Abstract

The Internet of Things (IoT) leads to the next phase of human interaction with technology. With the help of the IoT, physical objects can be given the ability to generate, receive, and seamlessly trade data with one another. The IoT includes a wide variety of applications, each of which focuses on automating a specific task and works to give inanimate objects the ability to act independently of human intervention. The currently available and upcoming IoT applications hold a great deal of promise for enhancing the level of convenience, productivity, and automation enjoyed by users. High levels of security, privacy, authentication, and the ability to recover from attacks are required for the implementation of such a world in a manner that is constantly expanding. In this light, it is necessary to make the necessary adjustments to the architecture of IoT applications to accomplish end-to-end security in IoT environments. In this article, a comprehensive review of the security-related challenges and potential sources of danger posed by IoT applications is provided. Following a discussion of security concerns, a variety of new and established technologies that are focused on achieving a high degree of trust in the applications of the IoT are covered. Machine learning, fog computing, edge computing, and blockchain are just a few of the technologies that help the IoT provide greater security.

Introduction

The Internet of Things (IoT) is one of the disruptive technologies of the 21st century that has revolutionized every business dimension across the globe. The Internet has become an essential component in daily activities such as e-commerce, e-learning, e-conferences, etc. According to [1], the number of IoT devices deployed may exceed 21.3 billion by 2022. Intelligent interfaces connect IoT devices in order to collaborate, gather, communicate, and store data. Deploying IoT devices enhances business productivity by optimizing operational procedures and maximizing the utilization of available resources. IoT brings innovation in the way things are monitored and managed remotely with the help of real-time data acquired from the sensors. With the unrestrained volumes of data generated by billions of connected devices, storage, management, and security are arduous. Security aspects such as authentication, authorization, privacy, confidentiality, availability, and integrity form the basis for information exchange in a trusted environment. Authentication is the process of identifying the genuineness of an entity. The authorization process allows only authenticated users to access the resources. The protection of privacy prevents hackers from accessing the user’s private information. Data confidentiality is an outcome of authentication and authorization. It prevents sensitive data from being reclaimed and exploited. A device’s availability confirms that IoT devices and services are resilient to attacks and are accessible all the time. Data integrity is the process of providing a tamper-proof platform for information communication, exchange, and storage.

Conclusion and future directions

In this paper, the methods that are currently available in the literature are discussed, and this paper also presents the methods that are currently available. The subsequent step is an in-depth discussion of the numerous security risks and the importance of threat modeling. The subsequent topic is a discussion of the various IoT applications’ necessary security precautions. It reviews the prior research on the numerous security issues and the solutions put forth by various researchers. The holes that were found while conducting this literature review served as inspiration for the current research work.

7.1. Research challenges
The billions of IoT devices deployed across the globe collect personal and sensitive data and exchange it with other networks using intelligent interfaces. Providing device authentication, authorization, data privacy, and security in such an un trusted environment is a challenge. Centralized trusted infrastructure cannot scale to the dynamic and ever intensifying traffic and, thus, leading to bottlenecks in the network. Distributed authentication can scale to the increasing demands of the IoT networks, but requires distributed trusted systems. The majority of the work in the IoT security domain focuses on embracing the security mechanisms of WSNs and conventional Internet-based applications. However, these mechanisms are far from being implemented in real-time scenarios due to the specific challenges of IoT, such as

Abstract

Despite providing unparalleled connectivity and convenience, the exponential growth of the Internet of Things (IoT) ecosystem has triggered significant cybersecurity concerns. These concerns stem from various factors, including the heterogeneity of IoT devices, widespread deployment, and inherent computational limitations. Integrating emerging technologies to address these concerns becomes imperative as the dynamic IoT landscape evolves. Machine Learning (ML), a rapidly advancing technology, has shown considerable promise in addressing IoT security issues. It has significantly influenced and advanced research in cyber threat detection. This survey provides a comprehensive overview of current trends, methodologies, and challenges in applying machine learning for cyber threat detection in IoT environments. Specifically, we further perform a comparative analysis of state-of-the-art ML-based Intrusion Detection Systems (IDSs) in the landscape of IoT security. In addition, we shed light on the pressing unresolved issues and challenges within this dynamic field. We provide a future vision with Generative AI and large language models to enhance IoT security. The discussions present an in-depth understanding of different cyber threat detection methods, enhancing the knowledge base of researchers and practitioners alike. This paper is a valuable resource for those keen to delve into the evolving world of cyber threat detection leveraging ML and IoT security.

Introduction

Brendan O’Brien astutely observed, ”If you think the Internet has changed your life, think again. The Internet of Things is about to change it all over again!” [1]. This is indeed the case, as the Internet of Things (IoT) has heralded unprecedented connectivity. The advancements in sensor technology, wireless communication, and data analytics have spurred an exponential increase in connected devices. This influx of connectivity, brought about by integrating IoT into various industries, cities, and households, promotes unmatched efficiency and convenience. As the backbone of IoT, sensors and actuators acquire and convert data from the physical world into digital signals. These compact devices amass a diverse range of data, thereby enabling real-time monitoring and control of numerous systems and processes.

However, the rapid proliferation and extensive integration of IoT devices into everyday life have ushered in various security challenges. These issues must be robustly addressed to ensure the safety and reliability of this expanding ecosystem. The sheer volume and variety of IoT devices and their often inconsistent security features and protocols engender a fragmented environment teeming with potential attack vectors. IoT devices frequently prioritize low cost and user simplicity over security, making them susceptible to breaches and exploitation. As a result, these devices are at risk of various cyber threats, including data breaches, Distributed Denial-of-Service (DDoS) attacks, and malware infections. Any security breach in these devices could significantly compromise privacy and crucial infrastructure systems, given the sensitive nature of the data they handle. Moreover, IoT devices, potentially serving as entry points, might allow attackers to infiltrate broader networks, amplifying the potential impact of security breaches. Another primary concern is the security of communication routes between IoT devices and networks, as many IoT devices utilize wireless communication protocols susceptible to interception or manipulation. These vulnerabilities can be exacerbated by the resource constraints of specific IoT devices, which prevent them from adopting contemporary encryption and authentication techniques. Furthermore, the long lifespan and widespread deployment of IoT devices compound the difficulty of managing security upgrades and patches, as many devices may not receive regular updates or may be difficult to access for maintenance. This could lead to an increased number of outdated or vulnerable devices, further exacerbating security concerns [2].

Conclusion

IoT devices have become fundamental to everyday life, offering increased connectivity and convenience. However, this accelerated development in IoT devices also brought several security challenges that need to be addressed to guarantee the safety and reliability of these interconnected systems. The survey was organized into five sections detailing the role of machine learning in enhancing IoT security. We have provided an overview of the current trends in ML for cyber threat detection in IoT environments. Furthermore, we survey recent cyber detection methods, define them, highlight the approach, and detail the attack surface utilized along with their evaluations. The utilized ML techniques are also discussed, defined, and compared regarding advantages and drawbacks in the relevant use cases. Moreover, open issues were discussed briefly, concluding that further research and development is required as no current solution can address the issues related to IoT cyber threat detection. A holistic strategy combining diverse techniques and strategies is required as a final recommendation. We present this survey as a reference for the current advancements in the field and point out the direction being taken. Our goal is for the survey to create a more secure and resilient IoT environment by identifying the current issues, understanding their root causes and possible implications, and developing innovative solutions.

Abstract

The Internet of Things (IoT) is becoming pervasive and with each new installation of IoT platforms new and legacy brokers have to be exploited. New internal brokers are those under the control of the platform, while legacy external brokers are those in place managed by third parties. The solution proposed addressed problems of (a) interoperability to reduce set up time to cope with unknown data structures (devices, entities) distributed via brokers; (b) performance by dimensioning both front-end and back-end processes to reach high rates in a broker-based platform, while preserving full capability features of the data warehouse. Interoperability aspects have been addressed by introducing our concepts and a reasoner into an IoT Directory tool to manage Internal and External brokers, automate device discovery and registration from both standard and customized data models. Despite the managed complexity, a broker-based solution turned out to provide high performance. To this end, a specific assessment and architecture tuning have been performed and reported in the paper to give evidence and validation. The proposed integrated IoT Directory has been developed in the context of the Herit-Data Project, and it is currently used in the whole Snap4City network of 18 tenants and billions of data. Snap4City is an open-source IoT platform for Smart Cities and Industry 4.0, which is an official FIWARE platform and solution, EOSC service and libs of Node-RED.

Introduction

The Internet of Things (IoT) defined a paradigm for the computation and communication amongst things, which is becoming every day more and more pervasive and adopted in many different domains [1,2]. It is partially due to the worldwide intense deployment campaign about Low-Power Wide Area Network technologies [3], as well as to many approaches and protocols for communications amongst devices (e.g., Message Queue Telemetry Transport or MQTT, Next Generation Service Interfaces or NGSI, Advanced Message Queuing Protocol or AMQP, Constrained Application Protocol or COAP). The approach is also covering the cloud and fog infrastructures [4,5]. Thus, IoT network infrastructures are becoming every day more complex to be managed due to the networks’ structure and existence of several protocols, formats, and concepts [1,6]. Hence, the complexity is growing in terms of data management, not only for huge amount of data but also for interoperability and abstraction levels needed to data managing. Relevant aspects to be considered are security and privacy [7] on specific data models and entities. To increase the complexity, there is a range of different owners and managers who may control different parts of such IoT networks [8].

In this context, the concept of Gateway is relevant for any segments of IoT networks connecting, as well as for the Web of Things, WoT [9]. Gateways may be integrated with one or more Brokers to send/receive data to/from Devices/entities. The Gateways and Brokers are typically compliant with a single protocol and may be managed by third parties with respect to data management platform. In some cases, they are provided as public services for several interested customers in the same area (for example: Proximus for LoraWAN services [10]). A Gateway may abstract from the IoT Broker level managing multiple brokers for multiple organizations/tenants (which can be regarded as customers of Gateway services to manage several Devices), via some API and/or Web user interface. Typical IoT Brokers can only manage one organization, and thus are single tenant, meaning they broker messages using topic/entity concepts (which can be regarded as the key for subscription on that specific device/entity) without any internal partition of services, but as a sort of family of devices and subscriptions. Some IoT Brokers can be multi-tenant, such as the FIWARE Orion Broker [11,12], which provides support for partitioning the served devices/entities/topics in groups, and each of them may have a dedicated service/path for a specific scope (or a specific customer). Furthermore, devices of different tenants could exist physically in different places (even having identical identifiers, IDs), and the subscription to the broker’s tenant may imply receiving all messages/services in the partition in push. That is feasible only if the subscriber knows the service/path identifier and, in the event of access control, the subscriber has the grant to access the broker’s services. Different IoT Devices connected to the same broker adopt the same protocol, may use different data structures/models and have the same semantic information.

Conclusions

The proliferation of IoT devices, brokers, networks, data models, operators and tenants, makes the harmonization and management of IoT Platform a hard goal. This paper offers an analysis and a comparison amongst relevant existing platforms, and it points out the basic requirements to achieve such aims. These identified requirements are in most cases not addressed by main platforms which prefer to stay on their own end-to-end solutions with limited interoperability and capacity of exploiting legacy IoT networks in place, in terms of performance. The proposed solution addressed problems of (a) interoperability by reducing set up time to efficiently detect and learn how to process unknown data structures (devices, entities) distributed via brokers; (b) performance by dimensioning the front-end and back-end processes to reach high rates in a broker-based platform, while preserving full capabilities features of data warehouse.

As to interoperability, the main identified and solved problems are those related to a large variety of Data Models coming from non-controllable External Brokers. The issue has been solved by designing and implementing a harvester and reasoner that is capable to automatically recognize/understand and map the new data models/types into those already known by Knowledge Base. This approach, together with the definition of a comprehensive meta model and dictionary, has allowed to speed up the process more than 800 times. The harvesting and comprehension process can be periodically performed to keep the platform updated with any newly defined data models by third party brokers. Furthermore, the process is helped by Km4City ontology and Data Dictionary to recognize the new data types and models according to the semantic domain.

Abstract

The lightweight Unmanned Aerial Vehicle (UAV) flight activities are constrained, particularly in the UAV range or activity span and perseverance, by the strategic correspondence link capabilities. This paper tends to the different overlap issue of trading off a set of mission prerequisites, the UAV execution parameters, and strategic credibility; thus compromising between the communication load characterized by a crucial, communication link transmitting power necessities, power accessibility onboard UAV as a weight-restricted parameter, and the UAV security.

Introduction

In current years, an outcome of decreased sizes and weights concentrated charges and amplified functionalities of various sensors and devices. The Unmanned Aerial Vehicles are attracting progressively general trendy varied variety of internal submissions (atmosphere monitoring, surveillance, photography, search-and-rescue, etc.) [1]. Through the marketplace develops and the diversity of applications increases, additional necessities are present-day being compulsory in spreading the flight variety and growing adaptability toward complex assignments of UAV structures [2].

UAV stands for Unmanned Aerial Vehicle, which is commonly considered to be a drone or an aircraft with no pilot on board. UAVs can be remote-controlled aircraft [3]. The unmanned aerial vehicle contains cameras, sensors, communication belonging as well as other payload devices [4]. It was created for military usage, and civilian usage to protect the border. UAVs are widely used in the military [5]. Unmanned aircraft system manufacturing was started by the United States department of defense (DOD) in 2005 [6]. Presently, leading UAV manufacturing countries are the USA, Israel, China, Iran, and Russia [7]. Rustom series of Indian UAVs are under development [8]. The Building of a UAV is easier, it is less expensive and it can be created at a reasonable price. UAVs are made of essential components.

Conclusion

In this paper, we reviewed UAV systems and communication design, and methods used to connect and send data to the ground station. Further, we discussed data processing via sensors and applications with security. We provide key definitions and concepts of UAV systems and details of the development of communication systems and GCS. We provide reviews and analyses of the security of UAVs and the types of attacks that are launched to disturb the communication of the UAV system. Finally, open research areas are given for further development of the UAV system and communication, which leads to better UAV technology for the industry.

Abstract

Keywords

1. Introduction

2. Sensors

3. Wireless Communication Technologies

4. Cloud Computing

5. Machine Learning Techniques

6. Fog/Edge Computing

7. Applications

8. Conclusion

Declaration of Competing Interest

References

Vitae

Abstract

The goal of Internet of Medical Things (IoMT) and digital healthcare systems is to provide people with the ease of receiving quality healthcare at the comfort of their homes. Hence, the aim of IoMT is the ubiquitous deployment of home-based healthcare systems. Making such systems intelligent and efficient for timely prediction of critical diseases can save millions of lives while simultaneously reducing the burden on the traditional healthcare systems e.g., hospitals. The advancement in IoT has enabled both patients and doctors to access real time data. This advancement has reduced the cost and energy consumption of digital healthcare systems by using efficient sensors and communication technologies. This paper provides a comprehensive review of various studies conducted for the development and improvement of IoMT. It analyses different sensors used for measurement of various parameters ranging from physiological to emotional signals. It also provides a detailed investigation of different communication technologies being used, their advantages, and limitations. Moreover, digital healthcare systems are now deploying machine learning technology for the prediction of health status of patients. These techniques and algorithms are also discussed. Data security and prediction accuracy are the main concerns in the development of this area. In conclusion, this paper reviews the various digital system designs in the context of healthcare, their methodology, limitations, and the present challenges faced by the e-health sector.

1. Introduction

Quality healthcare is a basic human right, but one which fails to be provided adequately worldwide. The economic, environmental, and social development of this world and subsequent lifestyle changes have led to a drastic increase in chronic diseases such as heart disease, cancer, and diabetes. These chronic illnesses symbolize the greatest threat to human health. Moreover, each time an infectious disease breaks out, the hospitals are flooded with people which takes a huge toll on healthcare services. For example, currently there is a continuous stress on the worlds healthcare resources with the rampant spread of COVID-19. This kind of situation leads to inefficiency in managing patients and their data.

Experts believe that digital healthcare systems in Internet of Things (IoT) environments seem to be a compelling solution to this major healthcare problem. The building blocks and general architecture of a system in the IoT environment is shown in Fig. 1. In the context of medical services, the traditionally proposed remote health monitoring system architectures are divided into three layers: the vitals data collection layer from sensors; the transmission layer; and the analysis layer. The collection layer consists of sensors in the body area network (BAN). BAN collects the sensor data and transmits it to a gateway node. The transmission layer stores that data and analyzes it using conventional threshold values to report any abnormality. Additionally, the data may also be sent to the cloud for storage and heavy computations.

Abstract

     The latest 5G technology is being introduced the Internet of Things (IoT) Era. The study aims to focus the 5G technology and the current healthcare challenges as well as to highlight 5G based solutions that can handle the COVID-19 issues in different arenas. This paper provides a comprehensive review of 5G technology with the integration of other digital technologies (like AI and machine learning, IoT objects, big data analytics, cloud computing, robotic technology, and other digital platforms) in emerging healthcare applications. From the literature, it is clear that the promising aspects of 5G (such as super-high speed, high throughput, low latency) have a prospect in healthcare advancement. Now healthcare is being adopted 5G-based technologies to aid improved health services, more effective medical research, enhanced quality of life, better experiences of medical professionals and patients in anywhere–anytime. This paper emphasizes the evolving roles of 5G technology for handling the epidemiological challenges. The study also discusses various technological challenges and prospective for developing 5G powered healthcare solutions. Further works will incorporate more studies on how to expand 5G-based digital society as well as to resolve the issues of safety–security–privacy and availability–accessibility–integrity in future health crises.

Introduction

     The latest wireless mobile phone technology, Fifth Generation (5G), was first widely deployed in 2019. While 5G network is at developing stage, some countries including China, South Korea, the UK and the US commercially deployed 5G networks, and some other developed countries are being expecting to put forward commercial 5G networks by 2025 [1]. Comparing to the existing wireless networks, 5G provides high data rate, lower latency, high volume of devices connectivity with energy efficiency, high reliability and mobility support [2]. Thus, the services offered by 5G mobile networks are generally categorized into three group, namely (i) “enhanced mobile broadband (eMBB)”, (ii) “ultra-reliable low latency communications (URLLC)”, and (iii) “massive machine-type communications (mMTC)” [3]. In 2019, 67% of the world people have subscribed to cellar devices, of which 65% have used smart phones [4], and 204 billion apps have been downloaded [5]. About 3.8 billion public have eagerly used social media reported in January 2020 [6]. As the number of digital devices connectivity with 5G is dramatically increasing, the extent of variation in exposure to radiofrequency arenas is still under investigation.

Conclusion

     The use of 5G network in integrating other digital technologies (like AI and machine learning, IoMTs, big data analytics, cloud computing, etc.) is now a reality in healthcare. From the literature, it is clear that 5G enabled technologies as well as intelligent systems have a prospect to improve healthcare services, such as medical diagnosis, health monitoring, treatment, management, etc. As the 5G poses non-ionizing radiation, there is no significant health risk of 5G RF wave below the recommended safety level. There is no evidence that 5G signal causes the spread of the coronavirus. Moreover, 5G enabled digital technologies have been used for limiting the COVID-19 outbreak, and enhancing the public-health strategies in 2020. Some advanced technology giants are conducting their researches on 5G related applications that will combat against the health risks of any undesirable epidemics. The successful utilization of 5G technology to tackle healthcare challenges will enhance the acceptance of such technologies for other sectors in the future.

     As it is a new field of research, some issues are to be considered in deployment of 5G network in healthcare including infrastructure development, technical standards, effective regulations and policy, privacy protection and security, as well as availability of research data. Further works will incorporate more study on how to expand 5G based digital society as well as to resolve some issues, such as safety–security–privacy, availability–accessibility–integrity, and to augment resilience to future health crises. It is also required to compile laws and prepare proper guidelines to deal with ethical and legal counteractions in managing the online services during the emergency situations. With the new technological innovations, 5G network will provide a complete pathway for a smart society with various promising healthcare applications.

ABSTRACT

1. INTRODUCTION

2. BACKGROUND

3. PERSPECTIVES OF IOT IN LOGISTICS

4. KEY IOT-BASED APPLICATIONS IN LOGISTICS

5. KEY CHALLENGES OF IOT ADOPTION IN LOGISTICS

6. CONCLUSIONS

FUNDING

NOTES

REFERENCES

Abstract

     The Internet of Things (IoT) vision enables multiple of resource-constrained embedded devices, objects, and humans to connect together through the Internet protocol for a ubiquitous data exchange. Logistics is considered to be a key player poised from this vision to achieve the full visibility and transparency through leveraging the pervasive interconnectivity to collect reliable and safe real-time data. In addition, the valuable information extracted and transformed from the IoT data can be exploited to create intelligent services and applications to improve the logistics activities as well as the overall performance of logistics operations. This paper aims at reviewing the state-of-the-art applications of IoT in the logistics sector. Although the adoption of IoT potentially gains huge benefits, there still exist barriers preventing the full adoption of IoT in the logistics. This paper also discusses such challenges that promise to expose a wide range of open research regarding the integration of IoT in the logistics domain.

Introduction

     In reality, logistics is increasingly identified as a core industry sector promoting the strong development of the economy as well as other relevant sectors such as transportation, education. Due to various points of view and considered scopes, there exists a variety of definitions regarding the logistics. For example, the authors in [1] defined logistics as “the process of planning, implementing and controlling the efficient, effective flow and storage of goods, services and related information from their point of origin to point of consumption for the purpose of conforming to customer requirements”. Meanwhile, Islam et al. pointed out that “logistics involves an integrated approach with the integration of information, transportation, inventory, warehousing, material handling, and packaging, and recently added security” [2]. They also sketched out five central components in the logistics managements including transportation, warehousing, inventory control, packaging, and information processing. From a broad view, many researchers such as authors in [3] considered the logistics as a combination of three flows, namely, physical flow, information flow, and financial flow. In this view, the ultimate role of logistics management is to ensure a full synchronization of three flows throughout the involved logistics activities, actors, and stakeholders. Regardless of multiple perspectives, the above concepts emphasize on the importance of information to achieve the ultimate goal of logistics services: right products are delivered to right customers at the right time at the right place and in the right condition [4]. The information will become more significant in the management of modern logistics since the logistics processes became significantly more complex and dynamic due to ever-increasing global competition, shorter product life-cycles, mass customization, and stricter quality requirements of customers.

Conclusions

    The application of IoT concept has brought the transformation of logistics into innovated model, which potentially drives a greater operation efficiency and sustainability thanks to advanced abilities of IoT-enabled technologies. This paper, firstly reviews developments of current logistics that encompasses several paradigms such as smart logistics, green logistics, and physical Internet. Toward obtaining a global efficiency and sustainability, such logistics models are relevant heavily on ICT to optimize the logistics activities efficiency through efficient exploitations of shared information. Particularly, recent IoT technologies that are recognized as an ultimate means for information exchange solution herald new ways of greater efficiency gains. Thus, this promises a potential perspective with respect to the application of IoT in the logistics. This paper also describes the perspective through sketching out an IoT ecosystem that can be applied for the logistics fields. The system encompasses key enabling IoT technologies with data-driven orientation, and key building blocks including smart objects, smart networks, and smart logistics information management system. On the basis of IoT ecosystem, the state-of-the-art development of IoT-based solutions is presented to highlight their applications to the logistics activities such as product condition monitoring, warehousing, freight transportation, and last-mile delivery. However, the practical integration of IoT-logistics pair is still in an infancy stage since there are still many immature in systematic, network, and collaboration perspectives. These barriers are discussed intensively for further open researches regarding new development space IoT-based solutions for the logistics enterprises.

Abstract

CONTEXT

     Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task.

OBJECTIVE

     Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring.

METHODS

     It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms.

RESULTS AND CONCLUSION

     The result showed that 33 variables measured by various sensors were demonstrated in these studies while 10 actuations were successfully integrated with Ag-IoT platforms. Perennial crops, which introduced less disturbance to Ag-IoT platforms than annual crops, were selected by 64% of researchers. Furthermore, studies in Ag-IoT system development were more focused on outdoor than indoor environments. Ag-IoT systems based on Arduino were most common among the studies while commercial platforms were least adopted, likely due to their inflexibility in customized developments. More researchers focused on agricultural applications than the IoT technology itself. Soil water content-based irrigation scheduling and controlled environment monitoring and controlling were the main applications. Other application areas included soil nutrient estimation, crop monitoring based on multiple vegetation indices, pest identification, and chemigation.

SIGNIFICANCE

     Several potential future research directions were identified at the end of the review, including integration of satellite-based internet connectivity to improve the IoT networks in non-connected farms, development of mobile IoT platforms (drones and autonomous ground vehicles) with continuous connectivity, and the use of edge-computing and machine-learning/deep-learning to enhance the capability of the Ag-IoT systems.

Introduction

     Crops are essential for human life because they provide food, animal feed, fuel, and raw materials for clothing and shelter. Crop yield has to be doubled in 2050 compared to 2009 in order to meet the demand of a growing population while increasing the food quality and reducing production inputs (Fukase and Martin, 2020). Potential solutions to enhance global food security include closing crop yield gaps, reducing food waste, changing dietary habits, and reducing inefficiencies in resource use (Foley et al., 2011). Reducing inefficiencies in input resources (such as water and nitrogen) can be achieved by continuously monitoring crops, soil, and microclimate, and then properly controlling inputs without sacrificing the yield and quality of the crop. Internet of Things (IoT) becomes a key technology that enables continuous monitoring and control in this scenario. The ability to generate (near) real-time quantitative data with high spatiotemporal resolution is a major advantage of IoT systems (Liao et al., 2017). IoTs are considered big data systems due to volumes, velocities, and varieties of data they generate. These data are mined and modeled to elucidate the relationships between inputs and outputs (Tsai et al., 2014). Correlation, trend analysis, classification, and numerical prediction are implemented on the data to reach meaningful control decisions. Compared with the conventional wireless sensor networks, the holistic approach of IoT technology allows users to incorporate data analytics on the big data collected by IoT sensor devices. Generally, connected actuators are enabled to control the inputs to achieve desired application rates. For example, an internet-connected soil water content (SWC) sensor network measures the plant water deficit and uploads data to a cloud-based data analysis platform. The analysis will find the trend of soil water deficit to determine the best time and quantity to apply irrigation water.

Conclusions and future directions

Conclusions

     Ag-IoT is a promising technology that would increase resource use efficiency in agricultural systems, and is an essential tool for digital agriculture transformation. In this paper, we have overviewed impactful research related to Ag-IoT in the past decade. The data collected from these papers were categorized and analyzed under six main Ag-IoT system design parameters namely sensors, sensing platforms and main control board, communication technology and IoT protocols, cloud platforms, power and energy management, and actuators. According to the analyzed data, it is revealed that there is an increased global attention towards the Ag-IoT system-related research in the recent years. However, there are certain research gaps found in the literature. One of them is that the implementations of the sensors and the actuators seem to be limited to soil and environmental parameter monitoring and irrigation controlling. Furthermore, crop macro and micronutrient demand analyses are still at the infant stage due to the non-availability of sensors that can measure nutrients in real-time. Therefore, it is essential to improve the sensor and actuator applications in crop monitoring and controlling. In addition, heterogeneity of the system parameters (such as data, platforms, required power) is a major challenge to the Ag-IoT systems implementation, to which the improvement of the context-awareness could be a solution. Power harness options for Ag-IoT nodes need more exploration as there are limited options available and it would be a big advantage for the perennial crop monitoring. The perception and the network layers of Ag-IoT systems require more improvements to meet the sensor implementation ergonomics and long-range high-throughput data transmission, respectively. Edge computing can be a replacement of the high throughput long-range communication, but to the best of our knowledge, only a limited number of practical applications have been developed based on edge computing to date. Mobile Ag-IoT platforms such as unmanned aerial and ground vehicles have a huge potential to increase the spatiotemporal resolution in Ag-IoT-based monitoring and controlling.

Abstract

     Internet of things has been one of the predominant research areas for the past two decades. Many application domains have embraced it to solve challenges that have long been considered hurdles. A recent trend in information communication technologies is integrating miniature sensing devices to elevate the experience in serious games. Serious games are games whose sole aim is not entertainment but rather to serve as a source of information and learning in a playful manner. Serious games are becoming one of the sizzling literature topics and are applied to every part of human lives, such as education, healthcare, and physical training, to name a few. Internet of Things, the biggest provider of modern-day games via personal mobiles, can be utilized to design serious games. However, deploying serious games in the Internet of Things environment engenders new challenges. This paper aims to provide a comprehensive survey on Internet of things-enabled serious games and investigate the challenges towards their realization. First, we highlight serious game domains and spot the evolution and motivation that lead to Internet-of-Things-enabled Serious Games. Later, we classify the state-of-the-art by devising a comprehensive taxonomy. In the end, we present numerous unaddressed open challenges in the current form of the state-of-the-art and identify future directions.

Introduction

     We live in a COVID era in which all the normal operations have been disrupted. From offline to online classes in education, emergency situation in hospitals, and the persistent restriction affecting people’s mental health are some of the worst effects of this era. Serious games have been effective in education and healthcare. Still, its true potential has yet to be unearthed, which can play a pivotal role in remedying mental health and maintaining quality education considering online mode. Serious games in which the focus is tilted towards a serious job rather than entertainment [1] is termed as serious games. Serious games have proven to be remarkably productive not only as a learning tool in many domains such as engineering, healthcare, physics, and history [2] but also in playfully achieving a particular job.

     Artificial intelligence (AI) technologies have moved games to the next level. The gameplay in earlier serious games was based on experience and prior knowledge and sometimes secured from humans in the form of surveys and questionnaires [3]. Nevertheless, these experts-based data get outdated with the passage of time since the nature of the data is static and is not updated with the newer strategies of the players. [4], [5] is the realization of a vision where everything in the real world would interconnect and make smart decisions [6]. The plethora of miniaturized sensing devices generates real-time contextual information that can be exploited to make informed decisions [6]. Over the past few years, many application domains have embraced IoT to provide smart services and add more value to human lives [7].

Conclusion

     Serious games are a promising way to engage users to perform a serious job in a more fun way. In the last five years, the emerging trend in different domains of serious games paved the way to explore the possibility of integrating them with IoT to reach its true potential. Conventionally, serious games are played with a dedicated terminal, which was limited in scope and did not offer a scalable and ubiquitous aspect. The IoT’s inherent ubiquitous and pervasive characteristics have paved the way for a more pervasive experience in serious games. While the adoption of IoT in serious games makes them more scalable and smart, its implementation engenders significant challenges in play on-demand and on-premise.

     In this survey, the integration of IoT with serious games is studied comprehensively. To this end, we first described the history and background of serious games and identified the core application areas. Additionally, we also demonstrated the never-fading research trend since the last century and emphasized game evolution in this period. The need to evolve serious games forms a basis for IoT discussion. Thus, the core architecture of IoT and the adoption of IoT with complementary technologies have been elucidated in terms of distinguished attributes of serious games. Furthermore, we have classified the IoTeSG based on existing state-of-the-art and presented a comprehensive taxonomy. Finally, numerous challenges and open issues are identified in the light of the current state-of-the-art.

Abstract

     The last two decades have experienced a steady rise in the production and deployment of sensing-and-connectivity-enabled electronic devices, replacing “regular” physical objects. The resulting Internet-of-Things (IoT) will soon become indispensable for many application domains. Smart objects are continuously being integrated within factories, cities, buildings, health institutions, and private homes.

     Approximately 30 years after the birth of IoT, society is confronted with significant challenges regarding IoT security. Due to the interconnectivity and ubiquitous use of IoT devices, cyberattacks have widespread impacts on multiple stakeholders. Past events show that the IoT domain holds various vulnerabilities, exploited to generate physical, economic, and health damage. Despite many of these threats, manufacturers struggle to secure IoT devices properly.

     Thus, this work overviews the IoT security landscape with the intention to emphasize the demand for secured IoT-related products and applications. Therefore, (a) a list of key challenges of securing IoT devices is determined by examining their particular characteristics, (b) major security objectives for secured IoT systems are defined, (c) a threat taxonomy is introduced, which outlines potential security gaps prevalent in current IoT systems, and (d) key countermeasures against the aforementioned threats are summarized for selected IoT security-related technologies available on the market.

Introduction

     The Fourth Industrial Revolution, also commonly referred to as Industry 4.0, is expected to alter almost every business sector with unprecedented velocity fundamentally. Industry 4.0 is characterized by the blurring lines between physical and virtual reality. One cornerstone of this technological revolution is Internet-of-Things (IoT) [1]. IoT is defined as an overall, intelligent system with comprehensive awareness, reliable transmission, and intelligent processing of data [2].

     With the increasing ubiquity of IoT devices, the number of devices to be used in potential attacks increases, respectively [3], [4]. Currently, around 31 billion “things” are connected, and it is estimated that this number will rise to 75 billion by 2025 [4], [5]. Most of these devices used by private consumers are Smart Home devices, like TVs, set-top boxes [6], entertainment systems, speakers or lighting, and heating sensors [7]. These apparatuses can theoretically monitor people without drawing attention from their victims. Consumers expect monitoring activities, such that gadgets can provide their intended functionality. E.g., an intelligent light system is expected to listen to voice commands. However, a user cannot control that only commands are being processed. The private conversations may be listened to, processed, or stored.

Summary and conclusions

     While the market of IoT in general grows at a strong pace, the market for IoT security is still in its infancy. Vulnerabilities of IoT devices have been and will be exploited in cyberattacks. The Mirai Botnet or the computer worm Stuxnet will not be the last ones of their kind. However, recognizing threats posed by insecure IoT devices, their use in dedicated scenarios, and identifying the need for basic (or additional) security measures are the first step in the right direction.

     For the development of IoT security measures, it is essential to question why it is technically challenging to secure IoT devices. The analysis of particular characteristics of IoT devices revealed clearly that features like usability, limited resources, ubiquity, short time-to-market, and interconnectivity prove that traditional security measures cannot be applied one-to-one. Dedicated models and, in turn, products are needed to secure the IoT domain. The list of IoT security objectives compiled, and the threat taxonomy developed can serve as a guideline for manufacturers to specify, design, and implement secure devices and to decrease the number of attack vectors an adversary can potentially use to target an attack. Gratifyingly, as Section 9 outlines, several promising technologies and products on the market exist, which can make the use of IoT technology secure. In addition to these products available, institutions and working groups unite their forces and knowledge to formulate guidelines such that manufacturers can build secure IoT devices in the first place. However, there is room for additional security products and services as the markets’ growth trajectories demand.

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Abstract

This review expands our insight into the ways the required adaptation to digital channels caused by COVID-19 has affected the creation of brand equity through social media marketing. Based on a systematic literature review, we propose a conceptual framework that answers the following research question: How can businesses, amidst and after the COVID-19 pandemic, adapt their social media marketing strategy to create positive brand equity? The conceptual framework describes four components as the basis for a potential social media marketing strategy. First, (1) businesses need to develop a clear perspective on their current social media marketing activities, and (2) evaluate current branding elements. Based on this, (3) the timeline of marketing activities must be postponed or adapted to the needs of consumers. Lastly, (4) businesses must adapt their messaging to show empathy and deliver relevant information. Within this process, governmental parties, financial institutions, influencers, and consumers are identified as stakeholders who influence and assist businesses in optimizing their social media marketing strategy. These findings are relevant for academics and businesses to further understand the long-term effects of COVID-19 on social media marketing. Additionally, they highlight that the roles of online channels and the consumer are expanding in the future.

1. Introduction

The COVID-19 pandemic has created substantial growth for internet-based businesses as an increasing number of consumers are shopping online [1]. In 2020, the global ecommerce market has seen an increase of 27.6%, surpassing over 4.28 trillion US dollars [2]. In the past few years, organizations have been subject to the introduction of digital technologies that transform organizations, interactions with consumers, and the creation of value [3–5]. Online shopping has created the opportunity to compare products more conveniently and consumers can easily choose the products that fit their needs the most [6]. Mostly young consumers have shifted to online spending, affecting the manner in which businesses have to operate as young consumers are more likely to shop through social media [7]. Additionally, 62% base their purchase decisions on the impact that these have on society, e.g., sustainable and green products, in comparison to 53% of middle-aged consumers and 44% of older consumers [7].

Subsequently, as more consumers and businesses are adapting to buying and selling through online channels, the digital marketing sphere is becoming more competitive [8]. When looking at the state of digital marketing amidst the COVID-19 pandemic, a special emphasis should be put on Social Media Marketing (SMM) as it offers a wide range of tools to target customers more effectively. Social media has also become a large influence on online shopping, as research shows that 54% of purchases made by younger consumers are from independent retailers they discovered via social media [7]; this is 43% for middle-aged consumers and 25% for older consumers. This is especially promising as social media has become a primary communication channel for many and the time consumers spent online has drastically increased since the start of the pandemic [1,9]. As SMM is aimed at the creation of interactions with consumers in the digital settings where they already spend time, businesses can enhance their customer experience by analyzing customers and their conversations [10].

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Abstract:

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

1. Introduction

The Internet of Things (IoT) is envisioned as a network of billions of devices that can sense, communicate and share data which can then be analyzed to unlock a wealth of intelligence useful for planning, management and decision making [1]. IoT promises huge benefits for a variety of domains such as healthcare, manufacturing, agriculture, telecommunication and transportation. Despite the popularity of the IoT concept and its promised benefits, its adoption has been significantly slower than expected [2,3]. Some of the major reasons behind this include: (1) security, privacy, policy and trust issues [3–5]; (2) organisational inertia, long capital cycles and shortage of specialist workforce needed to successfully implement IoT [2,5]; and (3) lack of convincing use cases with clear return on investment (ROI) in some sectors.

COVID-19 has impacted all walks of life so much that we may never return to the old normal. This pandemic is proving to be a catalyst for digital transformation because COVID19 has created or expanded applications and use cases of digital technologies [6–8]. It has also forced governments, organisations and individuals to change/adapt their priorities, their views on societal/ethical issues, and the way they operate. In many cases, this has addressed or mitigated many of the above mentioned reasons behind the slower-thanexpected adoption of IoT across many verticals. For example, governments have invested huge amounts of resources in IoT and other technologies to combat COVID-19. Lifestyle changes brought about by COVID-19 such as work/study from home have also provided new use cases of IoT with clear ROI such as remote asset control, workforce tracking and remote employee collaboration [9]. Consequently, many organisations have increased investment in IoT and the pace of their IoT projects [10,11]. The fight against COVID-19 has resulted in a less strict stance on privacy issues, higher trust in technology and fasttracked approval procedures. This is also paving the way for accelerated adoption of IoT in various verticals. Furthermore, regulatory changes such as stricter cleaning and tracking requirements for businesses are also accelerating the IoT adoption in smart buildings.