International Journal of Sensors Wireless Communications and Control - Volume 12, Issue 5, 2022

With the availability of inexpensive devices like storage and data sensors, collecting and storing data is now simpler than ever. Biotechnology, pharmacy, business, online marketing websites, Twitter, Facebook, and blogs are some of the sources of the data. Understanding the data is crucial today as every business activity from private to public, from hospitals to mega mart benefits from this. However, due to the explosive volume of data, it is becoming almost impossible to decipher the data manually. We are creating 2.5 quintillion bytes per day in 2022. One quintillion byte is one billion Gigabytes. Approximately, 90% of the total data is created in the last two years. Naturally, an automatic technique to analyze the data is a necessity today. Therefore, data mining is performed with the help of machine learning tools to analyze and understand the data. Data Mining and Machine Learning are heavily dependent on statistical tools and techniques. Therefore, we sometimes use the term – “Statistical Learning” for Machine Learning. Many machine learning techniques exist in the literature and improvement is a continuous process as no model is perfect. This paper examines the influence of variance, a statistical concept, on various machine learning approaches and tries to understand how this concept can be used to improve performance.

Background: There is a task of autonomous determination of the position latitude of unmoved vehicles. Also, there is another task of the initial value latitude determination as a prepared operation of gimbaled and strap-down inertial navigation systems. For both cases, it is necessary to have an inertial measurement unit (IMU) with triad gyroscopes and triad accelerometers. The output signals of micromechanical gyroscopes and accelerometers have large noise components when using the IMU in the micromachined electromechanical systems (MEMS) technology. Objective: Normally to filter output signals of MEMS sensors, averaging and filtering are used. However, for Kalman filtering, it is necessary to find the exact mathematical model of the sensors and a lot of their initial random characteristics. The study of the possibility of the wavelet transform usage to filter the output signals MEMS accelerometers and gyroscopes for the latitude autonomous determination was conducted in the paper. Methods: The wavelet transform approach was used to filter the output signals of MEMS accelerometers and gyroscopes in order to improve the accuracy of the autonomous position latitude determination. The autonomous latitude determination efficiency of IMU based on MEMS gyroscopes and accelerometers has been experimentally confirmed. The projections of the Earth’s angular rate and gravitational acceleration were obtained from the MEMS IMU. After that, the signals of the IMU gyroscopes and accelerometers were filtered, using the wavelet ‘Daubechies’ in decomposition, and averaged. These signals were used in a computational algorithm to determine the latitude. Results: The results showed that Unlike the well-known Kalman filter wavelet de-noising reduced calculation error by almost twice. Conclusion: Wavelet de-noising could be used for output signals filtering of micromechanical gyroscopes and accelerometers for the autonomous determination of position latitude.

Background: In this article, an approach to harvesting electrical energy from a mechanically excited piezoelectric element has been described. Three PMN-xPT compositions were used with x taking the values of 0.31, 0.33, and 0.35 in order to study the most important properties of piezoelectric PMN-PT in energy harvesting. Objectives: This study uses a detailed analysis of three Relaxer (1-x)PMN-xPT ceramic compositions, emphasizing the influence of content on piezoelectric, dielectric, and electromechanical characteristics. Methods: Prototypes have been made and characterized, validating these energy thresholds. From this comparative analysis of the three compositions, it was found that PMN-35PT has the highest recoverable energy density. On the other hand, the pressure change in the radial artery was calculated using the pressure sensitivity of the sensor and systolic and diastolic characteristic points in the pressure pulse wave (PPW). Results: The results show that piezoelectric, dielectric, and electromechanical properties are all directly associated with chemical composition and that the examined ceramics outperform their PZT counterparts, making them very suitable for energy harvester systems and sensing device applications. Therefore, the structure developed is an external patch of 5x3cm², placed on the arm and able to recover 3.46 mW for PMN-35PT during human walking. Conclusion: Results indicate that the suggested method demonstrated reliable accuracy of systolic blood pressure (SBP). The technology has the potential to be used for long-term continuous blood pressure monitoring. The piezoelectric sensor was placed on the skin above the radial artery and measured for 10 sec to obtain the continuous pressure waveform.

Aim: For a Wireless Sensor Network (WSN), the sensor node deployment is a critical issue since it reflects the coverage quality. It is the most fundamental issue in WSNs, and it has a great effect on the overall WSN application performance. When it is necessary to deploy randomly sensor nodes in a field of interest to form a WSN, ensuring a high coverage quality becomes difficult. In the random deployment scenario, the most difficult aspect of sensing coverage is to find how well the sensor nodes cover the field of interest. In this paper, we analyze the intrusion detection in a WSN which is defined as a mechanism for monitoring and detecting any intruder in a field of interest, with the objective of enhancing the detection quality in a random WSN. It is required to establish more specific measurements of node density and sensor range that impact the overall system performance especially in the intrusion detection application. To enhance the quality of intrusion detection, several probabilistic models are adopted for heterogeneous WSN in the random deployment scenario. Methods: Multi-intrusion detection model probability in a heterogeneous wireless sensor network for random deployment was used. Results and Discussion: In both homogeneous and heterogeneous WSNs, we analyzed our probabilistic model for multi-intrusion detection in single and multi-sensing detection. Conclusion: Our probabilistic models are useful in selecting the critical parameters of WSN in order to meet the detection quality requirement.

Background: In free space optics (FSO) communication system, turbulence in the atmosphere leads to the intensity fluctuations of the received signal at the receiver end. Due to this reason, the performance of the free space optical communication system gets affected and results in fading of the received signal. Objectives: Performance improvement of FSO system using sophisticated channel modelling and fading mitigation techniques. Methods: This paper presents the performance improvement of the FSO system using a modified Exponentiated Weibull (MEW) channel model. Moreover, the performance of the FSO system is compared using different channel models such as Gamma-Gamma and Exponentiated Weibull. Results: The results in bit error rate (BER) with respect to different signal to noise ratio (SNR) are obtained using MEW, Gamma-Gamma, and Exponentiated Weibull channel models. In addition, BER is calculated for different aperture sizes such as 1.8, 5, and 13 mm using the proposed channel model to improve the performance of the FSO system. Similarly, the relay assisted technique is utilized to calculate the BER using the proposed channel model. Conclusion: An improvement of free space optical communication system is presented in terms of fading using channel modelling at different atmospheric turbulence conditions. An appropriate channel model is proposed for improving the performance of the FSO communication system using fading mitigation techniques. The proposed MEW channel model best describes the strong atmospheric turbulence induced fading. In addition, the performance of the free space optics system is improved using fading mitigation techniques such as aperture averaging and relay assisted FSO system.

Aims: This paper presents agent-based localization using the mobile sink in wireless sensor networks. This proposed scheme is less expensive than GPS. It has a longer lifetime and is more energy-efficient. The mobile sink has a large and easily replenishable energy reserve and is movable within the sensor network's deployment area. Objective: In this work, a mobile sink is proposed that traverses inside the network's boundary and gathers information at a low energy cost. Methods: The use of a mobile sink in localization introduces a new way to reduce energy consumption in WSNs. The location of a mobile sink beacon signal is known to all the sensor nodes and is also communicated periodically to all the sensor nodes. The distance measurements of the three beacon signals broadcasted by the mobile sink moving in a predetermined path and time slot are considered in this scheme, which uses the trilateration method to compute the position of the node. For isolated nodes, location estimation is performed for non-GPS equipped nodes to derive from the network their locations by using the reference node beacon signals and performing multilateration. For nodes that receive only one beacon signal from the sink, position estimation is performed by considering the reference node beacon signal by iterative multilateration technique. Results: In this scheme, reference nodes are those nodes that are localized by the sink, and no GPS is included. The proposed algorithm is simulated using C language, and some of the performance parameters used for the evaluation of the scheme are localization time, localization error, data gathering time, and communication overhead. Conclusion: The proposed work is compared to the centralized algorithm and the proposed work has been found to outperform the existing networks.

Background and Objective: The core problem of wireless body area network (WBAN) nodes is their limited energy resource. Thus, an energy-efficient routing protocol is crucial to this type of network. A major drawback of current routing protocols is that they do not consider the monitoring of a large number of patients, which may be problematic in the event of disasters. In addition, most routing algorithms fail to consider balancing energy consumption between nodes. Furthermore, routing overhead (RO) and reduced complexity are required in this type of network. Methods: In this paper, we propose a control packet aware on-demand routing (CPAOR) protocol to overcome the aforementioned drawbacks. The proposed protocol decreases the number of control packets and adds awareness for the transmission of control packets, which can extend the network lifetime. Results and Discussion: We analyze and examine the simulation results of the CPAOR protocol using MATLAB in terms of energy consumption, the energy factor, RO, and changing the number of nodes to more than 60 nodes. The CPAOR protocol demonstrates an improvement of 70.9% compared to the DMQoS, ORACE-Net, and DLQoS protocols in terms of energy consumption. This finding proves that the CPAOR protocol is an efficient routing protocol for WBANs. Conclusion: The CPAOR protocol has been proven to be an effective routing protocol for WBANs.