Applications of the Internet of Things (IoT) are rapidly utilized in smart buildings and smart cities to reduce energy consumption. This advancement has caused a knowledge gap in applying IoT effectively by experts in the built environment to achieve energy efficiency. The study aims to provide an extensive review of IoT applications for energy savings in buildings and cities. This study contributes to the field of IoT by guiding and supporting built environment experts to utilize IoT technologies. This paper performed a thorough study using a systematic review that covered an overview of IoT concepts, models, applications, trends and challenges that can be encountered in the built environment. The findings indicated limitations in developing IoT strategies in buildings and cities by professionals in this field due to insufficient comprehension of technologies and their applied methods. Additionally, the study found an indefinite implementation and constraints on using IoT when integrated into the built environment. Finally, the study provides critical arguments and the next steps to effectively utilize IoT in terms of energy efficiency.

Vehicular ad hoc networks (VANETs) allow vehicles to contact one another to provide safety and comfort applications. However, mobility is a great challenge in VANETs. High vehicle speed causes topological changes that result in unstable networks. Therefore, most previous studies focused on using clustering techniques in roads to reduce the effect of vehicle mobility and enhance network stability. Vehicles stop moving at intersections, and their mobility does not impact clustering. However, none of previous studies discussed the impact of vehicle stopping at intersections on base stations (BSs). Vehicles that have stopped moving at intersections continue to send the same information to BSs, which causes duplicated information. Hence, this study proposes a new method named dynamic multiagent (DMA) to filter cluster information and prevent duplicated information from being sent to BSs at intersections. The performance of the proposed method was evaluated through simulations during the use of DMA and without-DMA (W-DMA) methods based on real data collected from 10 intersections in Batu Pahat City, Johor, Malaysia. Overall, the proposed DMA method results in a considerable reduction in duplicated information at intersections, with an average percentage of 81% from the W-DMA method.

The Internet of Things (IoT) is a rapidly evolving field that provides seamless connections to a physical object, making it part of a smart environment. To fully realize the potential power of the connections between these objects in IoT, trust between them is critical. Conventional security measures are not sufficient to provide comprehensive security for this smart world. Trust is used to reduce the risk of insecurity when nodes are connected to the Internet. In an IoT environment, various trust models have been proposed for Wireless Sensor Networks (WSNs) and Radio Frequency Identification (RFID). Nevertheless, these are not fully adapted to the dynamic and uncertain environment of the intelligent IoT. Therefore, this article reviews the characteristics of recent works in IoT trust models. A comprehensive study has been conducted on the classification of trust models. A set of factors are discussed, such as trust characteristics, trust architecture, trust distribution, trust aggregation technique, trust model type, and attack type. In addition, this paper provides readers with an understanding of the current existing trust model and directs future works to propose new models that satisfy all characteristics that should be considered when developing a trust model. Finally, some research challenges and directions are identified.

Over the past decade, the vehicular Ad-hoc networks (VANETs) has testified significant attention from authors to safe people's lives, reduce money consumption and accident. Most of the previous studies focused on clustering especially on electing cluster-head to increase cluster stability. However, no one addressed the effect of the number of connection at the base station (BS). This is because when CH was connected to the base station, it still reserved the link until the vehicle was turned off or moved from BS to another BS. In this paper, proposed a novel method called intelligent cluster head (ICH) to increase cluster stability, and at the same time reduce the number of uplink connection at the base station (BS). In this study, the same mobile BS was used if there was no need for establishment of new BS for VANETs. The simulation was done by using MATLAB through a real data. The results showed that the proposed method provided less number of uplink connections than other previous studies and enhancing the stability of the system. The proposed method resulted in less number of CH by 53.92 % from previous methods.

In the application of the Internet of Things (IoT), sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery life-time since there is additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this study is to increase the battery life of the IoT sensor node. To do so, this paper proposes an efficient real-time data collection model for multivariate sensors in IoT/WSN applications named RDCM. The general structure of RDCM is composed of two main levels; IoT sensor board level and fusion center level. IoT sensor board level is implemented in real-time by all IoT sensor boards simultaneously in each cycle and fusion center level is executed by the fusion center. IoT sensor board level includes various stages as follows; check the physical conditions of the IoT edge device (board) stage, update data strategy stage, data validation stage and sensed data reduction stage. The average of the total percentage of energy saved by application of RDCM to real-time data sets injected with various percentages of errors for all nodes is 98%. In summary, RDCM has a very high performance in terms of energy consumption compared to other algorithms. This paper concludes with the limitation of the current study and some further research opportunities.