Hierarchical Wireless Sensor Networks Research Articles

SEE2PK: Secure and energy efficient protocol based on pairwise key for hierarchical wireless sensor network

SEE2PK: Secure and energy efficient protocol based on pairwise key for hierarchical wireless sensor network

Improved User Authentication in Hierarchical Wireless Sensor Networks

Improved User Authentication in Hierarchical Wireless Sensor Networks

An Energy Efficient Cluster Head Selection in WSN Based on Enhanced Chicken Swarm Optimization

Clustering of data are ab effective energy saving method for wireless sensor networks (WSN). However, they incur additional costs in data collections from sensor nodes and sending them to base station (BS) after aggregations. Cluster heads (CHs) in hierarchical cluster-based WSNs use more energy. Therefore, choosing a suitable CHs is essential to conserve energy for sensor nodes and prolong the life of the WSN. Provide energy-saving cluster head selection techniques in this work. where enhanced fuzzy means clustering is used to perform the first cluster construction. Enhanced Chick Swarm optimization (ECSO) selects CHs which is evaluated strictly for varying WSN scenarios and counts of CHs and nodes for values of remaining energies of sensor nodes, sink distances, and distances within clusters. To demonstrate that the proposed technique is superior, the results are compared with those of several other currently used algorithms.

A Perfect Security Key Management Method for Hierarchical Wireless Sensor Networks in Medical Environments

Wireless sensor networks (WSNs) have developed during the past twenty years as a result of the accessibility of inexpensive, short-range, and simple-to-deploy sensors. A WSN technology sends the real-time sense information of a specific monitoring environment to a backend for processing and analysis. Security and management concerns have become hot topics with WSN systems due to the popularity of wireless communication channels. A large number of sensors are dispersed in an unmonitored medical environment, making them not safe from different risks, even though the information conveyed is vital, such as health data. Due to the sensor′s still limited resources, protecting information in WSN is a significant difficulty. This paper presents a hierarchical key management method for safeguarding heterogeneousWSNs on hybrid energy-efficient distributed (HEED) routing. In the proposed method, the Bloom scheme is used for key management and a pseudo-random number generator (PRNG) to generate keys in an efficient method to keep sensor resources. In addition, using cipher block chaining-Rivest cipher 5 (CBC-RC5) in this method achieved cryptography goals such as confidentiality. A comparison is made between the proposed and existing methods such as dynamic secret key management (DSKM) and smart security implementation (SSI) under the same circumstance to determine the performance of the new method. The data transmission in WSN consumes about 71 percent of a sensor′s energy, while encryption computation consumes only 2 percent. As a result, our method reduces the frequency with which data transmissions are made during the key management process. The simulation findings demonstrated that, in comparison to earlier techniques, the proposed method is significantly more secure, flexible, scalable, and energy-efficient. Our proposed method is also able to prevent classifications of node capture attacks.

Retraction Note to: Advertisement valid time triggered firefly and fruit-fly inspired approach for efficient cluster formation and standby CH selection in hierarchical wireless sensor network

Retraction Note to: Advertisement valid time triggered firefly and fruit-fly inspired approach for efficient cluster formation and standby CH selection in hierarchical wireless sensor network

Cascading Failure Analysis of Hierarchical Industrial Wireless Sensor Networks under the Impact of Data Overload

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively.

EECH/CF: An Energy-efficient Cluster Head Election and Cluster Formation Algorithms for WSNs

Background and Objective: Wireless sensor networks (WSN) consist of sensor nodes with a limited battery life and limited communication distance. The minimal energy expended by the sensor nodes and network can be achieved either by reducing the number of communications or by controlling the topology. Thus, energy consumption can be optimized by employing several techniques, such as clustering. Clustering allows the network to be divided into a set of clusters, each of which is managed by a cluster head. In a hierarchical cluster-based WSN, the cluster head receives the data from its sensor members, aggregates it and sends the result to the base station, which leads to an extra overload. So, the selection of appropriate cluster head plays a very important role in conserving the energy of the sensor nodes and extend the lifetime of the network. This paper introduces an energyefficient cluster head election and cluster formation algorithm for WSNs, called EECH/CF. To select cluster heads, our proposal election algorithm uses the initial and residual energy level of the sensor nodes, and efficiently creates the different clusters using an appropriate mechanism. Performance evaluation has shown a significant improvement in energy conservation and the network lifetime for EECH/CF in comparison to some existing clustering algorithms. Methods: Our proposal election algorithm uses the initial and residual energy level of the sensor nodes, and efficiently creates the different clusters using an appropriate mechanism. Results and Discussion: Performance evaluation has shown a significant improvement in energy conservation and the network lifetime for EECH/CF in comparison to some existing clustering algorithms. Conclusion: Results show that our clustering algorithms improve the lifetime of the network as we expected. Indeed, the delay before the death of the first node can be up to seven times longer with EECH/CF.

Multi agent dynamic weight based cluster trust estimation for hierarchical wireless sensor networks

Multi agent dynamic weight based cluster trust estimation for hierarchical wireless sensor networks

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node’s position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round.

Comprehensive analysis of energy efficient secure routing protocol over sensor network

To make hierarchical wireless sensor network transmission more secure, node life longer, and network operation efficiency higher, a WSN shared vital management scheme based on LEACH protocol is proposed. The program has good integrity and timeliness, dynamic critical security, node security independence, and critical survival effectiveness. It also can resist risks such as man-in-the-middle attacks and DDoS attacks, and it is expensive to distribute shared keys. The energy is lower, and the network operation efficiency is higher.

Data fusion based wireless temperature monitoring system applied to intelligent greenhouse

In intelligent greenhouses, wireless sensor networks with uneven temperature distribution and low collection efficiency may lead to poor monitoring effects in real time. To improve the performance of the temperature monitoring system in intelligent greenhouses, a real-time fusion strategy for a hierarchical wireless sensor network (WSN) is proposed. The designed WSN has three layers. In the bottom, sensors collect and preprocess the temperature data of the greenhouse by an improved unscented Kalman filter. In the middle layer, each cluster-head sensor, as a local fusion center, is used to fuse the data collected from the bottom sensors by a parallel inverse covariance intersection fusion algorithm. In the top, a global fusion center is utilized to fuse the temperature data from the middle layer to reflect the global temperature of the greenhouse by an improved extreme learning machine algorithm. The designed algorithm applied in each layer ensures the efficiency and accuracy of data fusion in real time. Simulation results show that the designed fusion strategy effectively improves the fusion accuracy and realizes the real-time fusion. The performance of the designed temperature monitoring system is greatly improved.

Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection

Purpose Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues. Design/methodology/approach This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station. Findings The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results. Originality/value This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.

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.

A Systematic Review of Data Aggregation Techniques in Wireless Sensor Networks

A Wireless Sensor Network (WSN) consists of a number of sensor nodes which can sense, communicate, and store data, and it has a battery capacity. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, that in turn improve the energy effectiveness and increases the life span of energy constrained WSNs. The current article presents a systematic review of data aggregating schemes in flat and hierarchical WSNs, which are compared in light of different factors, including the node heterogeneity, mobility of aggregator nodes, and types of networks and algorithms. By comparing these remarkable techniques, a set of aspects are suggested to be dealt with in future works. Index words: Data aggregation, WSNs, network lifetime, Flat Network, Hierarchical network.

An enhanced superframe structure of IEEE 802.15.4 standard for adaptive data requirement

In this paper, the superframe structure of IEEE 802.15.4 standard is improved to meet the adaptive data traffic requirements of hierarchical Wireless Sensor Networks (WSNs). In the IEEE 802.15.4 standard, sensor nodes send their data implicitly by using the Guaranteed Time Slots (GTS) to their PAN coordinator. The standard does not meet the adaptive data requirements of GTS requesting nodes. If requesting GTSs in an active period are more or less than the available limit, then either the requested nodes will not be entertained or GTSs remain underutilized. Consequently, it may cause unnecessary delays or poor GTS utilization. In this work, an Enhanced Superframe offering Adaptive Duty cycle (ESAD) is proposed. ESAD adapts the active period in accordance with the requested data. In addition, the actual size of GTS has been halved to accommodate more GTS requesting nodes. The shortest job first algorithm is applied to scrutinize the requested GTS nodes. Simulation results verify that the proposed scheme increases the link utilization, improves the data transmission, reduces the delay, and allows more nodes to transfer their data in a specified superframe.

MSCR: multidimensional secure clustered routing scheme in hierarchical wireless sensor networks

For hierarchical wireless sensor network (WSN), the clustered routing protocol can effectively deal with large-scale application requirements, thereby, how to efficiently elect the secure cluster heads becomes very critical. Unfortunately, many current studies only focus on improving security while neglecting energy efficiency and transmission performance. In this paper, a lightweight trust management scheme (LTMS) is proposed based on binomial distribution for defending against the internal attacks. Simultaneously, distance domain, energy domain, security domain and environment domain are considered and introduced to propose a multidimensional secure clustered routing (MSCR) scheme by using dynamic dimension weight in hierarchical WSNs. The simulation results show that LTMS can effectively prevent a malicious node from being elected as a cluster head, and MSCR can achieve a balance between security, transmission performance and energy efficiency under the requirements of environmental applications.

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.

Cluster-Based Routing Approach in Hierarchical Wireless Sensor Networks toward Energy Efficiency using Genetic Algorithm

Energy efficiency is one of the important factors when exploiting Wireless Sensor Networks, especially for increasing lifespan and performance. In the network nowadays, the number of sensor nodes can reach hundreds or thousands and can be arranged in complex hierarchical architecture. Besides, the current sensor nodes have a small size, limited battery source but are operated in vast areas. The clustered-based method has been an effective and potentially extensible means of boosting the management and operation of such large-scale networks and minimizing the overall energy consumption. In this paper, the issue of arranging and routing the nodes in the sensor network in a hierarchical manner is investigated, in which each lowest level sensor nodes are grouped in a cluster with a common cluster head, then the cluster-head plays an intermediate role transmit the information back and forth between the sensor nodes and the base station. In this way, the route to exchange information can not only be optimized with respect to the distance but also for energy spent on the communication. In order to do so, this paper proposed a novel method based on a Genetic Algorithm to establish a routing protocol to achieve energy optimization. The results demonstrate that this approach can decrease the energy consumption according to the optimized routing through clustering and increase the performance superior to the other clustering schemes.

Key Management for Hierarchical Wireless Sensor Networks: A Robust Scheme

Secure data transmission within the wireless sensor networks (WSNs) is a critical issue as they are mostly deployed in the open areas. Moreover, the communication between the cluster head (CH) and the base station(BS) in a hierarchical WSN is required to be more secure since the CH is responsible for data collection, aggregation and its forwarding to the BS. Thus, this paper aims to design a hybrid key management scheme (KMS) for the hierarchical WSNs for enhancing security between the CH and BS by using some asymmetric cryptographic technique while applying the secret key based communication among the member nodes to reduce their computational overheads. The security analysis of our proposed scheme exhibits its robustness against the node capture attack and its ability to support the node revocation. The performances of proposed scheme are also evaluated in term of data freshness, average number of keys established, throughput and computational cost to demonstrate its efficiency.

FPS‐MAC: Fuzzy priority scheduling‐based MAC protocol for intelligent monitoring systems

SummaryRecent advances in microelectronics have encouraged the implementation of a wireless sensor network (WSN) in intelligent monitoring systems (IMSs). The IMS for time‐critical applications requires timely and reliable data delivery without sacrificing the energy efficiency of the network. This paper proposes FPS‐MAC, a fuzzy priority scheduling‐based medium access control protocol, designed for event critical traffic in hierarchical WSN. The FPS‐MAC allows time‐critical event traffic to opportunistically steal the data slots allocated for periodic data traffic in event‐based situations. Additionally, a fuzzy logic‐based slot scheduling mechanism is introduced to provide guaranteed and timely medium access to emergency traffic load and ensures the quality‐of‐service (QoS) requirements of IMSs. Both analytical and simulation results for data throughput, energy consumption, and transmission delay of FPS‐MAC, TLHA, E‐BMA, and BMA‐RR have been analyzed to demonstrate the superiority of the proposed FPS‐MAC protocol.