Green Internet of Things (GIoT): Agriculture and Healthcare Application System (GIoT-AHAS)

Green IoT for Agriculture and Healthcare

Anil L. Wanare1* and Sahebrao N. Patil2

1JSPM’s BSIOTR, Pune Dept. of Electronics & Telecommunication Engineering, Savitribai Phule Pune University, Pune, India

2JSPM’s BSIOTR, Pune Dept. of Electrical Engineering, Savitribai Phule Pune University, Pune, India


In the last couple of years, there are applications relevant to Green Internet of Things (GIoT) and the major focus on two development trending and admired technologies is upcoming: Green Cloud Computing Application (GCCA) and GIoT are current buzz discussions in the field of crop growing (agriculture) and medical related things, i.e., healthcare industry–based applications. Motivated by achieving a sustainable globe, this article discusses a variety of technology and issue concerning GCCA and GIoT and, additionally, further improves the conversation with the suppression of energy utilization of the combination of these two techniques (CCA and GIoT) in farming industry, i.e., one is agriculture-based and the other is healthcare industry–based system. The past and perception of the hot green information and communication technologies (GICTs) which enabled GIoT have been discussed rigorously. Green mathematical computational calculations opens first and, furthermore, or we can say, afterward focuses on the modern significant works completed concerning of these two upcoming emerging technologies in both agriculture and healthcare cases. In addition, this post has contributed significant information by presenting GIoT farming and healthcare applications linear time-invariant system (GIoT-AHAS) using digital wireless sensor cloud discrete integration or digital summation modelling. Finally, we have summarized the limitations, advantages, challenges, and prospects of the research guidelines associated to emerging and advanced green-based application oriented development in relevant field. The aim of our article is to research and create broad green area and also to make contribution to sustainable application around the globe.

Keywords: Green Internet of Things (GIoT), green cloud computing applications, GIoT-based agriculture, healthcare, sensor cloud, ubiquitous computing

9.1 Introduction

The solar system now contains a number of components and relevant objects around it. Like the Internet of Things (IoT) [1, 2], so out of all, the most technologically advanced objects in world, it aims to connect various objects or objects via the internet (e.g., laptops, advanced computers, advanced cars, various digital gadget, and more functional objects) with different e-mail addresses or smart mobile phone application. Internet (IP) enables them to communicate with others and everywhere in world (worldwide). A lot of devices come up to develop a number of technological devices and objects are linked or joined to the internet with unusual levels of Internet of Things (IoT) imagination [2, 3]. The IoT-based on many applications are there, viz., transportation from one place to another, advanced agriculture, recent health systems, programmable logic controller-based industrial automation, and emergency responses to natural disasters, disasters happened due to human beings, and combinations wherever human decision-making is extremely difficult in such situation. Among many applications are there to enable by IoT, which focuses mainly on two applications one is agricultural and the other is related to healthcare in this chapter. The many sensors connected to the network, through the wireless sensors network to human body or embedded in our environment, make it achievable to collect effective data that reflects a person’s physical and mental health [3, 4].

The IoT empowers us to observe, pay attention to think and perform the tasks by enabling them to converse jointly, and share information and directives for the decision. Ultimately, all aspects of the human being cyber, substantial, societal, and psychosomatic world would be interrelated and rationally in the intellectual world (globally). As we know most recent stage in human times gone by, the intelligent world has received much attention from the world of education, business, industry, government, and many other organizations. In addition, the Green IoT (GIoT) is the raw material network aims at a robust (global) smart world device by suppressing the internet power utilization or consumption of components used in such applications [5, 6].

The green cloud computing (GCC) is one of the most popular and upcoming technologies, the upcoming and promise paradigm that offers computer use as an aid [7, 8]. It offers the most up-to-date software practice, mass data access, large data storage space services, and additional online mathematical computational calculations and helps customers payment resources based on the pay as you go representation system [5, 7]. Customers are only charged more for the way they use it properly. It is very expensive. The immense gain of cloud computing is that users can get computing for their statistics and huge data storage services on demand with no much investment in computer infrastructure (e.g., advanced computational tools). Díez, C. Hacia et al. provided statistics; all the world’s data centers use 30 billion watts of electricity out of 2000, and 2000 is equivalent to the output of 30 nuclear energy and power generation plants [8]. Electrical energy that can be powered 5 million homes in 1 year is needed to cool all these servers and all big data centers in 1 year [9]. We should therefore look for new strategies to increase the energy demand of these large data centers, namely, cloud [8, 9]. Basic agricultural and networking methodology is described in Figure 9.1.

GIoT data–based computing is nothing but the environmentally friendly computer. It refers to efforts to suppress or to reduce energy consumption and energy consumption issues and to reduce costs and emissions of carbon dioxide (CO2) [10]. The sensor is connected to that, i.e., the digital sensor cloud architecture concept integrates cloud infrastructure and sensor network, thus enabling instantaneous monitoring of detailed applications that are often distributed in geographically distributed areas [11, 12]. Wireless digital-based networks are widely used to apply health related applications, as shown in Figure 9.1, to monitor the patients with diabetes mellitus, blood pressure, heart beats, mental condition, and sleep patterns [12, 13].

Schematic illustration of basic Green IoT for both agriculture and health network.

Figure 9.1 Basic Green IoT for both agriculture and health network.

In such applications, the health facility take the essential steps or decision based on useful data collected from patients. It is very tricky task to observe the state of health condition of patient remotely, where the patient travels arbitrarily anywhere. Therefore, a well-organized computer system is very much needed to observe the situation of patients when they move at random. The most important information, time-varying sensor networks, can advantage from the complex incorporation of computer and storage space resources provided by cloud computing application for large statistics processing related to data collections [12, 15]. Therefore, green cloud sensor platforms are becoming increasingly popular in this era. This blog presents the GIoT agricultural and healthcare system using the cloud-sensor integration model (GIoT-AHAS).

The GIoT is transforming things from conventional to highly intelligent by exploits its basic technology such, at the same time, as ubiquitous computers, advanced embedded equipment, communication technology, sensory network, and internet set of rules and application [14]. Through IoT, recent technologies which relate to cloud computing are extremely different prospect technologies coming from both that are already a part of our global life [15]. The IoT is over and over again characterized by small, extensively distributed real-world objects, with inadequate storage and handing out power, including concern about dependability, performance, safety, and confidentiality. In other ways, GCC has limitless storage capacity and processing power, highly advanced technology, and most of the IoT that are slightly solved using new IoT-related technology. Therefore, any application where cloud and IoT are both integrated technologies is predictable to disturb the existing and prospect and upcoming internet [15, 16]. This article is organized into section as follows. The second section provides a comprehensive introduction of computational statistics, ubiquitous usage necessities, and a brief introduction of the green computational devices and advanced computer. In the third section, we attempted to present the proposed construction, requirements, application of GIoT in the construction of the proposed buildings, and information and communication technology (GICT)–enabled green components such as green radio-frequency identity device (GRFID) frequency, green wireless sensor network (GWSN), GCC, green machine to machine (GM2M), green direct power supply (GDPC), and green infrastructure (GIoT-AHAS). Finally, we have completed the conclusion, and prospect indicators are discussed in the further section of this blog in general.

9.2 Relevant Work and Research Motivation for GIoT-AHAS

9.2.1 Ubiquitous Computing

Computing everywhere is one of the best ways, lifestyle and engineering, new technologies at the same time: it basically refers to a type of technology that can access all aspects of the user’s life and work behind their scenes, providing value without getting in the way. It is sometimes referred to as a full computer [17]. The concept of ubiquitous computing techniques began to emerge in 1988, when a scientist named Mark D. Weiser first introduced it to the council community [17, 18]. The ubiquitous computer has been described as “representing a powerful revolution in integration, in which people be alive, occupation, and engage in recreation in a seamless computer environment”. An ever present computer lays the ground where public is enclosed by computer devices and the computer infrastructure we support everything [18, 19].

In the ubiquitous computer, individuals are surrounded by many computers that have networks, spontaneously but interoperable, a number of which are old or portable, some of which they encounter on the shift, many of which serve devoted purpose as part of the material, all using automatic, imperceptible, and limited human attention. In other ways, computing all over the place will carry the next epoch of more advanced computers around one user and become a more noticeable part of the substantial environment, and its mechanism will be disseminated on all scales day by day and usually turn to a clear quotidian edge. There are four key components that are everywhere in a computer: portable devices, customized nodes, network equipment, and smart labels related to the green internet (GIoT). To achieve ubiquitous computer services, rather than the so called five main goals of ubiquitous accessibility such as accessibility, transparency, shamelessness, awareness, and integrity (ATSAT). Presentation by the ubiquitous service from the ubiquitous computer view, by looking at five major GIoT-AHAS related methods: diversity, communication, adaptability, credit, and easy to use (SCALE) [19, 20]. Network platform of GIoT-AHAS or, we can say, structure for GIoT Agriculture and Healthcare Application System (AHAS) is shown in Figures 9.2a and b to collect the significant data for analysis to take decision for corrective actions.

Schematic illustration of GIoT-AHAS network platform with gateway.

Figure 9.2 (a) GIoT-AHAS network platform with gateway.

Schematic illustration of GIoT AHAS network platform with security management system.

Figure 9.2 (b) GIoT AHAS network platform with security management system.

9.2.2 Ubiquitous Agriculture and Healthcare Application Requirement

By considering the universal applications, there are three objectives, namely, to reduce the time lost due to disposal, to reduce intermediate costs, and to reduce the inconvenience of conventional medical flow according to the literature [21]. The lag is a necessary time for printing by hand and paper delivery or by human-based information transmission which creates delays that could represent a major cause of possible financial losses in the medical field. The collateral reduction will suppress the gap between the information being recorded in linear time-invariant (LTI) system and when it is accessible for digital system out data or information analysis and processing using GIoT devices.

In addition, ubiquitous agricultural consumers and healthcare providers will send information from a variety of sources to get instantaneous information and expertise and seek relevant and useful information as shown in Figure 9.2. If the above conditions are met, then the application will be everywhere by using digital system. It will be a straightforward local program, performing one or a hardly any volunteer tasks. It will spread everywhere, connect digital green devices, and be embedded in such a way that communication is invisible and available in real time. It will get to know the context and link the change in the surroundings with digital computer programs. It will be transportable, using advanced technology while delivering [21]. It will have been worn out, using advanced digital devices while the human hands, voice, eyes, or awareness are aggressively involved in the corresponding physical surroundings. It will look good quality, see its nature, and respond appropriately [22]. It will also be good for ambient, working in concert to support community in day-to-day activities, activities and culture in a simple and recognizable way using information and cleverness hidden in the GIoT-based wireless digital network that connects these devices [22]. Figure 9.3 showed everything about the GIoT-based agriculture and healthcare network establishment using digital sensors network with platform and also described protocols which are required for the same. Requirement of software structure and highrarchical process is also shown in Figure 9.3.

Schematic illustration of green IoT agriculture and healthcare applications (GIoT-AHAS).

Figure 9.3 Green IoT agriculture and healthcare applications (GIoT-AHAS).

9.2.3 Green Cloud Computing

The idea of a green computer began to spread in the last few years, gaining popularity. In adding to the extensive compassion to ecological issues, such attention is also ambitious by financial requirements, as both the energy expenses and energy requirements of the IoT industry internationally replicate ever increasing trend [23]. Green computational process is the natural make use of advanced digital computer and other related resource as per compatibility. Such actions include the use of dynamic power plants, servers, and restrictions as well as the reduction of resource use and proper disposal of e-waste [22, 23]. The green computational analysis process must to study in details and operation of efficient and eco-friendly computing and the purpose of using energy-saving codes to get software before using less powerful hardware, rather than continuing to use the same code on a smaller amount powerful hardware.

Green computing, GICT according to the Internationally Federation of GICT and GIFG Standard, raw GIT, or GICT sustainability is the research and development of an environmentally friendly computer or IT [24]. V. Murugesan et al. describe GIoT and investigate and intend for the plan, manufacture, process and discarding of computers, servers, and correlated sub-LTI systems such as monitor, printer, storage space devices, and digital communication and digital communication systems professionally and efficiently without negligible environmental impact [25]. Also, it lists these four major parallel approaches to accurately and efficiently monitor the environmental effects of computer statistics that should be targeted at the green computer. Some of the terminologies used related to our GIoT-AHAS are described as follows:

  1. 1. Green Utilization: To minimize the energy utilization of computer systems and other information system and to use all of them in an eco-friendly ways.
  2. 2. Green Waste: Repairing and reuse of old computers and reuse of unwanted computers and other digital electronic-based equipment (reusable).
  3. 3. Green Intend: Designing power-efficient and environmentally forthcoming machinery, devices, digital computers, various faults, high capacity servers in terms of memory, and advanced cooling equipment consume low power in a day.
  4. 4. Raw Production: The production of digital-based electronic equipment and various relevant advanced components, digital computers, and other high-tech devices with negligible impact or very low-impact environmental programs.

GCC identifies many areas and functions, including ecological sustainability, power-efficient computer, power administration, archiving, construction, configuration and position, computer digital server performance, appropriate discarding and recycle, control compliance, metrics raw materials, testing tools, and methodology, to reduce environmental hazards, the use of advanced renewable power devices to get eco-friendly power from renewable process, and the labeling of GIoT-AHAS product.

9.2.4 Green IoT Agriculture and Healthcare Applications (GIoT-AHAS)

We focused on ubiquitous computer communication, the needs to achieve universal usage, and the basics of cloud computing to integrate with the GIoT-AHAS. In this part of this chapter, we have defined the fundamental concept of the GICT technology components associated with the GIoT-AHAS and introduced the construction of the GIoT-AHAS using the concept of cloud integration. GIoT-AHAS Architecture

Green Sensor cloud computing (GScS) is considered one of the most powerful advanced agricultural technology and healthcare monitoring systems. GScS is a new green cloud computational model that can be utilized visual digital sensors to collect its information and transfer all sensory data to advanced cloud computing infrastructure. Control sensor statistics can be used by many monitoring system. Initially, we will try see the definitions of cloud sensors as below.

In the concept presented by Intelli Sys et al., it is an infrastructure that allows for complete statistics computation using digital advanced sensors as a visual associated between substantial and comprehensive sets of statistics such as cyber core and the GIoT as a source of communication [26].

From another available book by Micro Strains et al., sensor cloud definition is a unique, visually sensitive information and remotely managed audio amplifier that uses commanding cloud computing technology to provide the best statistical information, visualization, and comprehensible analysis. Innovative advances are designed to compatible reliable longterm transmission of microstrain wireless digital active sensors, and green sensor cloud now supports any IoT-based web connected to other network device, wireless digital sensor, or digital wireless active sensor network using the open facts application programming interface (API) [27, 28]. To attract ever increasing attention from both educational and engineering industrial communities, sensor cloud computing (SCC) is actually a new paradigm, driven by the fulfillment of 1. ability to get ubiquitous statistics and significant information collection for wireless network networks and 2. the ability to store data and process GSCC data.

Specifically, the basic sensor-based cloud application model has ubiquitous digital sensors or body sensors; it is easily accessible and commonly worn sensors such as accelerometer digital wireless sensors, digital proximity sensors, and luminosity and temperature lamps [28, 29] provided by the wireless sensor network (WSN) provider to collect a variety of sensory monitored information from various locations. Combined all sensor information is also transferred to the cloud provide by a cloud service contributor for storing and further processing of information. After the cloud has stored and processed raw digital wireless sensor statistics with data center, the used or value sensor data is delivered to the user’s applications for each service and requirement. In this full scenario, the network providers act as information sources for cloud service provider for analysis purposes (GIoT-AHAS). Users of the service are the ones asking for large data from cloud service providers. With the integration of cloud sensor (SCI), there are many interesting benefits [31, 32], which benefit users and the WSN and cloud like this: Users can access their required data or information collected by sensors from the cloud whenever and wherever there is a GIoT system connection get established, instead of clinging to their desks, that is, 24/7. The utilization of a green digital WSN can be increased, allowing it to run various relevant applications. The cloud of services provided is able to be greatly improved, by being capable to recommend the services provided by the digital WSN (e.g., agriculture and healthcare monitoring in this system which is GIoT-AHAS). Specifically, to improve the performance (e.g., information processing speed, impulse response time, and visibility) of a large storage network and cloud performance, investigative results have shown that the green cloud sensor can make a conventional wireless network, by ever increasing sensory lifetime by 3.35% and reducing power utilization by 37.55%. All of this is very appealing to the smart world and the green HEIs in India when used in the right ways [33]. GIoT-AHAS Requirements

We have written down and summarized the GICT requirements for the construction of GIoT-AHAS as follows:

  1. 1. Turn off unwanted areas: If the buildings remain functional, then it will consume a lot of power. However, if buildings are opened only when necessary, then power consumption will get reduced in such applications. For example, sleep planning is one of the most extensively used way to save energy on digital WSNs, by making the wireless digital sensor nodes wake up stronger and fall asleep.
  2. 2. Launch only the necessary information: Transferring big data (e.g., multimedia statistics) consumes a lot of power. Sending data only needed to users can accumulate a maximum of power utilization or consumption by the system. The prediction of data prediction is based on user performance analysis; it is one of the ways to make available information needed to user.
  3. 3. Reduce the distance end to end of the data pathway: This is as well a straightforward way to decrease the power consumption. Route scheme in view of the length of the selected data pathway can be very well energy efficient. In adding together, setup of network operating systems that meet route requirements is, in addition, an effective way to obtain a to a great extent shorter data pathway.
  4. 4. Suppress the distance end to end of the wireless data transmission pathway: In terms of reducing the length of the wireless data transmission path within network in the wireless network, energy-efficient construction designs for wireless communication systems can be considered. In addition, a collaborative transmission system must be present for wireless communication also promises efficient power, through the transmission nodes to hear the broadcast and digital signal transmission to the target node, which has resulted in significant gain of diversity.
  5. 5. Trade in communication processing: As new sensible ways to hear the signal at a much lower rate of relative proportions as long as the causal signal is small, the pressing sensation is also capable of improving energy efficiency in both systems.
  6. 6. Improved communication: In terms of green communication, sophisticated communication methods are emerging. For example, recent trends of cognitive radio system (CRS) that is knowledgeable about its nature and can change its operating systems (frequency, voice fluctuations, wave form, and transmitting power). With advanced software and hardware deception, it is possible to improve frequency spectrum band efficiency and reduce the difficulties of overcrowding by using appropriate algorithms.
  7. 7. Renewable green energy sources: Unlike traditional sources, renewable energy sources, namely, oxygen, clean water, solar energy, timber, biomass, fuel cell, and geothermal energy, are natural resources that can be replaced and can be reused. Therefore, the use various types of renewable green energy source will have a significant impact on reducing oil dependence and emissions of carbon dioxide.

Table 9.1 summarizes of impact by adapting some important things to get significant result related to reduce energy consumptions to get efficient results while constructing the GIoT and healthcare application system. Applying Green Internet of Things to Agriculture and Healthcare System

While discussing the GIoT, we should first see a variety of GIoT-related definitions, and it is considered the subsequently wave when cloud deployment is expected to be outside the conventional desktop area [34]. By considering the same, in line with this awareness and thinking of available literature, a new concept called the GIoT gained momentum in the last not many existence. GIoT refers to a universal global digital network of connected objects that is specially designed according to the common rules of communication its purpose is to connect to the Internet. The best GIoT is inspired researchers by the most recent development in various digital devices and advanced digital communication technology, but GIoT facilitates devices that are not only as multifaceted as smart transportable phones devices but also contains everyday substance [34]. They are able to work together to achieve the same objectives in this applications related to GIoT to AHAS [35, 36].

Table 9.1 Impact of requirement in GIoT-AHAS.

Requirement Impact to GIoT-AHAS
Shutdown the services that are not really required If the some part of buildings remains functional, then it will consume a lot of power. However, if buildings are opened only when necessary, then power consumption will get reduced in such applications.
For example, sleep planning is one of the most extensively used ways to save energy on digital WSNs, by making the wireless digital sensor nodes wake up stronger and fall asleep.
Keep active system when the data that are really required/needed Transferring or communicating the digital information or relevant data (e.g., multimedia computation statistics) consumes a lot of power. Sending data only when required or really needed to users can accumulate a lot of power consumption.
Reduce data transmission path as per requirement in the digital wireless sensors network This is as well a straight forward way to decrease the power consumption by low power generation devices required. Route scheme in view of the length of the selected data transmission pathway can be very well energy efficient. In adding together, setup of wireless network operating systems that meet route requirements is, in addition, an effective way to obtain a to a great extent shorter data pathway.
Minimization of length of wireless data broadcasting path (Transmitter and Receiver) In terms of reducing the length of the wireless data path in the wireless network, energy-efficient construction designs for wireless communication systems can be considered.
In addition, a collaborative transmission system must be present for wireless communication also promises efficient power, through the transmission nodes to hear the broadcast and digital signal transmission to the target node, which has resulted in significant gain of diversity.
Trade off processing for communications in IoT-based system As new sensible ways to receive the signal at a much lower rate of relative proportions as long as the causal signal is small, the pressing sensation is also capable of improving energy efficiency in both systems.
Advanced digital communication techniques adaptation in such applications In terms of green communication, sophisticated communication methods are emerging. For example, recent trends of Cognitive Radio System (CRS) that is knowledgeable about its nature and can change its operating systems (frequency, voice fluctuations, wave form, transmitting power).
With advanced software and hardware deception it is possible to get better frequency spectrum band efficiency and reduce the difficulties of overcrowding by using appropriate algorithms.
Renewable green power sources which are easily available in the market specially for agriculture and healthcare applications Unlike traditional sources, Renewable Energy Sources, namely, oxygen, clean water, solar energy, timber, biomass, fuel cell, and geothermal energy, are natural resources that can be replaced and can be reused.
Therefore, the use various types of “Renewable Green Energy Source” will have a significant impact on reducing oil dependence and emissions of carbon dioxide.

A key quality characteristic in GIoT is, without any doubt, its impact on the daily lives of probable consumer. GIoT has significant special effects in the workplace and the home environment, wherever it can play very significant role in the future prospect such as assisted livelihood, physical condition, crop growing, transports, and many other applications. Significant business outcomes are expected (e.g., logistic, industrial automation, advanced logistics, agricultural surveillance, security, and health employment). Factors in the IoT environment [37, 38] are presented in Figure 9.4. Particularly, there are six sub-blocks in GIoT such as recognition, hearing, communication or broadcasting technology based on advanced green digital coding techniques, and arithmetic, various relevant services and semantics. Connectivity of all subsystems related to GIoTbased GIoT-AHAS is shown in Figure 9.4.

Classification and identification play a very important position in designing and comparing various services and their needs. Example of diagnostic techniques is used in GIoT digital electronic device product codes, digital codes everywhere. Pay attention to collect a variety of information from related substance and send it to the database, database, and data analysis center. The data collected also analyzes the data based on the necessary services [39]. Sensors can be wetness or moisture detecting sensor, high sensitive temperature detecting sensors, sensing reading devices, cell phones, etc. Advanced digital communication devices are installed by using IoT-based techniques to connect diversified substances together to provide precise services as per the prerequisite. The accessible GIoT communication protocols are wireless fidelity (Wi-Fi), standard IEEE 802.15.4 protocol, Bluetooth, Z-wave which is one of the best wireless communication protocol, long-term evolution (LTE) advanced, Near Field wireless broadcasting or Communication (NFC), ultra bandwidth wide frequency protocol (UWB), etc. [40].

Schematic illustration of building blocks of GIoT to AHAS connected to IoT-based system.

Figure 9.4 Building blocks of GIoT to AHAS connected to IoT-based system.

For cloud computing, hardware-based digital signal processing units [e.g., sophisticated microcontrollers, emerging microprocessors, and required various LTI system on chips (SoCs, RC, etc.), field planning gateways (FPGAs), and software-based algorithms and applications highly developed numerous hardware (cards) platforms (e.g., Arduino, UDOO, Friendly ARM, Intel Galileo, Raspberry PI, and Gadgeteer)] are being built and using a variety of software platforms (e.g., Tiny OS, Lite OS, and Riot OS) [34, 35]. The cloud platform is an integral part of GIoT computation calculation, as it has great potential for the processing of large amounts of data in real time and extracts all kind of important information from the collected statistics.

GIoT-based services can be divided into three categories: proprietary-related services, integration services, and known mutual ubiquitous services. Proprietary-related services place the establishment for various types of services, because all applications that map real-world substance to the physical world need to recognize objects primarily. Data collection or integrating services gather and summarize raw information that needs to be processed and report to required sections of the system. The information obtain is also used by mutual services to build decision and act in response properly. The ubiquitous services tender common services to everywhere on require, anytime wherever required. Semantic refers to the ability to take out information significantly and intelligently to provide the essential services applications [41].

This development more often than not includes resource acquisition, resource utilization, data modeling, data identification, and analysis. The most normally used semantic technologies are resource development framework, i.e., RDF interpreting frameworks (RDF), web language ontology (OWL), and active XML exchange (EXI) [42, 43].

9.2.5 Green IoT for AHAS (GIoT-AHAS)

To simplify raw GIoT, IoT should be separated by energy efficiency. In particular, all agricultural equipment and health applications must be there prepared with emotional and communication additives so that they can hear and correspond well in a timely manner, and they will need a lot of power. In adding together, determined by increasing interest rates and sustain from various organization, the requirement for energy will be to a great extent greater than before. All of these create the GIoT focused on dropping the utilization of GIoT energy as a requirement, in provisions of satisfying the elegant world through sustainability. If you consider energy effectiveness as a key feature throughout the expansion, then expansion of GIoT, raw IoT can be defined as below [43].

The well-organized process for energy consumption (hardware or software) adopt by GIoT possibly will either reduce the thermal impact of presented application or decrease the impact of GIoT’s thermal effect. In the previous case, the utilization of GIoT will assist to decrease the impact of heat, at the same time as in the further development of IoT conservatory printing will be taken care of. The lifelong cycle of raw GIoT should focus on raw materials, raw production, raw resources, and, ultimately, green waste discarding/recycle with little or no environmental impact [44, 45]. GIoT to AHAS Components

In this particular section, the GICT framework and the raw technology for the G-AHAS are discussed. GICT is an umbrella term linked with several relevant applications, coming up technology and application for information and data broadcasting communication, enabling users to access, store with memory devices, broadcast, and use a variety of information. Any required items are listed below, with regard to the identification, hearing, communication, and calculation of IoT devices presented in this chapter.

  1. 1. Radio-Frequency Identification (RFID): A very tiny advanced electronic device consisting of a very large-scale integrated circuits and aerial as an antenna, which automatically identifies and tracks tag attached to objects, operating at a certain frequency.
  2. 2. Wireless sensor network (WSN): A digital network consisting of independent distributed sensors that work together to supervise physical or ecological circumstances such as temperature, sound, vibration, pressure, and movement.
  3. 3. Wireless local area network (WLAN): A groundless wireless network of digital devices connected to an individual’s workplace.
  4. 4. Wireless physical network (WPAN): A wireless digital network consisting of portable computer devices (e.g., sensors and actuators) available on or off the body.
  5. 5. Local area network (LAN): A type of LAN, which connects existing digital devices within or within the immediate neighborhood of the system.
  6. 6. Neighborhood Network (NAN): A network contains wireless fidelity hotspots and WLAN, enable users to connect to the Internet faster and to make it a much cost effective system.
  7. 7. Machine to Machine (M to M): This is one of the advanced technologies that allow both guided communication and digital wireless devices to proper communicate with other devices which are compatible with the same.
  8. 8. GCC: One of the advance computing models of a novel to enable easy network access, required for configuration resources (e.g., various network, big servers, big data storage memory devices, relevant applications, and many relevant services). To integrate GCC into a portable environment, green transportable cloud computing (MCC) can continue to load large amounts of digital information and data processing for analysis and storage functionality from smart mobile devices to the cloud.
  9. 9. Big Data Center (BDC): A database (visible or virtual) for storing, managing, and disseminating data and information.
  10. 10. Green Radio-Frequency Identification Model (G-RFIDM): RFIDM incorporates high-frequency RFID tags and an extremely small separation of tag reader. The G-RFIDM, or we can say, tags, is a small microprocessor-based integrated circuit (i.e., microchip) attached to the radio (used to receive and broadcast signal), with only one of its kind identification code.

Summary of the methods for optimization of the efficient energy of a variety of components at different positions in GIoT-based agriculture and healthcare applications LTI systems is described in Table 9.2.

The purpose of the RFID tags is to store information related to the attachment [46, 47, 49]. The fundamental procedure is that the flow of information caused by the readers of the RFID models or tags by transmitting the self-generated signal for questions followed by the answers of the RFID models [48].

Table 9.2 Typical class of components used for energy-efficient techniques toward GIoT in agriculture and healthcare applications.

Components used Cause of power consumption Energy-efficient techniques
Digital active and passive sensors Sensing data on site and continuous sensing and storing the information Selection of sensing devices, self-powering digital sensors, sleep mode scheduling as per need, compression sense about the information
Radio Frequency Identification Device (RFID) Detection of sensed information and data on site for specific purposes Green passive sensing device to utilize whenever needed or required
Sink nodes in digital wireless network Distribution and analyze the information Optimization utilization of processing at sensing end task to core mapping
Data center connected to cloud Processing the data or information in cloud, highcomputational task perform Need to distribute the load to node as per requirement, reducing the data transmission pathway to collect or to send the data within network
Gateway nodes by combining the both the system agriculture and healthcare system Broadcasting between digital wireless sensors and WSN N/W Controlled storage space and setting up with triggered events as per requirement
Resource allocation as per need and priority Distribution of information/ statistics to resources from gateway in the GIoT-based agriculture and healthcare system Modification and allotment of sensing devices and processing mechanism to access the scheduling throughput, interoperability
Processors and cloud system High-computational tools, relocate the different task to different central part of the linear time-invariant system, circumstance aware allotment of servers Use of vapor computation and edge computation, dynamic redundant data packets downloading, suppressing the data path dynamics task distribution and scheduling for quality-of-service cost

In general, the broadcasting range of radio-frequency detection systems is very low down range radio system (i.e., a few distances in meter). In addition, it is used to perform multi-band transmission (for example, it is very low frequency range from 124 to 135 kHz up to ultra high frequencies at 860 to 960 MHz). Two types of RFID tags [i.e., Active Tags and Passive Tags (ATPT)] are available. Active markers have battery that facilitate signal transmission by using digital signal processors and increase transmission range, while entry tag do not have internal battery and require harvesting power from the student signal with the goal of input [50, 51].

  • • Reducing the size of RFID tags should be considered as reducing the number of non corrosive materials used in their production (e.g., visible RFID models and printed RFID tag), because that models themselves are easier said than done to reproduce often.
  • • Energy-saving algorithms and processes are supposed to be used to increase tag proportions, to adjust the power transfer capacity, to keep away from tag collisions, to avoid hearing.

9.2.6 Green Digital Wireless Sensor Networks

Green digital wireless sensor networks (GWSNs) usually contain a definite number of digital active sensor nodes and a base station (e.g., wireless sensor nodes). The sensor nodes have low down processing power, partial power, and required memory devices or, we can say, storage capacity, and the base channel has high power [52, 53]. The sensors nodes are connected to each other with several active and passive sensors in the system, take the reading (e.g., temperature, humidity, and speed) from the first circuit. They then collaborate and transmit sensory information to a standard ad-level channel. The most commonly used WSN solution for sale is based on the standard IEEE 802.15.4 benchmark, which includes low and medium access control (MAC) levels of low power and low level of communication [53, 54]. Agriculture-based farm remote monitoring in agriculture and low-power GWSN topology have been shown in Figures 9.5 and 9.6 in details to get clear idea about the green WSN which is used for agriculture and healthcare-based application system. It shows how to connect all the required sensors to WSN and advanced microcontroller-based connectivity embedded system prototype, and it shown in Figure 9.5.

Schematic illustration of agriculture and farm remote monitoring and low-power green WSN topology.

Figure 9.5 Agriculture and farm remote monitoring and low-power green WSN topology.

Schematic illustration of remote monitoring in healthcare system and low-power green WSN topology.

Figure 9.6 Remote monitoring in healthcare system and low-power green WSN topology.

In the system which relates to green wireless sensors network (GWSN), the following methodologies must be adopted:

  • • Build the sensors node work only when desirable, while spending their entire lives in sleep mode saving energy consumption in that span.
  • • Decrease in energy utilization (e.g., unguided wireless charging, energy harvest methods like renewable energy system [54], for example, wind and solar, kinetic energy, and vibration, by using some material like negative temperature coefficient variations, etc.). Radio production methods [e.g., power manage, voice fluctuations, collaborative communication, directional horns, and high efficiency radio (CR)];
  • • Ways to reduce unnecessary statistics (e.g., integration, dynamic model, compression, and network coding)
  • • Power saving routing strategies (e.g., group configuration, power such as traffic flow metrics, duplicate route, transmission node setting, and node flow).

9.2.7 Green Cloud Computing

At G-CC, resources have been managed to get proper functionality of the system, namely, Infrastructure Services (IS), Platform as Service (PS), and Software as a Service (SS). Depending on the needs of the users, the GCC provides a variety of resources (e.g., high-end computer and high-capability storage) to user. Instead of owning and organization their resources, the user shares a more number of resources and also manages resource with easy access. As increasing applications are deployed in the cloud, more resources need to be distributed and used more energy, leading to more ecological issues and emission of CO2 [55, 56].

  1. 1. Acceptance of early advance hardware and recent software that reduces power consumption in such system. In this look upon, advance hardware solutions are supposed to focus on the design and production of low power devices. Necessary software solutions must try to provide effective software implementations that use a smaller amount power with a smaller amount of resource use;
  2. 2. Energy-saving equipment strategies (e.g., VMT integration, VMT migration, VMT placement, and VMT distribution);
  3. 3. Various forms of energy resource allocation (e.g., auction equipment distribution and gossip-based resource allocation);
  4. 4. Effective and accurate working models and testing methods with respect to energy-saving policy;
  5. 5. G-CC scheme based on cloud support technology (e.g., digital wireless sensors network N/W, advanced communications, and location and allotment of nodes in network).

9.2.8 Green Machine-to-Machine

In requisites of Machine-to-Machine (M to M) interactions, large M to M nodes intelligently collect the specified statistics used in the M to M domain. On a wireless set of connections domain, the unguided network transfers data collected to the processing center for processing [56]. The processing station also supports a variety of M to M applications via the network in the application area: M to M machine.

In this case, the Green M to M and the more number of machines get involved in the M to M interaction digitally will use a lot of energy, especially from the M to M applications [57].

  1. 1. Cleverly adjust the broadcasting relevant transmission power (e.g., at the required stage).
  2. 2. Design well-organized significant communication protocols (e.g., tracking channels) and algorithmic computer distribution and distribution strategies.
  3. 3. Functional configuration, where the purpose is to control other nodes to less energy consumption/inactive mode so that only a set of interconnected various nodes are used when maintaining the working system, i.e., functionality of the system (e.g., data/information collection) of the original wireless sensors digital network.
  4. 4. Shared power or energy conservation measures (e.g., over safety and resource allotment).
  5. 5. Use energy harvest relevant and aesthetics (e.g., frequency band sensitivity, frequency band management, minimization distortion, use of electrical energy) for green perception radio (GPR).

9.2.9 Green Data Processing Center (GDPC)

The main function of Green Data Processing or handling Center (GDPC) is to store the such a big data or information, manage big data, and process and distribute a variety of statistics and applications, user generated, objects, systems, etc. Typically, to deal with a wide range of specifications and applications, in some cases, GDPC devices consume more amounts of power with expensive operating manpower with their costs and huge amounts of CO2. Furthermore, with the ever increasing generation of big data with a extensive range of widely available and all GIoT-based advanced equipment (e.g., smart phones and digital sensors) by considering world scenario, the energy efficiency of GDPCs becomes more imperative in such applications [58].

With regard to GDPCs, existing strategies for improving use of energy utilization effectiveness can be found in the following factors [15, 59, 60].

  1. 1. Use of renewable or green energy sources
    • • (e.g., wind, water, solar energy, heat pumps, geothermal, and fuel cell)
  2. 2. Use dynamic and efficient energy control technology
    • • (e.g., turbo development and location [61])
  3. 3. Create the most effective hardware tools for different strengths and styles
    • • (e.g., exploits the benefits of dynamic energy system and time span, i.e., frequency measurement [61, 62])
  4. 4. Construction of energy-efficient data center buildings to achieve energy efficiency
    • • (e.g., raw data centers)
  5. 5. Design advanced power transmission capabilities to integrate traffic flow into a sub-network set and turn off idle device
  6. 6. Build large, efficient, and accurate electrical equipment with highly efficient performance [63]
  7. 7. Highly supportive advance digital communication system and highly configured computer systems
    • • (e.g., improved visual broadcasting or communication system, machine migration in machine migration, and installation performance [64]).

9.3 Conclusion

In this chapter, we have discussed the statistics of ubiquitous computational process, the needs of ubiquitous tools and applications, and the green computer. Continuing to review technologies such as green information and computer technologies that allow emerging technologies, the Green Internet Material (GIoT) for GIoT-AHAS architecture using a combination of SCC and the benefits that outlined sensor cloud and GIoT-AHAS are introduced. As per the theory given in this article as a literature review and conducting some surveys and by visited some typical places, we have overcome the some important challenges to establish the GIoT system for agriculture and healthcare system. Typical or major five challenges category-wise correspond to architecture and healthcare system of GIoT system: (i) green measurement equipment, (ii) GIoT-based information broadcasting system, (iii) big data storage space and predictive analytics by computing system, (iv) significant performance and development of GIoT-AHAS, and (v) assignment understanding and support. WSNs alone have some of the traditional challenges that digital sensor cloud green infrastructure can offer: 1) green data management system, 2) well-organized and significant uses of resources, and 3) high GIoT-AHAS operating costs. Green semi-conductive material equipped infrastructure is a very cost effective method, where the accessible GCC-based platform can be utilized. In conclusion, prospect identified indicators associated to the GIoT for both, one is agriculture and other is healthcare application system (GIoT-AHAS) for buildings with digital green sensor cloud integration, are presented as follows:

  1. 1. The design of the system should be close to the GIoT-AHAS with a view to energy efficiency, in terms of satisfactory service, superior superiority and critical performance.
  2. 2. Personality features and the use of various applications require a better understanding.
  3. 3. Models for the real-world use of the (GIoT-AHAS are required.
  4. 4. Cost issues and access to the digital sensor cloud service require both a green digital service contributor and a mathematical computer and a GIoT-based cloud system provider. GIoT-based cloud system provider must have independent user controls, services and efficient management, payment and pricing advanced techniques, and pricing system.


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