Energy-aware Clusters Research Articles

An efficient trust‐based secure energy‐aware clustering to mitigate trust distortion attack in mobile ad‐hoc network

SummaryTrust‐aware clustering plays a vital role in addressing the security issues faced by mobile ad‐hoc networks (MANETs). The trust worthiness of each participated node is to be estimated for ensuring the secure communications in MANETs. In this article, we propose a new trust‐based secure energy‐aware clustering (TSEAC) model to mitigate the malicious nodes from the network and form secure energy aware clusters with a cluster head which is more stable and trustworthy. Moreover, two novel algorithms namely energy‐efficient trust‐aware secure clustering algorithm and filtering untrustworthy recommendation (FUR) algorithm are proposed in this work. Here, the trust value of a node is measured by both direct trust estimation and indirect trust estimation methods using the Beta distribution technique. The trust value of the node is estimated in terms of the behavior of the node. The role of the FUR algorithm is to enhance the clustering process by mitigating the trust‐distortion attack. Thus, from the simulation results it is observed that the proposed work, TSEAC outperforms in improving the lifetime of the network by 38% than the other existing work such as CBTRP, AOTDV, and CBRP. Furthermore, TSEAC shows an improvement of 20% to 24% when compared to CBTRP, 27% to 31% in contrast to AOTDV and 35% to 42% superior to CBRP in terms of packet delivery ratio. Similarly, TSEAC shows 22% to 26%, 28% to 33%, and 34% to 38% better throughput in contrast to CBTRP, AOTDV, and CBRP, respectively.

An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks

This article proposes an Energy Efficient Dynamic Scheduling Hybrid MAC Protocol (EDS-MAC) for Traffic Adaptive Wireless Sensor Networks. The proposed approach consists of two stages. (i) cluster formation, and (ii) data transmission. In the first stage, a variable step size firefly algorithm (VSSFFA) is proposed for generating energy-aware clusters by optimal selection of cluster heads. The VSSFFA reduces the cost of locating optimal position for the head nodes in a cluster. Additionally, we proposed the VSSFFA-based approach within the cluster rather than base station, which makes it a semi-distributed method. The selection criteria of the objective function are based on the residual energy, intra-cluster distance, node degree and head count of the probable cluster heads. Data communication is done using data transmission stage, which reduces the latency, delay, and control overhead. Compared to IH-MAC, EDS-MAC reduces the size of control packet by introducing short node address (1 byte instead of 8 bytes in IH-MAC). By enabling lower duty cycle operation and by performing efficient clustering, it reduces overhearing and reduces the overall latency experienced by the sensor network. Hence, introduction of EDS-MAC protocol in WSN can help the users with prolonged network life span and minimize the energy and overhead utilization of the overall network. The efficiency of the proposed MAC protocol has been analyzed using the NS-2 simulator. WSN employing EDS-MAC is compared with IH-MAC and is found to experience 50% reduction in overall energy consumption and 72% reduction in overall delay experienced by the network.

Energy Aware Heuristic approach for Cluster Head Selection in Wireless Sensor Networks

Wireless sensor networks idea is envisioned and defined as self-deployed, error prone, long living inexpensive communication devices that are densely deployed to collect data from physical space. Sensor nodes communicate with each other to detect events depending on the application, to collect and process data, and to transmit the sensed information to the base station by hopping the data from node to node. Major challenge in wireless network is energy consumption, since the sensor devices are deployed on unattended wide areas and replacing their batteries is not very feasible. Therefore, designing energy-aware algorithms becomes a major challenge for extending the lifetime of sensors. The lifetime of the network can be maximized by selecting the most optimal cluster head. In this paper, a heuristic decision making approach is proposed for producing energy-aware clusters with optimal selection of cluster head. This helps to improve the efficiency and accuracy and overcome the drawbacks like getting trapped at a local extreme in the optimization process.

Energy Aware Clustering Scheme in Wireless Sensor Network Using Neuro-Fuzzy Approach

Nowadays sensor plays an important role in the day today life. People uses wireless technology along with sensor for monitoring home held devices, security alerts, natural disasters alert, building supervision, industrial quality management, etc. Wireless Sensor Network (WSN) consists of thousands of economical and feasible disposable sensors, deployed in the environment to sense parameters related to the surroundings such as temperature, moisture level, pressure etc., Number of sensor nodes are connected in these networks for communication. Each nodes are self-organized, having the capacity of sense, process, and aggregate data. Energy utilization in WSN is major issue in networks for improving network lifetime. Conventional clustering schemes are created with static cluster heads that die past than the normal nodes that degrade the network performance in routing. It is very vital area to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network life time. In this paper, a Energy Aware Clustering using Neuro-fuzzy approach (EACNF) is proposed to form finest and energy aware clusters. The proposed scheme consists of fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. EACNF used neural network that provide effective training set related to energy and density of all nodes to estimate the expected energy for Uncertain cluster heads. Sensor nodes with higher energy are trained with various location of base station to select energy aware cluster heads. Fuzzy if–then mapping rule is used in fuzzy logic part that inputs to form clusters and cluster heads. EACNF is designed for WSN that handling Trust factor for security to the network. EACNF used three metric such as transmission range, residual energy and Trust factor for improving network life time. The proposed scheme EACNF is compared with related clustering schemes namely Cluster-Head Election Mechanism using Fuzzy Logic and Energy-Aware Fuzzy Unequal Clustering. The experiment results show that EACNF performs better than the other related schemes.

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach.

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

An adaptive clustering algorithm based on improved particle swarm optimisation in wireless sensor networks

In wireless sensor networks WSNs, the lifetime of networks is a critical issue. In order to expand the lifespan of WSNs, an efficient technique is need to reduce energy consumption of WSNs. In this paper, an improved nonlinear dynamic adaptive particle swarm optimisation NDAPSO is applied for producing energy-aware clusters with selection of optimal cluster heads. The fitness function used for evaluating the particles consider four features, such as energy consumption, intra-cluster distance, the proportion of cluster heads energy and degree of energy consumption equilibrium. And a new cluster head competition mechanism is introduced in this paper. Then in the process of NDAPSO, the optimal cluster heads are selected by comparing quality of particles, which is evaluated by the fitness function. The simulation results show that this algorithm effectively reduces the communication energy consumption and improves the networks lifespan.

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

AbstractThe wireless sensor networks have long been an attractive field to the researchers and scientists for its ease in deployment and maintenance. In this research, we focus on the maximization of network lifetime which has become a critical issue in sensor networks. Clustered organization of nodes with aggregation of data at the cluster head becomes one of the significant means to extend life expectancy of the network. This paper proposes Particle Swarm Optimization (PSO) approach for generating energy-aware clusters by optimal selection of cluster heads. The PSO eventually reduces the cost of locating optimal position for the head nodes in a cluster. In addition, we have implemented the PSO-based approach within the cluster rather than base station, which makes it a semi-distributed method. The selection criteria of the objective function are based on the residual energy, intra-cluster distance, node degree and head count of the probable cluster heads. Furthermore, influence of the expected number of packet retransmissions along the estimated path towards the cluster head is also reflected in our proposed energy consumption model. The performance evaluation of our proposed technique is carried out with respect to the well-known cluster-based sensor network protocols, LEACH-C and PSO-C respectively. Finally, the simulation clarifies the effectiveness of our proposed work over its comparatives in terms of network lifetime, average packet transmissions, cluster head selection rounds supported by PSO and average energy consumption.

Energy-aware Cluster Head Selection Using Particle Swarm Optimization and Analysis of Packet Retransmissions in WSN

In sensor networks the maximization of network lifetime is a critical issue because the sensor nodes are characterized by restricted and non-replenishable energy supply. Therefore, proper organization of nodes (clustering) becomes one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. In this paper, Particle Swarm Optimization (PSO) approach is applied for producing energy-aware clusters with optimal selection of cluster head. The PSO ultimately reduces the cost of locating optimal position for the cluster head nodes. The PSO implementation is performed within the cluster rather than base station, which makes it a semi-distributed approach. The selection criteria of the objective function are based on the residual energy, minimum average distance from the member nodes and head count of the probable head nodes. Moreover, the effect of expected number of packet retransmissions is also demonstrated in our proposed energy model. The performance evaluation of our proposed technique is compared with the cluster-based sensor network protocols, LEACH-C and PSO-C respectively. The simulation results clearly reveal the effectiveness of our proposed work over its comparatives.