Coverage is a typical problem in wireless sensor networks to fulfil the issued sensing tasks. In general, sensing coverage represents how well an area is monitored by sensors. The quality of a sensor network can be reflected by the levels of coverage and connectivity that it offers. The coverage problem has been studied extensively, especially when combined with connectivity and energy efficiency. Constructing a connected fully covered, and energy efficient sensor network is valuable for real world applications due to the limited resources of sensor nodes. In this paper, we survey recent contributions addressing energyefficient coverage problems in the context of static WASNs, networks in which sensor nodes do not move once they are deployed and present in some detail of the algorithms, assumptions, and results. A comprehensive comparison among these approaches is given from the perspective of design objectives, assumptions, algorithm attributes and related results.

Presently, industrial automation has become a popular field because of its various advantages includes higher production rates, more efficient use of materials, better product quality, improved safety, and requires minimum labours. This is achieved by utilizing Local Networking Standards (LNSs), remotely monitoring and controlling industrial devices by utilizing Raspberry Pi and Embedded Web Server (EWS) technology. This proposed research provides an idea of utilizing Internet of Things (IoT) for monitoring and controlling the automation process through Wi-Fi or wireless medium by utilizing Raspberry pi as a server system. Additionally, the prediction and error detection utilized in the machine learning process for this Improved Random Forest (IRF) method. The proposed IRF uses Out of Bag (OoB) bagging estimation technique to randomly select sub-datasets for overcoming the optimization problem. The OoB estimation is the technique used to find prediction error in IRF as every samples are not used when each tree in IRF is trained. So for all those bags unused samples estimate the prediction error for a particular bag in prediction process. IRF recursively partition the data into categories and the derived RF model and maps the results. The proposed IRF method overcomes the overfitting problem, receives the command and applies action according to it. The IRF method uses only eight devices to control and monitor more than thirty devices and this entire process is represented as IoT based Machine Learning (ML) Automation Algorithm. The experimental results show that the IRF method provides better outcomes in terms of accuracy, precision, F-measure, and recall. The RMSE values obtained for the proposed IRF model shows 0.0386 lesser error values when compared with the existing models that had achieved RMSE as 3.607 for Long Short Term Memory-Recurrent Neural Network (LSTM-RNN), Artificial Neural Network (ANN) as 1.226 and Fuzzy Gain Scheduling (FGS) of 0.4247.

Today, Internet is the best place to communicate and share information among the people throughout the world and gives an endless support of knowledge and entertainment. The main objective of Internet technology is to increase efficiency and decrease human effort. With the introduction of Internet of Things (IoT) in the last decade, we have been pushing for ubiquitous computing in all spheres of life. Physically challenged people are also using the Internet with the help of Speech commands (SC). The main objective of this paper is to minimize the effort and increase efficiency of the Voice recognition and IoT based secured automation, which is named as Raspberry pi and IoT based Speech automation (RASP-IoT-SA) technique. This RASP-IoT-SA system has only worked for authenticated users which improves the security in home and industrial automations. This RASP-IoT-SA system consists of four different Process such as secured speaker prediction, voice data transmission, web update and voice data Receiver processing. In this research work, Mel frequency Cepstral Coefficient (MFCC) technique is used for Feature Extraction (FE). Artificial neural network (ANN) technique is used for two different process such as, speaker prediction and speech data recognition (SR). The experimental result shows that the MFCC and ANN based technique provides better results in terms of accuracy, precision, false measure and recall. The RASP-IoT-SA technique delivers 99% of speech recognition accuracy, which was higher than the existing Speech automation technique.

As wireless sensor networks (WSNs) continue to attract more and more researchers attention, new ideas for applications are continually being developed, many of which involve consistent coverage with good network connectivity of a given area of interest. For the successful operation of the wireless Sensor Network, the active sensor nodes must maintain both sufficient sensing coverage, and also sufficient network connectivity. These are two closely related essential prerequisites and they are also very important measurements of quality of service (QoS) for wireless sensor networks. This paper presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. Our method utilizes a hybrid approach that provides sufficient sensing coverage and ensured network connectivity.In this paper, we incorporate the solution for eliminating the coverage holes. Simulation results show that our Lifetime prolonged Coverage, Connectivity Configuration (LPC 3 ) Protocol can effectively reduce the number of active sensors and prolongs the network lifetime. Consequently, it realizes that the energy is best used and at the same time the sensor network lifetime is prolonged effectively, Key Words: coverage, connectivity, energy conservation, power control INTRODUCTIONIn this paper we will present a simple yet important relationship between coverage and connectivity in wireless sensor networks [1].Wireless sensor networks have attracted a lot of attention recently. Such environments may consist of many inexpensive nodes, each capable of collecting, storing, and processing environmental information and communicating with neighboring nodes through wireless links. For a sensor network to operate successfully, sensors must maintain both sensing coverage and network connectivity. This issue has been studied in [2]& [3], both of which reach a similar conclusion that coverage can imply connectivity as long as sensors' communication ranges are no less than twice their sensing ranges [8]Sensing is only one responsibility of a sensor network. To operate successfully a sensor network must also provide satisfactory connectivity so that nodes can communicate for data fusion and reporting to base stations. Connectivity affects the robustness and achievable throughput of communication in a sensor network. None of the above coverage maintenance protocols addresses the problem of maintaining network connectivity. On the other hand, several other protocols (e.g., ASCENT [4], SPAN [5], AFECA [6], and GAF [7]) aim to maintain network connectivity, but do not guarantee sensing coverage. Unfortunately, satisfying only coverage or connectivity alone is not sufficient for a sensor network to provide sufficient service. Without sufficient connectivity, nodes may not be able to coordinate effectively or transmit data back to base stations.In most of the usages, we are looking at reliable monitoring of the environment, where there are no holes in the sensing area of cove...

In wireless sensor networks, sensor nodes play the most important role. These sensor nodes are mainly un-chargeable, so it an issue regarding lifetime of the network.  The main objective of this research is concerning clustering algorithms to minimize the energy utilization of each sensor node, and maximize the sensor network lifetime of WSNs. In this paper, we propose a novel clustering algorithm for wireless sensor networks (WSN) that decrease the networks energy consumption and significantly prolongs its lifetime. Here main role play distribution of CHs ( Cluster Heads) across the network. Our simulation result shows considerable decrease in network energy utilization and therefore increase the network lifetime.