Journal of Computer Networks and Communications

This article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting technique. The fingerprint of a location, in this case, is a set of Wi-Fi ToFs between the target device and an access point (AP). Therefore, the number of APs in the area dictates the set size. The location fingerprinting technique requires a collection of fingerprints of various locations in the area to build a reference database or map. This database or map contains the information used to carry out the main task of the location fingerprinting technique, namely, estimating the position of a device based on its location fingerprint. For that task, we propose using a fully connected deep neural network (FCDNN) model to act as a positioning engine. The model is given a location fingerprint as its input to produce the estimated location coordinates as its output. We conduct an experiment to analyze the impact of the available AP pair in the dataset, from 1 unique AP pair, 2 AP pairs, and more, using WKNN and FCDNN to compare their performance. Our experimental results show that our IPS, DeepIndoor, can achieve an average positioning error or mean square error of 0.1749 m, and root mean square error of 0.5740 m in scenario 3, where 1–10 AP pairs or the raw dataset is used.

The 3rd generation partnership project (3GPP) standards organizations makes great efforts in order to reduce the latency of 5G mobile networks to the least possible extent. Recently, these networks are associated with big buffers to maximize the network utilization and minimize the wasted wireless resources. However, in existence of the TCP congestions, having bottlenecks are still expected on radio access networks (RANs) data paths. Consequently, this influences the network performance and reduces its quality of services (QoSs). Apparently, studying and improving the behavior of buffers deployed at 5G mobile networks devices can contribute to solving these problems (at least by reducing the queuing time at these buffers). In this paper, we study the buffer behavior of base stations in a 5G mobile network at steady state. We consider a cellular mobile network consisting of finite number of users (stations, terminals, and mobiles). At any time-slot, a station may be using the channel (busy) or not using the channel (idle). Since system analysis of cellular mobile networks in general form is rather complex, solutions are always obtained in closed forms or by numerical techniques. A two-dimensional traffic system for cellular mobile networks is presented, and the main performance evaluations are derived. Moreover, different moments of the base station buffer occupancy are calculated. The study reveals that there is a correlation between the state of the mobile stations (busy or idle) and the expected buffers occupancy of the base station. In addition, the results discussions demonstrate some important factors and parameters that affect the base station buffers and the overall network performance. These factors can be further worked on and controlled to obtain the least possible latency in next generation mobile networks.

Home automation systems are gaining a lot of attraction globally and changing the way we live. They simplify our lives, reduce workloads, improve home safety and security, and pave the way for newer developments. It is no wonder why these systems are in such high demand and why modernization is needed to keep up with consumer needs. Nevertheless, utilizing home automation system technology can be energy-intensive and costly—especially for middle-class families in developing countries. In this article, we discuss SECHA, a smart, energy-efficient, and cost-effective home automation system. It empowers users to automate their homes with IoT regardless of the residence type. SECHA is developed with the goal of being energy-efficient, simple to use, and open-source for everyone’s benefit. SECHA has developed a low-cost smart home automation system that incorporates Wi-Fi and GSM technology, enabling remote monitoring and control of appliances through an Android application. This solution enables users to easily monitor and manage their homes. An automation system has been developed using an ESP32 microcontroller equipped with Wi-Fi and GSM SIM800. This impressive setup is further enhanced by the integration of several sensors that enable monitoring of temperature, humidity, movement, and other aspects at home.

Precision agriculture (PA) is the next generation of a technological revolution in smart farming, where sensing technology is the core technological player. Energy-efficient data transmission in PA via sensing technology is possible only when additional security measures are synchronized. Nevertheless, security considerations often introduce additional overhead. Thus, it is necessary to develop an efficient mechanism to achieve an optimal trade-off between security and resource efficiency. The prime purpose of the proposed study is to introduce a lightweight communication protocol that can ensure an adequate balance between energy efficiency and maximum-security demands to benefit the success of PA. This paper proposes a synchronized framework where unique public-key encryption has been used, unlike any existing approach to facilitate the participation of legitimate on-field sensors in PA. On the other hand, an algorithm for energy efficiency where unique structural management of routing is discussed concerning aggregator nodes. In contrast, the security algorithm discusses a uniquely progressive and noniterative mechanism to perform secure data aggregation with a parallel validation technique. The proposed logic is scripted in MATLAB, considering a suitable PA environment where comparative assessment is carried out on a uniform testbed. The study outcome exhibited the effectiveness of the proposed scheme concerning better energy efficiency and higher resiliency from threats in contrast to existing schemes.

With the continuous development of mobile communication and satellite navigation technologies, the positioning requirements of 5G smart communication base stations are becoming higher and higher. With the opening of GNSS raw observation data, research on the positioning of a 5G smart communication base station has become a research hotspot in the surveying and mapping disciplines. In this paper, based on the GNSS observation data of the 5G smart communication base station, the quality of the original GNSS observation data of the 5G smart communication base station is studied and analyzed. A method based on Doppler smoothing pseudorange solves the problem that the original pseudorange observation values of the 5G smart communication base station are noisy and prone to multipath errors due to the limitations of the base station chips and processes, which makes the traditional data processing methods unable to meet the demand for higher accuracy positioning. This method uses Doppler observations to smooth the pseudorange and determines the Doppler smoothing strategy and closure values to improve the data quality. The experimental data show that Doppler smoothing pseudorange can improve data quality and positioning accuracy by 67.9% in the E direction, 64.8% in the N direction, and 65.5% in the U direction. The future world will develop in the direction of intelligence, and the wireless network 5G technology used to support the construction of this intelligent system will become the core driver for the development of a leading intelligent society. 5G network signals have higher reliability and lower latency and can meet the specific needs of smart manufacturing, autonomous driving, and other industrial applications. This new base station product can meet the construction needs of future 5G base stations, adapt to the future intensive, miniaturized, intelligent station construction mode, and realize safe and fast station construction, providing the necessary hardware support for 5G network coverage.

Software Defined Networking (SDN) is the novel networking paradigm where decoupling of the control plane from the data plane has its inherent advantages. Controller Placement Problem (CPP) involves placing the optimal number of controllers at the appropriate locations while meeting prerequisites such as latency, load balancing, energy and computational time. To achieve scalability, deployment of multiple controllers on large-scale SDN is one of the key challenges. CPP can be addressed as a multi-objective combinatorial optimization problem whose solution is a trade-off between multiple optimization parameters. In this paper, a novel population-based meta-heuristic algorithm viz. Naked Mole-Rat (NMR) Algorithm has been proposed to optimize the location for controller placement based on Switch-Controller (SC), Controller-Controller (CC) latency while maintaining load balancing among the controllers. The ideas and mechanisms are illustrated using two publicly available standard topologies viz. Ernet and Savvis. The controller localization approach implemented with NMR algorithm has slightly a better result as compared with the Bat algorithm.