In recent years using wireless sensor networks (WSNs) in applications, such as disaster management and security surveillance have been increased. A lot of sensors in these applications are expected to be remotely deployed in unattended environments autonomously. To support scalability, nodes are often grouped into disjointed and mostly non-overlapping Clusters. Every Cluster has a leader that is known as a Cluster-Head (CH). The CH may be selected by the sensors in the network or pre-assigned by the network designer. These networks require effective communication protocols to be energy efficient and increase network quality. In this paper, a self-organization routing protocol for wireless sensor networks is presented by using hierarchical protocols and considering the position of CHs regarding to each other which is called "Probabilistic Selection of Cluster-Head based on the Nearest possible Distance of Cluster-Head". In addition to increase network lifetime, it causes to increase scalability of the network, optimal use of communication bandwidth and improve some of qualitative parameters of the sensor networks. Proposed method has little overHead control and can find appropriate CHs with local information. In this paper, simulation is done by the NS-2 simulator, and simulation results show this protocol could lead to increase environment monitoring, improve network lifetime, throughput and some qualitative sensor network parameters by improving the Clustering process of all the routing protocol. WSNs that aren’t considered CHs distribution (LEACH protocol here).

A wireless sensor network consists of many inexpensive sensor nodes that can be used to confidently extract data from the environment. Nodes are organized into Clusters and in each Cluster all non-Cluster nodes transmit their data only to the Cluster-Head. The Cluster-Head transmits all received data to the base station. Because of energy limitation in sensor nodes and energy reduction in each data transmission, appropriate Cluster-Head election can significantly reduce energy consumption and enhance the life time of the network. In the proposed algorithm, a modified fuzzy logic approach is presented in order to improve the Cluster-Head election based on four descriptors energy, concentration, centrality, and distance to base station. Cluster-Head is elected by the base station in each round by calculating the chance each node has to elect as a Cluster-Head by considering descriptors. Network life time is evaluated based on first node dies metric, so energy depletion of one node causes the network to die. Simulation shows that the proposed algorithm can effectively increase the network life time. Sensor network is also simulated when sensor nodes move with random velocity in random direction in each round. Simulation shows that network life time is increased by considering this assumption in the proposed algorithm and can develop a better performance.

Wireless sensor networks consist of a large number of sensor nodes scattered over a limited geographical area. The main challenge of these networks is energy consumption. Clustering is a well-known way to save energy and extend network's lifetime. Many studies iteratively change the Cluster formation to increase the network's lifetime, however, this issue imposes high energy consumption on Clusters. Also, some Clustering methods select individual Cluster Heads for near Clusters, which leads to more energy consumption. Another major issue is selecting untrusted and unreliable nodes as Headers because it leads to unreliable interactions between nodes and reduces the security of the network. The proposed method aims to provide an efficient Clustering method that, in addition to having the benefits of energy consumption management, can provide a secure path for interaction and communication between nodes by identifying malicious nodes and not selecting them as Headers. For this purpose, each node's chance is calculated using the fuzzy approach, and nodes that have the highest chances are considered Cluster Heads. The efficiency of the proposed method is compared with state-of-the-art methods. Also, the process of Cluster formation is done by fuzzy logic and by defining the objective function consisting of residual energy, distance to the base station, and the average intra-Cluster distance. The statistical analysis indicates that the proposed method on average provides better results than other competitors and the results demonstrate how this method at least improves life time and residual energy by 59. 83% and 14. 75%, respectively.

Wireless sensor networks, which consist of numerous sensor nodes deployed in a specific environment, have been the focus of extensive research for monitoring physical and environmental activities. The crucial component of a sensor node is its battery power supply. As these batteries cannot be easily recharged in complex environments, achieving optimal energy consumption is a significant challenge for applications requiring a long network lifetime. Various methods have been proposed to address this challenge. One of most commonly used methods, is Clustering of nodes and aggregation their data, as well as hierarchical transmission of sensed data to the base station. In this study, three types of network topologies—1) one-level, 2) two-level with maintaining dependence on the first layer Cluster, and 3) two-level without dependence on the first layer Cluster —were simulated using three methods of Cluster Head selection: 1) random, 2) sorted, selecting nodes with higher energy , and 3) weight-oriented, based on the remaining energy of the nodes and the distance with other selected Cluster Heads. Selecting the optimal Cluster Head is an NP-complete algorithm, which entails high computational complexity for implementation. The simulation results demonstrate that selecting the Cluster Head based on the weight-oriented method in the assumed network types increases the network lifetime, reduces energy consumption, and prolongs the stability period, measured from network initiation to the first node failure.

The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most efficient techniques to save more energy in these networks. Therefore, the proper selection of the Cluster Heads plays important role to save the energy of sensor nodes for data transmission in the network. In this paper, we propose an energy efficient data transmission by determining the proper Cluster Heads in wireless sensor networks. We also obtain the optimal location of the base station according to the Cluster Heads to prolong the network lifetime. An efficient method is considered based on particle swarm algorithm (PSO) which is a nature inspired swarm intelligence based algorithm, modelled after observing the choreography of a flock of birds, to solve a sensor network optimization problem. In the proposed energy-efficient algorithm, Cluster Heads distance from the base station and their residual energy of the sensors nodes are important parameters for Cluster Head selection and base station localization. The simulation results show that our proposed algorithm improves the network lifetime and also more alive sensors are remained in the wireless network compared to the baseline algorithms in different situations.

Wireless sensor network includes hundreds or even thousands of sensor nodes, which are devices with low energy and often impossible to be replaced or recharged. For this reason, efficient use of energy is one of the major challenges in these networks. One way for increasing energy efficiency in such networks is Clustering. Clustering protocols such as LEACH, TEEN, SEP and DEEC randomly select the Cluster Heads for the Clustering process. This causes the Cluster Heads to be close together, decreasing the efficiency of protocols. For this reason, we proposed a new approach called EBDCH. In this method, when Cluster Heads are close together, we distribute them based on the energy situation. The proposed approach runs on the Clustering protocols. The simulation results show that with the implementation of the proposed method, throughput is increased and network lifetime is improved compared to protocols LEACH, DEEC, SEP and EEHC respectively at 26%, 28%, 37% and 36%.

This paper presents stress and strain analysis of outer shaft shoulder (OSS) and inner shaft shoulder (ISS) of a new paddy harvesting Head (PHH) using finite element method (FEM). Snapping grain unit is a part of this Head, with OSS and ISS as its members. The analysis was performed using the ABAQUS software with Dynamics Explicit Solution Method. Eight-node block and four-node tetrahedral elements were used to mesh the parts of the Head. After the analyses, the stress and strain curves, and their maximum values were estimated for various parts. Analytical method was used to verify the FEM results and to calculate the factor of safety (FS) of the OSS and ISS components and also to estimate the number of cycles to failure of each component. The maximum amount of force applied to each tooth of shoulder was 4. 29 Nmm-1. The maximum stress in both shoulders was obtained as 44. 43 MPa. Other results showed, fatigue factors of safety for all components were less than their relevant yield factor of safety. Therefore, the fatigue in components would occur first. The study showed the predicted life for OSS and ISS components is more than 106 cycles, thus the components have an infinite-life. A fitted regression line to the data showed that the calculated stresses from analytical method lie within bounds of
7. 89 % of the predicted values with a coefficient of determination of 0. 98. Hence, it could be concluded that there is a good agreement between the analytical and FEM results