Jamming detection approach based on fuzzy assisted multicriteria decision‐making system for wireless sensor networks

The jamming detection approach based on fuzzy assisted multicriteria decision‐making system (JDA) is proposed to detect the presence of jamming in downstream communication for Cluster based Wireless Sensor Network (CWSN). The proposed approach is deployed in cluster head (CH). The JDA functions in two aspects: First, the CH periodically measures the jamming detection metrics namely Packet Delivery Ratio (PDR) and Received Signal Strength Indicator (RSSI) of every node in the cluster to determine the behavior of the sensor nodes. In order to determine the behavior of members in the cluster, the CH compares the measured PDR with the PDR threshold. If the measured PDR is lesser than the PDR threshold, then CH applies the TOPSIS method on the PDR and RSSI metrics to determine the presence of jamming. These metrics are considered as the criteria and the nodes or the members are considered to be the alternatives. Next, the fuzzy logic is applied on the results obtained from the TOPSIS method to optimize the jamming detection metrics and identify the presence of jamming accurately. The proposed jamming detection approach detects well and arrives at 99.6% jamming detection rate as shown in simulation.     

Cluster‐based mobility management algorithms for wireless mesh networks

Wireless mesh networks (WMNs) have been the recent advancements and attracting more academicians and industrialists for their seamless connectivity to the internet. Radio resource is one among the prime resources in wireless networks, which is expected to use in an efficient way especially when the mobile nodes are on move. However, providing guaranteed quality of service to the mobile nodes in the network is a challenging issue. To accomplish this, we propose 2 clustering algorithms, namely, static clustering algorithm for WMNs and dynamic clustering algorithm for WMNs. In these algorithms, we propose a new weight‐based cluster head and cluster member selection process for the formation of clusters. The weight of the nodes in WMN is computed considering the parameters include the bandwidth of the node, the degree of node connectivity, and node cooperation factor. Further, we also propose enhanced quality of service enabled routing protocol for WMNs considering the delay, bandwidth, hopcount, and expected transmission count are the routing metrics. The performance of the proposed clustering algorithms and routing protocol are analyzed, and results show high throughput, high packet delivery ratio, and low communication cost compared with the existing baseline mobility management algorithms and routing protocols.     

Betweenness centrality based connectivity aware routing algorithm for prolonging network lifetime in wireless sensor networks

Network lifetime is the key design parameter for wireless sensor network protocols. In recent years, based on energy efficient routing techniques numerous methods have been proposed for enhancing network lifetime. These methods have mainly considered residual energy, number of hops and communication cost as route selection metrics. This paper introduces a method for further improvement in the network lifetime by considering network connectivity along with energy efficiency for the selection of data transmission routes. The network lifetime is enhanced by preserving highly connected nodes at initial rounds of data communication to ensure network connectivity during later rounds. Bassed on the above mentioned concept, a connectivity aware routing algorithm: CARA has been proposed. In the proposed algorithm, connectivity factor of a node is calculated on the basis of Betweenness centrality of a node and energy efficient routes are found by using fuzzy logic and ant colony optimization. The simulation results show that the proposed algorithm CARA performs better than other related state-of-the-art energy efficient routing algorithms viz. FML, EEABR and FACOR in terms of network lifetime, connectivity, energy dissipation, load balancing and packet delivery ratio.     

A cluster‐based fault‐tolerant routing protocol for wireless sensor networks

Energy conservation and fault tolerance are two critical issues in the deployment of wireless sensor networks (WSNs). Many cluster‐based fault‐tolerant routing protocols have been proposed for energy conservation and network lifetime maximization in WSNs. However, these protocols suffer from high frequency of re‐clustering as well as extra energy consumption to tolerate failures and consider only some very normal parameters to form clusters without any verification of the energy sufficiency for data routing. Therefore, this paper proposes a cluster‐based fault‐tolerant routing protocol referred as CFTR. This protocol allows higher energy nodes to become Cluster Heads (CHs) and operate multiple rounds to diminish the frequency of re‐clustering. Additionally, for the sake to get better energy efficiency and balancing, we introduce a cost function that considers during cluster formation energy cost from sensor node to CH, energy cost from CH to sink, and another significant parameter, namely, number of cluster members in previous round. Further, the proposed CFTR takes care of nodes, which have no CH in their communication range. Also, it introduces a routing algorithm in which the decision of next hop CH selection is based on a cost function conceived to select routes with sufficient energy for data transfer and distribute uniformly the overall data‐relaying load among the CHs. As well, a low‐overhead algorithm to tolerate the sudden failure of CHs is proposed. We perform extensive simulations on CFTR and compare their results with those of two recent existing protocols to demonstrate its superiority in terms of different metrics.     

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.     

Reduce‐complexity fuzzy‐inference‐based iterative multiuser detection for wireless communication systems

In recent years, combining multiuser detection and intelligence computer scheme have received considerable attention. In this paper, adaptive fuzzy‐inference multistage matrix wiener filtering (FI‐MMWF) techniques, based on the minimum mean‐square error criterion, are proposed for ultra‐wideband (UWB) impulse radio communication systems. These FI‐MMWF‐based algorithms employ a time‐varying fuzzy‐inference‐controlled filter stage. Consequently, the proposed approaches accomplish a substantial saving in complexity without trading off the system performance and dynamic‐tracking characteristic. In addition, the fuzzy‐logic‐controlled matrix conjugate gradient algorithm is adopted to reduce the system complexity without trading off the bit‐error‐rate (BER). Simulations are conducted to evaluate the convergence and tracking behavior of the proposed MMWF algorithm, and the BER of the time‐hopping‐UWB system in a realistic UWB channel is investigated. Copyright © 2011 John Wiley & Sons, Ltd.     

A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

The performance of underwater wireless sensor network gets affected by the working of a cluster in the network. The cluster head (CH) or cluster member (CM) fails because of energy depletion or hardware errors that increase delay and message overhead of the network. To recover the affected cluster, a technique is required to identify the failed CH or CM. We propose a fault detection and recovery technique (FDRT) for a cluster‐based network in this paper. Primarily, while selecting the CH, a backup cluster head (BCH) is selected using fuzzy logic technique based on parameters such as node density, residual energy, load, distance to sink, and link quality. Then, failure of CH, BCH, and CM is detected. If fault is detected at CH, then the BCH will start performing the task of failed CH. Simultaneously, when BCH failed, any other CM will be elected as BCH. If any of the CM appears to be nonperforming, then CH will detect the communication failure and request BCH to transfer the data from the failed CM to CH. The comparison of proposed FDRT is performed with existing FDRTs EDETA, RCH, and SDMCGC on the basis of packet drop, end‐to‐end delay, energy consumption, and delivery ratio of data packets. By simulation results, it is shown that FDRT for cluster‐based underwater wireless sensor network results in quicker detection of failures and recovery of the network along with the reduction in energy consumption, thereby increasing the lifespan of the network.     

SEPFL routing protocol based on fuzzy logic control to extend the lifetime and throughput of the wireless sensor network

In this paper a new protocol using fuzzy logic control has been proposed. The protocol is based on Stable Election Protocol (SEP). Fuzzy logic control based on three variables, distance of nodes form base station, density of nodes and the battery level of nodes along with the traditional threshold values used in SEP are used to enhance the process of cluster head election in the existing SEP protocol and improve the lifetime and throughput of the Wireless Sensor Network. The result of the simulation which has been done in MATLAB simulator indicates that Stable Election Protocol based on fuzzy logic is more energy efficient and improves the lifetime and throughput of the network by 73.2 and 68.54 % respectively comparing with the existing SEP protocol.     

A green cluster‐based routing scheme for large‐scale wireless sensor networks

In wireless sensor networks (WSNs), clustering has been shown to be an efficient technique to improve scalability and network lifetime. In clustered networks, clustering creates unequal load distribution among cluster heads (CHs) and cluster member (CM) nodes. As a result, the entire network is subject to premature death because of the deficient active nodes within the network. In this paper, we present clustering‐based routing algorithms that can balance out the trade‐off between load distribution and network lifetime “green cluster‐based routing scheme.” This paper proposes a new energy‐aware green cluster‐based routing algorithm to preventing premature death of large‐scale dense WSNs. To deal with the uncertainty present in network information, a fuzzy rule‐based node classification model is proposed for clustering. Its primary benefits are flexibility in selecting effective CHs, reliability in distributing CHs overload among the other nodes, and reducing communication overhead and cluster formation time in highly dense areas. In addition, we propose a routing scheme that balances the load among sensors. The proposed scheme is evaluated through simulations to compare our scheme with the existing algorithms available in the literature. The numerical results show the relevance and improved efficiency of our scheme.     

Energy Efficient QoS Aware Hierarchical KF-MAC Routing Protocol in Manet

The paper proposes an energy efficient quality of services (QoS) aware hierarchical KF-MAC routing protocol in mobile ad-hoc networks. The proposed KF-MAC (K-means cluster formation firefly cluster head selection based MAC routing) protocol reduces the concentration of QoS parameters when the node transmits data from source to destination. At first, K-means clustering technique is utilized for clustering the network into nodes. Then the clustered nodes are classified and optimized by the firefly optimization algorithm to find cluster heads for the clustered nodes. The transmission of data begins in the network nodes and TDMA based MAC routing does communication. The observation on KF-MAC protocol performs well for QoS parameters such as bandwidth, delay, bit error rate and jitter. The evaluation of proposed protocol based on a simulation study concludes that the proposed protocol provides a better result in contrast to the existing fuzzy based energy aware routing protocol and modified dynamic source routing protocol. With KF-MAC protocol, the collision free data transmission with low average energy consumption is achieved.     

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.     

Nelder‐Mead–based power optimization for secrecy enhancement in amplify‐and‐forward cooperative relay networks

Cooperative communication based on relaying nodes has been considered as a promising technique to increase the physical layer security (PLS) performance in wireless communications. In this paper, an optimal power allocation (OPA) scheme based on Nelder‐Mead (NM) algorithm is proposed for improving the secrecy rate of amplify‐and‐forward (AF) cooperative relay networks employing cooperative jamming (CJ) scheme. The proposed hybrid jamming scheme allows the source and selected relay to transmit the jamming signal along with the information to confound the eavesdropper. The path selection probability of ant colony optimization (ACO) algorithm is used for selecting the relay for transmission. The performance based on secrecy rate is evaluated for “n” trusted relays distributed dispersedly between the source and destination. Gradient‐based optimization and three‐dimensional exhaustive search methods are used as benchmark schemes for comparison of the proposed power optimization algorithm. The secrecy performance is also compared with conventional AF scheme and CJ scheme without power optimization (EPA). The impact of single and multiple relays on secrecy performance is also evaluated. Numerical results reveal that, compared with the gradient method and exhaustive search algorithm, the proposed power allocation strategy achieves optimal performance. Also, the derived OPA results show a significantly higher secrecy rate than the EPA strategy for both CJ and AF schemes.     

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.     

Square partition‐based node scheduling algorithm for wireless passive sensor networks

Wireless passive sensor networks play an important role in solving the energy limitation of nodes in the Internet of Things, and node scheduling is a significant method used to improve the energy utilization of nodes. In this work, an unused energy model based on analyzing the energy consumption characteristics of passive nodes is proposed because no unified model of passive sensor nodes is reported in previous studies. A rapid square partition clustering method is proposed according to the analysis of the relation between the sensing and communication radii of nodes, and the secondary grouping and node scheduling in each cluster are implemented to ensure the coverage rate of networks. Experimental results show that the state distribution of nodes in the proposed algorithm is favorable. The performance of the proposed algorithm is significantly affected by the P ratio between the working and charging powers of nodes. When the value of P is less than 100, the network coverage and connectivity rate are maintained at more than 95% and 90%, respectively, and are both higher than the existing algorithm.     

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

In an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.     

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.     

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.     

Trust‐based secure routing and the prevention of vampire attack in wireless ad hoc sensor network

The recent development in technologies is the driving force for development in sensors, especially in military applications. Due to the openness nature of the ad hoc sensor network, the system gets easily affected, which may lead to some packet drop, transmission delay, high network overhead, and more energy consumption. In this paper, the security during data transmission is provided to the network by using the proposed Secure Atom Search Routing (SASR) algorithm, which is adopted from the behavior of molecular dynamics. For global optimization problems, this algorithm provides an effective solution based on the constraint and interaction force of atoms. Moreover, the performance of SASR is improved by providing a proper balance between exploitation and exploration. Since the knowledge base processes all the data, the computational complexity is reduced and the lifetime of the network is increased. The simulation and performance are carried out for the proposed Knowledge and Intrusion Detection based Secure Atom Search Routing (KID‐SASR) protocol and is compared with the existing methods based on the metrics trust, delay, throughput, energy, packet delivery ratio, network lifetime, trust detection rate, and communication cost. The results obtained show improvement in the overall performance of the system.     

NEAR OPTIMAL CLUSTER-HEAD SELECTION FOR WIRELESS SENSOR NETWORKS

Clustering in wireless sensor networks is an effective way to save energy and reuse band- width. To our best knowledge, most of the clustering protocols proposed in literature are of a dynamic type, where cluster heads are selected in each period, followed by cluster formation. In this paper, a new static type clustering method called Hausdorff clustering, which is based on the location of sensor nodes as well as communication efficiency and network connectivity, is proposed. The cluster head, however, is rotated within the cluster by a fuzzy logic algorithm that optimizes the network lifetime. Simulation results show that this approach can significantly increase the lifetime of the sensor network.     

A CVD epitaxial deposition in a vertical barrel reactor: processmodeling using cluster-based fuzzy logic models

A chemical vapor deposition (CVD) epitaxial deposition process modeling using fuzzy logic models (FLM's) has been proposed. The process modeling algorithm consists of a cluster estimation method and backpropagation algorithm to construct a number of modeling structures from the training data. A decision rule based on the multiple correlation factor is used to obtain the optimum structure of the fuzzy model using the testing data. Upon the optimum structure being reached, the gradient-descent method is used to refer the parameters of the final fuzzy model using both training and testing data. The algorithm has been applied to a nonlinear function and a vertical chemical vapor deposition process. The results demonstrate the efficiency and effectiveness of the proposed fuzzy logic model in comparison with existing fuzzy logic models and artificial neural network models