Probabilistic data structures in smart city: Survey,applications,challenges,and research directions

7.1.1. Evolution of healthcare

Various authors and researchers in their studies are categories the healthcare system in different generations (Fig. 11) (Healthcare 1.0–Healthcare 5.0). The different functionality evolution of healthcare system are discussed as:

Healthcare 1.0: – In the year between 1970–1990 the first evolution ‘Healthcare 1.0’ was introduced. Due to limited digital resources, it was restricted to paper documentation. It was mainly focused on improving the efficiency of health services and a reduction in paperwork. The revolution transformed the home remedy system and the untrained physicians who provide paternal care into a more sophisticated, intelligent, and data-driven system that can be called the “medical complex”. In the 1830s the British government start piping the water to homes, when the plague was caused due to drinking of polluted water [114,171]. Shortly thereafter, the scientific vaccine theory was established [48]. In the 19th century, a better environment for healthy living integrated measures for sanitation, infection control, vaccination, and epidemiology surveys have been created.

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Fig. 11.

Health generation.

Healthcare 2.0: – The era of Healthcare 2.0 was between 1991–2005, the main focus was to combine with digital technology. Industrial machinery kept working and changing. In the 20th century to increase the productivity of cheap products, the automobile industry introduces the concept of mass production [120]. The healthcare system also follows the same. At the end of the 19th century, few large pharmaceutical companies were formed [214]. A few years later various antibiotics were introduced, with the advent of mass-industrial manufacturing technology [299]. Also at that time in medical education importance was given to both clinical training and basic science education [93]. Hospitals are expanding, being provided by more specialists, and doctors are being trained to deal with more patients with complex conditions. The main focus is on building a part of healthcare 2.0 [58]. The second version of healthcare is aimed at improving productivity and data sharing. The focus on information sharing is not limited to within the organization but among a group of other healthcare providers. The new version is entrenched in response to the symptoms, illness, and needs of the individual. Information was shared with other organizations with privacy and security. It was electrical energy-oriented.

Healthcare 3.0: – The era of healthcare 3.0 was between 2006–2015. In this evolution, Electronic Health Records (EHRs) were introduced. It helps the doctors in accessing patient records through cloud gateway. It was a step towards creating a value-based model based on Telecommunication and information communication technology (TICT), which provides database enhancement and additional data efficiency to prevent medical-related problems [1]. The advent of microcontrollers in the 1980s allowed for the production of small computers and fast-tracking environments, as well as large data storage [127,255]. With advanced computer technology, tomography jumped from single images to redesigned images, and doctors could diagnose ulcers with more information and diagnose diseases earlier. Doctors are provide evidence-based medicine after diagnosis disease [85]. They have additional information gathered from e-libraries using fast computer technologies. Healthcare 3.0 focused on providing emergency care and was able to ensure preventive care before the onset of illness or symptoms of the disease. Internet changes learning because much medical literature is available at e-libraries. In Healthcare 3.0 major concern is to use technologies like BDA and IoT-based wearable devices along with advanced E-Medical record databases.

Healthcare 4.0: – From 2016 to 2020, the era of Healthcare 4.0 was introduced with patient-centric healthcare services with the advent of new technologies and IoT devices [261]. This healthcare evolution is inspired by ‘Industry 4.0’ by establishing personalized healthcare platforms and augmented virtualization [271]. It was focused on smart devices, involving capabilities of empowering data analytics with ML, DL, AI, and IoT for the detection of diseases, [151]. It is the successive approach of 1.0, 2.0, and 3.0. Patients get medicine from suppliers by using their websites and also get medical assistance through blogs [240]. In this case, patient records are shared with healthcare professionals via an e-Health record over the cloud or on the LAN, where many patients and healthcare workers can be connected. It helps the physician to access patient records anywhere and also communicate with fellow doctors for better treatment. However, data sharing has introduced new challenges such as authentication, security, and authority, and so on, [280]. New hands & a new brain, which includes robots, mini-laboratories, wearable devices, and 3D printers. Every device works faster and more efficiently; illnesses can be quickly diagnosed using a drop of blood; custom-designed surgery of body joints can be performed, and bone framework can be prepared using 3D printing [280]. Healthcare 4.0 also involves technologies like robotic surgery, CC, CPS, information security, and many more.

Healthcare 5.0: – The era of healthcare 5.0 began in 2020 and is going on. This machine includes AI features such as a robot nurse, a smart IoT device, and a 6G network speed. [186]. With latency (10–100 ms) and reliability (99.9999%) [150], and based on ultra-high accuracy for remote connections [314], 6G communication addresses reliability and latency issues in smart cities. Why not 5G? According to [141] 5G will fall short of meeting future demands for big data connections such as holographic communications (e.g., 3D video conferencing), games, and telesurgery. This evolution comes up with device-to-device, machine-to-machine, and human-to-machine communication. The human-machine cooperation and participation improve the diagnosis system and fast results. This system is more secure than its predecessors. A blockchain-based architecture for healthcare applications that automatically collects data and removes unreliable systems from external companies. The system is not dependent on a single-point failure due to a decentralized network [150].

The evolution of the healthcare system has also been succinct in Table 4.

Facilities in Smart Healthcare: – The main participants in smart healthcare are research institutes and hospitals, patients, and doctors. Smart healthcare has many benefits, like disease prevention and monitoring, diagnosis and treatment, hospital management, health decision-making, and medical research. ICT, IoT, AI, 5G internet, big data, and modern biotechnology are the foundations of the intelligent healthcare room, according to [108]. Doctors use Laboratory Information Management system, Electronic medical record etc. for managing health data [207].

Trusted Communication: – Many medical devices experience network failure, which is unacceptable when real-time data access is required. It is challenging to maintain connections on mobile devices such as wearable devices while moving anywhere a patient walks, crossing boundaries, and covering areas. In mobile devices, switching the network to the most powerful available signal also affects data generation. The different amounts of data are generated while switching to the network, which is dependent on the roaming relationship between the SIM provider and the location of the feed. As there are many subcategories for mobile connectivity, we should also adjust the network type and location to the value, speed, audio, and video required by our device.

Cyber security: – Cyber security is one of the challenges when we talk about smart healthcare. As it requires internet access and IoT devices, the data may be stolen or attackers may attack data or modify the data. So the first priority of the healthcare system is to ensure the privacy of patients and their data. There are some private IoT-based networks (e.g. APNs, VPNs, and IPsec protocol) that are available, which create private areas only accessible by authorized users or devices.

Scalable Platforms: – The smooth functioning of a smart healthcare system, a scalable platform is required. For the effectiveness of a smart healthcare system to be enhanced, it must be integrated and supported seamlessly with patients and their big data. So, the authorized professionals, physicians, and patients use the devices and system easily for monitoring remotely.

Cost: – Meet, the requirements of the healthcare system are also cost-effective. The healthcare system may take less time for decision-making and information gathering. So, it must require new tools and techniques for efficient storage and retrieval.

Data Availability: – The availability of data is facing some issues like resource management and device identification. Systems don’t need any redundancy; they only need consistent data. So it requires identifying the resources and storage management with new technologies in healthcare systems.

Data Security: – While accessing the data, it is mandatory that only the authorized user can access the concerned data. It still needs to be improved.

Unique Identification: – In the healthcare system, it is required to uniquely identify patients by their doctor and vice versa. This is required for providing or getting the best and correct treatment.

Privacy Issue: – This is one of the major issues while we are talking about big data and IoT devices in a smart city. Many kinds of research provide many tools and techniques. But still, it needs to be improved.

Device Communication: – Machine to machine, a device to machine, and human to machine communication is challenging. Patients need a quick response to their records, medical tests, and medical reports. A 5G/6G network is required for the smooth transformation of data communication. It is cost-effective, and also required some tools and algorithms for privacy and data exchanges.

Data Integrity: – Ensuring the integrity of healthcare data is also challenging because it is important for providers to use it in making decisions about patient care. It is also required for information exchange between doctors and patients.

7.1.3. Application of PDS in smart healthcare

Due to the rapid growth in the population and an epidemic, hospitals are overburdened and overcrowded. Patients are struggling to find the doctor’s office and pre-body check-up center. Patients and their guardians also face the issue of understanding medical terms, and they need someone’s help to get the proper information. For that, a proper healthcare system is required with the use of new technologies like sensors, IoT, ICT, etc. On one hand, the IoT has benefited patients, physicians, hospitals, and health insurance companies. On the other hand, challenges have increased. Because sensors and IoT have produced massive amounts of data, To handle this data, the following is the application of PDS which may be used in smart healthcare systems:

Data Processing: – The data generated by devices is homogeneous or heterogeneous, semi-structured or unstructured, which will result in the traffic load on switches. The storage capacity of switches is limited, so the performance of routing may be compromised. A scheme ‘BloomStore: Dynamic Bloom Filter-based Secure rule-space management’ is proposed for Software Defined Networking (SDN) [245]. The scheme uses two self-dependent hash functions for security checks and also manages the network resources to handle the traffic in data. Smart healthcare systems provide system-assistance analysis for dedicated medical care to patients. The security of this data is in danger. The data security features are improved to prevent unauthorized access to information in healthcare systems. BF is also useful in data transmission elimination [300].

Data Sharing: – The Garbled Bloom filter is used to support authentic search results and secure data sharing for multiple users [260] in the Verifiable Multi-Key Searchable Encryption (VMKSE). This scheme supports single-keyword search. The author also compares his programme with a modern solution to evaluate its effectiveness. This scheme helps in doctor-patient data sharing. The multi-keyword search verification mechanism is introduced based on pseudo-random and it is IoT-cloud-enabled for healthcare data systems [283]. The mechanism also takes care of authentication and advanced encryption with advanced privacy of data. BF is used in a secure two-dimensional calculation protocol, to compare a unit of characters and record [277].

Data Security and Privacy-Preserving System: – The use of IoT in smart cities plays a big role in smart healthcare. A huge amount of data is exchanged between machines, humans, and devices. This big data can be encrypted and then stored on a cloud so that only authenticated users can access it. There are also privacy issues. A scheme for efficiently sharing data is proposed to address this issue. The author uses attribute-based encryption with attribute BF to control access. While transforming data or sharing sensitive information, a lot of privacy concerns may arise. Xu et al. propose a scheme using BF, which is privacy-preserving for patient health information for sharing information [293]. The author uses an encrypted search method that allows numerical search for encrypted data. The BF and message verification code are used to filter patient data and check the accuracy of search results. Liu et al. design cooperative privacy preservation for wearable devices that ensure authenticity and consideration for controlling data access in the context of space and time information [162]. In space-aware, they use MinHash-based authentication, and in time-aware, attribute-based encryption is applied. They adopted BF to determine the existence of sensitive data in storage without exposing secret information, and for secure data interaction, positive and negative filters are used. Seham A., et al. [11] introduce a system for infection control with the use of Blockchain for privacy-preserving. In this system, one leader elected by authority updates the two BFs, one for infected users and the other for close contact users. Two BF are used for infected and suspected users, which reduces the storage space. The block size of the COVID-19 status ledger with and without using BF is also compared (Fig. 12).

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Fig. 12.

Status ledger’s block size with and without using BF. [11].

Fast Response and Congestion control: – The number of connected devices in the network (like traffic lights, vehicles, laptops, smartphones, etc.) is increasing exponentially. For resource sharing and effective communication, all devices with different configurations have to be integrated into the same network. Due to the rapid growth of these devices, the traffic in the network also increases, causing difficulty in predicting traffic patterns. G. S. Aujla et al. propose an approach to handle these issues. “Blockchain-as-a-Service for Software Defined Network (SDN) in Smart City Applications” is mentioned [23]. Multiple etiquette firmware can be integrated into a single network using the SDN controller [22].

In “Software-defined Content Dissemination Scheme for Internet of Healthcare Vehicles in COVID-19 like Scenarios”, [111] introduces a new way to find the right online distribution channel described in the healthcare ecosystem software. Internet of Healthcare Vehicle (IoHV) is an emerging concept that depends on smart transport, including ambulances and additional healthcare vehicles (testing Covid-19 immediately). To connect to the internet, especially when possible, for testing COVID-19 immediately and contact tracing, this IoHV is helpful and is deployed across a smart city setup. All healthcare vehicles are connected to each other through different types of links: vehicle-to-pedestrian (V2P), vehicle-to-vehicle (V2V), infrastructure-to-infrastructure (I2I), vehicle-to-infrastructure (V2I), and cellular links [147]. To overcome congestion and improve response, the global information of devices is stored in the deletable BF in the proposed framework. While implementing in the real world, there are many ambient intelligence challenges. Some of them related to healthcare are listed below:

Adopting Advance healthcare technology: Almost all medical devices are connected to IoT devices. The management systems like appointments, patient administration, laboratory information, etc. are now handled by ML and AI. So it is necessary to develop a connected network of healthcare leaders, clinics, manufacturers, and software development companies. which enhances the new business models and also helps adopt the new technologies.

Integration between healthcare services: The massive amount of data is generated while using medical devices and AI-integrated applications. Many top healthcare companies lack data management systems and new architecture.

Rising Healthcare cost: The rising costs of healthcare are always a serious issue, which includes the manufacturing cost of healthcare vehicles and disease detection and testing processes. Due to this, many patients skip lab tests and do regular follow-ups.

Payment Verification: – In this digital world and epidemic, the maximum number of transactions is done digitally and payment verification is required. In this lieu, Pratim et al. a lightweight payment verification based on the blockchain using BF is proposed as IoT-Assisted e-Healthcare [223]. If BLWN simply asks for the full location of a small set of blocks/topics, there may be a chance for privacy leaks as the full area can peer into BLWNs’ assets. It may allow the full site to call for Denial of Services (DoS) and the insignificant liaison service for funds available from BLWN. Therefore, it may be completely overwhelmed by the great difficulty in its ability to make a computer while crashing its system.

Real Time Analysis/Support: – Wearable devices are mainly used in real-time Electronic Health Records (EHR) collection. Obviously, encryption is required for searching targeted EHRs by medical institutions. In this reference, Yuan et al. [259] propose a scheme in which medical institutions can search and access EHRs in the cloud. They improve the search accuracy and privacy of users in EHRs. To improve search efficiency, the Cuckoo filter is used and gives a facility to data owners for modification (insert and delete) in their EHRs in the cloud.

7.2.1. Application of smart transportation

To meet the increased demand of citizens, the easy option is to provide then better transport services. With this the growth in supply of automobile is increases, due to that the traffic congestion is also increases. The attraction to the development of smart transport system took a lot of attention to addressing traffic congestion and rapid urban growth [71]. It is identified that the implementation of smart technologies is the key factor in gaining intelligence and stability [107,233]. Also several researchers shown that how this sustainability achieving the environmental and economic efficiency [71,115]. Therefore, sustainable transportation is very important in today’s society. There are various application under smart transportation are discussed as follows:

Smart Street Light: – Smart city should also needs to upgrade Road street light to smart street light. It helps in reducing the energy consumption by dimming the lights. This saved energy are then used for other services like pollution monitoring, update about whether, GPS etc. and also help in signing available parking in nearby area. But smart street lights are depending on its feature and requirements, and involve a combination of cameras and sensors. Sensors and Cameras are collecting the data can either process locally if street light have computing device or propagate through network. These devices can detect the movement which enable dynamic lighting and dimming [102]. Chen et al. present a system which used for controlling the street lights using TX2 device and also help in updating the parking status to users (Fig. 13).

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Fig. 13.

Smart street light scenario [59].

Intelligent Transportation System (ITS): – Today, the state-of-the-art transport system is heavily influenced by Machine Learning (ML) and Dynamic Range Learning (DRL) based strategies to detect autonomous vehicles, dispatch passengers in a safe manner and ensure the safety of vehicles. ITS uses the various advance technologies like senros, IoT devices, ICT and many more [302]. The huge amount of data is being generated by these IoT devices and sensors which contributes to the concept of intelligent cities and the future of ITS [312]. The techniques like AI, ML, and especially DRL play an important role as an integral part of sustainable in precisely monitoring and measuring real-time data traffic flow in an urban area [301,305].

The various applications of ITS are discussed by [302] like: The intelligent highway in Britain, which reduces the traffic and accident rate, The CRITER system in Lyon, France, offers transportation workers a schematic plan like a map and also predicts the bottleneck points. In Japan, electronic toll collections (ETC) measure the physical characteristics of vehicles and check and deduct charges automatically if they are in ETC. It is useful to avoid illegal entry into the city. Include IoT in ITS to build a system where communication between road facilities, vehicles, and management equipment is done without barriers. The Global Positioning System (GPS) is replaced by Radio Frequency Identification (RFID) in the IoT to become the Smart Transportation System (STS). The author also discusses the services for passengers on public transport, like service range, charging, security control, and administration.

Availability of Parking: – With the rapid growth of vehicles in the city, it is a trick for drivers to find available parking. This dilemma is seen as an opportunity to increase the efficiency of parking facilities, thereby decreasing road accidents and taking less time to find free space in a smart city. The troubles related to parking and traffic congestion could be solved if drivers were aware in advance of the availability of parking in the area and surrounding areas [39]. A smart and automated system that can detect empty parking spaces can reduce search time, by finding out where parking is available and bypassing lawful information to drivers. Maria et al. proposed an image processing system that takes video as input from a drone and feeds it into a frame extraction block [176]. These frames are then preprocessed to reduce complexity. These systems may be improved if the availability of data and the techniques to manage this data were improved or changed with new technologies.

Street lights can also play an important role in detecting empty parking spaces in open environments. Traditional parking (sensor-based) occupancy systems are more expensive, as demonstrated by [59]. They use Jetson TX2, an NVIDIA’s Computer Unified Device Architecture embedded artificial intelligence supercomputer, which has high power efficiency. This system works both day and night with an on-off street parking smart control system. Parking space is detected by marker-based image processing using the onboard camera of an Unmanned Aerial Vehicle (UAV) [67].

Traffic Control: – The authorities are facing chaos trying to manage the traffic with an increase of vehicles on the roads. Because of a lack of human resources, authorities are moving toward smart traffic control to manage the city’s traffic. To reduce congestion in the context of VANETs, robots can play a key role. The aim of smart robots is to give information to avoid ideal roads and manage traffic congestion in urban areas. To detect illegal traffic behaviour or traffic violations, the system uses street cameras [66]. But it is not possible to install street cameras everywhere in the city. Modern cars with video storage cameras have been introduced to control traffic violations in the city. These cars capture the videos in the city and report any violations that happen to the authorities. Rathore et al. propose a system to detect the front car and road line using the Single Shot MultiBox Detector (SSD) and Hough transform for self-driving. A violation detection algorithm is designed for the fog device smart to identify driving violations, U-turns, and driving central dividers [222]. Steve Mazur has also presented a traffic control system (Fig. 14) in [180].

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Fig. 14.

Smart traffic control [180].

Automated Toll Collection: – To decrease the fuel consumption used in automobiles, the use of the cream road is required. The government and road contractors are working on making the new highways and flyovers in the cities, and contractors are installing their toll on those roads to complete their expenses. Motorists and commuters are spending their valuable time at the toll plazas paying the amount of tax. Due to this, the parking problem, traffic congestion, and pollution are increasing near the toll plaza. Commuters are also facing delays, which increases the travel time for their journey [34]. Automatic toll collection is on the rise nowadays, both by governments and researchers. The main concern with this automated toll collection system, the RIFD tag, is that it is installed on the windshield of vehicles. To collect the required amount, the vehicles pass through the sensor system before the tollgate [134]. Regular user may also have facility of prepaid smart cards. So that the traffic at tollgates can be avoided. M.A. Berlin et al. propose an alert message based toll collection system using smart Road Side Unit (RSU) [34]. This system also helps in stopping the payment violation by send the alert message if the any vehicle violate the toll payment. This system is totally man free and barricade free, which also help in rush hours to handle the traffic [34]. As the use RIFD tags not only the time is saved but also eliminate the corruption in toll plazas [8].

Smart Mobility: – Smart mobility is also the main concern in smart transportation as it is consistent with the development of a sustainable world [101]. In particular, either ICT is the initiative to smart mobility or a complete failure [31]. Smart Mobility is providing solutions to users by using new technologies like IoT, ICT etc. Some tourists use apps to plan their journey, but they get limited information and priorities for travel recommendations [205]. Smart mobility is also helpful for citizens to roam and move freely in the vicinity of a smart city. Smart mobility also helps in improving the traffic control system by giving access to other routes in emergencies or traffic jams. Intelligent navigators facilitate providing routes and navigating to essential services like ambulances and government. can be facilitated by intelligent navigation. In the coming year, smart mobility transform into mobility as a service paradigm, like car-sharing [205].

Figure 15 explores a picture of smart transportation in the city. Smart transportation has many benefits, some of them are discussed as.

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Fig. 15.

Smart transportation [180].

Smart Transportation is safer: – In smart transport the integration of ML with IoT, 5G help in reducing traffic and road accidents. In these IoT devices, cameras and other safety devices help in monitoring the traffic situation and intimate the same to users for improving road safety.

Smart Transportation is better managed: – Smart transportation facilitates the public administration by allowing to monitor the performance of road safety and traffic. It also gives information on critical sources of problems and tracks where maintenance is required.

Smart Transport is very effective: – With better management of resources in smart transport gives more efficient results. If we are having quality data, then easily identify the areas where improvement is required. It also provides better-quality filling rates.

Smart Transportation is more cost-effective: – Smart transportation also helps in reducing the cost by providing the best shortest routes, giving information regarding facilities available with approx distance and price, and many more. Commuters also take benefit if they get affordable public transport as compared to hiring a private CAB.

Smart Parking Management: – In smart parking the driver or car owner can’t face the problem of finding the available space for parking. The system provides the information by collecting real-time data from connected devices and sensors.

Smart Traffic Management: – With the smart traffic management system users can get information about congestion on road. So that they can plan their journey accordingly.

Smart Transportation provides instant information: – Smart transport system can also provide information instantly regarding the issue in the city, traffic congestion, and problem areas using traffic management centers. They also ensure public safety and provide information on affordable insurance plans.

Integrated Ticket Systems: – It will also provide diverse services to citizens by providing the intelligent ticket system in some local services.

A large number of vehicles in major cities around the world has posed major transportation and stability challenges, like air pollution, traffic congestion and energy problems [284]. Following are some challenges to smart transportation.

Security: – Vulnerable to cyberattacks is one of the biggest fears among smart city dwellers. Cyber attacks are more common to criminals as the world’s connectivity to the internet increases. The data flow during the smart transport management system are may be hacked or used to thief the vehicle if there is no secure communication. The security of data used in toll collection systems is also in danger. During finding the available parking space user share their location, this also may create problems in user security.

Data Privacy: – User data cannot be retrieved without their knowledge. The user’s personal identity must not be identified or traced. Data privacy in smart transport is the main concern. Under the new law, data processing must have legitimate.

Supply Chain: – Due to epidemics like Covid-19 the global supply chain is affected. While transportation may face various problems during this epidemic. Due to that many businesses are affected. When the drivers may ill and move from one region to another it may cause public health.

Environmental Problem: – With the rapid growth in the automobile industry the traffic on roads also increases. This may affect the air quality and water pollution in nearby residential areas. The environmental problems caused by the IoT devices are currently serious and need to be addressed urgently [180].

Health Concern: – Health concern is also one of the challenges to smart transportation. If the transport system is not connected to hospitals, then it may cause major problems or may loss their lives in road accidents. The system needs to improve its service on road, and proper intimation of the concerns (hospitals and police stations).

7.2.4. Applications of PDS in smart transport

The traffic on roads in both rural and urban areas is increasing day by day. It needs to smartly manage the traffic and distribute the traffic load by making the transport system to be smart. It includes smart parking, smart street lights, violation check on the roads, traffic control system etc.

Data Dissemination: – It means the statistical data is transmitted or distributed to end-users. In Pursuing a Pub/Sub Internet(PURSUIT) project use BF to store the path information in source routing. The main scenario of this project is general data dissemination [16].

Privacy Preserving: – The IoT devices and sensors are used to collect and exchange a huge amount of data, to improve the transport system. As Cloud Computing progressed, more sensitive information (like vehicle registration number, chassis number, insurance detail,etc.) was released to the cloud. The most accurate way to protect data privacy is to encrypt data before extracting it [253]. Enabling keyword searches directly over encrypted data is a desirable way to make the best use of encrypted data. Wang et al. has proposed a brand new idea for acquiring multiple keywords (compound keywords) in random search [282]. Unlike most existing keyword search programs, the program eliminates the requirement of a predefined keyword dictionary.

7.3.1. Challenges in smart environment

The major challenges for the environment are water pollution, air quality, and radiation. To achieve sustainable growth by maintaining a healthy society proper vigilance is needed in the world. With the development of IoT and smart sensors, Smart Environmental Monitoring (SEM) is the system for environmental monitoring, in the latest years [273]. Figure 16 shows various issues of environment like temperature, radiation, dust, humidity, ultraviolet signal etc. For establishing the system Silvia et al. used the WSN which provides an interface between smart sensors and IoT Devices [273].

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Fig. 16.

Challenges in smart environment.

Air quality Monitoring: – Due to the rapid increase in traffic and industries, air pollution is one of the primary concerns of our epoch. The earth is becoming increasingly polluted due to the emissions of harmful gases like CO, NO2, SO2, and CO2. These toxic gases can’t be predicted because there are dissolve in the air. So, the air quality needs to be checked, and for that, an IoT-based tool is required. An IoT device can collect and analyze the data to predict the air quality either good or bad. Sensors using Raspberry Pi/ Arduino and IoT devices can monitor the local air quality [175]. Dhingra et al. develop an application i.e. “IoT-Mobair”, which is mobile-based use to monitor and detect the air pollution of the concerned area [76]. This mobile-based application has various features like air quality, daily forecasts, health-related tips, and risks, air quality map generations etc. But, when dealing with big data generated from sensors, then this application has faced some computational complexity problems. For that Dhingra et al. have suggested using fog computing instead of cloud computing. The IoT is a global system of “smart devices” which can detect and communicate with the environment and interact with users and other applications. Qian et al. found that due to low sensitivity and low accuracy the exiting monitoring system does not work well and it also requires laboratory analysis [218]. The data is highly correlated in the case of air pollution monitoring, where these systems are leads to a lot of obsolete information. To the data delivery cost and to alleviate data neglect Qian et al. the system i.e. ‘Content-centric IoT-based Air pollution Monitoring (CIAM)’. In CIAM, the content method is used to compile and integrate air pollution data.

Water quality Monitoring: – Monitoring of water quality is important in determining water safety and related public health [256]. Water quality parameters are determined by the same factors as physical, chemical, and biological. Bacterial contamination, turbidity, dissolved oxygen, dispersion, free chlorine, and pH are the typical parameters of water quality [215,219]. The various research papers have been studied in terms of intelligent water pollution control systems using ML, IoT, and smart sensors. The pollution of water in the lake can be predicted using an ML-based neural network for machine-reading which analyzes the sensed image [160]. The water is classified as pure or polluted, and we studied the separation of water pollution with the use of ML methods and IoT devices [61]. The prediction of water quality parameters using AI and neural networks and the amounts of sulfate or chloride present in water were studied [220]. In order to separate the pollutants in water using SVM, the analysis of big data and problems faced during the separation of water pollution were discussed [46]. AI-SVM is a classification system used for real-time monitoring and technology used for testing and its separation from non-drinking water [41,128]. Video-based monitoring of water quality and pollution was investigated, which used IoT video surveillance and ML tools to separate dirty and clean water [208]. To predict the future and quality of water before use, another function which is a feature-based model, also helped in analyzing the water suggested [311]. Different ML models were used to test the concentration of chlorophyll-A in pond water and were also recommended for real-time water management system [163].

Agriculture Monitoring System: – The growth of industrial and robust agricultural production methods has accelerated to ensure the quality and quantity of the growing demand for food [249]. In “smart or green agriculture”, Smart Environment Monitoring (SEM) plays an important role as agriculture is the relevant growth factor for any nation [210]. It also helps in product development and sustainable growth to handle major challenges in the agriculture sector [196,239]. Ullo et al. refer to the smart agriculture scenarios (Fig. 17), where the SEM system is a smart agricultural monitoring system in real. In the agriculture sector, various factors are very important for achieving sustainable production, like water level, water pollution level, moisture analysis, soil health etc. These features are included in the smart agricultural monitoring system, which is monitored and controlled using IoT devices, smart sensors for agriculture data capturing, and WSN to transmit data into the cloud [273].

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Fig. 17.

Smart agriculture monitoring system using IoT devices and sensors.

In the new agricultural era, there is a growing market for IoT that offers a few creative solutions. The various studies and research on Smart Agricultural Monitoring systems (SAM) are discussed, which include fertilizer control, crop monitoring measures, pest control, etc. Kumar et al. propose a system for plant growth monitoring i.e. ‘gCrop’ using IoT, ML and WSN [148]. They use a 3rd-degree regression model and provide a prediction with a high computational complexity of 98% accuracy. Shinde and Pathak et al. performed a crop quality test to monitor the quality of paddy rice using Synthetic Aperture Radar(SAR) data [242]. In the rice quality test, SVMs were used with limited sample size and back distribution features. The land and its size play an important role in checking the growth level of different crop species that are either satisfactory or not. To measure the leaf index Hosseini et al. propose a system with a Gaussian process model [91] and using SVM as ML method and reported 89% with a limited sample size [119]. To determine the level of fertilizer, pesticides, and water quantity used for plant irrigation, an expert system using AI was developed [78] using the Naive Bayes [17] method and studying ML using sensory data taken from agriculture. UAV is used [77] to investigated the crop quality tests [230] and soil health for phenological data of soybean crop [49]. Smart farming [42], pest monitoring [164], and crop monitoring [311] are important in the various uses of SEM systems. Weather and the environment also affect the health and growth of plants. Ullu et al. propose a technique that checks the condition of the soil, moisture, air, and water quality, temperature etc. in the context of SEM using IoT devices, AI, and smart sensors [273]. The data analysis is performed while smart agriculture provides estimation, assisting protection, decision making, and storage management [249]. The data is moving while performing the techniques to achieve smart agriculture, and various challenges are faced by both farmers and researchers. Some of them are addressed below.

To increase food production, farmers will face many challenges. The production will increase 70% by the year 2050 [122]. Various challenges in agriculture have been discussed (Fig. 18) as.

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Fig. 18.

Challenges in smart agriculture.

Irrigation management: – One of the objectives of an irrigation system is to calculate the water requirement for crops based on collected data and water flow without interference from humans. Irrigation systems use dispersed sensors to monitor the different soils, water bodies, vegetation, and microscopic elements. Climate is one of the most important variables in estimating agricultural water requirements. A farmer can adjust his irrigation system in a variety of ways according to soil and weather conditions [216]. The entire farm can track, manage, and forecast weather from almost anywhere. IoT will help in developing the new infrastructure for irrigation in a very exciting way. Smart IoT-operated irrigation systems use embedded sensors in the field to monitor soil structures, climate, and agricultural irrigation conditions.

Soil management: – Soil monitoring is one of the most challenging agricultural activities for both businesses and farmers. Various soil parameters like pH, humidity, etc. are involved in soil management and IoT sensors can be used to calculate these parameters. Soil management helps in finding the right kind of plants and helps to identify fertilizer requirements in the soil. Crop production can be affected by soil testing due to a number of environmental concerns. The process and patterns of farming can easily be understood, if these types of problems are well defined. Crop production may improve and fertilization practices can be promoted based on findings of a soil survey study for farmers [37]. The moisture and humidity sensors can monitor the moisture in the soil, and IoT technology identifies the contaminated soil and shields the field from over-fertilization and damage to crops. Agricultural productivity and quality may increase, pollution can be avoided, and input costs may be reduced due to soil management.

Climate management: – Climate has a profound effect on crop production. With the use of an IoT-enabled weather forecast system, farmers can determine the best time to plant, irrigate, and harvest. With the help of distant sensors attached to the field, farmers can learn about natural conditions like humidity, soil moisture, and air temperature. On the basis of historical results, to maximize the yield, farmers should properly prepare and market the harvest and irrigation season. By editing and updating the collected data, farmers should take immediate steps to ensure a safe crop yield. Many of the right things are put together to maintain and establish a good plant environment while living under stringent limits like airflow, temperature, CO2, and O2 levels. With the use of IoT-enabled systems, where for advanced decisions data can be exchanged between intelligent sensors and devices, this can be achieved [288].

Accurate farming: – The traditional method of farming to increase yields and preserve crops was based on physical examination. If any issue was found, then it was resolved by trial and error after being involved in a serious incident on the farm. Farmers will face various different types of challenges while farming, like less water, floods, lack of suitable planting space, and cost control. Productivity can be improved with the use of IoT in agriculture. With the use of gathered information, farmers can organize their farming activities, including what seeds they should sow, what crop yields should be expected, the time to harvest, and how much fertilizer to use. Example: The natural soil diversity of a field is an accurate agricultural practice. The plants can be planted thicker and the irrigation can be used sparingly if the soil in a certain area holds more water. Alternatively, if the site is used for grazing, we can take more cattle than an equivalent area with a lower level of the soil.

Nutritional management: – As the human body needs proper nutrition to grow, the same plants may require accurate nutrition. Nutrients help to produce the best yield when given at the right prices and at the right times. Too much and too limited nutrients for plants will affect the environment. For example, too much phosphorus, ammonia, or nitrogen may reduce water levels. To grow in one place, the selection of the best crop cycle allows for balancing soil fertility. While reducing environmental degradation and economic costs, achieving sustainable agriculture, nutrition, and technology are essential [6].

Garbage Management: – The wastage of water, soil, and seeds is common during farming. This needs to be controlled, but in an intelligent manner. For garbage collection, create smart trash cans using IoT sensors, which can smartly sense and collect the garbage. The collected data related to network disposal is used to read, store and transmit with the help of these smart trash cans. Garbage management can be done with the help of some smart and systematic algorithms [10].

Livestock monitoring: – Livestock plays an important role in agriculture, so they need intention, proper care, timely feeding, etc. It is a growing worldwide issue to provide enough food to the world’s people with the growing agricultural production. As a result, the importance of livestock management on farms is crucial to survival. To improve the quality and quantity of agricultural products, new technology like IoT advances is important. It also improves the quality of livestock by allowing farmers to make decisions based on data-driven. To monitor the livestock’s welfare remotely and identify their habitats, cloud-based technologies are used with power communication sensors [50]. The health condition of livestock like respiratory rate, digestion, blood pressure, heart rate, and other day and night vital signs can be checked by farmers using connected sensors. But the data flow between these sensors and smart devices is interrupted or tempered, so it needs to be secure and well managed to get efficient results.

Farm Management System (FMS): – Smart farming promotes productivity while minimizing environmental influences, but this smart agriculture technique is merely possible with the help of FMS [97]. With the help of WSN and GSM in FMS, farmers can track the entire farm and capture the data with a small controller [199]. With the use of sensors and smart devices in the field, the identifier is used to provide appropriate awareness of soil, fertility, and weather, to the farmers. The data collection and storage, monitoring, and analysis of the farm operations can be automated using an IoT-based farm management system. It can also help in managing agricultural budgets and business operations. The irrigation scheme helps in protecting the farms from animals and pests. But also automatic irrigation systems can increase the water consumption [105,137].

Tracing and tracking: – Satyanarayana et al. [236] develop the structures to remotely track soil structure and its status in accordance with the needs of plant culture. The different agricultural areas and locations are tracked using GPS devices and wireless network connections. The real-time data processing is tracked and approved by connecting WSN and ZigBee to other devices like the Central Monitoring Station (CMoS), GSM, and GPRS. The GPS also enables the farmer to take actions based on notifications sent to the farm manager through SMS or MMS. It is often used in agriculture to detect precise location and control capacity, despite it having high operating and maintenance costs.

Plant management: – The growth of a farmer’s crop is most important in farming. Farmers use good seeds, organic fertilizers, proper watering etc. for best results. For farmers to protect themselves from them, they use chemical medicines and fertilizers, which may later affect the human body. So, it required an intelligent system that protects the crop from insects and does not affect the human body. This can be done by plant management, which involves monitoring and recording the welfare of the crop. The plant and its diseases can be detected using RFID chips and IoT sensors. The farmers can process the data remotely and take necessary steps like keeping the insects from plants. The production of rice for a specific country with a Chinese monitoring station using SVM [152]. Farmers can also prevent the risk and plan their farming practices by demonstrating an effective calculation strategy for coffee fruit [68].

Water Management: – The major challenge in greenhouses is to determine how much water is required i.e. water management [281]. Intelligent sensors are installed to control the waste of water and operate them by using a variety of IoT techniques. Automated drip irrigation is used to control the soil moisture in irrigation and storage of water in greenhouses. The farmers are checking the water levels in a water tank with their Android phones. With the use of IoT devices and sensors, the whole water management is done like the motor is automatically started and stopped by checking the level water level. Due to over-irrigation in conventional irrigation systems, up to 50% of water is lost [241]. A smart Irrigation system(SIS) provides a system to overcome this issue. This system helps the farmers to avoid water wastage and improve the quality of their crops through timely irrigation. SIS also transmits the knowledge of the field to the farmer using temperature and soil sensors. Farmers may also plan and modify their irrigation according to the local weather information. For Water Distribution System (WDS) an architecture WDSchain’, which is blockchain-based in MATLAB, is proposed [172]. For security, various consensus mechanisms are used, and results show a trade-off is required between data validation and system complexity (Fig. 19).

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Fig. 19.

Water distribution systems chain architecture [172].

Blockchain with Agricultural IoT: – To improve agricultural intelligence, data-driven technologies can be allowed a secure data storage system. The data collection is often very costly where the inventory, agricultural contracts, and information about farm conditions from a reliable source can be provided. Developing the trust between providers and consumers and establishing a reliable food supply chain, the blockchain technology helps in tracking the food for timely payments to stakeholders [191].

To monitor the farm and collect the data remotely, smart sensors and cameras were used. To adopt the current condition of agricultural land, farmers can use IoT devices as they use smartphones anywhere in the world. New technologies like IoT have the potential to increase global productivity and reduce the cost of crop production. To face the challenges on the farm, the agricultural sector can be restructured by providing different IoT-based tools and techniques. A massive amount of unstructured data has also been generated and the PDS has the potential to solve issues like handling big data with real-time response.

Energy efficiency and traffic awareness: – To improve energy efficiency and traffic awareness. Yousef et al. propose a scheme in underwater WSN for water pollution monitoring [297]. BF is used in the preprocessing step to reduce the number of transmissions and eliminate redundancy to save precious energy. This type of project is mainly used in deep water like the sea and ocean. Mahmoud et al. use BF for customer’s identity and privacy-preserving of transferred data in WDS [173]. They also suggest the optimal parameters of BF i.e. for 200 customers with 2000 numbers of bits and 7 hash functions. In WDSchain, BF is used to match the authentication of network nodes in proof-of-authentication(PoAuth).

High-performance communication networks: – IoT devices contain many sensors, routers, actuators, and base stations that need communications between them and send millions of data that need to deliver with high-performance communication networks. IoT devices that may have limited power resources or limited integration areas have an important research challenge. To increase the performance of IoT communication networks alassery et al. propose an efficient mechanism based on BF [12]. BF is used to store the routing information after aggregating packets to send receiving packets to upstream routers.

Query Optimization: – Under the big data domain has been developed an air monitoring system, which poses major challenges to data analysis. Peng et al. proposed a scheme for query optimization of air quality big data using BF index [211]. The efficiency of data collected in the air quality monitoring system is improved. They create a Hive data repository in the Optimized Row Columnar (ORC) file format and the Row Group Index (RGI). For basic data types 64-bits ThomasWang’s hash function is used and Murmur3 64-bit hash algorithm for string and binary type by BF in ORC.

Privacy-preserving Data Aggregation and Analysis: – A data aggregation algorithm is proposed using BF for privacy data analysis in mobile crowdsensing system [212].

7.4.1. Industry evolution

The farming and handicraft economizing processes changed to be monopolized by industry and manufacturing machines in the Industry Revolution. These changes transformed society fundamentally in terms of living and working styles. In the 18th century Britain began this process and spread all over the world. The production of manufactured goods and the use of natural resources have increased after these technological changes [294]. The industrial evolution has been categorized in Fig. 20 and succinct as given in Table 5.

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Fig. 20.

Industry 1.0–5.0.

Industry 1.0: – In the years 1760–1850, the first revolution was introduced with the mechanical theme [225]. It used steam, coal, and water mechanization for the manufacturing process. Production through machines had increased, so the rate of production had increased by eight times for conventional methods [270]. It also increased the standard of life by creating various goods in massive amounts. Human productivity was increased by the use of steam power [83]. Machines, stream power, and water power played an important role in the first industrial revolution’s growth. If we talk about the developing sectors in this revolution, textile manufacture, chemicals, paper machines, cement, iron industry, gaslighting, transportation, agriculture, railways, and many more are existing there [291].

Industry 2.0: – In the year 1880–1973 this revolution was introduced with industrialization [131]. With the adoption of new technologies like telephone, electric power, sewage, internal combustion engine, etc. this revolution manufactured mass production. The sectors that are developed in Industry 2.0 through technology are steel, paper making, petroleum, automobile, fertilizer, Iron, chemical, rubber, electrification, machine tools, telecommunications, and many more. Mainly this revolution occurs in America, Britain, and Germany [193].

Industry 3.0: – In the year 1989–2013, the industrial revolution ‘Industry 3.0’ was started. In this industry the production was automatically done without human interference so, the production sector grew in the engineering field. The automation was fully computerized, which increases the efficiency and reliability of the industrial system. But automation also affects employment. With the growth in technology, many industries start using robots and reducing manpower. Industry robots are designed with programmable integrated circuits and give accurate and efficient results. Robots can do painting, welding, testing, labeling, etc. It is estimated by the International Federation of Robotics there are 1.64 million robots used in industry worldwide [51].

Industry 4.0: – In 2011 the industrial revolution ‘Industry 4.0’ was first proposed by the German government. In this revolutionary trend, computerization is used in manufacturing [149]. The cyber-physical system was developed and all systems are communicated using IoT devices, cloud computing, and machine learning [272,278]. These IoT devices help ‘Industry 4.0’ for providing services and also in manufacturing. This industry transform information through Industrial IoT (IIoT) [190]. The key components of this revolution include IoT, Cloud Computing, Big Data, Cyber Security, Cognitive computing, etc. [286]. German was initiative the Industry 4.0 as “smart manufacturing for the future” [155]. This revolution has emerged with the aim of achieving mass production and increasing productivity using innovative technologies i.e. similar to previous revolutions [25,213].

Industry 5.0: – This revolution in the industry is declared by the European Commission, after discussions with various funding agencies, and organizations in Research and Innovation workshops in January 2021. To provide services to humanity this industry focuses on and highlights innovation and research. It uses the blockchain concept to integrate the generated data from different industries. To achieve social goals like employment, the standard of living and development this industry plays a key role [43].

Industry 5.0 is not entirely new it is the upgrade version of industry 4.0. With growing technology, artificial intelligence uses in industries also improved. The capability of humans, interaction with computers, and robot workers gives efficient and effective results [279]. This industry proposed the 3D techniques (like 3D printing or additive manufacturing is used for creating 3D objects) with the use of IoT [80].

Data Security: – As the rapid growth of technologies in the smart industry a huge amount of data is moving between IoT devices. The security of data is also a major challenge in the smart industry. The data protected and safe from unauthorized access is also a challenge to the industry.

Data Management: – This is also the major challenge when a huge amount of data is following over the network and between devices. Data need to be well structured and in a good manner to access and get efficient results. Storage: – Storage of industrial big data is very tough both for users and developers. Data generated from various resources IoT devices are scattered and not filtered. The storage of that type of data is not easy.

7.4.3. Applications of PDS in smart industry

Remove ambiguity: – The data generated in the smart industry is ambiguous. Wang et al. proposed a technique “Fingerprint Summary” for cluster data de-duplication which is time and space-efficient. They use BF in this technique in each node, for reducing data duplication. For efficient detect and remove duplicate data [57] proposed a new data structure i.e. ‘Improved Streaming Quotient Filter (ISQF)’.

Validation: – BF is used for data validation. BF is used to reduce memory consumption and bypass the unnecessary comparisons in the validation process [109]. This process is space-efficient.

There are various opportunities and some challenges brought by energy big data. Some challenges are listed here:

Effectively collection of energy big data: It is very difficult to collect data from energy resources for giving efficient and effective decision-making and quick responses.

Management storage of energy big data: This is also one of the major challenges for energy big data to provide better services to customers.

Mining and analyzing of energy big data: The mining of data for cleaning big raw data and analysis is a very tough job.

Lack of effective and efficient decision making: The decision making is one of the impotent key points of a smart energy system, it also needs to be improved by joining efficient techniques like PDS.

Privacy preserving of energy big data: This is one of the major concerns to giving security to energy big data. With the use of IoT devices, a huge amount of data is flowing without any security measures.

To achieve the above-discussed challenges efficient and effective tools and techniques like PDS are required.

7.5.2. Applications of PDS in smart energy

Privacy Preserving: Zhang et al. proposed a mechanism Cuckoo-RPL (Routing Protocol for Low-Power and Lossy Networks) to defend from Advanced metering infrastructure (AMI) network from blackhole attack. Cuckoo-RPL is also useful to defend from other attacks like version number and gray hole attack [303].

Network traffic management: Chaudhary et al. designed a “SDN-enabled multi-attribute-based secure communication protocol” in the Smart Grid environment for their entity communication. They use a cuckoo filter for fast forwarding of data [56].

Load Balancing: Debnath et al. present a scheme i.e. ‘BloomFlash for flash storage device. This scheme also achieved the load balancing of elements across the BF component. This scheme proves that the BF is useful in load balancing techniques [72].

Adopting Big data technologies: Some developing countries are already adopting big data technologies in smart governance. But many are facing problems, due to their bad handling and management, lack of knowledge, cost-effectiveness, and data available being either unstructured or semi-structured.

Data Privacy: Much confidential data is uploaded by people and used by public administration. So, it needs to be protected from malicious users.

Applications of PDS in Smart Governance: As such, we have not found any existing use of PDS in smart governance. So here the scope of use of PDS is highly recommendable. PDS is an efficient data structure for managing data storage and is also compatible with new technologies. PDS also helps in protecting data.

7.7.1. Society evolution

Society is coexistence with nature, and according to ethnographers, society 1.0 had begun with the birth of humans known as the hunting society (Society 1.0). In 13,000 BC the settlements had been firmly established and irrigation techniques had been developed known as an agrarian society (Society 2.0). At the end of the 18th century, the steam locomotives were invented and mass production had started, it was the industrial society (Society 3.0). In the latter 20th century computers were invented and the distribution of information was started, this is the information society (Society 4.0). At the beginning of the 21st century, “super-smart society” has introduced and known as (Society 5.0). The social evolution has categorized and concise as given below (Fig. 24).

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Fig. 24.

Society evolution 1.0–5.0 [95].

Society 1.0: – This society’s evolution begins with the birth of humans. This society is also called a hunting society. People used simple tools for full fill their daily needs including food. People have changed their habits on the availability of resources [138].

Society 2.0: – In 13000 BC this social evolution was introduced with new developing agriculture techniques and it is also known as an agrarian society. With the advancement of technology and demographical changes, this revolution is transformed from the earlier society revolution [29]. In Mesopotamia, the hand-made pottery and the cultivation of barley and wheat were found at early stages [14].

Society 3.0: – This societal revolution begin at the end of the 18th century, when modern physics, gravity law, and the invention of the steam engine had discovered and also called Industrial Society. This society changes the face of the earth forever. As already discussed industry revolutions are one of the growing fields in terms of economy as well as academia [254]. This also builds the relationship through transportation, environment, society, and many more.

Society 4.0: – This is the Information Society, initially planned in 1972 in Japan. This society aims for a new era after the post-industrial revolution in the year 1985 [179]. Here the production of information promotes human creativity, and the transition, and development of society.

Society 5.0: – At the beginning of the 21st century this revolution was introduced with a vision of a “super-smart society”. This society provides solutions to many social problems through technologies because it is human-centered. This also improves the quality of life, the use of robots also increases, and also environment-friendly [14].

The smart society is directly concerned with citizens and daily lives. The main applications are discussed below (Fig. 25).

Smart Home/Houses: – To increase the quality of life and independence, the homes are equipped with technologies called “smart home” [73]. Smart homes include home appliances like television, air conditioners, smart fans and lights, etc. are connected with IoT devices to effectively deliver the services. To achieve the goals of providing the services efficiently, the Smart Home Reasoning System (SHRS) plays an impotent role to make decisions [181]. Reducing environmental emissions, energy management, and increasing home automation is the primary objective of smart home [229].

Smart Living/People: – Smart living gives new opportunities to citizens to increase their standard of living. It needs to follow an inclusive strategic approach across all age groups and demographics [203]. It provides solutions that are controllable, productive, sustainable, economical, and efficient. Smart living is changing people’s lives with the emergence of new technologies. Yan et al. propose an architecture that controls home lighting using a Bluetooth-based Android smartphone [295]. Peoples are safer in their homes, if they have to face any problem, they call independently also the objective of smart living [153].

Smart Buildings: – Smart building is defined as it is efficient energy management, a convenient and comfortable environment with reasonable investment, and is designed to provide service and management [169]. A smart building also includes automated processes like security, heating, lighting, air conditioning, ventilation, and many more [221]. To develop the smart building the things required are future-proof devices, IT skilled team, and robust wireless infrastructure. The basic components of smart building for security includes CCTV system, access control, intrusion system, gate automation etc [184].

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Fig. 25.

Applications of smart society.

Smart Education: – With the advancement of technologies everything may be interconnected, instrumented with AI [313]. Smart education is also an emerging area nowadays and also needs attention from both researchers and academics [135]. Various smart education projects [121] have already been performed in recent years, in which the first smart education project is carried out by Malaysia in 1997 [52]. Smart education [62] faces certain issues like accessing student knowledge, comparing behavioral patterns of a student, data integration, data mining, detecting effective and emotional state of the student, and many more [60].

Efficiency: – All resource uses in a smart society have their limits like battery power, memory storage, and bandwidth required for communication. They directly affect efficiency, which also increases with the rapid growth in data.

Heterogeneous data: – The data generated in the super-smart city is heterogeneous, which leads to challenges in processing, analyzing, and mining data. Getting adequate information from big data in heterogeneous information networks is also a big challenge.

Privacy preserving: – On increasing of smart technologies in a smart society the issue of security, and privacy is the main concern. Challenges in all aspects like access control, authentication, policy enforcement etc.

Applications of PDS in Smart Society: As such we have not found any existing use of PDS in the smart society. So here the scope of use of PDS is highly recommendable. In lieu of the above challenges, there are many studies and researches available that PDS and its variants give an efficient result with memory management, and storage, also to handle heterogeneous data. PDSs also provide security to the data.

Collection of data: – For developing sustainability a huge amount of big data is required at a place.

Storage management: – The management of this data in a proper manner is a big challenge.

Privacy and Security: – Security and privacy of this data is also the main concern.

Applications of PDS in Smart Sustainability: As such we have not found any existing use of PDS in smart city sustainability. So here the scope of use of PDS is highly recommendable. The PDS and its variant are very much effective in data collection with less time and efficient storage management. It also provides security to the data movement in smart sustainability.