Smart Garden Planning and Design Based on the Agricultural Internet of Things

To improve the effect of urban agricultural garden landscape planning and design, this paper combines the agricultural Internet of Things technology to construct a smart garden planning and design system. Moreover, this paper selects the LEACH protocol that can support monitoring for a long time according to actual application needs, introduces the latest swarm intelligence optimization algorithm, the gray wolf algorithm, to optimize some of the problems in the LEACH protocol, and conducts simulation experiments on the improved algorithm. The simulation experiment results show that the improved algorithm has obvious advantages in cluster head selection, data transmission within the cluster, and route maintenance. After constructing a smart garden planning system based on the agricultural Internet of Things, the effect of the agricultural Internet of Things data processing in this paper is evaluated. Finally, this paper constructs a garden simulation system and analyzes the performance of the system. The results verify that the agricultural Internet of Things has a good effect in the planning and design of smart gardens.

An Improved Clustering Using by Likely Attributable Function and Informed Selection in WSN for Science of Management and Engineering

Wireless sensors networks (WSNs) are traditionally composed of large number of tiny homogenous sensors nodes connected through a wireless network that gather data to be treated locally or relayed to the sink node through multi-hop wireless transmission. The low-energy adaptive clustering hierarchy (LEACH) protocol is one of the Famous protocols used in the wireless sensor networks (WSNs). The LEACH protocol in wireless sensor network suffers from many Bugs and many researchers proposed different methods to mitigate them. In this paper, we propose two ideas in a format for improving leach protocol. For Cluster head selection we used a Likely Attributable Function that in this function used from a factor. This factor that we called the informed selection factor helps to farther nodes not selection for cluster head. This significantly decreases the energy consumption and increases the lifetime of associated nodes. Simulation is conducted in using MATLAB results are analyzed for energy consumption.

Improved uplink throughput and energy efficiency of LoRaWAN using 2-hop LEACH protocol

The low power wide area networks (LPWAN) is the new connectivity technology that is geared towards energy constrained internet of things (IoT) devices, is starting to become one of the drivers of the re-accelerating IoT market and has one goal: ensure the wide range distance while reducing the battery energy consumption. We focus in this paper on the evaluation of the uplink throughput of the long-range wide area networks (LoRaWAN) then we attempt optimize the throughput and power dissipation using low energy adaptive clustering hierarchy (LEACH) protocol. Therefore, we exploit a novel module developed in NS-3 simulator for obtaining the first measurements scenario, then the LEACH algorithm for the second optimization case. As result, the simulation analysis will help us to add a new LoRaWAN routing protocol feature.

Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids

Aiming at the problem of uneven clustering and the unreasonable energy consumption of LEACH protocol in the perception layer of IoT-based microgrids of static nodes; in this paper, we propose a stationary-node energy-based routing protocol (SERP). First, we select a dynamic cluster radius for clustering to meet the actual needs of the network during clustering. Then, to solve the problem that the number of cluster heads is difficult to determine, a dynamic optimal cluster head ratio is adopted. The dynamic optimal cluster head ratio can be obtained by minimizing the total energy consumption of cluster formation and the stable transmission phase, which can improve the efficiency of network transmission. Finally, by setting the residual energy factor and distance factor to improve the calculation of the cluster head election threshold, the energy load of the network is more uniform, and the location of the cluster head is more reasonable. Compared with the LEACH protocol and the HEED protocol, the simulation results show that the SERP protocol can effectively prolong the lifetime of the whole network.

E-FLEACH: An Improved Fuzzy Based Clustering Protocol for Wireless Sensor Network

Clustering is one of the most energy-efficient techniques for extending the lifetime of wireless sensor networks (WSNs). In a clustered WSN, each sensor node transmits the data acquired from the sensing field to the leader node (cluster head). The cluster head (CH) is in charge of aggregating and routing the collected data to the Base station (BS) of the deployed network. Thereby, the selection of the optimum CH is still a crucial issue to reduce the consumed energy in each node and extend the network lifetime. To determine the optimal number of CHs, this paper proposes an Enhanced Fuzzy-based LEACH (E-FLEACH) protocol based on the Fuzzy Logic Controller (FLC). The FLC system relies on three inputs: the residual energy of each node, the distance of each node from the base station (sink node), as well as the node’s centrality. The proposed protocol is implemented using the Castalia simulator in conjunction with OMNET++, and simulation results indicate that the proposed protocol outperforms the traditional LEACH protocol in terms of network lifetime, energy consumption, and stability.

Enhance the Performance of Wireless Sensor Network based on Software Define Network and Kalman Filter

The paper proposes a methodology for estimating packet flowing at the sensor level in SDN-WSN based on the partial congestion controller with Kalman filter. Furthermore, the actual purpose of proposing such methodology for predicting in advance the subsequent step of packet flow, and that will consequently contribute in reducing the congestion that might happen. The model proposed (SDN with Kalman filter) is optimized using congestion controller, the methodology of proposed work, the first step random distributed of random node, the apply the Kmean cluster of select the head cluster node in, the connected the network based on LEACH protocol. in this work proposed SDN with Kalman filter for control on network and reduce error of data, where achieve by add buffer memory for each nodes and head cluster to store the data, and SDN control on transmit ion data and receiver data, before transmit apply the Kalman filter on data to reduce error data. The proposed technique, according to simulation findings, extends the network's lifetime by over 30% more than typical WSNs, the reduce the average density of memory to 20% than traditional WSN, and the increase the average capacity of memory to 20% than traditional WSN.