Opportunities for Transmission Power Control Protocols in Wireless Sensor Networks

This study investigates the opportunities for transmission power control (TPC) protocols in resource constrained wireless sensor networks (WSNs). The paper begins by creating a generalised model to describe the relationship between transmission power, communication reliability and energy consumption. Applying this model to the performance of state-of-the art radio hardware, the maximum potential energy savings achievable through the implementation of a TPC protocol are identified. From this, previous assumptions about the limited impact of protocols and mechanisms, such as TPC, which seek to reduce the energy consumed by wireless communication activities through targeting the distance dependent term are disproven. This paper concludes by presenting guidelines on the link conditions which offer the greatest opportunities for a TPC protocol

A Survey of Link Quality Properties Related to Transmission Power Control Protocols in Wireless Sensor Networks

Transmission Power Control (TPC) protocols are poised for wide spread adoption in wireless sensor networks (WSNs) to address energy constraints. The link quality properties that need to be captured in order to identify the optimum transmission power (TP) have not been clearly defined and previous works have presented conflicting views on the matter. This has led to several current TPC protocols using vastly different link quality properties and reporting unreliable, unstable and inefficient network performance. In this work, observations from several empirical studies on low-power wireless links are applied to identify the most critical properties of link quality for a TPC protocol. Comparing the requirements against currently available link quality estimators, it is shown that link quality estimation in WSNs is still very much an open challenge and one that must be addressed in order to implement an accurate and reliable TPC protocol

Computing: Looking Back and Moving Forward

The Internet and computer commercialization have transformed the computing systems area over the past sixty years, affecting society. Computer systems have evolved to meet diverse social needs thanks to technological advances. The Internet of Things (IoT), cloud computing, fog computing, edge computing, and other emerging paradigms provide new economic and creative potential. Therefore, this article explores and evaluates the elements impacting the advancement of computing platforms, including both long-standing systems and frameworks and more recent innovations like cloud computing, quantum technology, and edge AI. In this article, we examine computing paradigms, domains, and next-generation computing systems to better understand the past, present, and future of computing technologies. This paper provides readers with a comprehensive overview of developments in computing technologies and highlights promising research gaps for the advancement of future computing systems

Predicting customer behavioural patterns using a virtual credit card transactions dataset

Nowadays, many businesses are resorting to data mining techniques on their data, to save costs and time, as well as to understand customers’ needs. Analysing such data can leader to higher profits and higher customer satisfaction. This paper presents a data mining study that is applied on millions of transactional records collected for a number of years, by a leading virtual credit card company based in Malta. In this study, 2 machine learning techniques, namely Artificial Neural Networks (ANN) and Gradient Boosting (GBM), are analysed to identify the best modelling framework that predicts the churning behaviour of this company’s customers. Apart from helping the marketing department of this firm by providing a model that predicts churning customers, we contribute to literature by identifying the minimum amount of customer activity needed to predict churn. In addition, we also analyse the “cold start” problem by performing a time-series experiment based on the few data available at the beginning of the customer purchase history.peer-reviewe

Autonomous Irrigation Management in Decision Agriculture

In this chapter, the important application of autonomous irrigation management in the field decision agriculture is discussed. The different types of sensor-guided irrigation systems are presented that includes center pivot systems and drip irrigation systems. Their sensing and actuator components are with detailed focus on real-time decision-making and integration to the cloud. This chapter also presents irrigation control systems which takes, as an input, soil moisture and temperature from IOUT and weather data from Internet and communicate with center pivot based irrigation systems. Moreover, the system architecture is explored where development of the nodes including sensing and actuators is presented. Finally, the chapter concludes with comprehensive discussion of adaptive control systems, software, and visualization system design

Underground Wireless Channel Bandwidth and Capacity

The UG channel bandwidth and capacity are vital parameters in wireless underground communication system design. In this chapter, a comprehensive analysis of the wireless underground channel capacity is presented. The impact of soil on return loss, bandwidth, and path loss is discussed. The results of underground multi-carrier modulation capacity are also outlined. Moreover, the single user capacity and multi-carrier capacity are also introduced with an in-depth treatment of soil texture, soil moisture, and distance effects on channel capacity. Finally, the chapter is concluded with a discussion of challenges and open research issues

Underground Phased Arrays and Beamforming Applications

This chapter presents a framework for adaptive beamforming in underground communication. The wireless propagation is thoroughly analyzed to develop a model using the soil moisture as an input parameter to provide feedback mechanism while enhancing the system performance. The working of array element in the soil is analyzed. Moreover, the effect of soil texture and soil moisture on the resonant frequency and return loss is studied in detail. The wave refraction from the soil–air interface highly degrades the performance of the system. Furthermore, to beam steering is done to achieve high gain for lateral component improving the UG communication. The angle enhancing the lateral wave depends upon dielectric properties and usually ranges from 0∘ to 16∘. These dielectric properties change with the change in soil moisture and soil texture. It is shown from the experiments that optimal UG lateral angle is high at lower soil moisture readings and decreases with decrease in soil moisture. A planar structure of antenna array and different techniques for optimization are proposed for enhanced soil moisture adaptive beamforming. UG channel impulse response is studied from the beamforming aspect to identify the components of EM waves propagating through the soil. An optimum steering method for beamforming is presented which adapts to the changing values of soil moisture. Finally, the limitations of UG beamforming are presented along with the motivation to use it

Soil Moisture and Permittivity Estimation

The soil moisture and permittivity estimation is vital for the success of the variable rate approaches in the field of the decision agriculture. In this chapter, the development of a novel permittivity estimation and soil moisture sensing approach is presented. The empirical setup and experimental methodology for the power delay measurements used in model are introduced. Moreover, the performance analysis is explained that includes the model validation and error analysis. The transfer functions are reported as well for soil moisture and permittivity estimation. Furthermore, the potential applications of the developed approach in different disciplines are also examined

Signals in the Soil: Underground Antennas

Antenna is a major design component of Internet of Underground Things (IOUT) communication system. The use of antenna, in IOUT, differs from traditional communication in that it is buried in the soil. Therefore, one of the main challenges, in IOUT applications, is to establish a reliable communication. To that end, there is a need of designing an underground-specific antenna. Three major factors that can impact the performance of a buried antenna are: (1) effect of high soil permittivity changes the wavelength of EM waves, (2) variations in soil moisture with time affecting the permittivity of the soil, and (3) difference in how EM waves propagate during aboveground (AG) and underground (UG) communications. For the third challenge above, it is to be noted that lateral waves are dominant component in EM during UG2UG communication and suffer lowest attenuation as compared to other, direct and reflected, components. Therefore, antennas used for over-the-air (OTA) communication will not be suitable for UG communication because of impedance mismatch. This chapter focuses on developing a theoretical model for understanding the impact of soil on antenna by conducting experiments in different soil types (silty clay loam, sandy, and silt loam soil) and indoor testbed. The purpose of the model is to predict UG antenna resonance for designing efficient communication system for IOUT. Based on the model a wideband planar antenna is designed considering soil dispersion and soil–air interface reflection effect which improves the communication range five times from the antennas designed only for the wavelength change in soil. Furthermore, it also focuses on developing an impedance model to study the effect of changing wavelength in underground communication. It is also discussed how soil–air interface and soil properties effect the return loss of dipole antenna

Decision Agriculture

In this chapter, the latest developments in the field of decision agriculture are discussed. The practice of management zones in digital agriculture is described for efficient and smart faming. Accordingly, the methodology for delineating management zones is presented. Modeling of decision support systems is explained along with discussion of the issues and challenges in this area. Moreover, the precision agriculture technology is also considered. Moreover, the chapter surveys the state of the decision agriculture technologies in the countries such as Bulgaria, Denmark, France, Israel, Malaysia, Pakistan, United Kingdom, Ukraine, and Sweden. Finally, different field factors such as GPS accuracy and crop growth are also analyzed