Definition of an energy optimization protocol of a wireless sensor network

International audienceNowadays the WSN (Wireless Sensor Network) implemented in different domains such as environment, medical, military, etc. At the same time, the WSN has some limitations, among them and the major one is the energy resource and limitation in the battery's lifetime due to the fact of the small size of the sensor node. The clustering is the most efficient method for increasing the network's lifetime which most routing protocols based on it and LEACH (Low Energy Adaptive Clustering Hierarchy) are the first energy-efficient clustering hierarchical routing protocol. This clustering programs choose randomly the CH (Cluster Head) and just if the sensor node has a few energy. Thus in LEACH transmission, every CH sends data immediately to the BS (Base Station), so they consume more energy that decreases the network lifetime.In this paper, we propose a new approach based on LEACH protocol that purpose to extend the network's lifetime by balancing the energy of sensor nodes. Our model allows to chose CHs according to the current energy. Thus, data archives the BS by multi-hop method

An enhanced energy-efficient routing protocol for wireless sensor network

Recent few years, Wireless Sensor Network (WSN) has been an increasingly important technology that has been applied in almost all domains, even in complex environments where human activity is impossible. In WSN, various factors are impacted energy consumption, such as communication protocols, packet data transmission, and limited battery. So, the lifespan of the WSNs is limited. In this context, energy efficiency is the factor most attracted by many researchers. In this paper, we proposed a new improved LEACH routing protocol. This proposed protocol based on the current energy to select cluster-heads, and it uses a root cluster-head with more current energy and low distance to the sink to gather all data, then sends it to the sink. The simulation results in MATLAB confirmed that the proposed algorithm performed better than the conventional LEACH protocol, and increased the network lifetime in WSN

Multiple solutions based particle swarm optimization for cluster-head-selection in wireless-sensor-network

Wireless sensor network (WSN) has a significant role in wide range of scientific and industrial applications. In WSN, within the operation area of sensor nodes the nodes are randomly deployed. The constraint related to energy is considered as one of the major challenges for WSN, which may not only affect the sensor nodes efficiency but also influences the operational capabilities of the network. Therefore, numerous attempts of researches have been proposed to counter this energy problem in WSN. Hierarchical clustering approaches are popular techniques that offered the efficient consumption of the energy in WSN. In addition to this, it is understood that the optimum choice of sensor as cluster head can critically help to reduce the energy consumption of the sensor node. In recent years, metaheuristic optimization is used as a proposed technique for the optimal selection of cluster heads. Furthermore, it is noteworthy here that proposed techniques should be efficient enough to provide the optimal solution for the given problem. Therefore, in this regard, various attempts are made in the form of modified versions or new metaheuristic algorithms for optimization problems. The research in the paper offered a modified version of particle-swarm-optimization (PSO) for the optimal selection of sensor nodes as cluster heads. The performance of the suggested algorithm is experimented and compared with the renowned optimization techniques. The proposed approach produced better results in the form of residual energy, number of live nodes, sum of dead nodes, and convergence rate

A CLUSTERING OPTIMIZATION FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORK USING K-MEANS ALGORITHM

The limitation of sensors energy make energy efficiency still a priority issue in Wireless Sensor Network (WSN) technology. One effort that can be done to overcome this problem is to design the right data transmission path (or better known as routing). Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the most widely used cluster-based routing protocols because it is considered capable of minimizing the amount of energy consumption through the formation of clusters or groups of nodes. Unfortunately, this protocol will experience a significant decrease in energy as the amount of data transmission increases. This is partly due to the clustering process which is carried out randomly and causes an imbalance in the distribution of the number of nodes between clusters. This study proposed a method to optimize the clustering process in the LEACH protocol by integrating the K-Means algorithm, which is called LEACH-KMe. A simulation was conducted to determine the effectiveness of the proposed method by considering 4 main parameters, namely total energy consumption, number of alive nodes, number of dead nodes, and residual energy. The test results proved that the LEACH-KMe protocol provides better performance than the conventional LEACH protocol (more even distribution of nodes, less total energy consumption and number of dead nodes, as well as a larger number of alive nodes and residual energy)

Improved LEACH protocol for increasing the lifetime of WSNs

Recently, wireless sensor network (WSN) is taking a high place in several applications: military, industry, and environment. The importance of WSNs in current applications makes the WSNs the most developed technology at the research level and especially in the field of communication and computing. However, WSN’s performance deals with a number of challenges. Energy consumption is the most considerable for many researchers because nodes use energy to collect, treat, and send data, but they have restricted energy. For this reason, numerous efficient energy routing protocols have been developed to save the consumption of power. Low energy adaptive clustering hierarchy (LEACH) is considered as the most attractive one in WSNs. In the present document, we evaluate the LEACH approach effectiveness in the cluster-head (CH) choosing and in data transmission, then we propose an enhanced protocol. The proposed algorithm aims to improve energy consumption and prolong the lifetime of WSN through selecting CHs depending on the remaining power, balancing the number of nodes in clusters, determining abandoned nodes in order to send their data to the sink. Then CHs choose the optimal path to achieve the sink. Simulation results exhibit that the enhanced method can decrease the consumption of energy and prolong the life-cycle of the network

Optimization of Energy Efficient Advance Leach Protocol

In WSNs, the only source to save life for the node is the battery consumption. During communication with other area nodes or sensing activities consumes a lot of power energy in processing the data and transmitting the collected/selected data to the sink. In wireless sensor networks, energy conservation is directly to the network lifetime and energy plays an important role in the cluster head selection. A new threshold has been formulated for cluster head selection, which is based on remaining energy of the sensor node and the distance from the base station. Proposed approach selects the cluster head nearer to base station having maximum remaining energy than any other sensor node in multi-hop communication. The multi hop approach minimizing the inter cluster communication without effecting the data reliability

Residual Energy Based Cluster-head Selection in WSNs for IoT Application

Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%

Comprehensive Energy Efficient Algorithm for WSN

Wireless sensor networks has been widely used. Energy problem is one of the important problems influencing the complete application. Sensor nodes use batteries as power source and have quite limit lifetime. So, efficiency of energy management becomes a key requirement in wireless sensor network design. Based on particle swarm optimization and ant colony optimization, a comprehensive algorithm with weight analysis has been proposed in the paper. In the algorithm, optimization method would be firstly used to determine the nodes number; then, particle  swarm optimization would be used to divide the networks into some clusters; finally, ant colony optimization is used to require the best transmission path and select the cluster head. The simulation results show that the new algorithm has higher energy efficiency and balanced energy consumption. It can extend the network lifetime

An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches

Wireless sensor network (WSN) is widely acceptable communication network where human-intervention is less. Another prominent factors are cheap in cost and covers huge area of field for communication. WSN as name suggests sensor nodes are present which communicate to the neighboring node to form a network. These nodes are communicate via radio signals and equipped with battery which is one of most challenge in these networks. The battery consumption is depend on weather where sensors are deployed, routing protocols etc. To reduce the battery at routing level various quality of services (QoS) parameters are available to measure the performance of the network. To overcome this problem, many routing protocol has been proposed. In this paper, we considered two energy efficient protocols i.e. LEACH and Sub-cluster LEACH protocols. For provision of better performance of network Levenberg-Marquardt neural network (LMNN) and Moth-Flame optimisation both are implemented one by one. QoS parameters considered to measure the performance are energy efficiency, end-to-end delay, Throughput and Packet delivery ratio (PDR). After implementation, simulation results show that Sub-cluster LEACH with MFO is outperforms among other algorithms.Along with this, second part of paper considered to anomaly detection based on machine learning algorithms such as SVM, KNN and LR. NSLKDD dataset is considered and than proposed the anomaly detection method.Simulation results shows that proposed method with SVM provide better results among others