MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication

With the rapid development of future wireless networks, device-to-device (D2D) technology is widely used as the communication system in the Internet of Things (IoT) fifth generation (5G) network. The IoT 5G network based on D2D communication technology provides pervasive intelligent applications. However, to realize this reliable technology, several issues need to be critically addressed. Firstly, the device’s energy is constrained during its vital operations due to limited battery power; thereby, the connectivity will suffer from link failures when the device’s energy is exhausted. Similarly, the device’s mobility alters the network topology in an arbitrary manner, which affects the stability of established routes. Meanwhile, traffic congestion occurs in the network due to the backlog packet in the queue of devices. This paper presents a Mobility, Battery, and Queue length Multipath-Aware (MBMQA) routing scheme for the IoT 5G network based on D2D communication to cope with these key challenges. The back-pressure algorithm strategy is employed to divert packet flow and illuminate the device selection’s estimated value. Furthermore, a Multiple-Attributes Route Selection (MARS) metric is applied for the optimal route selection with load balancing in the D2D-based IoT 5G network. Overall, the obtained simulation results demonstrate that the proposed MBMQA routing scheme significantly improves the network performance and quality of service (QoS) as compared with the other existing routing schemes

The Barriers To Accessing Sanitary Facilities Experienced By Adolescent Girls In An Urban Slum Of Patna : A Cross Sectional Study.

Aim:  The barriers to accessing hygienic sanitary facilities experienced by adolescent girls in an urban slum. Materials and Method: The study included 98 adolescent females (10-19 years) living in an urban slum of Patna. Result: Mean age of adolescent girls in the present study was 15.44 ±2.2 years (Range:12 to 19 years) and majority of them were in high school (60.2%). About half (42%) of the study subject were living in semipucca house and only 38% had access to an independent toilet facility, 9% were practicing open defecation and remaining 51% were using public toilets. Conclusion: The availability of sanitation facility and latrine utilization rate of the households were satisfactory.Privacy is a concern in public toilets.   &nbsp

Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System

Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position based on a received signal strength indicator (RSSI) by using a traditional dataset and a deep learning classifier. To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets. The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise. In this scheme, the RSSI-based dataset is preprocessed and noise samples are removed by CNES. This experiment was carried out in a dynamic environment, and we evaluated the lab simulation results of CNES using deep learning classifiers. The results showed that applying CNES to the test database to eliminate noise will increase the success probability of fingerprint location. The lab simulation results show that after using CNES, the average positioning accuracy of margin-zero (zero-meter error), margin-one (two-meter error), and margin-two (four-meter error) in the database increased by 17.78%, 7.24%, and 4.75%, respectively. We evaluated the simulation results with a real time testing experiment, where the result showed that CNES improved the average positioning accuracy to 22.43%, 9.15%, and 5.21% for margin-zero, margin-one, and margin-two error, respectively

Investigation of Open‐Loop Transmit Power Control Parameters for Homogeneous and Heterogeneous Small‐Cell Uplinks

In Long Term Evolution (LTE) cellular networks, the transmit power control (TPC) mechanism consists of two parts: the open loop (OL) and closed loop. Most cellular networks consider OL/TPC because of its simple implementation and low operation cost. The analysis of OL/TPC parameters is essential for efficient resource management from the cellular operator's viewpoint. In this work, the impact of the OL/TPC parameters is investigated for homogeneous small cells and heterogeneous small‐cell/macrocell network environments. A mathematical model is derived to compute the transmit power at the user equipment, the received power at the eNodeB, the interference in the network, and the received signal‐to‐interference ratio. Using the analytical platform, the effects of the OL/TPC parameters on the system performance in LTE networks are investigated. Numerical results show that, in order to achieve the best performance, it is appropriate to choose αsmall = 1 and Po‐small = –100 dBm in a homogenous small‐cell network. Further, the selections of αsmall = 1 and Po‐small = –100 dBm in the small cells and αmacro = 0.8 and Po‐macro = –100 dBm in the macrocells seem to be suitable for heterogeneous network deployment

Side-Information-Aided Preprocessing Scheme for Deep-Learning Classifier in Fingerprint-Based Indoor Positioning

Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor positioning. During fingerprint database construction, all received signal strength indicators from each access point are combined without any distinction. Therefore, the database is created and utilised for deep-learning models. Meanwhile, side information regarding specific conditions may help characterise the data features for the deep-learning classifier and improve the accuracy of indoor positioning. Herein, a side-information-aided preprocessing scheme for deep-learning classifiers is proposed in a dynamic environment, where several groups of different databases are constructed for training multiple classifiers. Therefore, appropriate databases can be employed to effectively improve positioning accuracies. Specifically, two kinds of side information, namely time (morning/afternoon) and direction (forward/backward), are considered when collecting the received signal strength indicator. Simulations and experiments are performed with the deep-learning classifier trained on four different databases. Moreover, these are compared with conventional results from the combined database. The results show that the side-information-aided preprocessing scheme allows better success probability than the conventional method. With two margins, the proposed scheme has 6.55% and 5.8% improved performances for simulations and experiments compared to the conventional scheme. Additionally, the proposed scheme, with time as the side information, obtains a higher success probability when the positioning accuracy requirement is loose with larger margin. With direction as the side information, the proposed scheme shows better performance for high positioning precision requirements. Thus, side information such as time or direction is advantageous for preprocessing data in deep-learning classifiers for fingerprint-based indoor positioning

Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application

In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices