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.