized game theoretical clustering algorithm (LGCA), and energy‐efficient clustering algorithm based on game theory (ECAGT). Simulation experiments validate that the proposed algorithm enhances the network's lifetime up to 47.49% compared to existing clustering algorithms.

Wireless sensor networks (WSNs) consist of a large number of small, low data rate and inexpensive nodes that communicate in order to sense or control a physical phenomenon. The major difference between the WSN and the traditional wireless network is that sensors are very sensitive to energy consumption. Moreover, the performance of the sensor network applications highly depends on the lifetime of the network and we expect the lifetime of several months to several years. Thus, energy saving is crucial in designing long-lived wireless sensor networks. Many researchers have focused on developing energy efficient cluster based protocols for WSNs, but there has not been much research on event driven WSNs and, their focus is on continuous driven networks. In this paper, we propose a modified algorithm of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a well known energy efficient clustering algorithm for WSNs. Our modified protocol called “Adaptive and Energy Efficient Clustering Algorithm for Event-Driven Application in Wireless Sensor Networks (AEEC)” is aimed at prolonging the lifetime of a sensor network by balancing energy usage of the nodes. AEEC makes the nodes with more residual energy have more chances to be selected as cluster head. Also, we use elector nodes which take the responsibility of collecting energy information of the nearest sensor nodes and selecting the cluster head. We compared the performance of our AEEC algorithm with the LEACH protocol using simulations .

Wireless sensor networks (WSNs) face challenges in maintaining network lifetime due to energy limitations. To optimize energy usage, techniques such as node clustering and data transmission through the shortest path are employed. However, the selected Cluster Head (CH) node eventually becomes inactive as its energy is depleted during continuous transmission to the sink. To overcome this issue, we propose the utilization of Energy-Efficient Bat-Moth Flame Optimization (EEBMFO) in WSNs, aiming to enhance network lifetime. Our strategy makes use of the echolocation signal pattern that bats use to identify prey within a certain range. In a similar manner, nodes fall into clusters within the range of the CH, and the CH is chosen by looking at the highest residual energy. In addition, we use spiral path data transmission and Moth Flame optimization to route data from the source node to the CH. By combining bat and moth flame optimizations and considering each node’s residual energy, we aim to improve the network’s lifespan. With the use of simulations and performance measures such network lifetime, throughput, latency, dependability, and network stabilization, this research provides a thorough analysis of the suggested EEBMFO approach in WSNs. When compared to current techniques, the results show a noteworthy 11–16% increase in network longevity. These findings validate the efficacy of EEBMFO in prolonging the lifespan of WSNs, offering a promising solution for energy-efficient and sustainable wireless sensor networks.

Wireless sensor networks (WSNs) consist of an enormous number of tiny sensor nodes deployed in huge numbers which are able to sense, process and transmit environmental information to the base station (BS) for a variety of applications. Energy efficiency is one of the primary concerns for maintaining WSN in operation. In this study, an energy efficient clustering protocol based on K‐means algorithm named EECPK‐means has been proposed for WSN where midpoint algorithm is used to improve initial centroid selection procedure. The proposed approach produces balanced clusters to ultimately balance the load of cluster heads (CHs) and prolong the network lifetime. It considers residual energy as the parameter in addition to Euclidean distance used in basic K‐means algorithm for appropriate CH selection. Multi‐hop communication from CH nodes to BS takes place depending on their distances from BS. Simulation result shows that the proposed approach outperforms LEACH‐B, balanced parallel K‐means (BPK‐means), Park's approach and Mk‐means with respect to network lifetime and energy efficiency. Simulation result also demonstrates that the proposed approach can reduce the energy consumption at most 50% compared to LEACH‐B, 14% compared to BPK‐means protocol, 10% compared to Park's approach and 6% compared to Mk‐means.

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

For the purpose of saving the resources of wireless sensor networks, it is necessary to fuse the existing data in wireless sensor networks. When using the current method to fuse data in wireless sensor networks, the fusing time is too long to remove the noise signal in wireless sensor networks effectively. In the process of fusing, data packet loss is easy to occur. There are some problems such as low fusion efficiency, poor denoising performance and high packet loss rate. A multi-path data hierarchical fusion method based on routing algorithm is proposed in this paper. The redundant node decision method based on mesh partitioning is combined with the grid algorithm and EECRS protocol to establish a routing protocol based on redundant node elimination, and the redundant data existing in the redundant nodes of the wireless sensor network is removed. Based on the statistical data of the data, the probability density function is deduced and analyzed, and the noise filtering matrix is constructed to optimize the measurement matrix and remove the noise existing in the wireless sensor network. The multi-path scheduling technology is used to synthesize the pheromone table and the data forwarding flow table, and the ant is used to actively monitor the link QOS real-time information in the path finding process to update the pheromone table and the flow table, and calculate the link idle rate according to the update result. According to the calculation result, the data transmission path is selected, and the multi-path data layer fusion is completed. The experimental results show that the proposed method has high fusion efficiency, good denoising performance and low packet loss rate.

ABSTRACTWireless sensor networks, or WSNs, are essential for energy monitoring, home automation, the medical industry, computing, and other fields. The two problems with wireless sensor network are the network lifetime and data transmission delay. The cluster head selection (CHS) is a difficult task. Therefore, a novel Hybrid Archimedes and Arithmetic Optimization Algorithm for Cluster Head Selection and Multipath Routing in Wireless Sensor Network (HYB‐AO‐AOA‐CHS‐WSN) is proposed in this paper. The proposed HYB‐AO‐AOA integrates Archimedes and Arithmetic optimization algorithm (AO‐AOA). The stimulation of sensor nodes initiates the CHS process. The stimulated sensor nodes are evaluated separately for delay, energy, node degree, intercluster distance, and intracluster distance for determining the cluster heads. Then, the multipath routing is handled by the HYB‐AO‐AOA technique. The proposed HYB‐AO‐AOA‐CHS‐WSN model is implemented in MATLAB. The metrics, like delay, number of alive nodes, throughput, and residual energy is examined. The proposed SRD‐DCGAN approach attains 25.72%, 21.32% and 19% higher throughput, 26.56%, 21.35% and 32.09% lower delay than the existing techniques.

Wireless Sensor Networks (WSNs) are widely used for different applications such as monitoring and surveillance in both civilian and military domains. Mobile data collection has significant impact on the performance of WSN. Therefore it is essential to have mechanisms to improve it. In this paper we proposed a three-layered approach that is used to collect data in wireless sensor network. The three layers include sensor layer which is made up of collection of sensors, cluster head layer which is made up of all cluster heads, and the mobile collector layer. The proposed approach also takes care of distributed load balanced clustering. The aim of the proposed approach is to achieve scalable solution with low data collection latency and energy efficiency. While sensors are organized into clusters the distributed algorithm is applied so as to ensure that the efficiency of the network is achieved. We built a prototype application using Microsoft .NET which simulates the proposed approach. The empirical results revealed that the proposed layered approach is energy efficient, scalable and achieves low latency in data collection.

Routing Algorithm determines the optimal route to transmit data packet from source to destination. The nodes in wireless sensor networks (WSN) are devoid of abundant energy and hence energy efficiency is the vital benchmarks for data transmission in WSN. The basic importance of wireless sensor networks (WSN) in present scenario is to reduce the node energy consumption such that it improves the data transmission efficiency and reliability to extend entire network life time. In these respect, we need to search the optimal route for data transmission in WSN. Here an Improved Ant Colony Optimization (IACO) based energy efficient routing algorithm is proposed in WSN to solve the issue. The function of improved Low Energy Adaptive Clustering Hierarchy (LEACH) routing algorithm is to select cluster heads (CH) by considering residual energy and the distance in between the nodes. To balance the energy consumption between the nodes, residual energy is used as an energy control factor, which affects the network life time and enhances the data transmission efficiency. An ACO algorithm is applied to search the optimal path from the various available routes in between CH and sink for data transmission. Simulation results show that the proposed method Improved Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (IACOLEACH) is found to yield better results over other conventional algorithms.

Clustering is an effective approach to hierarchically organizing network topology and to prolong the lifetime of a wireless sensor network. Current clustering protocols usually utilize two techniques: selecting cluster heads with more residual energy and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster. Most of the researches in this field has focused on energy-efficient solutions, but caring less about the communication between Cluster Head (CH) nodes and Base Station (BS). When the sensor field is far away from the base station, the cluster heads are burdened with heavier relay traffic and tend to die much faster. To mitigate the problem, we propose an Energy-Efficient Hierarchical Routing Protocol (EEHRP) for long range transmission in the wireless sensor networks. It uses a number of gateway nodes, which do not engage in clustering, to connect the CHs and base station. They are responsible for transmitting packets received from the CHs to the base station, thus the CHs can preserve some energy in data forwarding and the gateway nodes can ease their burden by not participating in clustering. Simulation results show that EEHRP obviously increases the network lifetime and well balances the energy consumption among the sensor nodes.

In this paper, we propose a novel encryption schema (ECDH – RSA) based on Elliptic Curves Diffie – Hellman algorithm (ECDH) and RSA algorithm to secure transmitted data in Heterogeneous Wireless sensor networks (HWSNs). The proposed encryption is built under Cheesboard Clustering Routing Method (CCRM). The CCRM used to balancing node energy consumption. To achieve good scalability and performance by deploying a small number of powerful high-end sensors in addition to a large number of lowend sensors. ECDH is used to exchange public and private keys due to its ability to provide high security with small key size. The proposed encryption key is 176-bit and is produced by combining it’s key, node identification number, and distance to its cluster head (CH). To reduce energy consumption of CH, RSA encryption is used to allow CH to aggregate the encrypted data without having to decrypt them. We demonstrated that the proposed method is capable to work with different sensing environments that need to capture text data as well as images. The Simulation results demonstrated that our proposed method greatly improve the network performance in terms of energy consumption and highest paths.

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

Wireless sensor network is consisting of power-limited sensors that sense the surrounding environment and transmit data to another sensor or to the particular gateway. Thus, a considerable amount of energy is dissipated which impact the overall performance and network lifetime. In order to mitigate the failure-based energy, routing and clustering mechanisms should be energy aware. Accordingly, and in the literature, many hierarchical clustering techniques are proposed in term to extend the network alive time such as LEACH and SEP. The role of the elected cluster head is the main objective of these techniques and appear the differences between them. In this paper, two energy efficient clustering techniques are evaluated in 100 uniformly distributed wireless sensor networks in term of number of alive nodes, residual energy, and stability period. The election of cluster head is achieved in heterogeneous conditions where some of the nodes are powered higher than others. The results show how the heterogeneity of wireless sensor network in term of energy remaining at nodes influences the overall performance and increase the stability region before first node is dying. SEP is outperformed LEACH in term of long stability zone by 4.2% when 20% of the nodes (m=0.2) are energized more energy by 100% (α=1). Moreover, when m=0.2 and α=.3, SEP has better stable region than LEACH by 26%. In general, the election of cluster head mechanism in LEACH clustering leads to reduce the stable region and the node die faster comparing to SEP. In which, the cluster head election is totally based on the remaining energy.