Internet of things dataset for home renewable energy management

Smart cities, as well as smart homes research, are becoming of concern, especially in the field of energy consumption and production. However, there is a lack in the dataset that can be used to simulate smart city energy consumption and prediction or even smart homes. Therefore, this paper provides a carefully generated dataset for smart home energy management simulation. Five datasets are generated and analysed to ensure suitability, including 20, 50, 100, and 200 homes across 365 days. For more accurate data, energy consumption and production for 50 homes are generated based on real input taken from a dataset for homes in Saudi Arabia. Due to the unavailability of a comprehensive dataset related to the complex scenario of smart home sensors, energy consumption, and peer-to-peer data exchange, synthetic data was generated to support the simulation of smart home energy generation and consumption. This synthetic data plays a crucial role in situations where simulating uncommon events, ensuring data availability, facilitating extensive experimentation and model validation, and enabling scalability are paramount. It offers a valuable opportunity to incorporate these rare yet significant occurrences into the simulation, particularly in the context of infrequent events, such as abnormal energy consumption patterns observed in smart homes. The generated data is analysed and validated in this article, ready to be used for many smart home and city research.

Emerging Sensor Communication Network-Based AI/ML Driven Intelligent IoT

At present, the field of the Internet of Things (IoT) is one of the fastest-growing areas in terms of Artificial Intelligence (AI) and Machine Learning (ML) techniques [...].

Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink

With the advances in sensing technologies, sensor networks became the core of several different networks, including the Internet of Things (IoT) and drone networks. This led to the use of sensor networks in many critical applications including military, health care, and commercial applications. In addition, sensors might be mobile or stationary. Stationary sensors, once deployed, will not move; however, mobile nodes can move from one place to another. In most current applications, mobile sensors are used to collect data from stationary sensors. This raises many energy consumption challenges, including sensor networks' energy consumption, urgent messages transfer for real-time analysis, and path planning. Moreover, sensors in sensor networks are usually exposed to environmental parameters and left unattended. These issues, up to our knowledge, are not deeply covered in the current research. This paper develops a complete framework to solve these challenges. It introduces novel path planning techniques considering areas' priority, environmental parameters, and urgent messages. Consequently, a novel energy-efficient and reliable clustering algorithm is proposed considering the residual energy of the sensor nodes, the quality of wireless links, and the distance parameter representing the average intra-cluster distance. Moreover, it proposes a real-time, energy-efficient, reliable and environment-aware routing, taking into account the environmental data, link quality, delay, hop count, nodes' residual energy, and load balancing. Furthermore, for the benefit of the sensor networks research community, all proposed algorithms are formed in integer linear programming (ILP) for optimal solutions. All proposed techniques are evaluated and compared to six recent algorithms. The results showed that the proposed framework outperforms the recent algorithms.

ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because it is a key part of how well data are sent. At the same time, energy consumption in battery-based sensors is another challenge. Therefore, efficient clustering and routing are techniques that can be used to save sensors energy and guarantee reliable message delivery. With this in mind, this paper develops an energy-efficient and reliable clustering protocol (ERCP) for WSNs. First, an efficient clustering technique is proposed for sensor nodes' energy savings considering different clustering parameters, including the link quality metric, the energy, the distance to neighbors, the distance to the sink node, and the cluster load metric. The proposed routing protocol works based on the concept of a reliable inter-cluster routing technique that saves energy. The routing decisions are made based on different parameters, such as the energy balance metric, the distance to the sink node, and the wireless link quality. Many experiments and analyses are examined to determine how well the ERCP performs. The experiment results showed that the ECRP protocol performs much better than some of the recent algorithms in both homogeneous and heterogeneous networks.

Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms

Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique (SMOTE) to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods.

Hierarchal Fuzzy Logic Controller and Internet of Things (IoT) Information

With the advances of networks and sensing technologies, it is possible to benefit from the surrounding environment's data in enhancing peoples' life. Currently, we have different types of networks such as Wireless Sensor Networks (WSNs), Vehicle Ad Hoc Networks (VANETs), Cellular Networks (CNs), and Social Networks (SNs) along with underlying computing such as Cloud computing. These types of networks provide huge data about the surrounding environments including weather information, peoples' relations, peoples' interest, and location information. This paper examines the suitability of hierarchal fuzzy logic controller in classifying the IoT data. The paper also tries to answer “if-else “questions about the effect of each of the input parameters. The authors' test case in this paper is related to the disease spreading prediction problem. This test case is highly important to the health care organizations. Different case studies are generated to examine the efficiency of the proposed framework and methodologies.

Efficient Data Reporting in a Multi-Object Tracking Using WSNs

Object tracking is one of the most important applications in wireless sensor networks (WSNs). Many recent articles have been dedicated to localization of objects; however, few of these articles were concentrated on the reliability of network data reporting along with objects localization. In this work, the authors propose an efficient data reporting method for object tracking in WSNs. This paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in the authors' model where the sensed data can reach the sink node in a more reliable way. This work first formulates the problem as 0/1 Integer Linear Programming (ILP) problem, and then proposes a SWARM intelligence for solving the optimization problem. Through simulation, the performance of proposed method to report information about the detected objects to the sink is compared with the previous works related to the authors' topic, such as LR-based object tracking algorithm, SEB, EPWSN, and ACO.

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

CAM

Sensor nodes of Wireless Sensor Network (WSN) possess very limited power resources normally a battery and a solar cell could exist in some cases, which requires efficient usage of these resources to extend the network's lifetime. Accordingly, several research areas have been investigated to prolong the network longevity, Clustering was proposed to WSN as one of these areas that could help in decreasing the amount of consumed energy. A number of clustering algorithms were devised but to the authors' knowledge, this is the first work to consider clustering in Multimodal WSN, where a node can report more than one feature e.g. temperature and humidity. We compared the two general clustering algorithms K-Mean and Fuzzy C-Means to LEACH-C and LEACH, which are two clustering algorithms specially designed for WSN. Fuzzy C-Means and K-Means showed better performance using the techniques proposed in this work over LEACH-C and LEACH.