In Wireless Sensor Networks (WSN), Efficient-Energy Coverage (EEC) is one of the important issues for considering the (WSNs) implementation. In this study, we have developed the new algorithm ECO (Enhanced Coverage Optimization) for solving the EEC problem effectively. The proposed algorithm uses three types of major work for effectively solving the problem. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI) and the other is used to form a sensor set that has as many senses as an ant has selected the number of active sensors by using the former pheromone. This study also introduces one technique that leads to a more realistic approach to solving the EEC problem that is to utilize the probabilistic sensor detection model. The main goal of ECO is Efficient Coverage on target area with minimum energy consumption and increased network's lifetime.

In heterogeneous wireless networks, connection choosing relates to the method for automatically determining the most suitable one among a variety of readily accessible alternatives according to features including quality of signal, connectivity, and preferences of users. The network choice with heterogeneous wireless systems includes the procedure for choosing a suitable system from an array of available alternatives, taking into consideration variables that include signal quality, bandwidth, and customer preferences, in order to guarantee ideal interaction as well as efficiency for wireless gadgets in a variety of environments. This is a dynamic choice that seeks to maximize the user experience by replying to shifting network situations and demands. Network selection study for heterogeneous wireless systems is crucial since it boosts wireless transmission by means of sophisticated algorithms, enhancing effectiveness, dependability, and overall performance while tackling obstacles from diverse technologies alongside rising connectivity demands, thus improving consumers along with allowing innovations in the Internet of Things and 5G wireless networks. The GRA (Grey Relational Analysis) is a decision-making tool which assesses the value of many variables in a grey structure, helping in the selection of the most appropriate alternative. In accordance with grey system theory, method examines reference and comparison sequences employing grey relational parameters, permitting impartial rankings especially supporting difficult decisions in the fields of engineering, leadership, and economics. The Evaluation parameters are Bandwidth, Jitter, Delay and Cost. Alternate parameter is Wi-Fi. For the analysis, the final rank is where “Wi-Fi 1” is in the 1st rank, “Wi-Fi 3”is in the 2nd rank, “Wi-Fi 4” is in the 3rd rank, “Wi-Fi 2” is in the 4th rank, and “Wi-Fi 5” is in the 5th rank. The best one is “Wi-Fi 1” which holds up the 1st rank.

Weather forecasting has several applications in our daily lives, ranging from agriculture to event planning. Previous weather forecasting models relied on a complex combination of mathematical instruments, which was insufficient to achieve a higher categorization rate. We offer fresh revolutionary approaches for estimating monthly rainfall using machine learning algorithms in this study. Weather forecasts are created by gathering quantitative information about the current state of the atmosphere. Machine learning algorithms may learn complicated mappings from inputs to outputs using only samples and with little effort. The dynamic nature of the atmosphere makes accurate weather prediction challenging. The fluctuation in weather conditions in previous years must be used to anticipate future weather conditions. It is extremely likely that it will match within the next two weeks of the preceding year. We proposed using linear regressions with the Random forest algorithm to forecast weather using characteristics such as temperature, humidity and wind. It will forecast weather based on prior records thus, this prediction will be accurate.

In certain regions where it is utilized to maintain consistent data rates and throughput with low traffic, the cellular network signal has numerous difficulties. For this reason, it is necessary to test the cellular network under various conditions in order to verify its resilience. In this implementation, we test the cellular signal under various conditions to see how well it maintains steady state responses. The usefulness of the cellular signal is verified by measuring its intensity in a variety of locations, including the kitchen, hallway, bedroom, bathroom, terrace, and a remote area.

Wireless Sensor Network (WSN) has several applications such as military, industry. and environment. The importance of WSNs in current applications makes the WSN technology highly relevant and significant to the field of communication and computing. However, WSN's performance deals with number of challenges. Energy consumption is the most considerable 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 dev eloped to save the consumption or power. Low energy adaptive clustering hierarchy (LEACH) is considered the most attractive one in WSNs, in this projects we evaluate the LEACH approach effectiveness in the cluster-head (CHJ choosing and in data transmission, then we propose an enhanced protocol. 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 nudes in order to send their data to the sink. Then CHs choose the optimal path to reach the sink, we propose a new approach to achieve better enhancement of WSN in terms of network lifetime and data transmission time represented by reducing the packet delay time. Then, we compare the simulated result of the proposed algorithm with the basic LEACH protocol.