Multi-stage secure clusterhead selection using discrete rule-set against unknown attacks in wireless sensor network

Security is the rising concern of the wireless network as there are various forms of reonfigurable network that is arised from it. Wireless sensor network (WSN) is one such example that is found to be an integral part of cyber-physical system in upcoming times. After reviewing the existing system, it can be seen that there are less dominant and robust solutions towards mitigating the threats of upcoming applications of WSN. Therefore, this paper introduces a simple and cost-effective modelling of a security system that offers security by ensuring secure selection of clusterhead during the data aggregation process in WSN. The proposed system also makes construct a rule-set in order to learn the nature of the communication iin order to have a discrete knowledge about the intensity of adversaries. With an aid of simulation-based approach over MEMSIC nodes, the proposed system was proven to offer reduced energy consumption with good data delivery performance in contrast to existing approach

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency

Localization method for low-power wireless sensor networks

Context awareness is an important issue in ambient intelligence to anticipate the desire of the user and, in consequence, to adapt the system. In context awareness, localization is very important to enable a responsive environment for the users. Focusing on this issue, this paper presents a localization system based on the use of Wireless Sensor Networks devices. In contrast to a traditional RFID, these devices offer the possibility of a collaborative sensing and processing of environmental information. The proposed system is a range-free localization algorithm that uses fuzzy inference to process the RSSI measurement and to estimate the position of mobile devices. The main goal of the algorithm is to reduce the power consumption and the cost of the devices, especially for the mobiles ones, maintaining the accuracy of the inferred position

On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network

In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, Particle Swarm Optimisation (PSO) has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on Ant-Colony Optimisation (ACO) has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab Simulations

A Centralized Clustering approach for Wireless Sensor Networks

Wireless Sensor Networks consists of hundreds and thousands of micro sensor nodes that monitor a remote environment by data aggregation from individual nodes and transmitting this data to the base station for further processing and inference. The energy of the battery operated nodes is the most vulnerable resource of the WSN, which is depleted at a high rate when information is transmitted, because transmission energy is dependent on the distance of transmission. In a clustering approach, the Cluster Head node looses a significant amount of energy during transmission to base station. So the selection of Cluster Head is very critical. An effective selection protocol should choose Cluster Heads based on the geographical location of node and its remaining energy. In this work a centralized protocol for Cluster Head selection in WSN is discussed, which is run at the base station, thus reducing the nodes' energy consumption and increasing their life-time. The primary idea is implemented using a fuzzy-logic based selection of Cluster Head from among the nodes of network, which is concluded depending on two parameters, the current energy of the node and the distance of the node from the base station. The protocol is named LEACH-C(ED)-Centralized LEACH based on Energy and Distance, and is run periodically at the base station where a new set of cluster heads are selected at every round, thus distributing the energy load in the network and increasing the network lifetime. The simulation results show that the proposed approach is more effective than the existing LEACH-Centralized protocol

Modern Clustering Techniques in Wireless Sensor Networks

Wireless sensor networks (WSNs) are employed in various applications from healthcare to military. Due to their limited, tiny power sources, energy becomes the most precious resource for sensor nodes in such networks. To optimize the usage of energy resources, researchers have proposed several ideas from diversified angles. Clustering of nodes plays an important role in conserving energy of WSNs. Clustering approaches focus on resolving the conflicts arising in effective data transmission. In this chapter, we have outlined a few modern energy-efficient clustering approaches to improve the lifetime of WSNs. The proposed clustering methods are: (i) fuzzy-logic-based cluster head election, (ii) efficient sleep duty cycle for sensor nodes, (iii) hierarchical clustering, and (iv) estimated energy harvesting. Classical clustering approaches such as low energy adaptive clustering hierarchy (LEACH) and selected contemporary clustering methods are considered for comparing the performance of proposed approaches. The proposed modern clustering approaches exhibit better lifetime compared to the selected benchmarked protocols

Fuzzy logic based cluster head election led energy efficiency in history assisted cognitive radio networks

The performance and the network lifetime of cooperative spectrum sensing (CSS) infrastructure-based cognitive radio (CR) networks are hugely affected by the energy consumption of the power-constrained CR nodes during spectrum sensing, followed by data transmission and reception. To overcome this issue and improve the network lifetime, clustering mechanisms with several nodes inside a single cluster can be employed. It is usually the cluster head (CH) in every cluster that is responsible for aggregating the data collected from individual CR nodes before it is being forwarded to the base station (BS). In this article, an energy-efficient fuzzy logic-based clustering (EEFC) algorithm is proposed, which uses a novel set of fuzzy input parameters to elect the most suitable node as CH. Unlike most of the other probabilistic as well as fuzzy logic-based clustering algorithms, EEFC increments the fuzzy input parameters from three to four to obtain improved solutions employing the Mamdani method for fuzzification and the Centroid method for defuzzification. It ensures that the best candidate is selected for the CH role by obtaining the crisp value from the fuzzy logic rule-based system. While compared to other well-known clustering algorithms such as low-energy adaptive clustering hierarchy (LEACH), CH election using fuzzy logic (CHEF), energy-aware unequal clustering using fuzzy logic (EAUCF), and fuzzy logic-based energy-efficient clustering hierarchy (FLECH), our proposed EEFC algorithm demonstrates significantly enhanced network lifetime where the time taken for first node dead (FND) in the network is improved. Moreover, EEFC is implemented in the existing history-assisted energy efficient infrastructure CR network to analyze and demonstrate the overall augmented energy efficiency of the system

Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio