The growing demand for food in terms of quality and quantity has increased the need for industrialization and intensification in the agriculture field. Internet of Things (IoT) is a highly promising technology that is offering many innovative solutions to modernize the agriculture sector. Research institutions and scientific groups are continuously working to deliver solutions and products using IoT to address different domains of agriculture. This paper presents a systematic literature review (SLR) by conducting a survey of IoT technologies and their current utilization in different application domains of the agriculture sector. The underlying SLR has been compiled by reviewing research articles published in well-reputed venues between 2006 and 2019. A total of 67 papers were carefully selected through a systematic process and classified accordingly. The primary objective of this systematic study is the collection of all relevant research on IoT agricultural applications, sensors/devices, communication protocols, and network types. Furthermore, it also discusses the main issues and challenges that are being investigated in the field of agriculture. Moreover, an IoT agriculture framework has been presented that contextualizes the representation of a wide range of current solutions in the field of agriculture. Similarly, country policies for IoT-based agriculture have also been presented. Lastly, open issues and challenges have been presented to provide the researchers promising future directions in the domain of IoT agriculture.

Role of IoT Technology in Agriculture: A Systematic Literature Review

Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes-either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

https://www.mdpi.com/2072-6694/13/7/1590/pdf

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data.

The Atmosphere-Space Interactions Monitor (ASIM) is an instrument suite on the International Space Station (ISS) for measurements of lightning, Transient Luminous Events (TLEs) and Terrestrial Gamma-ray Flashes (TGFs). Developed in the framework of the European Space Agency (ESA), it was launched April 2, 2018 on the SpaceX CRS-14 flight to the ISS. ASIM was mounted on an external platform of ESA’s Columbus module eleven days later and is planned to take measurements during minimum 3 years. The instruments are an x- and gamma-ray monitor measuring photons from 15 keV to 20 MeV, and an array of three photometers and two cameras measuring in bands at: 180–250 nm, 337 nm and 777.4 nm. Additional objectives that can be addressed with the instruments relate to space physics like aurorae and meteors, and to Earth observation such as dust- and aerosol effects on cloud electrification. The paper describes the scientific objectives of the ASIM mission, the instruments, the mission architecture and the international collaboration supported by the ASIM Science Data Centre. ASIM is the first space mission with a comprehensive suite of instruments designed to measure TLEs and TGFs. Two companion papers describe the instruments in more detail (Ostgaard et al. in Space Sci. Rev., 2019; Chanrion et al. in Space Sci. Rev., 2019).

/pdf/the-asim-mission-on-the-international-space-station-30a43our20.pdf

The ASIM Mission on the International Space Station

The Kingdom of Saudi Arabia is known for its extreme climate where temperatures can exceed 50 °C, especially in summer. Improving agricultural production can only be achieved using innovative environmentally suitable solutions and modern agricultural technologies. Using Internet of Things (IoT) technologies in greenhouse farming allows reduction of the immediate impact of external climatic conditions. In this paper, a highly scalable intelligent system controlling, and monitoring greenhouse temperature using IoT technologies is introduced. The first objective of this system is to monitor the greenhouse environment and control the internal temperature to reduce consumed energy while maintaining good conditions that improve productivity. A Petri Nets (PN) model is used to achieve both monitoring of the greenhouse environment and generating the suitable reference temperature which is sent later to a temperature regulation block. The second objective is to provide an Energy-Efficient (EE) scalable system design that handles massive amounts of IoT big data captured from sensors using a dynamic graph data model to be used for future analysis and prediction of production, crop growth rate, energy consumption and other related issues. The design tries to organize various possible unstructured formats of raw data, collected from different kinds of IoT devices, unified and technology-independent fashion using the benefit of model transformations and model-driven architecture to transform data in structured form.

/pdf/an-intelligent-iot-based-system-design-for-controlling-and-2cen08gdiw.pdf

An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature

The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments.

/pdf/energy-harvesting-techniques-for-internet-of-things-iot-3gwdaz1e0d.pdf

Energy Harvesting Techniques for Internet of Things (IoT)

Low-power wide-area networks (LPWANs) are gaining popularity in the research community due to their low power consumption, low cost, and wide geographical coverage. LPWAN technologies complement and outperform short-range and traditional cellular wireless technologies in a variety of applications, including smart city development, machine-to-machine (M2M) communications, healthcare, intelligent transportation, industrial applications, climate-smart agriculture, and asset tracking. This review paper discusses the design objectives and the methodologies used by LPWAN to provide extensive coverage for low-power devices. We also explore how the presented LPWAN architecture employs various topologies such as star and mesh. We examine many current and emerging LPWAN technologies, as well as their system architectures and standards, and evaluate their ability to meet each design objective. In addition, the possible coexistence of LPWAN with other technologies, combining the best attributes to provide an optimum solution is also explored and reported in the current overview. Following that, a comparison of various LPWAN technologies is performed and their market opportunities are also investigated. Furthermore, an analysis of various LPWAN use cases is performed, highlighting their benefits and drawbacks. This aids in the selection of the best LPWAN technology for various applications. Before concluding the work, the open research issues, and challenges in designing LPWAN are presented.

/pdf/low-power-wide-area-networks-a-broad-overview-of-its-3qenlqza.pdf

Low-Power Wide-Area Networks: A Broad Overview of Its Different Aspects

In the nano-scale era, Network-on-Chip (NoC) interconnection paradigm has gained importance to abide by the communication challenges in Chip Multi-Processors (CMPs). With increased integration density on CMPs, NoC components namely cores, routers, and links are susceptible to failures. Therefore, to improve system reliability, there is a need for efficient fault-tolerant techniques that mitigate permanent faults in NoC based CMPs. There exists several fault-tolerant techniques that address the permanent faults in application cores while placing the spare cores onto NoC topologies. However, these techniques are limited to Mesh topology based NoCs. There are few approaches that have realized the fault-tolerant solutions on an FPGA, but the study on architectural aspects of NoC is limited. This paper presents the flexible placement of spare core onto Torus topology-based NoC design by considering core faults and validating it on an FPGA. In the first phase, a mathematical formulation based on Integer Linear Programming (ILP) and meta-heuristic based Particle Swarm Optimization (PSO) have been proposed for the placement of spare core. In the second phase, we have implemented NoC router addressing scheme, routing algorithm, run-time fault injection model, and fault-tolerant placement of spare core onto Torus topology using an FPGA. Experiments have been done by taking different multimedia and synthetic application benchmarks. This has been done in both static and dynamic simulation environments followed by hardware implementation. In the static simulation environment, the experimentations are carried out by scaling the network size and router faults in the network. The results obtained from our approach outperform the methods such as Fault-tolerant Spare Core Mapping (FSCM), Simulated Annealing (SA), and Genetic Algorithm (GA) proposed in the literature. For the experiments carried out by scaling the network size, our proposed methodology shows an average improvement of 18.83%, 4.55%, 12.12% in communication cost over the approaches FSCM, SA, and GA, respectively. For the experiments carried out by scaling the router faults in the network, our approach shows an improvement of 34.27%, 26.26%, and 30.41% over the approaches FSCM, SA, and GA, respectively. For the dynamic simulations, our approach shows an average improvement of 5.67%, 0.44%, and 3.69%, over the approaches FSCM, SA, and GA, respectively. In the hardware implementation, our approach shows an average improvement of 5.38%, 7.45%, 27.10% in terms of application runtime over the approaches SA, GA, and FSCM, respectively. This shows the superiority of the proposed approach over the approaches presented in the literature.

/pdf/flexible-spare-core-placement-in-torus-topology-based-nocs-1xuskec5rs.pdf

Flexible Spare Core Placement in Torus Topology Based NoCs and Its Validation on an FPGA

In this paper, we present the design and prototype implementation of an ultra-low power wake-up radio for wireless IoT devices. The prototyped wake-up radio consumes only 580nA from 3V power supply, covers distance range of up to 55 meters and achieves a sensitivity of -49.5dBm. This wakeup radio module can easily be integrated into wireless IoT devices and thereby reducing the overall power consumption of the battery powered and energy harvesting based devices. The prolonged life time of the devices can reduce the overall costs when deployed in large scale.

Design and Implementation of an Ultra-Low Power Wake-up Radio for Wireless IoT Devices

Applications of Unmanned aerial vehicles (UAVs) have advanced rapidly in recent years. The UAVs are used for a variety of applications, including surveillance, disaster management, precision agriculture, weather forecasting, etc. In near future, the growing number of UAV applications would necessitate densification of UAV infrastructure (ground radio station (GRS) and ground control station (GCS)) at the expense of increased energy consumption for UAV communications. Maximizing the energy efficiency of this UAV infrastructure is important. Motivated by this, we propose joint resource allocation and UAV scheduling with GRS sleeping (GRSS). Further, we propose the use of coordinated multi-point (CoMP) with joint transmission (JT) and non-orthogonal multiple access (NOMA) along with GRSS to increase the coverage and data rates, respectively. Through exhaustive simulation results, we show that the proposed CoMP along with GRSS results in up to 10% higher energy savings and 24% increase in coverage. Further, NOMA along with GRSS results in up to 9% enhancement in throughput of the system.

/pdf/joint-resource-allocation-and-uav-scheduling-with-ground-379zk2iuee.pdf

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

With millions of devices connected together, the Internet of Things (IoT) has become an emerging technology for future wireless networks. The ever-increasing number of smart devices and data hungry applications demand a high Quality-of-Service (QoS) for IoT. In conventional networks, data being sent to cloud for computational purpose leads to poor QoS. In order to address QoS challenges, mobile edge networks have emerged as a promising solution. In edge networks, bringing the networks resources closer to the end devices results in improved QoS. The maneuverability and the ease of versatile deployment coupled with cost efficiency makes unmanned aerial vehicles (UAVs) a promising candidate for future edge networks. The UAVs can act as edge servers to provide computational capabilities and improved services to the edge devices. Due to the flying ability, UAVs can establish better line-of-sight link with the ground devices. In this paper, we consider that the edge devices in the area of interest have to be facilitated with a certain desired QoS, which is based on the notion of outage probability of the wireless link between the UAV and the edge devices. In this context, we first propose a novel method that computes the optimum height at which UAV should hover, resulting in maximum coverage radius with sufficiently small outage probability. Then the geographical area is divided in optimal number of clusters using a novel algorithm based on K-means clustering. The method computes the optimum number of UAVs required for covering the area of interest. Each of the UAVs utilizes 3D beamforming in order to cover its own coverage area. For this purpose, we are taking coordinate transformation of the original area and forming a wide beam to cover the desired area. The obtained results demonstrate the effectiveness of the proposed method when compared to existing methods, which validate the utilization of the proposed method over large scale network applications.

Linga Reddy Cenkeramaddi

Papers

Low-Power Wide-Area Networks: A Broad Overview of Its Different Aspects

Flexible Spare Core Placement in Torus Topology Based NoCs and Its Validation on an FPGA

Design and Implementation of an Ultra-Low Power Wake-up Radio for Wireless IoT Devices

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

Improving Quality-of-Service in Cluster-Based UAV-Assisted Edge Networks