Network Lifetime Of Wireless Sensor Networks Research Articles

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.

Wireless Sensor Network Lifetime Extension via K-Medoids and MCDM Techniques in Uncertain Environment

In this study, the multi-criteria decision-making (MCDM) technique is used in collaboration with K-medoids clustering to establish a novel algorithm for extending the lifetime of wireless sensor networks (WSNs) in the presence of uncertainty. One of the most important problems in WSNs is the energy consumption. Furthermore, extending the network lifetime in WSNs is highly dependent on selecting the appropriate cluster heads (CHs), and this can be a challenging task for the decision makers. In addition, parameters associated with WSNs are completely unexpected due to uncertainty. Therefore, after proposing K-medoids clustering and a MCDM technique, we have developed a novel algorithm for extending the lifetime of WSNs. As criteria, we have taken into account four important aspects of the proposed WSN: the distance from sink, average distance of cluster nodes, reliability of cluster and residual energy. To represent uncertain parameters in this work, we have considered triangular fuzzy numbers (TFNs). Finally, an experiment involving a WSN under uncertainty was investigated, and the findings have been graphically displayed. In this research, it has been observed that the proposed strategy with the novel algorithm exhibits 42% greater network lifetime as compared with a hybrid energy efficient distributed (HEED) algorithm and 11% and 18% with respect to optimal clustering artificial bee colony (OCABC) and particle swarm optimization (PSO) applied to a clustering optimization problem. We have also conducted statistical hypotheses for the purpose of confirming the presented outcomes.

Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

The internet of things (IoT) and industrial IoT (IIoT) play a major role in today’s world of intelligent networks, and they essentially use a wireless sensor network (WSN) as a perception layer to collect the intended data. This data is processed as information and send to cloud servers through a base station, the challenge here is the consumption of minimum energy for processing and communication. The dynamic formation of cluster heads and energy aware clustering schemes help in improving the lifetime of WSNs. In recent years, grey wolf optimization (GWO) became the most popular feature selection optimizing, swarm intelligent, and robust metaheuristics algorithm that gives competitive results with impressive characteristics. In spite of several studies in the literature to enhance the performance of the GWO algorithm, there is a need for further improvements in terms of feature selection, accuracy, and execution time. In this paper, we have proposed an energy-efficient cluster head selection using an improved version of the GWO (EECHIGWO) algorithm to alleviate the imbalance between exploitation and exploration, lack of population diversity, and premature convergence of the basic GWO algorithm. The primary goal of this paper is to enhance the energy efficiency, average throughput, network stability, and the network lifetime in WSNs with an optimal selection of cluster heads using the EECHIGWO algorithm. It considers sink distance, residual energy, cluster head balancing factor, and average intra-cluster distance as the parameters in selecting the cluster head. The proposed EECHIGWO-based clustering protocol has been tested in terms of the number of dead nodes, energy consumption, number of operating rounds, and the average throughput. The simulation results have confirmed the optimal selection of cluster heads with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by using minimum energy levels in WSNs. Using the proposed algorithm, there is an improvement in network stability of 169.29%, 19.03%, 253.73%, 307.89%, and 333.51% compared to the SSMOECHS, FGWSTERP, LEACH-PRO, HMGWO, and FIGWO protocols, respectively.

MOBILE SINK PATH FORMATION TO EXTEND THE NETWORK LIFETIME IN WSN

Wireless Sensor Network (WSN) is a group of sensor nodes that is utilized to observe and record the several physical, environmental, and significant real time data.Data traffic obtained through the Base Station (BS) in WSN minimizes the energy of close sensor nodes as equated to other sensor nodes. This issue is called as hot spot issue in the WSN. But, the Mobile Sink (MS) is proposed for solving this hot spot issue in the WSN. MS is used to gather the modified information from the Cluster Head (CH) and it forward to the BS. This approach presents a Mobile Sink Path Estimation (MSPE) to extend the network lifetime in WSN. The major objective of this article is to designing a MS route to minimizing the energy utilization and delay. In this approach each CH has a threshold level of the load ahead of that if there is an inflow of data, then the node routinely forwards an Mobile Sink Route Request (MS_REQ) for assist from the MS. Depend on the requests received from all CH nodes; the MS evaluates the most appropriate region to move other position. We examine the function of MSPE approach by applying Network simulator. Simulation results demonstrate that the introduced MSPE approach has enhanced the residual energy and minimized the network delay.   &nbsp

EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

In recent years, Internet of Things (IoT)-based constrained wireless sensor networks (WSN) have been observed with radical developments. The aligned phenomenon such as maximizing resource utilization and the delivery of efficient services is the need of the hour to suffice for the purpose of seamless connectivity. Typically, IoT devices have limited battery life and are deployed in remote areas. As a result, limited battery life limits the use of networks in some situations. To develop energy-efficient techniques for the IoT requires innovative ideas where the current techniques of using WSNs cannot directly apply to the IoT due to protocol issues, SLAs (service level agreements), scalability issues, and complexity levels. To overcome these issues, this article proposes an Enhanced Multitier Energy-Efficient Clustering Protocol integrated with the Internet of Things (EMEECP-IOT) based on the heterogeneous wireless sensor networks (HWSN) technique. The proposed approach has been compared with the current and standard protocols with respect to the parameters such as network stability, packets received, average throughput, standard deviation (SD), and residual energy. The simulation results show significantly better performance of the proposed EMEECP-IoT-based HWSN technique than the existing technique. Extensive performance factors show that the proposed EMEECP-IOT extends the network lifetime by 35% and decreases the network’s energy consumption by 21%. This proposed approach can be applied to save energy and enhance network lifetime for WSN and IoT-oriented applications.

A game theory‐based clustering and multi‐hop routing scheme in wireless sensor networks for energy minimization

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.

SEPCS: Prolonging Stability Period of Wireless Sensor Networks using Compressive Sensing

Compressive sensing (CS) is an emerging theory thatis based on the fact that a small number of linear projections of asparse data contains enough information for reconstruction. CScan break through the asymmetry between the data acquisitionand information processing that makes a great challenge to therestriction energy and computation consumption of the sensornodes. In this paper, we propose a routing protocol called SEPCSfor clustered wireless sensor networks (WSNs) using CS. SEPCScombines a new clustering strategy with CS theory for prolongingstability period and network lifetime in WSNs. Our simulationresults show that the proposed protocol can effectively prolongthe stability period and network lifetime compared with existingprotocols.

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Energy and security remain the main two challenges in Wireless Sensor Networks (WSNs). Therefore, protecting these WSN networks from Denial of Service (DoS) and Distributed DoS (DDoS) is one of the WSN networks security tasks. Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks. This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time. We examined the performance metrics of different machine learning classification categories such as K-Nearest Neighbour (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gboost, Decision Tree (DT), Naïve Bayes, Long Short Term Memory (LSTM), and Multi-Layer Perceptron (MLP) on a WSN-dataset in different sizes. The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets, and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics. The performance metrics used in these validations were accuracy, F1-score, False Positive Ratio (FPR), False Negative Ratio (FNR), and the training execution time. Moreover, the test results showed the Gboost algorithm got 99.6%, 98.8%, 0.4% 0.13% in accuracy, F1-score, FPR, and FNR, respectively. At training execution time, it obtained 1.41 s for the average of all training time execution datasets. In addition, this paper demonstrated that for the numeric statistical data type, the best results are in the size of the dataset ranging from 3000 to 6000 records and the percentage between categories is not less than 50% for each category with the other categories. Furthermore, this paper investigated the effect of Gboost on the WSN lifetime, which resulted in a 32% reduction compared to other Gboost-free scenarios.

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

Cost-based routing protocols are the main approach used in practical wireless sensor network (WSN) and Internet of Things (IoT) deployments for data collection applications with energy constraints; however, those routing protocols lead to the concentration of most of the data traffic on some specific nodes which provide the best available routes, thus significantly increasing their energy consumption. Consequently, nodes providing the best routes are potentially the first ones to deplete their batteries and stop working. In this paper, we introduce a novel routing strategy for energy efficient and balanced data collection in WSNs/IoT, which can be applied to any cost-based routing solution to exploit suboptimal network routing alternatives based on the parent set concept. While still taking advantage of the stable routing topologies built in cost-based routing protocols, our approach adds a random component into the process of packet forwarding to achieve a better network lifetime in WSNs. We evaluate the implementation of our approach against other state-of-the-art WSN routing protocols through thorough real-world testbed experiments and simulations, and demonstrate that our approach achieves a significant reduction in the energy consumption of the routing layer in the busiest nodes ranging from 11% to 59%, while maintaining over 99% reliability. Furthermore, we conduct the field deployment of our approach in a heterogeneous WSN for environmental monitoring in a forest area, report the experimental results and illustrate the effectiveness of our approach in detail. Our EER based routing protocol CTP+EER is made available as open source to the community for evaluation and adoption.

An Efficent Coverage and Maximization of Network Lifetime in WSN Through Metaheuristics

<span>In wireless sensor networks (WSNs), energy, connectivity, and coverage are the three most important constraints for guaranteed data forwarding from every sensor node to the base station. Due to continuous sensing and transmission tasks, the sensor nodes deplete more quickly and hence they seek the help of data forwarding nodes, called relay nodes. However, for a given set of sensor nodes, finding optimal locations to place relay nodes is a very challenging problem. Moreover, from the earlier studies, the relay node placement is defined as a non-deterministic polynomial tree hard (NP-Hard) problem. To solve this problem, we propose a multi-objective firefly algorithm-based relay node placement (MOFF-RNP) to deploy an optimal number of relay nodes while considering connectivity, coverage, and energy constraints. To achieve network lifetime, this work adopted energy harvesting capabilities to the sensor nodes and backup relay strategy such that every sensor node is always connected to at least one relay to forward the data. The optimal relay placement is formulated as an objective function and MOFF is applied to achieve a better solution. Extensive Simulations are carried out over the proposed model to validate the performance and the obtained results are compared with state-of-art methods)</span>

Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications.

The application of the Internet of Things (IoT) in wireless sensor networks (WSNs) poses serious challenges in preserving network longevity since the IoT necessitates a considerable amount of energy usage for sensing, processing, and data communication. As a result, there are several conventional algorithms that aim to enhance the performance of WSN networks by incorporating various optimization strategies. These algorithms primarily focus on the network layer by developing routing protocols to perform reliable communication in an energy-efficient manner, thus leading to an enhanced network life. For increasing the network lifetime in WSNs, clustering has been widely accepted as an important method that groups sensor nodes (SNs) into clusters. Additionally, numerous researchers have been focusing on devising various methods to increase the network lifetime. The prime factor that helps to maximize the network lifetime is the minimization of energy consumption. The authors of this paper propose a multi-objective optimization approach. It selects the optimal route for transmitting packets from source to sink or the base station (BS). The proposed model employs a two-step approach. The first step employs a trust model to select the cluster heads (CHs) that manage the data communication between the BS and nodes in the cluster. Further, a novel hybrid algorithm, combining a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), is proposed to determine the routes for data transmission. To validate the efficacy of the proposed hybrid algorithm, named PSOGA, simulations were conducted and the results were compared with the existing LEACH method and PSO, with a random route selection for five different cases. The obtained results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency, increased network throughput, high packet delivery rate, and high residual energy throughout the entire iterations.

Performance Estimation of a Wireless Sensor Network with a Mobile Sink Moving in Different Trajectories at Different Velocities

Background: Throughout the past few years, numerous Medium Access Control (MAC) protocols, routing protocols, node deployment mechanisms and duty cycle variation schemes have been designed for achieving high throughput, low delay and jitter, and long network lifetime in Wireless Sensor Networks (WSNs). In a WSN with static sink, voluminous sensors transmit their sensed data to the sink node. The coordinators mutually present in the range of sensors and sink have to forward a number of packets, which cause rapid depletion of their battery. These coordinators become dead too early resulting in the breakage of the communication channel and formation of energy holes. However, to save energy with a static sink, the duty cycle should be short. A mobile sink is a better option than a static sink if the duty cycle is long, as it balances energy consumption among the sensors. It is well observed that the mobile sink is capable of acquiring homogeneous energy depletion leading to stretched lifetime enhancing network performance. Methods: The vital benefaction of this paper is to present a simulation-based analysis of the network performance with a mobile sink having different trajectories of path traversed at different velocities. The intent is to find out the most appropriate and efficient trajectory and a particular velocity for a specific WSN with 100 nodes. The terrain area of the network is 210×210 m2 with the communication range of 20 m. The routing, network and MAC protocols implemented are Ad hoc On-Demand Distance Vector (AODV), Internet Protocol version 4 (IPv4) and Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 respectively. This paper has evaluated and analyzed the influence of lawn mower, elliptical and circular trajectories of a mobile sink moving at different velocities of 0.5, 1 and 2 m/s. The optimum performance is achieved at the velocity of 2 m/s for a circular trajectory of the mobile sink. It is observed that performance has significantly varied with the variation of trajectories and velocities. The notion of precise utilization of sink mobility improves the performance than a static sink. It is equally important to determine the most effective mechanism to implement mobile sinks and to find out the most appropriate scheme out of them. Results: The attainment parameters just as total messages received, average end to end delay (seconds), jitter (seconds), throughput (bits per second), number of packets dropped, number of packets dropped due to channel access failure, residual battery (mAh) and network lifetime (hours) for different trajectories such as lawn mower, elliptical and circular at different speeds of 0.5, 1 and 2 m/s of the sink node are evaluated and compared. The simulation results present that the circular trajectory and the velocity of 2 m/s have provided optimum performance. Conclusion: The objective was to precisely analyze and evaluate the influence of different trajectories of a mobile sink moving at different velocities in a WSN of 100 nodes to determine the most effective and appropriate trajectory and velocity to optimize the attainments. The intent is to uniform the power exhaustion amidst the sensors. The purpose was to gain the attention of researchers of this field to significantly contribute to novel research.

An Energy-Efficient Routing Protocol Based on Energy Band and Virtual Localization

Wireless Sensor Networks (WSN) are Important in various fields which includes military, environmental ,industries, healthcare, biological, home and other commercial applications. Our focus in this dissertation is to prolong the network lifetime of WSNs by reducing and balancing energy consumption during routing process by designing energy efficient clustering protocol for the wireless sensor networks based on energy band based virtual coordinates. The system should be designed in such a way that each node while transmission consumes least amount of energy. This will help in increasing the lifetime of the WSN.

Q-Learning based Routing Protocol to Enhance Network Lifetime in WSNs

In resource constraint Wireless Sensor Networks (WSNs), enhancement of network lifetime has been one of the significantly challenging issues for the researchers. Researchers have been exploiting machine learning techniques, in particular reinforcement learning, to achieve efficient solutions in the domain of WSN. The objective of this paper is to apply Q-learning, a reinforcement learning technique, to enhance the lifetime of the network, by developing distributed routing protocols. Q-learning is an attractive choice for routing due to its low computational requirements and additional memory demands. To facilitate an agent running at each node to take an optimal action, the approach considers node’s residual energy, hop length to sink and transmission power. The parameters, residual energy and hop length, are used to calculate the Q-value, which in turn is used to decide the optimal next-hop for routing. The proposed protocols’ performance is evaluated through NS3 simulations, and compared with AODV protocol in terms of network lifetime, throughput and end-to-end delay.

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out.

An enhanced energy-efficient routing protocol for wireless sensor network

Recent few years, Wireless Sensor Network (WSN) has been an increasingly important technology that has been applied in almost all domains, even in complex environments where human activity is impossible. In WSN, various factors are impacted energy consumption, such as communication protocols, packet data transmission, and limited battery. So, the lifespan of the WSNs is limited. In this context, energy efficiency is the factor most attracted by many researchers. In this paper, we proposed a new improved LEACH routing protocol. This proposed protocol based on the current energy to select cluster-heads, and it uses a root cluster-head with more current energy and low distance to the sink to gather all data, then sends it to the sink. The simulation results in MATLAB confirmed that the proposed algorithm performed better than the conventional LEACH protocol, and increased the network lifetime in WSN.

A Transmission Power Control Mechanism for 802.15.4+RPL-Operated Wireless Sensor Network

Background and Objective: Wireless Sensor Networks (WSNs) are typically formed by one or more sink nodes and a large number of sensor nodes that are able to sense, process and transmit data. Topology control mechanisms can be utilized to guarantee requirements such as connectivity, coverage, delay and network lifetime in WSNs. One effective way to control the topology is adjusting transmission power level in each node according to the link quality conditions. The goal of Transmission Power Control (TPC) is to set the transmission range of each node by adjusting its transmission power level. Methods: In this paper, we investigate TPC in 802.15.4+RPL WSNs. In our proposed mechanism, each node dynamically adjusts its transmission power based on channel conditions before sending every data and ACK packet. Result and Conclusion: The results of extensive simulations confirm that our power control method improves network performance, especially in terms of network lifetime, which is an essential issue in WSNs.

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

SummaryClustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.

A Hybrid Optimization Algorithm on Cluster Head Selection to Extend Network Lifetime in WSN

Wireless Sensor Networks (WSNs) comprise of a number of sensor nodes that are capable of sensing and aggregating the data from the monitoring environment. However, the process of recharging the limited energy sensor node batteries are highly difficult during adverse situations. This limitation of sensor nodes greatly crumbles the network lifetime to a maximum degree and degrades the level of reliable data dissemination. In this paper, a Novel Individual Updating Strategies-based Hybrid Elephant Herding Optimization Algorithm (NIUS-HEHOA) is planned for facilitating energy balanced cluster head selection for the objective of extending the network lifetime. It included energy-aware optimization process during the clustering schemes, since it is considered as a solution to the significant NP complete optimization problem. It is propounded as a swarm intelligent algorithms are identified to be the most applicable candidate for energy optimization that leads to significant improvement in network lifetime. It is contributed to maintain the deviation between exploitation and exploration such that least potential sensor nodes are prevented from being chosen as cluster heads. The simulation experiments confirmed that the proposed NIUSHEHOA scheme is better than the benchmarked schemes in terms of alive nodes, dead nodes, residual energy, network lifetime and throughput.

Fuzzy based Dynamic Security Parameters Determination Method to Improve Energy Efficiency

Fine dust is a harmful substance that floats in the atmosphere. It is not filtered well in the bronchus and causes health problems when it accumulates in the body. Fine dust that is present in the air outside can flow into buildings through windows and other openings, adversely affecting indoor air quality. It has negative health effects on people who live indoors. As attention to problems associated with fine dust gradually rises, the importance of continuously managing the quality of indoor air through air purification rises. Therefore, recently, research and development into systems to periodically purify indoor air are being carried out. This paper introduces an air purification system, a Wireless Sensor Networks (WSNs)-based Internet of Things (IoT) air purification system. In the WSNs-based IoT air purification system, the IoT air purifier is controlled based on event information that the WSNs senses, so it is important to maintain the security of the event information. To this end, a WSNs security protocol, Interleaved Hop by hop Authentication (IHA), is used in this system. IHA is a security protocol in which sensor nodes and a Base Station (BS) detect and drop false reports if the number of compromised sensor nodes does not exceed a security threshold. That is, because the false report injection attack that the number of compromised sensor nodes exceeds security threshold can’t defend by IHA, it is detected and defended through Data Calibration. However, considerable energy of the sensor nodes is unnecessarily consumed in the process of forwarding false reports. Thus, this paper proposes a method of decreasing and increasing security thresholds dynamically according to the network situation using fuzzy logic. This proposed scheme has the advantages of improvements in both the overall energy efficiency and network lifetime in WSNs.