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Wireless sensor networks are employed to monitor physical areas in different places. Fault tolerance and energy efficiency are main challenges of the networks. WSNs are designed to transmit sensed data to the base station when a part of the network is faulty. In this paper, an algorithm is presented to withstand the challenges. In the suggested alg...
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Wireless sensor Networks are often used for monitoring and sensing the various environmental conditions .It is collection of sensor nodes which are provided with a fixed battery. Millions of sensor nodes are scattered to monitor smart grids, which consume huge amount of energy. In Wireless sensor network energy wastage is more due to high latency a...
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... The most crucial part of the network was the sensor nodes which can be operated without the human interface. In mobile networks, the energy at the base station is very important to provide the network, but in WSN the energy network is reduced to increase the lifetime of the nodes [3]. The data sharing obtained between a base station and sensor nodes is obtained by various routing protocols. ...
Wireless Sensor Networks (WSNs) are utilized Several applications like industrial, transportation, buildings, etc. due to their flexible communication, reliable utilization, less cost, and high accessibility. However, due to many issues related to lifetime and energy consumption, the ability of WSNs to broadcast information collected through the network appears to be a sophisticated process. Several efforts have been made to enhance energy-aware networking operations through the clustering process. Existing works address the issue of optimizing energy efficiency and lifetime of the network through optimal cluster head selection (CHS), topology control, and scheduling for collision reduction. But, in the clustering procedure, the cluster head (CH) selection remains a complicated task while a proper selection of CH will enhance the network lifetime. Therefore, this paper proposes a novel ‘Hybrid Snake Whale Optimization (HSWO) Algorithm’ to select optimal CH from the cluster group that helps to manage the network in broadcasting information to the destination. Three main phases included in the proposed concept are the initialization phase, the route maintenance phase and the CHS phase. At the initialization phase, the network model, distance model, and energy model are formulated. Secondly, the HSWO algorithm is applied to select the most optimal CHs from the clusters by eliminating the worst ones with the consideration of constraints namely delay, energy, and distance. Finally, in the route maintenance phase, the efficient path is chosen to broadcast the sensed data to the destination without any link breakages. The effectiveness of the HSWO algorithm is validated using different performance measures and the results proved that the proposed HSWO algorithm yielded a superior network lifetime of 5600 rounds, and normalized network energy of 0.98 compared to other existing techniques.
... Finally, network coverage sends the collected data and signals to the monitoring center in a dynamic transmission mode. In practical applications, wireless sensor networks do not need stable and specialized network support and can work quickly in any environment [30]. According to our research in the literature, the frequency of use of wireless sensor networks in various fields is shown in Figure 3. and efficiency [27]. ...
... Finally, network coverage sends the collected data and signals to the monitoring center in a dynamic transmission mode. In practical applications, wireless sensor networks do not need stable and specialized network support and can work quickly in any environment [30]. According to our research in the literature, the frequency of use of wireless sensor networks in various fields is shown in Figure 3. ...
With the construction and development of modern and smart cities, people’s lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand. This paper provides a data governance solution based on a 6G environment. The shortcomings of critical technologies in wireless sensor networks are addressed through ZigBee energy optimization to address the shortage of energy supply and high energy consumption in the practical application of wireless sensor networks. At the same time, this improved routing algorithm is combined with embedded cloud computing to optimize the monitoring system and achieve efficient data processing. The ZigBee-optimized wireless sensor network consumes less energy in practice and also increases the service life of the network, as proven by research and experiments. This optimized data monitoring system ensures data security and reliability.
... DHSCA simulation proves that it improves the equalization of energy-consumption of the network, and outperform the other existing static clustering algorithm. In [19] to increase clustering-based energy efficiency and fault tolerance, the FTEC algorithm was devised. To cluster nodes and choose a CH, we employed the HEED method. ...
The energy resources available to nodes in wireless sensor networks are limited, so they must be wisely used. Clustering is a useful technique for reducing energy consumption and extending the life of a network. In this study, we presented an Energy-Saving Clustering Algorithm (ESCA) to reduce energy consumption and increase the network's lifetime. The clustering phase is based on cluster construction that is centralized and cluster heads that are distributed. The clustering is stationary and determined using a centralized K-means method, with the created clusters remaining static throughout the process. Subsequently, according to the varying amounts of energy in the nodes, it chooses and rotates the cluster heads (CHs) within those clusters to reduce energy expenditure before the data transmission phase to the base station (BS). The suggested ESCA is compared to the current two MOFCA, and IGHND clustering methods using a Python-based custom simulator. As a consequence, the suggested ESCA efficiently tackles the energy use issue while also greatly extending the network's lifetime.
... A fault tolerance and energy-efficient clustering algorithm (FTEC) is proposed to detect the fault and energy-efficient-based clustering in WSN based on hierarchical fault management with neighbor cooperation. 28 The FTEC algorithm employs the hybrid energy-efficient distributed clustering (HEED) algorithm for selecting a BCH. In the proposed method, the CH is responsible for collecting and transmitting data to the base station from all sensor nodes, and BCH is responsible for detecting and recovering the fault nodes in the network. ...
Wireless sensor networks (WSNs) are ad hoc networks that consist of tiny sensor nodes deployed in the sensed environment to monitor the natural phenomenon and send the monitored data to the base station for further processing. Due to the unfriendly deployment of the sensor nodes and their resource‐constrained nature, the sensor nodes are subjected to frequent node failure. Detecting sensor node failures and recovering them is a major challenge in WSN. Due to the failure of the nodes in WSN, there exist various problems such as a change in the network topology, communication link failure, a disjoint partition of the network, and failure to transmit the data to the sink. Various authors have proposed various techniques to detect and recover from node failure in the WSN. In this paper, a comprehensive analysis of the various node failure and node recovery techniques has been carried out to highlight the advantages and limitations along with the open research challenges for addressing the failure of the nodes in an effective manner. Detecting sensor node failures and recovering them is a major challenge in WSN. Due to the failure of the nodes in WSN, there exist various problems such as a change in the network topology, communication link failure, a disjoint partition of the network, and failure to transmit the data to the sink.
... Here, we focus on researches that took up two essential fields of enhancing LEACH clustering and fault-tolerance algorithms. Jassbi and Moridi [20], the authors developed a hybrid powerefficient distributed algorithm (HEED) by adding fault tolerance technique to the base station's decision on faults to take appropriate action and isolated defective nodes and take it as a value by using a weighted median process, and they can increase the corrected data rate. Salem and Shudifat [21], the authors attempted to resolve the limitations of WSN, low-cost and low-power tools that affect power exchange rates by enhancing LEACH protocol in which LEACH is extended by defining a cluster head at the shortest distance to the base station. ...
Energy-efficiency of wireless sensor networks (WSN) becomes an essential issue in the research area. This is because of the energy constraints in WSN that depend on a battery, which is difficult to replace or recharge; therefore, multiple clustering algorithms were proposed to achieve efficiency in using the available energy as much as possible. This paper proposed energy-efficient and fault-tolerance algorithms that enhance the low energy adaptive clustering hierarchy (LEACH) protocol by three algorithms. The first focuses on selecting the best cluster head, and the second focuses on minimizing the required nodes within the same cluster. Simultaneously, the third fault tolerance algorithm uses software engineering techniques like sleep schedules to increase network lifetime as much as possible. The testing results of the proposed algorithms prove the claim of enhancing the lifetime of WSN. In order to check improvement of lifetime of WSN we have compered the results of the proposed algorithms with standered algorthim. The results show prove the claim of enhancing the lifetime of WSN, where the total lifetime of WSN rise from about 550 rounds to reach 4100 when utilized self-checking process and rised up to 5200 after enhance minimum distans. This is an open access article under the CC BY-SA license.
... The sources of energy in the sensor networks are minimal due to the low-power batteries developing the energy problem. Clustering gives a prominent solution to the energy problem in wireless sensor networks [11][12][13][14]. Clustering divides the entire sensing area into the desired range of sub-areas; then, one of the sensor node from the cluster member sensor is selected as the cluster head node. ...
Maximizing network lifetime in wireless sensor networks is one of the critical issues, particularly for transmitting multimedia data. The wireless sensor network's lifetime is directly linked to energy conservation at each sensor node in the network. Clustering is the most energy-efficient technique for saving energy in sensor networks. The appropriate method for selecting the cluster head is still lagging. The sink node divides the deployment region into the optimal number of sub-regions depending upon its placement in the sensing region. The initial cluster heads are chosen randomly in each region, and this is not an energy-efficient method. The sink node adopts particle swarm optimization technique to select the cluster head in each region efficiently. The chosen cluster head in each region advertises its role to member nodes. Then, the cluster head node is chosen forms the new cluster. PSO optimization technique with the optimization parameters of clustering coefficient, the sensor node's remaining energy, and the distance from the sink and the head of the cluster to the members is adopted to select the cluster head sensor node. The cluster head spends most of its energy aggregating and transferring the data to the sink node. For unloading the cluster head responsibilities, the assistant cluster head and super cluster head are selected for the aggregation and transfer of data, respectively. The proposed energy efficient cluster head selection algorithm has improved the network lifetime by an average of 65 percent better than the existing clustering algorithms.
... In [19], the authors made enhance in leach protocol and take a residual energy at each node in the consideration as well as the distance from cluster head to a data collection station. While in [20], the authors developed a hybrid energy-efficient distributed algorithm in the fault tolerance stage by making the decision about faults occur in the base station. This is to do appropriate action and isolated faulty nodes and take its value by applying weighted median method to increase the corrected data rate. ...
Energy consumption problems in wireless sensor networks are an essential aspect of our days where advances have been made in the sizes of sensors and batteries, which are almost very small to be placed in the patient's body for remote monitoring. These sensors have inadequate resources, such as battery power that is difficult to replace or recharge. Therefore, researchers should be concerned with the area of saving and controlling the quantities of energy consumption by these sensors efficiently to keep it as long as possible and increase its lifetime. In this paper energy-efficient and fault-tolerance strategy is proposed by adopting the fault tolerance technique by using the self-checking process and sleep scheduling mechanism for avoiding the faults that may cause an increase in power consumption as well as energy-efficient at the whole network. this is done by improving the LEACH protocol by adding these proposed strategies to it. Simulation results show that the recommended method has higher efficiency than the LEACH protocol in power consumption also can prolong the network lifetime. In addition, it can detect and recover potential errors that consume high energy.
... In [19], the authors made enhance in leach protocol and take a residual energy at each node in the consideration as well as the distance from cluster head to a data collection station. While in [20], the authors developed a hybrid energy-efficient distributed algorithm in the fault tolerance stage by making the decision about faults occur in the base station. This is to do appropriate action and isolated faulty nodes and take its value by applying weighted median method to increase the corrected data rate. ...
Energy consumption problems in wireless sensor networks are an essential aspect of our days where advances have been made in the sizes of sensors and batteries, which are almost very small to be placed in the patient's body for remote monitoring. These sensors have inadequate resources, such as battery power that is difficult to replace or recharge. Therefore, researchers should be concerned with the area of saving and controlling the quantities of energy consumption by these sensors efficiently to keep it as long as possible and increase its lifetime. In this paper, an energy-efficient and fault-tolerance strategy is proposed by adopting the fault tolerance technique by using the self-checking process and sleep scheduling mechanism to avoid the faults that may cause an increase in power consumption and energy-efficient at the whole network. this is done by improving the LEACH protocol by adding these proposed strategies to it. Simulation results show that the recommended method has higher efficiency than the LEACH protocol in power consumption also can prolong the network lifetime. In addition, it can detect and recover potential errors that consume high energy
... These faults are divided into physical and software [5]. In this paper, there is a focus on these types of errors to reduce the consumed power under the limitation of energy of battery [6]. Therefore, a proposed algorithm for controlling these faults and creating a recovery solution as a part of fault tolerance. ...
... In [6], fault tolerance and energy efficient clustering algorithm in WSN was suggested to improve clustering-based energy efficiency and fault tolerance. This algorithm was used to cluster nodes and selected cluster head (CH). ...
The management of faults in Wireless Sensor Networks (WSN) has been considered recently. The problem of tolerating the detected fault is solved by presenting different methods from numerous researchers. Moreover, the software engineering approaches have been adopted to introduce methods with high reliability. In this paper, a fault tolerance method is proposed for WSN based on the software engineering self-checking process to deal with the faults that affect energy consumption in the network and make it drop earlier. The proposed method detects the appeared fault at any sensor node and recovers the faulted readings by computing the average value of its neighbor nodes. In addition, this process is continued until the faulty sensor is fixed by the maintenance team. The proposed method is tested over different case studies and the obtained results prove the claim of the paper's idea.
... However, they have shown that WSN exposed to various types of failures correlated with mobility and loss of signal sensed. WSN suffer from frail performance because of crashes, temporary or permanent failures, which cause enough to reduce network life [3]. So, many investigators directed to recognize of malfunctions and fading method of deployment via the routing protocol and regarded it as a vital style issue. ...
... The authors also estimated analysis results for the proposed classifiers used to analyze their detection accuracies by different performance metrics as ERELM. In [3], the FTEC algorithm for increasing the reliability of fault tolerance in WSN was suggested. The researchers used the false alarm rate to detect the faults of cluster nodes. ...
... In case of the probability of the sensor's energy depletion demonstrating by beacon message, the node is detected as fault node through the proposed SAAFA, then it isolated that node from network topology, hence the energy consumption of the network also decreases. To assess the efficiency of SAAFA, we compare the proposed FSA algorithm with the CLAFDM algorithm [14] and FTEC implemented in [3], for observing the overhead and consumption of energy for the proposed algorithm according to the various number of nodes. Experimentally, we extended the designed network and increased the faulty sensor nodes to vary from 50 to 250 on the deployment platform through Iter.#1; and then it expanded the network by adding the number of sensor nodes from 500 to1000 nodes through Iter.#2, respectively. ...
Wireless sensor networks (WSNs) have conquered comprehensive survey progressions in the regular control and management fields. Although WSN allows the spatial monitoring of real-world events, the mobility action depletes a huge part of a sensor’s energy cost in wireless communication. WSN sensors are often prone to various faults as frequent crashes and temporary or permanent failures. This is because it propagates them in very complex and harsh environments. So, we tend to design a Self-Adaptive based Autonomous Fault-Awareness (SAAFA) model, to limit the impact of such failures and filter them. In this paper, we incorporate the two of adaptive-filters FIR with RLS through three adaptive two-stages performed at the level of cluster head, for independent fault-correction during the propagation platform. The proposed model (SAAFA) included two stages, the first stage comprised self-detection the failure and self-aware for the lost scales, in which relied on responses of delay port and prior-knowledge of absent sensor-signals throughout monitoring, through adjusting the filter weights in the adaptive feedback loop for awarding convergent signals for the lost ones. The second stage is adaptive filtering the registered signals from the above stage for gaining pure measures and free of interferences. Compared to the state-of-the-art methods, the scheduled model attained a speed in diagnosing faults and awareness the missing readings with a rate of accuracy reached 98.8% improving the robustness of performance. Evaluation criteria revealed the progress of SAAFA in reducing the radio communication to ~ 97.47% that kept about 93.7% of battery-energy throughout the picked dataset sample. Hence, it expanded the whole network lifetime.
