Fuzzy logic–based jamming detection algorithm for cluster‐based wireless sensor network

In this paper, a fuzzy logic–based jamming detection algorithm (FLJDA) is proposed to detect the presence of jamming in downstream data communication for cluster‐based wireless sensor networks. The proposed FLJDA keeps an eye on the existing nodes and new node to determine their behavior by applying fuzzy logic on measured jamming detection metrics. To monitor the behavior of the nodes, the FLJDA computes the jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. The major features of this paper are the following: (1) The jamming detection algorithm is specifically implemented for downstream data communication, (2) cluster head estimates jamming detection metrics for detecting the jamming unlike the existing algorithms where individual nodes explicitly collect the jamming detection metrics, and (3) the proposed algorithm optimizes the jamming detection metrics on the basis of fuzzy logic unlike the existing approaches, which uses merely jamming detection threshold alone for jamming detection. The simulation results of the proposed system provide the true detection ratio as high as 99.89%.     

Multi-hop wireless network oriented multiple jammers localization algorithm

A multiple jammer localization algorithm in multi-hop wireless networks was proposed. The proposed algo-rithm contained three steps, packet delivery ratio (PDR) valley point determination based on gradient descent algorithm, received jamming signal strength (RJSS) peak point determination based on gradient ascent algorithm and cluster analysis. Firstly, the algorithm started from a few initial nodes and moved along the gradient descent direction of PDR to approach the jammers until reaches the PDR valley point. Then, the algorithm moved toward the jammers using power adaptation technique based on RJSS gradient ascent process until it reached the RJSS peak point. Finally, through applying cluster analysis on the neighbour nodes which fail to communicate with RJSS peak points, the number and positions of the jam-mers can be estimated. Experimental results have verified that the proposed algorithm can improve the accuracy of local-ization compared with existed localization algorithms. Furthermore, the performance of the proposed algorithm is promi-nent when the distance of jammers accords with constraint condition.     

A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

One of the famous approaches to decision making is named as multicriteria decision making (MCDM). In order to solve the MCDM issues, a better way is provided by the fuzzy logic. Expendability, cost, maintenance, availability of software, and performance characteristics are such problems considered by the decision. The precise estimation of the pertinent data is one of the vital phases in DM systems. This paper presents a fuzzy MCDM‐based cluster head (CH) selection and hybrid routing protocol to solve the most common issues. In this research article, the generalized intuitionistic fuzzy soft set (GIFSS) approach is utilized to select the optimal CH, and hybrid shark smell optimization (SSO), and a genetic algorithm (GA) is introduced for the effective routing. Initially, the wireless sensor network (WSN) system and energy models are designed, and then the nodes are grouped into several clusters. Next, based on the GIFSS, the CH nodes are selected, and finally, an effective routing is placed based on the hybrid optimizations. The implementation is performed on the NS2 platform, and the performances are evaluated by packet delivery ratio (PDR), delay, packet loss ratio (PLR), network lifetime, bit error rate (BER), energy consumption, throughput, and jitter. The existing approaches named energy centers examining using particle swarm optimization (EC‐PSO), variable dimension‐based PSO (VD‐PSO), energy‐efficient PSO‐based CH selection (PSO‐ECHS), low‐energy adaptive clustering hierarchy‐sugeno fuzzy (LEACH‐SF), SSO, and GA are compared with the proposed strategy. According to the implemented outcomes, it displays the proposed strategy and gives improved outcomes than the others.     

A Three Layered Peer-to-Peer Energy Efficient Protocol for Reliable and Secure Data Transmission in EAACK MANETs

The wireless body sensor network (WBSN) an extensive of WSN is in charge for the detection of patient’s health concerned data. This monitored health data are essential to be routed to the sink (base station) in an effective way by approaching the routing technique. Routing of tremendous sensed data to the base station minimizes the life time of the network due to heavy traffic occurrence. The major concern of this work is to increase the lifespan of the network which is considered as a serious problem in the wireless network functionalities. In order to recover this issue, we propose an optimal trust aware cluster based routing technique in WBSN. The human body enforced for the detection of health status is assembled with sensor nodes. In this paper, three novel schemes namely, improved evolutionary particle swarm optimization (IEPSO), fuzzy based trust inference model, and self-adaptive greedy buffer allocation and scheduling algorithm (SGBAS) are proposed for the secured transmission of data. The sensor nodes are gathered to form a cluster and from the cluster, it is necessary to select the cluster head (CH) for the effective transmission of data to nearby nodes without accumulation. The CH is chosen by considering IEPSO algorithm. For securable routing, we exhibit fuzzy based trust inference model to select the trusted path. Finally, to reduce traffic occurrence in the network, we introduce SGBAS algorithm. Experimental results demonstrate that our proposed method attains better results when compared with conventional clustering protocols and in terms of some distinctive QoS determinant parameters.     

CH selection and compressive sensing-based data aggregation in WSN using hybrid Golden circle-inspired optimization

The arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS-based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network-long short term memory (CNN-LSTM) is employed for the data aggregation process. The proposed HGCIO-based CNN-LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.     

An energy‐efficient clustering approach for collaborative data forwarding in heterogeneous MANET

Mobile ad hoc network consists of a group of mobile nodes that can communicate with each other without any infrastructure. Clustering of the mobile nodes ensures efficient use of available bandwidth and high network throughput. Various clustering schemes are developed to improve the energy efficiency and lifetime of the network. However, there is an increase in the energy consumption with the increase in the number of clusters for forwarding data. This paper presents an energy‐efficient clustering approach for collaborative data forwarding in mobile ad hoc network. The cluster head (CH) is selected based on the processing capability of the nodes and link connection metrics. The CH receives the data from the server and forwards the data to the member nodes at a corresponding data rate of the nodes. Data offloading technique manages the data traffic in the network. The CH rejoining approach enables load balancing in the network. The proposed clustering approach achieves a significant reduction in the energy consumption and data traffic and improvement in the throughput rate through stable routing.     

An optimised fuzzy clustering for wireless sensor networks

Minimising energy consumption has always been an issue of crucial importance in sensor networks. Most of the energy is consumed in data transmission from sensor nodes to the base station due to the long distance of nodes from the base station. In the recent past, a number of researchers have proposed that clustering is an efficient way of reducing the energy consumption during data transmission and enhancing the lifetime of wireless sensor networks. Many algorithms have been already proposed for cluster head selection. In this work, we analyse and compare the lifetime of the network with three different fuzzy-based approaches of cluster head selection. The three strong parameters which play an important role in lifetime enhancement – energy, centrality and node density – are considered for cluster head selection in our proposed fuzzy approaches. In the first approach, energy and centrality are considered simultaneously in a fuzzy system to select the cluster heads. In the second approach, energy and node density have been taken in a fuzzy system to select the cluster heads. In the third approach, node density and centrality are considered simultaneously by a fuzzy system to select the cluster heads. Simulation results of these fuzzy logic-based approaches show that all the three approaches are superior to the Low-Energy Adaptive Clustering Hierarchy (LEACH). Simulation results also show that the energy-centrality-based fuzzy clustering scheme gives best performance among all the three fuzzy-based algorithms and it enhances the lifetime of wireless sensor networks by a significant amount.     

SeC‐SDWSN: Secure cluster‐based SDWSN environment for QoS guaranteed routing in three‐tier architecture

Software defined wireless sensor network (SDWSN) is a recent evolution in networking that improves network performance and scalability. However, Quality of Service (QoS) and security are major the issues in SDWSN due to inefficient route selection (traffic load minimization algorithm) and insecure cryptography scheme (homomorphic algorithm). This paper proposes novel three‐tier architecture for secure cluster‐based SDWSN (SeC‐SDWSN) environment to ensure QoS and security for WSN using SDN. In the first tier, sensor nodes are segregated into multiple clusters by secure hash tree‐based clustering (SHTC) algorithm. Within each secure cluster, data transmission is performed through optimal route selected by adaptive spider monkey optimization (ASMO) algorithm in which two new fitness factors (F₁, F₂ ) are formulated by multiple QoS metrics. For data security, parallel advanced encryption standard with cipher block chaining (PAES‐CBC) algorithm is proposed. Aggregated ciphertext is transmitted to optimal switch in the second tier by using fuzzy weighted technique for order preference by similarity to ideal solution (FW‐TOPSIS) algorithm according to selection criteria. Switches forward the data to sink node based on flow rules deployed by SDN controllers in the third tier. SDN controllers provide global view on the entire network and deploy flow rules on switches in accordance to network status and security level. Extensive simulation in ns‐3 shows that the proposed three‐tier architecture achieves 5% throughput improvement, 7.8% PDR improvement, and 16% energy consumption improvement.     

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Due to inherent issue of energy limitation in sensor nodes, the energy conservation is the primary concern for large‐scale wireless sensor networks. Cluster‐based routing has been found to be an effective mechanism to reduce the energy consumption of sensor nodes. In clustered wireless sensor networks, the network is divided into a set of clusters; each cluster has a coordinator, called cluster head (CH). Each node of a cluster transmits its collected information to its CH that in turn aggregates the received information and sends it to the base station directly or via other CHs. In multihop communication, the CHs closer to the base station are burdened with high relay load; as a result, their energy depletes much faster as compared with other CHs. This problem is termed as the hot spot problem. In this paper, a distributed fuzzy logic‐based unequal clustering approach and routing algorithm (DFCR) is proposed to solve this problem. Based on the cluster design, a multihop routing algorithm is also proposed, which is both energy efficient and energy balancing. The simulation results reinforce the efficiency of the proposed DFCR algorithm over the state‐of‐the‐art algorithms, ie, energy‐aware fuzzy approach to unequal clustering, energy‐aware distributed clustering, and energy‐aware routing algorithm, in terms of different performance parameters like energy efficiency and network lifetime.     

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

Wireless sensor network (WSN) comprises automatic sensors that are dispersed into a huge region. WSN is constructed from huge sensors, which is allocated to a particular task and the majority of task involves reporting and monitoring. However, as the network can be extended to several sensor nodes, there is a high chance of collision. Thus, this paper devises a novel technique for performing both collision detection and mitigation in WSN. Initially, the simulation of WSN is performed, and then the selection of cluster head is done using fractional artificial bee colony (FABC). Here, the network-based parameter is extracted that involves received signal strength index (RSSI), priority level, delivery rate, and energy consumed. The deep recurrent neural network (DRNN) is adapted for collision detection. Here, the training of DRNN is done using lion crow search optimizer (LCSO). After collision detection, the collision mitigation is performed with a pre-scheduling algorithm, namely dolphin ant lion optimizer (Dolphin ALO). Here, fitness is considered for collision mitigation that includes energy, sleep index (SI), delivery rate, priority level, E-waste, and E-save. The proposed method outperformed with the smallest energy consumption of 0.185, highest throughput of 0.815, highest packet delivery ratio (PDR) of 0.815, and highest collision detection rate of 0.930.     

Trust-aware FuzzyClus-Fuzzy NB: intrusion detection scheme based on fuzzy clustering and Bayesian rule

The dynamic nature of the nodes on the mobile ad hoc network (MANET) imposes security issues in the network and most of the Intrusion detection methods concentrated on the energy dissipation and obtained better results, whereas the trust remained a hectic factor. This paper proposes a trust-aware scheme to detect the intrusion in the MANET. The proposed Trust-aware fuzzy clustering and fuzzy Naive Bayes (trust-aware FuzzyClus-Fuzzy NB) method of detecting the intrusion is found to be effective. The fuzzy clustering concept determines the cluster-head to form the clusters. The proposed BDE-based trust factors along with the direct trust, indirect trust, and the recent trust, hold the information of the nodes and the fuzzy Naive Bayes determine the intrusion in the nodes using the node trust table. The simulation results convey the effectiveness of the proposed method and the proposed method is analyzed based on the metrics, such as delay, energy, detection rate, and throughput. The delay is in minimum at a rate of 0.00434, with low energy dissipation of 9.933, high detection rate of 0.623, and greater throughput of 0.642.

     

ECRP: an energy-aware cluster-based routing protocol for wireless sensor networks

Energy conservation is the main issue in wireless sensor networks. Many existing clustering protocols have been proposed to balance the energy consumption and maximize the battery lifetime of sensor nodes. However, these protocols suffer from the excessive overhead due to repetitive clustering resulting in high-energy consumption. In this paper, we propose energy-aware cluster-based routing protocol (ECRP) in which not only the cluster head (CH) role rotates based on energy around all cluster members until the end of network functioning to avoid frequent re-clustering, but also it can adapt the network topology change. Further, ECRP introduces a multi-hop routing algorithm so that the energy consumption is minimized and balanced. As well, a fault-tolerant mechanism is proposed to cope up with the failure of CHs and relay nodes. We perform extensive simulations on the proposed protocol using different network scenarios. The simulation results demonstrate the superiority of ECRP compared with recent and relevant existing protocols in terms of main performance metrics.

     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.     

无线传感器网络RSSI测距方法与精度分析

基于RSSI的测距技术是一项低成本的距离测量技术.分析了接收信号强度指示器(RSSI)多种测距模型,结合采用IEEE802.15.4协议的CC2430芯片,设计了测距实验,获取了多组数据,通过对实验数据的分析,提出结合信标节点确定参数、高斯拟合确定测量值的RSSI测距处理方法.实验证明,该方法能提高RSSI测距的抗干扰能力,20 m内节点间的测距精度能达到1.5 m以下.     

An efficient approach for the detection of link failures in WBAN system for health care applications

Wireless body area network (WBAN) plays an important role in patient health care. The performance of this WBAN system is affected by link failures due to the presence of malicious sensor nodes. Hence, the detection and mitigation of this link failure is important for improving the efficiency of the WBAN system. This paper proposes a methodology for link failure detection using weight metric approach. The performance of the proposed methodology is analyzed in terms of packet delivery ratio (PDR), link failure detection latency, and link failure detection rate.     

DUCR: Distributed unequal cluster‐based routing algorithm for heterogeneous wireless sensor networks

Designing energy efficient communication protocols for wireless sensor networks (WSNs) to conserve the sensors' energy is one of the prime concerns. Clustering in WSNs significantly reduces the energy consumption in which the nodes are organized in clusters, each having a cluster head (CH). The CHs collect data from their cluster members and transmit it to the base station via a single or multihop communication. The main issue in such mechanism is how to associate the nodes to CHs and how to route the data of CHs so that the overall load on CHs are balanced. Since the sensor nodes operate autonomously, the methods designed for WSNs should be of distributed nature, i.e., each node should run it using its local information only. Considering these issues, we propose a distributed multiobjective‐based clustering method to assign a sensor node to appropriate CH so that the load is balanced. We also propose an energy‐efficient routing algorithm to balance the relay load among the CHs. In case any CH dies, we propose a recovery strategy for its cluster members. All our proposed methods are completely distributed in nature. Simulation results demonstrate the efficiency of the proposed algorithm in terms of energy consumption and hence prolonging the network lifetime. We compare the performance of the proposed algorithm with some existing algorithms in terms of number of alive nodes, network lifetime, energy efficiency, and energy population.     

A novel approach for mitigating route request flooding attack in MANET

Mobile ad-hoc network (MANET) is a temporary network in which the main requirement for establishing the communication path among nodes is that the nodes should be cooperative. However, in the presence of malicious node, the MANET’s routing protocol such as AODV is vulnerable to different types of flooding attacks. The flooding attack can be continuous or selective. In the available literature, although many researchers have analyzed the network under continuous flooding attack but they have not focussed on selective flooding attack in which an attacker can sometimes behave as a normal and sometimes behave as a malicious. Most of the existing schemes use constant threshold value which lead to a false positive problem in the network. In order to address this issue, a new mechanism called as Mitigating Flooding Attack Mechanism is proposed which is based on a dynamic threshold value and consists of three phases. It makes use of several special nodes called as Flooding-Intrusion Detection System (F-IDS) that are deployed in MANETs in order to detect and prevent flooding attack. The F-IDS nodes are set in promiscuous in order to monitor the behaviour of the node. The simulation results show that the proposed mechanism improves network performance metrics in terms of PDR, throughput and reduces the routing overhead as well as normalized routing load.     

A novel weight based clustering algorithm for routing in MANET

Cluster based routing in Mobile AdHoc Networks are considered one of the convenient method of routing. Existence of Cluster Head (CH) in a group of nodes for data forwarding improves the performance of routing in terms of routing overhead and power consumption. However, due to the movement of CH and frequent change in cluster members, cluster reformation is required and increases cluster formation overhead. The stability of the cluster highly dependent of stability of the CH and hence during CH selection special care should be taken so that the cluster head survives for longer time. In this paper a method of cluster formation is proposed which will take into account two most vital factor node degree and bandwidth requirement for construction of the cluster and selection of the cluster head. Further, when two clusters come closer to each other they merge and form a single cluster. In such case out of two CHs one has to withdraw the role and other will take over. A new mechanism of merging two clusters is also proposed in the paper. We call this method as an Improved Cluster Maintenance Scheme and primarily focused on minimizing CH changing process in order to enhance the performance. The stated method makes cluster more stable, and minimizes packet loss. The proposed algorithm is simulated in ns-2 and compared with Least Cluster head Change (LCC) and CBRP. Our algorithm shows better behavior in terms of number of clusterhead changes or number of cluster member changes.     

Network energy optimization and intelligent routing in WSN applicable for IoT using self-adaptive coyote optimization algorithm

The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self-adaptive meta-heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self-adaptive coyote optimization algorithm (SA-COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA-COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA-COA. From the simulation findings, the best value of the designed SA-COA-LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self-adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C-ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.     

Novel localization algorithm for wireless sensor network based on intelligent water drops

High localization rigor and low development expense are the keys and pivotal issues in operation and management of wireless sensor network. This paper proposes a neoteric and high efficiency algorithm which is based on new optimization method for locating nodes in an outdoor environment. This new optimization method is non-linear optimization method and is called intelligent water drops (IWDs). It is proposed that the objective function which need to be optimized by using IWDs is the mean squared range error of all neighboring anchor nodes. This paper affirms that received signal strength indicator (RSSI) is used to determine the interior distances between WSNs nodes. IWDs is an elevated performance stochastic global optimization tool that affirms the minimization of objective function, without being trapped into local optima. The proposed algorithm based on IWDs is more attractive to promote elevated localization precision because of a special features that is an easy implementation of IWDs, in addition to non cost of RSSI. Simulation results have approved that the proposed algorithm able to perform better than that of other algorithms based on optimization techniques such as ant colony, genetic algorithm, and particle swarm optimization. This is distinctly appear in some of the evaluation metrics such as localization accuracy and localization rate.