Human Activity Recognition (HAR) is a field that infers human activities from raw time-series signals acquired through embedded sensors of smartphones and wearable devices. It has gained much attraction in various smart home environments, especially to continuously monitor human behaviors in ambient assisted living to provide elderly care and rehabilitation. The system follows various operation modules such as data acquisition, pre-processing to eliminate noise and distortions, feature extraction, feature selection, and classification. Recently, various state-of-the-art techniques have proposed feature extraction and selection techniques classified using traditional Machine learning classifiers. However, most of the techniques use rustic feature extraction processes that are incapable of recognizing complex activities. With the emergence and advancement of high computational resources, Deep Learning techniques are widely used in various HAR systems to retrieve features and classification efficiently. Thus, this review paper focuses on providing profound concise of deep learning techniques used in smartphone and wearable sensor-based recognition systems. The proposed techniques are categorized into conventional and hybrid deep learning models described with its uniqueness, merits, and limitations. The paper also discusses various benchmark datasets used in existing techniques. Finally, the paper lists certain challenges and issues that require future research and improvements.

Water is always a crucial part of everyday life. Due to global environmental situation, water management and conservation is vital for human survival. In recent times, there were huge needs of consumer based humanitarian projects that could be rapidly developed using Internet of Things (IoT) technology. In this paper, we propose an IoT based water monitoring system that measures water level in real-time. Our prototype is based on idea that the level of the water can be very important parameter when it comes to the flood occurrences especially in disaster prone areas. A water level sensor is used to detect the desired parameter, and if the water level reaches the parameter, the signal will be feed in realtime to social network like Twitter. A cloud server was configured as data repository. The measurement of the water levels are displayed in remote dashboard.

A smart home, which is an extension of a traditional home, is equipped with ubiquitous sensors embedded in consumer appliances, connected via sensing technologies such as radio frequency, and communicating through the internet to gather information. They often communicate using appropriate protocols such as MQTT, CoAP, or HTTP to ensure the smooth transmission of data used by a variety of smart home services. Human activity recognition is one of the services provided by this IoT method of data collection from the sensor network when activated by residents. The obtained data can be subjected to extensive preprocessing and feature extraction tasks before being learned using appropriate machine learning or deep learning algorithms to generate a model capable of managing human activities more effectively. This technique is challenged by the nature of IoT technology and perceived data, as well as by human differences, which necessitated additional processing tasks to select significant features for the learning algorithms. In this work, we focus our review on activity recognition implementation strategies by examining various sensors and sensing technologies used to collect useful data from IoT devices, reviewing preprocessing and feature extraction techniques, as well as classification algorithms used to recognize human activities in smart homes. Many relevant works were examined and their achievements compared.The research demonstrates that IoT sensor technology for recognizing human activity in a smart home is practically feasible and efficient, even with individual differences in the smart home. However, it was discovered to be susceptible to issues such as insufficient and imbalanced data, annotation scarcity, and computational complexity. Finally, the study suggests that associating sensor data from the Internet of Things with numerous labels of activities based on time can help decrease computing overhead and improve activity recognition.

Recent advancements in smart home systems have increased the utilization of consumer devices and appliances in home environment. However, many of these devices and appliances exhibit certain degree of heterogeneity and do not adapt towards joint execution of operation. Hence, it is rather difficult to perform interoperation especially to realize desired services preferred by home users. In this paper, we propose a new intelligent interoperability framework for smart home systems execution as well as coordinating them in a federated manner. The framework core is based on Simple Object Access Protocol (SOAP) technology that provides platform independent interoperation among heterogeneous systems. We have implemented the interoperability framework with several home devices to demonstrate their effectiveness for interoperation. The performance of the framework was tested in Local Area Network (LAN) environment and proves to be reliable in smart home setting¹.