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In the application of the Internet of Things (IoT), a sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime , since there are 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 paper 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: the IoT sensor board level and the fusion center level. The IoT sensor board level is implemented in real time by all the IoT sensor boards simultaneously in each cycle and fusion center level is executed by the fusion center. The IoT sensor board level includes various stages as follows: check the physical conditions of the IoT edge device (board) stage and update data strategy stage, data validation stage, and sensed data reduction stage. The average of the total percentage of energy saved by the application of RDCM to real-time data sets injected with various percentages of errors for all nodes is 98%. In summary, the RDCM has a very high performance in terms of energy consumption compared with other algorithms. This paper concludes with the limitation of the current study and some further research opportunities.

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A wearable C-shaped antenna based on a fabric material operating at 2.4 GHz frequency is proposed for use in flexible/wearable IoT medical systems. The wearable IoT device plays a key role in medical applications, and the antenna is a key part of it. Loading the presented antenna on the body models showed a frequency detuned with the gain and efficiency reduced from 1. 28 to −9 dB and 90% to 10%. In addition, the SAR did not meet the safety health requirement defined by the FCC or ICNIRP standards. Therefore, an “Artificial Magnetic Conductor” structure (AMC) is added to the C-shaped antenna to overcome these problems. The AMC acts as shielding material between the human skin and the presented antenna because of its 0° reflection phase, which mimics the action of the Perfect Magnetic Conductor (PMC). The overall size of the proposed design was 54 × 54 × 3.9 mm 3 . Numerical and experimental findings indicated that integrating the AMC structures with a C-shaped antenna was robust for body deformation and load. The C-shaped antenna worked equally well with the AMC, whether positioned in free space or on the chest or the arm of the human body. The integrated antenna with AMC structures has excellent performances. The gain and efficiency without loading on the chest were 6.49 dB and 84%, respectively. While for loaded on the chest were 6.21 dB and 81%, respectively. It also decreased the back radiation and raised the Front to Back Ration (FBR) by 13.8 dB. SAR levels have been reduced by more than 90% between the FCC and ICNIRP standards compared to the C-shaped antenna alone, which does not comply with the standards. As a result, the C-shaped integration with AMC structures is highly suitable for assembly in any wearable system. Supplementary Information The online version contains supplementary material available at 10.1007/s11276-021-02770-4.

This paper presents a new metric to assess the performance of different multivariate data reduction models in wireless sensor networks (WSNs). The proposed metric is called Updating Frequency Metric (UFM) which is defined as the frequency of updating the model reference parameters during data collection. A method for estimating the error threshold value during the training phase is also suggested. The proposed threshold of error is used to update the model reference parameters when it is necessary. Numerical analysis and simulation results show that the proposed metric validates its effectiveness in the performance of multivariate data reduction models in terms of the sensor node energy consumption. The adaptive threshold improves the frequency of updating the parameters by 80% and 52% in comparison to the non-adaptive threshold for multivariate data reduction models of MLR-B and PCA-B respectively.

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.

The huge development in the number of Vehicle factories have resulted in many people having lost their life due to accident, which has made vehicular Ad-hoc networks (VANETs) hot topic to enable improved communication between vehicles aimed at reducing the loss of life. The main challenge in this area is vehicle mobility, which has direct effect on network stability. Thus, most previous studies that discussed clustering focused on cluster formation, cluster-head selection and the stability of cluster to reduce the impact of mobility in the network, with little attention given to the clusters when passing from base-station to neighbor base-station. Therefore, this study focused on handover problem that occurs after cluster formation and cluster-head election during cluster passing from base station to base station, known as overlapping area. As the cluster in an overlapping area receives two signals from different base stations, the signal arriving at the cluster becomes weak due to interference between two frequencies resulting in loss of cluster information in the overlapping area. In this study, proposed a novel method named Intelligent Cluster-Head (ICH), which is a controller on two clusters that are used to change uplink between clusters to solve the handover problem in the overlapping area. The proposed method was evaluated with VMaSC-1hop method. The proposed method achieved percentage of packet loss up to 0.8%, percentage of packet delivery ratio (PDR) 99%, percentage of number of disconnected links 0.12% and percentage of network efficiency 99% in the cells edge.

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